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define(factory) :\n (factory());\n}(this, (function () { 'use strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "(function(global) {\r\n /**\r\n * Polyfill URLSearchParams\r\n *\r\n * Inspired from : https://github.com/WebReflection/url-search-params/blob/master/src/url-search-params.js\r\n */\r\n\r\n var checkIfIteratorIsSupported = function() {\r\n try {\r\n return !!Symbol.iterator;\r\n } catch (error) {\r\n return false;\r\n }\r\n };\r\n\r\n\r\n var iteratorSupported = checkIfIteratorIsSupported();\r\n\r\n var createIterator = function(items) {\r\n var iterator = {\r\n next: function() {\r\n var value = items.shift();\r\n return { done: value === void 0, value: value };\r\n }\r\n };\r\n\r\n if (iteratorSupported) {\r\n iterator[Symbol.iterator] = function() {\r\n return iterator;\r\n };\r\n }\r\n\r\n return iterator;\r\n };\r\n\r\n /**\r\n * Search param name and values should be encoded according to https://url.spec.whatwg.org/#urlencoded-serializing\r\n * encodeURIComponent() produces the same result except encoding spaces as `%20` instead of `+`.\r\n */\r\n var serializeParam = function(value) {\r\n return encodeURIComponent(value).replace(/%20/g, '+');\r\n };\r\n\r\n var deserializeParam = function(value) {\r\n return decodeURIComponent(String(value).replace(/\\+/g, ' '));\r\n };\r\n\r\n var polyfillURLSearchParams = function() {\r\n\r\n var URLSearchParams = function(searchString) {\r\n Object.defineProperty(this, '_entries', { writable: true, value: {} });\r\n var typeofSearchString = typeof searchString;\r\n\r\n if (typeofSearchString === 'undefined') {\r\n // do nothing\r\n } else if (typeofSearchString === 'string') {\r\n if (searchString !== '') {\r\n this._fromString(searchString);\r\n }\r\n } else if (searchString instanceof URLSearchParams) {\r\n var _this = this;\r\n searchString.forEach(function(value, name) {\r\n _this.append(name, value);\r\n });\r\n } else if ((searchString !== null) && (typeofSearchString === 'object')) {\r\n if (Object.prototype.toString.call(searchString) === '[object Array]') {\r\n for (var i = 0; i < searchString.length; i++) {\r\n var entry = searchString[i];\r\n if ((Object.prototype.toString.call(entry) === '[object Array]') || (entry.length !== 2)) {\r\n this.append(entry[0], entry[1]);\r\n } else {\r\n throw new TypeError('Expected [string, any] as entry at index ' + i + ' of URLSearchParams\\'s input');\r\n }\r\n }\r\n } else {\r\n for (var key in searchString) {\r\n if (searchString.hasOwnProperty(key)) {\r\n this.append(key, searchString[key]);\r\n }\r\n }\r\n }\r\n } else {\r\n throw new TypeError('Unsupported input\\'s type for URLSearchParams');\r\n }\r\n };\r\n\r\n var proto = URLSearchParams.prototype;\r\n\r\n proto.append = function(name, value) {\r\n if (name in this._entries) {\r\n this._entries[name].push(String(value));\r\n } else {\r\n this._entries[name] = [String(value)];\r\n }\r\n };\r\n\r\n proto.delete = function(name) {\r\n delete this._entries[name];\r\n };\r\n\r\n proto.get = function(name) {\r\n return (name in this._entries) ? this._entries[name][0] : null;\r\n };\r\n\r\n proto.getAll = function(name) {\r\n return (name in this._entries) ? this._entries[name].slice(0) : [];\r\n };\r\n\r\n proto.has = function(name) {\r\n return (name in this._entries);\r\n };\r\n\r\n proto.set = function(name, value) {\r\n this._entries[name] = [String(value)];\r\n };\r\n\r\n proto.forEach = function(callback, thisArg) {\r\n var entries;\r\n for (var name in this._entries) {\r\n if (this._entries.hasOwnProperty(name)) {\r\n entries = this._entries[name];\r\n for (var i = 0; i < entries.length; i++) {\r\n callback.call(thisArg, entries[i], name, this);\r\n }\r\n }\r\n }\r\n };\r\n\r\n proto.keys = function() {\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push(name);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n proto.values = function() {\r\n var items = [];\r\n this.forEach(function(value) {\r\n items.push(value);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n proto.entries = function() {\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push([name, value]);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n if (iteratorSupported) {\r\n proto[Symbol.iterator] = proto.entries;\r\n }\r\n\r\n proto.toString = function() {\r\n var searchArray = [];\r\n this.forEach(function(value, name) {\r\n searchArray.push(serializeParam(name) + '=' + serializeParam(value));\r\n });\r\n return searchArray.join('&');\r\n };\r\n\r\n\r\n global.URLSearchParams = URLSearchParams;\r\n };\r\n\r\n var checkIfURLSearchParamsSupported = function() {\r\n try {\r\n var URLSearchParams = global.URLSearchParams;\r\n\r\n return (\r\n (new URLSearchParams('?a=1').toString() === 'a=1') &&\r\n (typeof URLSearchParams.prototype.set === 'function') &&\r\n (typeof URLSearchParams.prototype.entries === 'function')\r\n );\r\n } catch (e) {\r\n return false;\r\n }\r\n };\r\n\r\n if (!checkIfURLSearchParamsSupported()) {\r\n polyfillURLSearchParams();\r\n }\r\n\r\n var proto = global.URLSearchParams.prototype;\r\n\r\n if (typeof proto.sort !== 'function') {\r\n proto.sort = function() {\r\n var _this = this;\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push([name, value]);\r\n if (!_this._entries) {\r\n _this.delete(name);\r\n }\r\n });\r\n items.sort(function(a, b) {\r\n if (a[0] < b[0]) {\r\n return -1;\r\n } else if (a[0] > b[0]) {\r\n return +1;\r\n } else {\r\n return 0;\r\n }\r\n });\r\n if (_this._entries) { // force reset because IE keeps keys index\r\n _this._entries = {};\r\n }\r\n for (var i = 0; i < items.length; i++) {\r\n this.append(items[i][0], items[i][1]);\r\n }\r\n };\r\n }\r\n\r\n if (typeof proto._fromString !== 'function') {\r\n Object.defineProperty(proto, '_fromString', {\r\n enumerable: false,\r\n configurable: false,\r\n writable: false,\r\n value: function(searchString) {\r\n if (this._entries) {\r\n this._entries = {};\r\n } else {\r\n var keys = [];\r\n this.forEach(function(value, name) {\r\n keys.push(name);\r\n });\r\n for (var i = 0; i < keys.length; i++) {\r\n this.delete(keys[i]);\r\n }\r\n }\r\n\r\n searchString = searchString.replace(/^\\?/, '');\r\n var attributes = searchString.split('&');\r\n var attribute;\r\n for (var i = 0; i < attributes.length; i++) {\r\n attribute = attributes[i].split('=');\r\n this.append(\r\n deserializeParam(attribute[0]),\r\n (attribute.length > 1) ? deserializeParam(attribute[1]) : ''\r\n );\r\n }\r\n }\r\n });\r\n }\r\n\r\n // HTMLAnchorElement\r\n\r\n})(\r\n (typeof global !== 'undefined') ? global\r\n : ((typeof window !== 'undefined') ? window\r\n : ((typeof self !== 'undefined') ? self : this))\r\n);\r\n\r\n(function(global) {\r\n /**\r\n * Polyfill URL\r\n *\r\n * Inspired from : https://github.com/arv/DOM-URL-Polyfill/blob/master/src/url.js\r\n */\r\n\r\n var checkIfURLIsSupported = function() {\r\n try {\r\n var u = new global.URL('b', 'http://a');\r\n u.pathname = 'c d';\r\n return (u.href === 'http://a/c%20d') && u.searchParams;\r\n } catch (e) {\r\n return false;\r\n }\r\n };\r\n\r\n\r\n var polyfillURL = function() {\r\n var _URL = global.URL;\r\n\r\n var URL = function(url, base) {\r\n if (typeof url !== 'string') url = String(url);\r\n if (base && typeof base !== 'string') base = String(base);\r\n\r\n // Only create another document if the base is different from current location.\r\n var doc = document, baseElement;\r\n if (base && (global.location === void 0 || base !== global.location.href)) {\r\n base = base.toLowerCase();\r\n doc = document.implementation.createHTMLDocument('');\r\n baseElement = doc.createElement('base');\r\n baseElement.href = base;\r\n doc.head.appendChild(baseElement);\r\n try {\r\n if (baseElement.href.indexOf(base) !== 0) throw new Error(baseElement.href);\r\n } catch (err) {\r\n throw new Error('URL unable to set base ' + base + ' due to ' + err);\r\n }\r\n }\r\n\r\n var anchorElement = doc.createElement('a');\r\n anchorElement.href = url;\r\n if (baseElement) {\r\n doc.body.appendChild(anchorElement);\r\n anchorElement.href = anchorElement.href; // force href to refresh\r\n }\r\n\r\n var inputElement = doc.createElement('input');\r\n inputElement.type = 'url';\r\n inputElement.value = url;\r\n\r\n if (anchorElement.protocol === ':' || !/:/.test(anchorElement.href) || (!inputElement.checkValidity() && !base)) {\r\n throw new TypeError('Invalid URL');\r\n }\r\n\r\n Object.defineProperty(this, '_anchorElement', {\r\n value: anchorElement\r\n });\r\n\r\n\r\n // create a linked searchParams which reflect its changes on URL\r\n var searchParams = new global.URLSearchParams(this.search);\r\n var enableSearchUpdate = true;\r\n var enableSearchParamsUpdate = true;\r\n var _this = this;\r\n ['append', 'delete', 'set'].forEach(function(methodName) {\r\n var method = searchParams[methodName];\r\n searchParams[methodName] = function() {\r\n method.apply(searchParams, arguments);\r\n if (enableSearchUpdate) {\r\n enableSearchParamsUpdate = false;\r\n _this.search = searchParams.toString();\r\n enableSearchParamsUpdate = true;\r\n }\r\n };\r\n });\r\n\r\n Object.defineProperty(this, 'searchParams', {\r\n value: searchParams,\r\n enumerable: true\r\n });\r\n\r\n var search = void 0;\r\n Object.defineProperty(this, '_updateSearchParams', {\r\n enumerable: false,\r\n configurable: false,\r\n writable: false,\r\n value: function() {\r\n if (this.search !== search) {\r\n search = this.search;\r\n if (enableSearchParamsUpdate) {\r\n enableSearchUpdate = false;\r\n this.searchParams._fromString(this.search);\r\n enableSearchUpdate = true;\r\n }\r\n }\r\n }\r\n });\r\n };\r\n\r\n var proto = URL.prototype;\r\n\r\n var linkURLWithAnchorAttribute = function(attributeName) {\r\n Object.defineProperty(proto, attributeName, {\r\n get: function() {\r\n return this._anchorElement[attributeName];\r\n },\r\n set: function(value) {\r\n this._anchorElement[attributeName] = value;\r\n },\r\n enumerable: true\r\n });\r\n };\r\n\r\n ['hash', 'host', 'hostname', 'port', 'protocol']\r\n .forEach(function(attributeName) {\r\n linkURLWithAnchorAttribute(attributeName);\r\n });\r\n\r\n Object.defineProperty(proto, 'search', {\r\n get: function() {\r\n return this._anchorElement['search'];\r\n },\r\n set: function(value) {\r\n this._anchorElement['search'] = value;\r\n this._updateSearchParams();\r\n },\r\n enumerable: true\r\n });\r\n\r\n Object.defineProperties(proto, {\r\n\r\n 'toString': {\r\n get: function() {\r\n var _this = this;\r\n return function() {\r\n return _this.href;\r\n };\r\n }\r\n },\r\n\r\n 'href': {\r\n get: function() {\r\n return this._anchorElement.href.replace(/\\?$/, '');\r\n },\r\n set: function(value) {\r\n this._anchorElement.href = value;\r\n this._updateSearchParams();\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'pathname': {\r\n get: function() {\r\n return this._anchorElement.pathname.replace(/(^\\/?)/, '/');\r\n },\r\n set: function(value) {\r\n this._anchorElement.pathname = value;\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'origin': {\r\n get: function() {\r\n // get expected port from protocol\r\n var expectedPort = { 'http:': 80, 'https:': 443, 'ftp:': 21 }[this._anchorElement.protocol];\r\n // add port to origin if, expected port is different than actual port\r\n // and it is not empty f.e http://foo:8080\r\n // 8080 != 80 && 8080 != ''\r\n var addPortToOrigin = this._anchorElement.port != expectedPort &&\r\n this._anchorElement.port !== '';\r\n\r\n return this._anchorElement.protocol +\r\n '//' +\r\n this._anchorElement.hostname +\r\n (addPortToOrigin ? (':' + this._anchorElement.port) : '');\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'password': { // TODO\r\n get: function() {\r\n return '';\r\n },\r\n set: function(value) {\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'username': { // TODO\r\n get: function() {\r\n return '';\r\n },\r\n set: function(value) {\r\n },\r\n enumerable: true\r\n },\r\n });\r\n\r\n URL.createObjectURL = function(blob) {\r\n return _URL.createObjectURL.apply(_URL, arguments);\r\n };\r\n\r\n URL.revokeObjectURL = function(url) {\r\n return _URL.revokeObjectURL.apply(_URL, arguments);\r\n };\r\n\r\n global.URL = URL;\r\n\r\n };\r\n\r\n if (!checkIfURLIsSupported()) {\r\n polyfillURL();\r\n }\r\n\r\n if ((global.location !== void 0) && !('origin' in global.location)) {\r\n var getOrigin = function() {\r\n return global.location.protocol + '//' + global.location.hostname + (global.location.port ? 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'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "Array.prototype.flat||Object.defineProperty(Array.prototype,\"flat\",{configurable:!0,value:function r(){var t=isNaN(arguments[0])?1:Number(arguments[0]);return t?Array.prototype.reduce.call(this,function(a,e){return Array.isArray(e)?a.push.apply(a,r.call(e,t-1)):a.push(e),a},[]):Array.prototype.slice.call(this)},writable:!0}),Array.prototype.flatMap||Object.defineProperty(Array.prototype,\"flatMap\",{configurable:!0,value:function(r){return Array.prototype.map.apply(this,arguments).flat()},writable:!0})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"array-flat-polyfill\"\nimport \"focus-visible\"\nimport \"unfetch/polyfill\"\nimport \"url-polyfill\"\n\nimport {\n EMPTY,\n NEVER,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getOptionalElement,\n requestJSON,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountBackToTop,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantLoading,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget()\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? __search?.index || requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up instant loading, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantLoading({ document$, location$, viewport$ })\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"[href][rel=prev]\")\n if (typeof prev !== \"undefined\")\n prev.click()\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"[href][rel=next]\")\n if (typeof next !== \"undefined\")\n next.click()\n break\n }\n })\n\n/* Set up patches */\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, { viewport$, header$, target$ })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.component$ = component$ /* Component observable */\n", "self.fetch||(self.fetch=function(e,n){return n=n||{},new Promise(function(t,s){var r=new XMLHttpRequest,o=[],u=[],i={},a=function(){return{ok:2==(r.status/100|0),statusText:r.statusText,status:r.status,url:r.responseURL,text:function(){return Promise.resolve(r.responseText)},json:function(){return Promise.resolve(r.responseText).then(JSON.parse)},blob:function(){return Promise.resolve(new Blob([r.response]))},clone:a,headers:{keys:function(){return o},entries:function(){return u},get:function(e){return i[e.toLowerCase()]},has:function(e){return e.toLowerCase()in i}}}};for(var c in r.open(n.method||\"get\",e,!0),r.onload=function(){r.getAllResponseHeaders().replace(/^(.*?):[^\\S\\n]*([\\s\\S]*?)$/gm,function(e,n,t){o.push(n=n.toLowerCase()),u.push([n,t]),i[n]=i[n]?i[n]+\",\"+t:t}),t(a())},r.onerror=s,r.withCredentials=\"include\"==n.credentials,n.headers)r.setRequestHeader(c,n.headers[c]);r.send(n.body||null)})});\n", "import tslib from '../tslib.js';\r\nconst {\r\n __extends,\r\n __assign,\r\n __rest,\r\n __decorate,\r\n __param,\r\n __metadata,\r\n __awaiter,\r\n __generator,\r\n __exportStar,\r\n __createBinding,\r\n __values,\r\n __read,\r\n __spread,\r\n __spreadArrays,\r\n __spreadArray,\r\n __await,\r\n __asyncGenerator,\r\n __asyncDelegator,\r\n __asyncValues,\r\n __makeTemplateObject,\r\n __importStar,\r\n __importDefault,\r\n __classPrivateFieldGet,\r\n __classPrivateFieldSet,\r\n} = tslib;\r\nexport {\r\n __extends,\r\n __assign,\r\n __rest,\r\n __decorate,\r\n __param,\r\n __metadata,\r\n __awaiter,\r\n __generator,\r\n __exportStar,\r\n __createBinding,\r\n __values,\r\n __read,\r\n __spread,\r\n __spreadArrays,\r\n __spreadArray,\r\n __await,\r\n __asyncGenerator,\r\n __asyncDelegator,\r\n __asyncValues,\r\n __makeTemplateObject,\r\n __importStar,\r\n __importDefault,\r\n __classPrivateFieldGet,\r\n __classPrivateFieldSet,\r\n};\r\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n ReplaySubject,\n Subject,\n fromEvent\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch document\n *\n * Documents are implemented as subjects, so all downstream observables are\n * automatically updated when a new document is emitted.\n *\n * @returns Document subject\n */\nexport function watchDocument(): Subject {\n const document$ = new ReplaySubject(1)\n fromEvent(document, \"DOMContentLoaded\", { once: true })\n .subscribe(() => document$.next(document))\n\n /* Return document */\n return document$\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements(\n selector: string, node?: ParentNode\n): T[]\n\nexport function getElements(\n selector: string, node: ParentNode = document\n): T[] {\n return Array.from(node.querySelectorAll(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement(\n selector: string, node?: ParentNode\n): T\n\nexport function getElement(\n selector: string, node: ParentNode = document\n): T {\n const el = getOptionalElement(selector, node)\n if (typeof el === \"undefined\")\n throw new ReferenceError(\n `Missing element: expected \"${selector}\" to be present`\n )\n\n /* Return element */\n return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement(\n selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement(\n selector: string, node: ParentNode = document\n): T | undefined {\n return node.querySelector(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n return document.activeElement instanceof HTMLElement\n ? document.activeElement || undefined\n : undefined\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n debounceTime,\n distinctUntilChanged,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * Previously, this function used `focus` and `blur` events to determine whether\n * an element is focused, but this doesn't work if there are focusable elements\n * within the elements itself. A better solutions are `focusin` and `focusout`\n * events, which bubble up the tree and allow for more fine-grained control.\n *\n * `debounceTime` is necessary, because when a focus change happens inside an\n * element, the observable would first emit `false` and then `true` again.\n *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(document.body, \"focusin\"),\n fromEvent(document.body, \"focusout\")\n )\n .pipe(\n debounceTime(1),\n map(() => {\n const active = getActiveElement()\n return typeof active !== \"undefined\"\n ? el.contains(active)\n : false\n }),\n startWith(el === getActiveElement()),\n distinctUntilChanged()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n animationFrameScheduler,\n auditTime,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementOffset {\n x: number /* Horizontal offset */\n y: number /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffset(\n el: HTMLElement\n): ElementOffset {\n return {\n x: el.offsetLeft,\n y: el.offsetTop\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffset(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(window, \"load\"),\n fromEvent(window, \"resize\")\n )\n .pipe(\n auditTime(0, animationFrameScheduler),\n map(() => getElementOffset(el)),\n startWith(getElementOffset(el))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n animationFrameScheduler,\n auditTime,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\nimport { ElementOffset } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content offset (= scroll offset)\n *\n * @param el - Element\n *\n * @returns Element content offset\n */\nexport function getElementContentOffset(\n el: HTMLElement\n): ElementOffset {\n return {\n x: el.scrollLeft,\n y: el.scrollTop\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element content offset\n *\n * @param el - Element\n *\n * @returns Element content offset observable\n */\nexport function watchElementContentOffset(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(el, \"scroll\"),\n fromEvent(window, \"resize\")\n )\n .pipe(\n auditTime(0, animationFrameScheduler),\n map(() => getElementContentOffset(el)),\n startWith(getElementContentOffset(el))\n )\n}\n", "/**\r\n * A collection of shims that provide minimal functionality of the ES6 collections.\r\n *\r\n * These implementations are not meant to be used outside of the ResizeObserver\r\n * modules as they cover only a limited range of use cases.\r\n */\r\n/* eslint-disable require-jsdoc, valid-jsdoc */\r\nvar MapShim = (function () {\r\n if (typeof Map !== 'undefined') {\r\n return Map;\r\n }\r\n /**\r\n * Returns index in provided array that matches the specified key.\r\n *\r\n * @param {Array} arr\r\n * @param {*} key\r\n * @returns {number}\r\n */\r\n function getIndex(arr, key) {\r\n var result = -1;\r\n arr.some(function (entry, index) {\r\n if (entry[0] === key) {\r\n result = index;\r\n return true;\r\n }\r\n return false;\r\n });\r\n return result;\r\n }\r\n return /** @class */ (function () {\r\n function class_1() {\r\n this.__entries__ = [];\r\n }\r\n Object.defineProperty(class_1.prototype, \"size\", {\r\n /**\r\n * @returns {boolean}\r\n */\r\n get: function () {\r\n return this.__entries__.length;\r\n },\r\n enumerable: true,\r\n configurable: true\r\n });\r\n /**\r\n * @param {*} key\r\n * @returns {*}\r\n */\r\n class_1.prototype.get = function (key) {\r\n var index = getIndex(this.__entries__, key);\r\n var entry = this.__entries__[index];\r\n return entry && entry[1];\r\n };\r\n /**\r\n * @param {*} key\r\n * @param {*} value\r\n * @returns {void}\r\n */\r\n class_1.prototype.set = function (key, value) {\r\n var index = getIndex(this.__entries__, key);\r\n if (~index) {\r\n this.__entries__[index][1] = value;\r\n }\r\n else {\r\n this.__entries__.push([key, value]);\r\n }\r\n };\r\n /**\r\n * @param {*} key\r\n * @returns {void}\r\n */\r\n class_1.prototype.delete = function (key) {\r\n var entries = this.__entries__;\r\n var index = getIndex(entries, key);\r\n if (~index) {\r\n entries.splice(index, 1);\r\n }\r\n };\r\n /**\r\n * @param {*} key\r\n * @returns {void}\r\n */\r\n class_1.prototype.has = function (key) {\r\n return !!~getIndex(this.__entries__, key);\r\n };\r\n /**\r\n * @returns {void}\r\n */\r\n class_1.prototype.clear = function () {\r\n this.__entries__.splice(0);\r\n };\r\n /**\r\n * @param {Function} callback\r\n * @param {*} [ctx=null]\r\n * @returns {void}\r\n */\r\n class_1.prototype.forEach = function (callback, ctx) {\r\n if (ctx === void 0) { ctx = null; }\r\n for (var _i = 0, _a = this.__entries__; _i < _a.length; _i++) {\r\n var entry = _a[_i];\r\n callback.call(ctx, entry[1], entry[0]);\r\n }\r\n };\r\n return class_1;\r\n }());\r\n})();\n\n/**\r\n * Detects whether window and document objects are available in current environment.\r\n */\r\nvar isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined' && window.document === document;\n\n// Returns global object of a current environment.\r\nvar global$1 = (function () {\r\n if (typeof global !== 'undefined' && global.Math === Math) {\r\n return global;\r\n }\r\n if (typeof self !== 'undefined' && self.Math === Math) {\r\n return self;\r\n }\r\n if (typeof window !== 'undefined' && window.Math === Math) {\r\n return window;\r\n }\r\n // eslint-disable-next-line no-new-func\r\n return Function('return this')();\r\n})();\n\n/**\r\n * A shim for the requestAnimationFrame which falls back to the setTimeout if\r\n * first one is not supported.\r\n *\r\n * @returns {number} Requests' identifier.\r\n */\r\nvar requestAnimationFrame$1 = (function () {\r\n if (typeof requestAnimationFrame === 'function') {\r\n // It's required to use a bounded function because IE sometimes throws\r\n // an \"Invalid calling object\" error if rAF is invoked without the global\r\n // object on the left hand side.\r\n return requestAnimationFrame.bind(global$1);\r\n }\r\n return function (callback) { return setTimeout(function () { return callback(Date.now()); }, 1000 / 60); };\r\n})();\n\n// Defines minimum timeout before adding a trailing call.\r\nvar trailingTimeout = 2;\r\n/**\r\n * Creates a wrapper function which ensures that provided callback will be\r\n * invoked only once during the specified delay period.\r\n *\r\n * @param {Function} callback - Function to be invoked after the delay period.\r\n * @param {number} delay - Delay after which to invoke callback.\r\n * @returns {Function}\r\n */\r\nfunction throttle (callback, delay) {\r\n var leadingCall = false, trailingCall = false, lastCallTime = 0;\r\n /**\r\n * Invokes the original callback function and schedules new invocation if\r\n * the \"proxy\" was called during current request.\r\n *\r\n * @returns {void}\r\n */\r\n function resolvePending() {\r\n if (leadingCall) {\r\n leadingCall = false;\r\n callback();\r\n }\r\n if (trailingCall) {\r\n proxy();\r\n }\r\n }\r\n /**\r\n * Callback invoked after the specified delay. It will further postpone\r\n * invocation of the original function delegating it to the\r\n * requestAnimationFrame.\r\n *\r\n * @returns {void}\r\n */\r\n function timeoutCallback() {\r\n requestAnimationFrame$1(resolvePending);\r\n }\r\n /**\r\n * Schedules invocation of the original function.\r\n *\r\n * @returns {void}\r\n */\r\n function proxy() {\r\n var timeStamp = Date.now();\r\n if (leadingCall) {\r\n // Reject immediately following calls.\r\n if (timeStamp - lastCallTime < trailingTimeout) {\r\n return;\r\n }\r\n // Schedule new call to be in invoked when the pending one is resolved.\r\n // This is important for \"transitions\" which never actually start\r\n // immediately so there is a chance that we might miss one if change\r\n // happens amids the pending invocation.\r\n trailingCall = true;\r\n }\r\n else {\r\n leadingCall = true;\r\n trailingCall = false;\r\n setTimeout(timeoutCallback, delay);\r\n }\r\n lastCallTime = timeStamp;\r\n }\r\n return proxy;\r\n}\n\n// Minimum delay before invoking the update of observers.\r\nvar REFRESH_DELAY = 20;\r\n// A list of substrings of CSS properties used to find transition events that\r\n// might affect dimensions of observed elements.\r\nvar transitionKeys = ['top', 'right', 'bottom', 'left', 'width', 'height', 'size', 'weight'];\r\n// Check if MutationObserver is available.\r\nvar mutationObserverSupported = typeof MutationObserver !== 'undefined';\r\n/**\r\n * Singleton controller class which handles updates of ResizeObserver instances.\r\n */\r\nvar ResizeObserverController = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserverController.\r\n *\r\n * @private\r\n */\r\n function ResizeObserverController() {\r\n /**\r\n * Indicates whether DOM listeners have been added.\r\n *\r\n * @private {boolean}\r\n */\r\n this.connected_ = false;\r\n /**\r\n * Tells that controller has subscribed for Mutation Events.\r\n *\r\n * @private {boolean}\r\n */\r\n this.mutationEventsAdded_ = false;\r\n /**\r\n * Keeps reference to the instance of MutationObserver.\r\n *\r\n * @private {MutationObserver}\r\n */\r\n this.mutationsObserver_ = null;\r\n /**\r\n * A list of connected observers.\r\n *\r\n * @private {Array}\r\n */\r\n this.observers_ = [];\r\n this.onTransitionEnd_ = this.onTransitionEnd_.bind(this);\r\n this.refresh = throttle(this.refresh.bind(this), REFRESH_DELAY);\r\n }\r\n /**\r\n * Adds observer to observers list.\r\n *\r\n * @param {ResizeObserverSPI} observer - Observer to be added.\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.addObserver = function (observer) {\r\n if (!~this.observers_.indexOf(observer)) {\r\n this.observers_.push(observer);\r\n }\r\n // Add listeners if they haven't been added yet.\r\n if (!this.connected_) {\r\n this.connect_();\r\n }\r\n };\r\n /**\r\n * Removes observer from observers list.\r\n *\r\n * @param {ResizeObserverSPI} observer - Observer to be removed.\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.removeObserver = function (observer) {\r\n var observers = this.observers_;\r\n var index = observers.indexOf(observer);\r\n // Remove observer if it's present in registry.\r\n if (~index) {\r\n observers.splice(index, 1);\r\n }\r\n // Remove listeners if controller has no connected observers.\r\n if (!observers.length && this.connected_) {\r\n this.disconnect_();\r\n }\r\n };\r\n /**\r\n * Invokes the update of observers. It will continue running updates insofar\r\n * it detects changes.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.refresh = function () {\r\n var changesDetected = this.updateObservers_();\r\n // Continue running updates if changes have been detected as there might\r\n // be future ones caused by CSS transitions.\r\n if (changesDetected) {\r\n this.refresh();\r\n }\r\n };\r\n /**\r\n * Updates every observer from observers list and notifies them of queued\r\n * entries.\r\n *\r\n * @private\r\n * @returns {boolean} Returns \"true\" if any observer has detected changes in\r\n * dimensions of it's elements.\r\n */\r\n ResizeObserverController.prototype.updateObservers_ = function () {\r\n // Collect observers that have active observations.\r\n var activeObservers = this.observers_.filter(function (observer) {\r\n return observer.gatherActive(), observer.hasActive();\r\n });\r\n // Deliver notifications in a separate cycle in order to avoid any\r\n // collisions between observers, e.g. when multiple instances of\r\n // ResizeObserver are tracking the same element and the callback of one\r\n // of them changes content dimensions of the observed target. Sometimes\r\n // this may result in notifications being blocked for the rest of observers.\r\n activeObservers.forEach(function (observer) { return observer.broadcastActive(); });\r\n return activeObservers.length > 0;\r\n };\r\n /**\r\n * Initializes DOM listeners.\r\n *\r\n * @private\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.connect_ = function () {\r\n // Do nothing if running in a non-browser environment or if listeners\r\n // have been already added.\r\n if (!isBrowser || this.connected_) {\r\n return;\r\n }\r\n // Subscription to the \"Transitionend\" event is used as a workaround for\r\n // delayed transitions. This way it's possible to capture at least the\r\n // final state of an element.\r\n document.addEventListener('transitionend', this.onTransitionEnd_);\r\n window.addEventListener('resize', this.refresh);\r\n if (mutationObserverSupported) {\r\n this.mutationsObserver_ = new MutationObserver(this.refresh);\r\n this.mutationsObserver_.observe(document, {\r\n attributes: true,\r\n childList: true,\r\n characterData: true,\r\n subtree: true\r\n });\r\n }\r\n else {\r\n document.addEventListener('DOMSubtreeModified', this.refresh);\r\n this.mutationEventsAdded_ = true;\r\n }\r\n this.connected_ = true;\r\n };\r\n /**\r\n * Removes DOM listeners.\r\n *\r\n * @private\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.disconnect_ = function () {\r\n // Do nothing if running in a non-browser environment or if listeners\r\n // have been already removed.\r\n if (!isBrowser || !this.connected_) {\r\n return;\r\n }\r\n document.removeEventListener('transitionend', this.onTransitionEnd_);\r\n window.removeEventListener('resize', this.refresh);\r\n if (this.mutationsObserver_) {\r\n this.mutationsObserver_.disconnect();\r\n }\r\n if (this.mutationEventsAdded_) {\r\n document.removeEventListener('DOMSubtreeModified', this.refresh);\r\n }\r\n this.mutationsObserver_ = null;\r\n this.mutationEventsAdded_ = false;\r\n this.connected_ = false;\r\n };\r\n /**\r\n * \"Transitionend\" event handler.\r\n *\r\n * @private\r\n * @param {TransitionEvent} event\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.onTransitionEnd_ = function (_a) {\r\n var _b = _a.propertyName, propertyName = _b === void 0 ? '' : _b;\r\n // Detect whether transition may affect dimensions of an element.\r\n var isReflowProperty = transitionKeys.some(function (key) {\r\n return !!~propertyName.indexOf(key);\r\n });\r\n if (isReflowProperty) {\r\n this.refresh();\r\n }\r\n };\r\n /**\r\n * Returns instance of the ResizeObserverController.\r\n *\r\n * @returns {ResizeObserverController}\r\n */\r\n ResizeObserverController.getInstance = function () {\r\n if (!this.instance_) {\r\n this.instance_ = new ResizeObserverController();\r\n }\r\n return this.instance_;\r\n };\r\n /**\r\n * Holds reference to the controller's instance.\r\n *\r\n * @private {ResizeObserverController}\r\n */\r\n ResizeObserverController.instance_ = null;\r\n return ResizeObserverController;\r\n}());\n\n/**\r\n * Defines non-writable/enumerable properties of the provided target object.\r\n *\r\n * @param {Object} target - Object for which to define properties.\r\n * @param {Object} props - Properties to be defined.\r\n * @returns {Object} Target object.\r\n */\r\nvar defineConfigurable = (function (target, props) {\r\n for (var _i = 0, _a = Object.keys(props); _i < _a.length; _i++) {\r\n var key = _a[_i];\r\n Object.defineProperty(target, key, {\r\n value: props[key],\r\n enumerable: false,\r\n writable: false,\r\n configurable: true\r\n });\r\n }\r\n return target;\r\n});\n\n/**\r\n * Returns the global object associated with provided element.\r\n *\r\n * @param {Object} target\r\n * @returns {Object}\r\n */\r\nvar getWindowOf = (function (target) {\r\n // Assume that the element is an instance of Node, which means that it\r\n // has the \"ownerDocument\" property from which we can retrieve a\r\n // corresponding global object.\r\n var ownerGlobal = target && target.ownerDocument && target.ownerDocument.defaultView;\r\n // Return the local global object if it's not possible extract one from\r\n // provided element.\r\n return ownerGlobal || global$1;\r\n});\n\n// Placeholder of an empty content rectangle.\r\nvar emptyRect = createRectInit(0, 0, 0, 0);\r\n/**\r\n * Converts provided string to a number.\r\n *\r\n * @param {number|string} value\r\n * @returns {number}\r\n */\r\nfunction toFloat(value) {\r\n return parseFloat(value) || 0;\r\n}\r\n/**\r\n * Extracts borders size from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @param {...string} positions - Borders positions (top, right, ...)\r\n * @returns {number}\r\n */\r\nfunction getBordersSize(styles) {\r\n var positions = [];\r\n for (var _i = 1; _i < arguments.length; _i++) {\r\n positions[_i - 1] = arguments[_i];\r\n }\r\n return positions.reduce(function (size, position) {\r\n var value = styles['border-' + position + '-width'];\r\n return size + toFloat(value);\r\n }, 0);\r\n}\r\n/**\r\n * Extracts paddings sizes from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @returns {Object} Paddings box.\r\n */\r\nfunction getPaddings(styles) {\r\n var positions = ['top', 'right', 'bottom', 'left'];\r\n var paddings = {};\r\n for (var _i = 0, positions_1 = positions; _i < positions_1.length; _i++) {\r\n var position = positions_1[_i];\r\n var value = styles['padding-' + position];\r\n paddings[position] = toFloat(value);\r\n }\r\n return paddings;\r\n}\r\n/**\r\n * Calculates content rectangle of provided SVG element.\r\n *\r\n * @param {SVGGraphicsElement} target - Element content rectangle of which needs\r\n * to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getSVGContentRect(target) {\r\n var bbox = target.getBBox();\r\n return createRectInit(0, 0, bbox.width, bbox.height);\r\n}\r\n/**\r\n * Calculates content rectangle of provided HTMLElement.\r\n *\r\n * @param {HTMLElement} target - Element for which to calculate the content rectangle.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getHTMLElementContentRect(target) {\r\n // Client width & height properties can't be\r\n // used exclusively as they provide rounded values.\r\n var clientWidth = target.clientWidth, clientHeight = target.clientHeight;\r\n // By this condition we can catch all non-replaced inline, hidden and\r\n // detached elements. Though elements with width & height properties less\r\n // than 0.5 will be discarded as well.\r\n //\r\n // Without it we would need to implement separate methods for each of\r\n // those cases and it's not possible to perform a precise and performance\r\n // effective test for hidden elements. E.g. even jQuery's ':visible' filter\r\n // gives wrong results for elements with width & height less than 0.5.\r\n if (!clientWidth && !clientHeight) {\r\n return emptyRect;\r\n }\r\n var styles = getWindowOf(target).getComputedStyle(target);\r\n var paddings = getPaddings(styles);\r\n var horizPad = paddings.left + paddings.right;\r\n var vertPad = paddings.top + paddings.bottom;\r\n // Computed styles of width & height are being used because they are the\r\n // only dimensions available to JS that contain non-rounded values. It could\r\n // be possible to utilize the getBoundingClientRect if only it's data wasn't\r\n // affected by CSS transformations let alone paddings, borders and scroll bars.\r\n var width = toFloat(styles.width), height = toFloat(styles.height);\r\n // Width & height include paddings and borders when the 'border-box' box\r\n // model is applied (except for IE).\r\n if (styles.boxSizing === 'border-box') {\r\n // Following conditions are required to handle Internet Explorer which\r\n // doesn't include paddings and borders to computed CSS dimensions.\r\n //\r\n // We can say that if CSS dimensions + paddings are equal to the \"client\"\r\n // properties then it's either IE, and thus we don't need to subtract\r\n // anything, or an element merely doesn't have paddings/borders styles.\r\n if (Math.round(width + horizPad) !== clientWidth) {\r\n width -= getBordersSize(styles, 'left', 'right') + horizPad;\r\n }\r\n if (Math.round(height + vertPad) !== clientHeight) {\r\n height -= getBordersSize(styles, 'top', 'bottom') + vertPad;\r\n }\r\n }\r\n // Following steps can't be applied to the document's root element as its\r\n // client[Width/Height] properties represent viewport area of the window.\r\n // Besides, it's as well not necessary as the itself neither has\r\n // rendered scroll bars nor it can be clipped.\r\n if (!isDocumentElement(target)) {\r\n // In some browsers (only in Firefox, actually) CSS width & height\r\n // include scroll bars size which can be removed at this step as scroll\r\n // bars are the only difference between rounded dimensions + paddings\r\n // and \"client\" properties, though that is not always true in Chrome.\r\n var vertScrollbar = Math.round(width + horizPad) - clientWidth;\r\n var horizScrollbar = Math.round(height + vertPad) - clientHeight;\r\n // Chrome has a rather weird rounding of \"client\" properties.\r\n // E.g. for an element with content width of 314.2px it sometimes gives\r\n // the client width of 315px and for the width of 314.7px it may give\r\n // 314px. And it doesn't happen all the time. So just ignore this delta\r\n // as a non-relevant.\r\n if (Math.abs(vertScrollbar) !== 1) {\r\n width -= vertScrollbar;\r\n }\r\n if (Math.abs(horizScrollbar) !== 1) {\r\n height -= horizScrollbar;\r\n }\r\n }\r\n return createRectInit(paddings.left, paddings.top, width, height);\r\n}\r\n/**\r\n * Checks whether provided element is an instance of the SVGGraphicsElement.\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nvar isSVGGraphicsElement = (function () {\r\n // Some browsers, namely IE and Edge, don't have the SVGGraphicsElement\r\n // interface.\r\n if (typeof SVGGraphicsElement !== 'undefined') {\r\n return function (target) { return target instanceof getWindowOf(target).SVGGraphicsElement; };\r\n }\r\n // If it's so, then check that element is at least an instance of the\r\n // SVGElement and that it has the \"getBBox\" method.\r\n // eslint-disable-next-line no-extra-parens\r\n return function (target) { return (target instanceof getWindowOf(target).SVGElement &&\r\n typeof target.getBBox === 'function'); };\r\n})();\r\n/**\r\n * Checks whether provided element is a document element ().\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nfunction isDocumentElement(target) {\r\n return target === getWindowOf(target).document.documentElement;\r\n}\r\n/**\r\n * Calculates an appropriate content rectangle for provided html or svg element.\r\n *\r\n * @param {Element} target - Element content rectangle of which needs to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getContentRect(target) {\r\n if (!isBrowser) {\r\n return emptyRect;\r\n }\r\n if (isSVGGraphicsElement(target)) {\r\n return getSVGContentRect(target);\r\n }\r\n return getHTMLElementContentRect(target);\r\n}\r\n/**\r\n * Creates rectangle with an interface of the DOMRectReadOnly.\r\n * Spec: https://drafts.fxtf.org/geometry/#domrectreadonly\r\n *\r\n * @param {DOMRectInit} rectInit - Object with rectangle's x/y coordinates and dimensions.\r\n * @returns {DOMRectReadOnly}\r\n */\r\nfunction createReadOnlyRect(_a) {\r\n var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n // If DOMRectReadOnly is available use it as a prototype for the rectangle.\r\n var Constr = typeof DOMRectReadOnly !== 'undefined' ? DOMRectReadOnly : Object;\r\n var rect = Object.create(Constr.prototype);\r\n // Rectangle's properties are not writable and non-enumerable.\r\n defineConfigurable(rect, {\r\n x: x, y: y, width: width, height: height,\r\n top: y,\r\n right: x + width,\r\n bottom: height + y,\r\n left: x\r\n });\r\n return rect;\r\n}\r\n/**\r\n * Creates DOMRectInit object based on the provided dimensions and the x/y coordinates.\r\n * Spec: https://drafts.fxtf.org/geometry/#dictdef-domrectinit\r\n *\r\n * @param {number} x - X coordinate.\r\n * @param {number} y - Y coordinate.\r\n * @param {number} width - Rectangle's width.\r\n * @param {number} height - Rectangle's height.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction createRectInit(x, y, width, height) {\r\n return { x: x, y: y, width: width, height: height };\r\n}\n\n/**\r\n * Class that is responsible for computations of the content rectangle of\r\n * provided DOM element and for keeping track of it's changes.\r\n */\r\nvar ResizeObservation = /** @class */ (function () {\r\n /**\r\n * Creates an instance of ResizeObservation.\r\n *\r\n * @param {Element} target - Element to be observed.\r\n */\r\n function ResizeObservation(target) {\r\n /**\r\n * Broadcasted width of content rectangle.\r\n *\r\n * @type {number}\r\n */\r\n this.broadcastWidth = 0;\r\n /**\r\n * Broadcasted height of content rectangle.\r\n *\r\n * @type {number}\r\n */\r\n this.broadcastHeight = 0;\r\n /**\r\n * Reference to the last observed content rectangle.\r\n *\r\n * @private {DOMRectInit}\r\n */\r\n this.contentRect_ = createRectInit(0, 0, 0, 0);\r\n this.target = target;\r\n }\r\n /**\r\n * Updates content rectangle and tells whether it's width or height properties\r\n * have changed since the last broadcast.\r\n *\r\n * @returns {boolean}\r\n */\r\n ResizeObservation.prototype.isActive = function () {\r\n var rect = getContentRect(this.target);\r\n this.contentRect_ = rect;\r\n return (rect.width !== this.broadcastWidth ||\r\n rect.height !== this.broadcastHeight);\r\n };\r\n /**\r\n * Updates 'broadcastWidth' and 'broadcastHeight' properties with a data\r\n * from the corresponding properties of the last observed content rectangle.\r\n *\r\n * @returns {DOMRectInit} Last observed content rectangle.\r\n */\r\n ResizeObservation.prototype.broadcastRect = function () {\r\n var rect = this.contentRect_;\r\n this.broadcastWidth = rect.width;\r\n this.broadcastHeight = rect.height;\r\n return rect;\r\n };\r\n return ResizeObservation;\r\n}());\n\nvar ResizeObserverEntry = /** @class */ (function () {\r\n /**\r\n * Creates an instance of ResizeObserverEntry.\r\n *\r\n * @param {Element} target - Element that is being observed.\r\n * @param {DOMRectInit} rectInit - Data of the element's content rectangle.\r\n */\r\n function ResizeObserverEntry(target, rectInit) {\r\n var contentRect = createReadOnlyRect(rectInit);\r\n // According to the specification following properties are not writable\r\n // and are also not enumerable in the native implementation.\r\n //\r\n // Property accessors are not being used as they'd require to define a\r\n // private WeakMap storage which may cause memory leaks in browsers that\r\n // don't support this type of collections.\r\n defineConfigurable(this, { target: target, contentRect: contentRect });\r\n }\r\n return ResizeObserverEntry;\r\n}());\n\nvar ResizeObserverSPI = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserver.\r\n *\r\n * @param {ResizeObserverCallback} callback - Callback function that is invoked\r\n * when one of the observed elements changes it's content dimensions.\r\n * @param {ResizeObserverController} controller - Controller instance which\r\n * is responsible for the updates of observer.\r\n * @param {ResizeObserver} callbackCtx - Reference to the public\r\n * ResizeObserver instance which will be passed to callback function.\r\n */\r\n function ResizeObserverSPI(callback, controller, callbackCtx) {\r\n /**\r\n * Collection of resize observations that have detected changes in dimensions\r\n * of elements.\r\n *\r\n * @private {Array}\r\n */\r\n this.activeObservations_ = [];\r\n /**\r\n * Registry of the ResizeObservation instances.\r\n *\r\n * @private {Map}\r\n */\r\n this.observations_ = new MapShim();\r\n if (typeof callback !== 'function') {\r\n throw new TypeError('The callback provided as parameter 1 is not a function.');\r\n }\r\n this.callback_ = callback;\r\n this.controller_ = controller;\r\n this.callbackCtx_ = callbackCtx;\r\n }\r\n /**\r\n * Starts observing provided element.\r\n *\r\n * @param {Element} target - Element to be observed.\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.observe = function (target) {\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n // Do nothing if current environment doesn't have the Element interface.\r\n if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n return;\r\n }\r\n if (!(target instanceof getWindowOf(target).Element)) {\r\n throw new TypeError('parameter 1 is not of type \"Element\".');\r\n }\r\n var observations = this.observations_;\r\n // Do nothing if element is already being observed.\r\n if (observations.has(target)) {\r\n return;\r\n }\r\n observations.set(target, new ResizeObservation(target));\r\n this.controller_.addObserver(this);\r\n // Force the update of observations.\r\n this.controller_.refresh();\r\n };\r\n /**\r\n * Stops observing provided element.\r\n *\r\n * @param {Element} target - Element to stop observing.\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.unobserve = function (target) {\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n // Do nothing if current environment doesn't have the Element interface.\r\n if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n return;\r\n }\r\n if (!(target instanceof getWindowOf(target).Element)) {\r\n throw new TypeError('parameter 1 is not of type \"Element\".');\r\n }\r\n var observations = this.observations_;\r\n // Do nothing if element is not being observed.\r\n if (!observations.has(target)) {\r\n return;\r\n }\r\n observations.delete(target);\r\n if (!observations.size) {\r\n this.controller_.removeObserver(this);\r\n }\r\n };\r\n /**\r\n * Stops observing all elements.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.disconnect = function () {\r\n this.clearActive();\r\n this.observations_.clear();\r\n this.controller_.removeObserver(this);\r\n };\r\n /**\r\n * Collects observation instances the associated element of which has changed\r\n * it's content rectangle.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.gatherActive = function () {\r\n var _this = this;\r\n this.clearActive();\r\n this.observations_.forEach(function (observation) {\r\n if (observation.isActive()) {\r\n _this.activeObservations_.push(observation);\r\n }\r\n });\r\n };\r\n /**\r\n * Invokes initial callback function with a list of ResizeObserverEntry\r\n * instances collected from active resize observations.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.broadcastActive = function () {\r\n // Do nothing if observer doesn't have active observations.\r\n if (!this.hasActive()) {\r\n return;\r\n }\r\n var ctx = this.callbackCtx_;\r\n // Create ResizeObserverEntry instance for every active observation.\r\n var entries = this.activeObservations_.map(function (observation) {\r\n return new ResizeObserverEntry(observation.target, observation.broadcastRect());\r\n });\r\n this.callback_.call(ctx, entries, ctx);\r\n this.clearActive();\r\n };\r\n /**\r\n * Clears the collection of active observations.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.clearActive = function () {\r\n this.activeObservations_.splice(0);\r\n };\r\n /**\r\n * Tells whether observer has active observations.\r\n *\r\n * @returns {boolean}\r\n */\r\n ResizeObserverSPI.prototype.hasActive = function () {\r\n return this.activeObservations_.length > 0;\r\n };\r\n return ResizeObserverSPI;\r\n}());\n\n// Registry of internal observers. If WeakMap is not available use current shim\r\n// for the Map collection as it has all required methods and because WeakMap\r\n// can't be fully polyfilled anyway.\r\nvar observers = typeof WeakMap !== 'undefined' ? new WeakMap() : new MapShim();\r\n/**\r\n * ResizeObserver API. Encapsulates the ResizeObserver SPI implementation\r\n * exposing only those methods and properties that are defined in the spec.\r\n */\r\nvar ResizeObserver = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserver.\r\n *\r\n * @param {ResizeObserverCallback} callback - Callback that is invoked when\r\n * dimensions of the observed elements change.\r\n */\r\n function ResizeObserver(callback) {\r\n if (!(this instanceof ResizeObserver)) {\r\n throw new TypeError('Cannot call a class as a function.');\r\n }\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n var controller = ResizeObserverController.getInstance();\r\n var observer = new ResizeObserverSPI(callback, controller, this);\r\n observers.set(this, observer);\r\n }\r\n return ResizeObserver;\r\n}());\r\n// Expose public methods of ResizeObserver.\r\n[\r\n 'observe',\r\n 'unobserve',\r\n 'disconnect'\r\n].forEach(function (method) {\r\n ResizeObserver.prototype[method] = function () {\r\n var _a;\r\n return (_a = observers.get(this))[method].apply(_a, arguments);\r\n };\r\n});\n\nvar index = (function () {\r\n // Export existing implementation if available.\r\n if (typeof global$1.ResizeObserver !== 'undefined') {\r\n return global$1.ResizeObserver;\r\n }\r\n return ResizeObserver;\r\n})();\n\nexport default index;\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ResizeObserver from \"resize-observer-polyfill\"\nimport {\n NEVER,\n Observable,\n Subject,\n defer,\n filter,\n finalize,\n map,\n merge,\n of,\n shareReplay,\n startWith,\n switchMap,\n tap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementSize {\n width: number /* Element width */\n height: number /* Element height */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Resize observer entry subject\n */\nconst entry$ = new Subject()\n\n/**\n * Resize observer observable\n *\n * This observable will create a `ResizeObserver` on the first subscription\n * and will automatically terminate it when there are no more subscribers.\n * It's quite important to centralize observation in a single `ResizeObserver`,\n * as the performance difference can be quite dramatic, as the link shows.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n new ResizeObserver(entries => {\n for (const entry of entries)\n entry$.next(entry)\n })\n))\n .pipe(\n switchMap(observer => merge(NEVER, of(observer))\n .pipe(\n finalize(() => observer.disconnect())\n )\n ),\n shareReplay(1)\n )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n el: HTMLElement\n): ElementSize {\n return {\n width: el.offsetWidth,\n height: el.offsetHeight\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n el: HTMLElement\n): Observable {\n return observer$\n .pipe(\n tap(observer => observer.observe(el)),\n switchMap(observer => entry$\n .pipe(\n filter(({ target }) => target === el),\n finalize(() => observer.unobserve(el)),\n map(() => getElementSize(el))\n )\n ),\n startWith(getElementSize(el))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ElementSize } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content size (= scroll width and height)\n *\n * @param el - Element\n *\n * @returns Element content size\n */\nexport function getElementContentSize(\n el: HTMLElement\n): ElementSize {\n return {\n width: el.scrollWidth,\n height: el.scrollHeight\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n NEVER,\n Observable,\n Subject,\n defer,\n distinctUntilChanged,\n filter,\n finalize,\n map,\n merge,\n of,\n shareReplay,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport {\n getElementContentSize,\n getElementSize,\n watchElementContentOffset\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Intersection observer entry subject\n */\nconst entry$ = new Subject()\n\n/**\n * Intersection observer observable\n *\n * This observable will create an `IntersectionObserver` on first subscription\n * and will automatically terminate it when there are no more subscribers.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n new IntersectionObserver(entries => {\n for (const entry of entries)\n entry$.next(entry)\n }, {\n threshold: 0\n })\n))\n .pipe(\n switchMap(observer => merge(NEVER, of(observer))\n .pipe(\n finalize(() => observer.disconnect())\n )\n ),\n shareReplay(1)\n )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element visibility\n *\n * @param el - Element\n *\n * @returns Element visibility observable\n */\nexport function watchElementVisibility(\n el: HTMLElement\n): Observable {\n return observer$\n .pipe(\n tap(observer => observer.observe(el)),\n switchMap(observer => entry$\n .pipe(\n filter(({ target }) => target === el),\n finalize(() => observer.unobserve(el)),\n map(({ isIntersecting }) => isIntersecting)\n )\n )\n )\n}\n\n/**\n * Watch element boundary\n *\n * This function returns an observable which emits whether the bottom content\n * boundary (= scroll offset) of an element is within a certain threshold.\n *\n * @param el - Element\n * @param threshold - Threshold\n *\n * @returns Element boundary observable\n */\nexport function watchElementBoundary(\n el: HTMLElement, threshold = 16\n): Observable {\n return watchElementContentOffset(el)\n .pipe(\n map(({ y }) => {\n const visible = getElementSize(el)\n const content = getElementContentSize(el)\n return y >= (\n content.height - visible.height - threshold\n )\n }),\n distinctUntilChanged()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n startWith\n} from \"rxjs\"\n\nimport { getElement } from \"../element\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle\n */\nexport type Toggle =\n | \"drawer\" /* Toggle for drawer */\n | \"search\" /* Toggle for search */\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle map\n */\nconst toggles: Record = {\n drawer: getElement(\"[data-md-toggle=drawer]\"),\n search: getElement(\"[data-md-toggle=search]\")\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the value of a toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value\n */\nexport function getToggle(name: Toggle): boolean {\n return toggles[name].checked\n}\n\n/**\n * Set toggle\n *\n * Simulating a click event seems to be the most cross-browser compatible way\n * of changing the value while also emitting a `change` event. Before, Material\n * used `CustomEvent` to programmatically change the value of a toggle, but this\n * is a much simpler and cleaner solution which doesn't require a polyfill.\n *\n * @param name - Toggle\n * @param value - Toggle value\n */\nexport function setToggle(name: Toggle, value: boolean): void {\n if (toggles[name].checked !== value)\n toggles[name].click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value observable\n */\nexport function watchToggle(name: Toggle): Observable {\n const el = toggles[name]\n return fromEvent(el, \"change\")\n .pipe(\n map(() => el.checked),\n startWith(el.checked)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n share\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../element\"\nimport { getToggle } from \"../toggle\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Keyboard mode\n */\nexport type KeyboardMode =\n | \"global\" /* Global */\n | \"search\" /* Search is open */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Keyboard\n */\nexport interface Keyboard {\n mode: KeyboardMode /* Keyboard mode */\n type: string /* Key type */\n claim(): void /* Key claim */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether an element may receive keyboard input\n *\n * @param el - Element\n * @param type - Key type\n *\n * @returns Test result\n */\nfunction isSusceptibleToKeyboard(\n el: HTMLElement, type: string\n): boolean {\n switch (el.constructor) {\n\n /* Input elements */\n case HTMLInputElement:\n /* @ts-expect-error - omit unnecessary type cast */\n if (el.type === \"radio\")\n return /^Arrow/.test(type)\n else\n return true\n\n /* Select element and textarea */\n case HTMLSelectElement:\n case HTMLTextAreaElement:\n return true\n\n /* Everything else */\n default:\n return el.isContentEditable\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch keyboard\n *\n * @returns Keyboard observable\n */\nexport function watchKeyboard(): Observable {\n return fromEvent(window, \"keydown\")\n .pipe(\n filter(ev => !(ev.metaKey || ev.ctrlKey)),\n map(ev => ({\n mode: getToggle(\"search\") ? \"search\" : \"global\",\n type: ev.key,\n claim() {\n ev.preventDefault()\n ev.stopPropagation()\n }\n } as Keyboard)),\n filter(({ mode, type }) => {\n if (mode === \"global\") {\n const active = getActiveElement()\n if (typeof active !== \"undefined\")\n return !isSusceptibleToKeyboard(active, type)\n }\n return true\n }),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Subject } from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location\n *\n * This function returns a `URL` object (and not `Location`) to normalize the\n * typings across the application. Furthermore, locations need to be tracked\n * without setting them and `Location` is a singleton which represents the\n * current location.\n *\n * @returns URL\n */\nexport function getLocation(): URL {\n return new URL(location.href)\n}\n\n/**\n * Set location\n *\n * @param url - URL to change to\n */\nexport function setLocation(url: URL): void {\n location.href = url.href\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location\n *\n * @returns Location subject\n */\nexport function watchLocation(): Subject {\n return new Subject()\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { JSX as JSXInternal } from \"preact\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * HTML attributes\n */\ntype Attributes =\n & JSXInternal.HTMLAttributes\n & JSXInternal.SVGAttributes\n & Record\n\n/**\n * Child element\n */\ntype Child =\n | HTMLElement\n | Text\n | string\n | number\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Append a child node to an element\n *\n * @param el - Element\n * @param child - Child node(s)\n */\nfunction appendChild(el: HTMLElement, child: Child | Child[]): void {\n\n /* Handle primitive types (including raw HTML) */\n if (typeof child === \"string\" || typeof child === \"number\") {\n el.innerHTML += child.toString()\n\n /* Handle nodes */\n } else if (child instanceof Node) {\n el.appendChild(child)\n\n /* Handle nested children */\n } else if (Array.isArray(child)) {\n for (const node of child)\n appendChild(el, node)\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * JSX factory\n *\n * @template T - Element type\n *\n * @param tag - HTML tag\n * @param attributes - HTML attributes\n * @param children - Child elements\n *\n * @returns Element\n */\nexport function h(\n tag: T, attributes?: Attributes | null, ...children: Child[]\n): HTMLElementTagNameMap[T]\n\nexport function h(\n tag: string, attributes?: Attributes | null, ...children: Child[]\n): T\n\nexport function h(\n tag: string, attributes?: Attributes | null, ...children: Child[]\n): T {\n const el = document.createElement(tag)\n\n /* Set attributes, if any */\n if (attributes)\n for (const attr of Object.keys(attributes)) {\n if (typeof attributes[attr] === \"undefined\")\n continue\n\n /* Set default attribute or boolean */\n if (typeof attributes[attr] !== \"boolean\")\n el.setAttribute(attr, attributes[attr])\n else\n el.setAttribute(attr, \"\")\n }\n\n /* Append child nodes */\n for (const child of children)\n appendChild(el, child)\n\n /* Return element */\n return el as T\n}\n\n/* ----------------------------------------------------------------------------\n * Namespace\n * ------------------------------------------------------------------------- */\n\nexport declare namespace h {\n namespace JSX {\n type Element = HTMLElement\n type IntrinsicElements = JSXInternal.IntrinsicElements\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Truncate a string after the given number of characters\n *\n * This is not a very reasonable approach, since the summaries kind of suck.\n * It would be better to create something more intelligent, highlighting the\n * search occurrences and making a better summary out of it, but this note was\n * written three years ago, so who knows if we'll ever fix it.\n *\n * @param value - Value to be truncated\n * @param n - Number of characters\n *\n * @returns Truncated value\n */\nexport function truncate(value: string, n: number): string {\n let i = n\n if (value.length > i) {\n while (value[i] !== \" \" && --i > 0) { /* keep eating */ }\n return `${value.substring(0, i)}...`\n }\n return value\n}\n\n/**\n * Round a number for display with repository facts\n *\n * This is a reverse-engineered version of GitHub's weird rounding algorithm\n * for stars, forks and all other numbers. While all numbers below `1,000` are\n * returned as-is, bigger numbers are converted to fixed numbers:\n *\n * - `1,049` => `1k`\n * - `1,050` => `1.1k`\n * - `1,949` => `1.9k`\n * - `1,950` => `2k`\n *\n * @param value - Original value\n *\n * @returns Rounded value\n */\nexport function round(value: number): string {\n if (value > 999) {\n const digits = +((value - 950) % 1000 > 99)\n return `${((value + 0.000001) / 1000).toFixed(digits)}k`\n } else {\n return value.toString()\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n shareReplay,\n startWith\n} from \"rxjs\"\n\nimport { getOptionalElement } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location hash\n *\n * @returns Location hash\n */\nexport function getLocationHash(): string {\n return location.hash.substring(1)\n}\n\n/**\n * Set location hash\n *\n * Setting a new fragment identifier via `location.hash` will have no effect\n * if the value doesn't change. When a new fragment identifier is set, we want\n * the browser to target the respective element at all times, which is why we\n * use this dirty little trick.\n *\n * @param hash - Location hash\n */\nexport function setLocationHash(hash: string): void {\n const el = h(\"a\", { href: hash })\n el.addEventListener(\"click\", ev => ev.stopPropagation())\n el.click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location hash\n *\n * @returns Location hash observable\n */\nexport function watchLocationHash(): Observable {\n return fromEvent(window, \"hashchange\")\n .pipe(\n map(getLocationHash),\n startWith(getLocationHash()),\n filter(hash => hash.length > 0),\n shareReplay(1)\n )\n}\n\n/**\n * Watch location target\n *\n * @returns Location target observable\n */\nexport function watchLocationTarget(): Observable {\n return watchLocationHash()\n .pipe(\n map(id => getOptionalElement(`[id=\"${id}\"]`)!),\n filter(el => typeof el !== \"undefined\")\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n fromEvent,\n fromEventPattern,\n map,\n merge,\n startWith,\n switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch media query\n *\n * Note that although `MediaQueryList.addListener` is deprecated we have to\n * use it, because it's the only way to ensure proper downward compatibility.\n *\n * @see https://bit.ly/3dUBH2m - GitHub issue\n *\n * @param query - Media query\n *\n * @returns Media observable\n */\nexport function watchMedia(query: string): Observable {\n const media = matchMedia(query)\n return fromEventPattern(next => (\n media.addListener(() => next(media.matches))\n ))\n .pipe(\n startWith(media.matches)\n )\n}\n\n/**\n * Watch print mode\n *\n * @returns Print observable\n */\nexport function watchPrint(): Observable {\n const media = matchMedia(\"print\")\n return merge(\n fromEvent(window, \"beforeprint\").pipe(map(() => true)),\n fromEvent(window, \"afterprint\").pipe(map(() => false))\n )\n .pipe(\n startWith(media.matches)\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Toggle an observable with a media observable\n *\n * @template T - Data type\n *\n * @param query$ - Media observable\n * @param factory - Observable factory\n *\n * @returns Toggled observable\n */\nexport function at(\n query$: Observable, factory: () => Observable\n): Observable {\n return query$\n .pipe(\n switchMap(active => active ? factory() : EMPTY)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n catchError,\n from,\n map,\n of,\n shareReplay,\n switchMap,\n throwError\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * If the request fails (e.g. when dispatched from `file://` locations), the\n * observable will complete without emitting a value.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Response observable\n */\nexport function request(\n url: URL | string, options: RequestInit = { credentials: \"same-origin\" }\n): Observable {\n return from(fetch(`${url}`, options))\n .pipe(\n catchError(() => EMPTY),\n switchMap(res => res.status !== 200\n ? throwError(() => new Error(res.statusText))\n : of(res)\n )\n )\n}\n\n/**\n * Fetch JSON from the given URL\n *\n * @template T - Data type\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestJSON(\n url: URL | string, options?: RequestInit\n): Observable {\n return request(url, options)\n .pipe(\n switchMap(res => res.json()),\n shareReplay(1)\n )\n}\n\n/**\n * Fetch XML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestXML(\n url: URL | string, options?: RequestInit\n): Observable {\n const dom = new DOMParser()\n return request(url, options)\n .pipe(\n switchMap(res => res.text()),\n map(res => dom.parseFromString(res, \"text/xml\")),\n shareReplay(1)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n defer,\n finalize,\n fromEvent,\n map,\n merge,\n switchMap,\n take,\n throwError\n} from \"rxjs\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create and load a `script` element\n *\n * This function returns an observable that will emit when the script was\n * successfully loaded, or throw an error if it didn't.\n *\n * @param src - Script URL\n *\n * @returns Script observable\n */\nexport function watchScript(src: string): Observable {\n const script = h(\"script\", { src })\n return defer(() => {\n document.head.appendChild(script)\n return merge(\n fromEvent(script, \"load\"),\n fromEvent(script, \"error\")\n .pipe(\n switchMap(() => (\n throwError(() => new ReferenceError(`Invalid script: ${src}`))\n ))\n )\n )\n .pipe(\n map(() => undefined),\n finalize(() => document.head.removeChild(script)),\n take(1)\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport offset\n */\nexport interface ViewportOffset {\n x: number /* Horizontal offset */\n y: number /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport offset\n *\n * On iOS Safari, viewport offset can be negative due to overflow scrolling.\n * As this may induce strange behaviors downstream, we'll just limit it to 0.\n *\n * @returns Viewport offset\n */\nexport function getViewportOffset(): ViewportOffset {\n return {\n x: Math.max(0, scrollX),\n y: Math.max(0, scrollY)\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport offset\n *\n * @returns Viewport offset observable\n */\nexport function watchViewportOffset(): Observable {\n return merge(\n fromEvent(window, \"scroll\", { passive: true }),\n fromEvent(window, \"resize\", { passive: true })\n )\n .pipe(\n map(getViewportOffset),\n startWith(getViewportOffset())\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport size\n */\nexport interface ViewportSize {\n width: number /* Viewport width */\n height: number /* Viewport height */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport size\n *\n * @returns Viewport size\n */\nexport function getViewportSize(): ViewportSize {\n return {\n width: innerWidth,\n height: innerHeight\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport size\n *\n * @returns Viewport size observable\n */\nexport function watchViewportSize(): Observable {\n return fromEvent(window, \"resize\", { passive: true })\n .pipe(\n map(getViewportSize),\n startWith(getViewportSize())\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n map,\n shareReplay\n} from \"rxjs\"\n\nimport {\n ViewportOffset,\n watchViewportOffset\n} from \"../offset\"\nimport {\n ViewportSize,\n watchViewportSize\n} from \"../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport\n */\nexport interface Viewport {\n offset: ViewportOffset /* Viewport offset */\n size: ViewportSize /* Viewport size */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport\n *\n * @returns Viewport observable\n */\nexport function watchViewport(): Observable {\n return combineLatest([\n watchViewportOffset(),\n watchViewportSize()\n ])\n .pipe(\n map(([offset, size]) => ({ offset, size })),\n shareReplay(1)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n distinctUntilKeyChanged,\n map\n} from \"rxjs\"\n\nimport { Header } from \"~/components\"\n\nimport { getElementOffset } from \"../../element\"\nimport { Viewport } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
/* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport relative to element\n *\n * @param el - Element\n * @param options - Options\n *\n * @returns Viewport observable\n */\nexport function watchViewportAt(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n const size$ = viewport$\n .pipe(\n distinctUntilKeyChanged(\"size\")\n )\n\n /* Compute element offset */\n const offset$ = combineLatest([size$, header$])\n .pipe(\n map(() => getElementOffset(el))\n )\n\n /* Compute relative viewport, return hot observable */\n return combineLatest([header$, viewport$, offset$])\n .pipe(\n map(([{ height }, { offset, size }, { x, y }]) => ({\n offset: {\n x: offset.x - x,\n y: offset.y - y + height\n },\n size\n }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n fromEvent,\n map,\n share,\n switchMap,\n tap,\n throttle\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Worker message\n */\nexport interface WorkerMessage {\n type: unknown /* Message type */\n data?: unknown /* Message data */\n}\n\n/**\n * Worker handler\n *\n * @template T - Message type\n */\nexport interface WorkerHandler<\n T extends WorkerMessage\n> {\n tx$: Subject /* Message transmission subject */\n rx$: Observable /* Message receive observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n *\n * @template T - Worker message type\n */\ninterface WatchOptions {\n tx$: Observable /* Message transmission observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch a web worker\n *\n * This function returns an observable that sends all values emitted by the\n * message observable to the web worker. Web worker communication is expected\n * to be bidirectional (request-response) and synchronous. Messages that are\n * emitted during a pending request are throttled, the last one is emitted.\n *\n * @param worker - Web worker\n * @param options - Options\n *\n * @returns Worker message observable\n */\nexport function watchWorker(\n worker: Worker, { tx$ }: WatchOptions\n): Observable {\n\n /* Intercept messages from worker-like objects */\n const rx$ = fromEvent(worker, \"message\")\n .pipe(\n map(({ data }) => data as T)\n )\n\n /* Send and receive messages, return hot observable */\n return tx$\n .pipe(\n throttle(() => rx$, { leading: true, trailing: true }),\n tap(message => worker.postMessage(message)),\n switchMap(() => rx$),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getLocation } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Feature flag\n */\nexport type Flag =\n | \"content.code.annotate\" /* Code annotations */\n | \"content.tabs.link\" /* Link content tabs */\n | \"header.autohide\" /* Hide header */\n | \"navigation.expand\" /* Automatic expansion */\n | \"navigation.indexes\" /* Section pages */\n | \"navigation.instant\" /* Instant loading */\n | \"navigation.sections\" /* Section navigation */\n | \"navigation.tabs\" /* Tabs navigation */\n | \"navigation.tabs.sticky\" /* Tabs navigation (sticky) */\n | \"navigation.top\" /* Back-to-top button */\n | \"navigation.tracking\" /* Anchor tracking */\n | \"search.highlight\" /* Search highlighting */\n | \"search.share\" /* Search sharing */\n | \"search.suggest\" /* Search suggestions */\n | \"toc.integrate\" /* Integrated table of contents */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Translation\n */\nexport type Translation =\n | \"clipboard.copy\" /* Copy to clipboard */\n | \"clipboard.copied\" /* Copied to clipboard */\n | \"search.config.lang\" /* Search language */\n | \"search.config.pipeline\" /* Search pipeline */\n | \"search.config.separator\" /* Search separator */\n | \"search.placeholder\" /* Search */\n | \"search.result.placeholder\" /* Type to start searching */\n | \"search.result.none\" /* No matching documents */\n | \"search.result.one\" /* 1 matching document */\n | \"search.result.other\" /* # matching documents */\n | \"search.result.more.one\" /* 1 more on this page */\n | \"search.result.more.other\" /* # more on this page */\n | \"search.result.term.missing\" /* Missing */\n | \"select.version.title\" /* Version selector */\n\n/**\n * Translations\n */\nexport type Translations = Record\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Versioning\n */\nexport interface Versioning {\n provider: \"mike\" /* Version provider */\n default?: string /* Default version */\n}\n\n/**\n * Configuration\n */\nexport interface Config {\n base: string /* Base URL */\n features: Flag[] /* Feature flags */\n translations: Translations /* Translations */\n search: string /* Search worker URL */\n version?: Versioning /* Versioning */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration and make base URL absolute\n */\nconst script = getElement(\"#__config\")\nconst config: Config = JSON.parse(script.textContent!)\nconfig.base = `${new URL(config.base, getLocation())}`\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration\n *\n * @returns Global configuration\n */\nexport function configuration(): Config {\n return config\n}\n\n/**\n * Check whether a feature flag is enabled\n *\n * @param flag - Feature flag\n *\n * @returns Test result\n */\nexport function feature(flag: Flag): boolean {\n return config.features.includes(flag)\n}\n\n/**\n * Retrieve the translation for the given key\n *\n * @param key - Key to be translated\n * @param value - Positional value, if any\n *\n * @returns Translation\n */\nexport function translation(\n key: Translation, value?: string | number\n): string {\n return typeof value !== \"undefined\"\n ? config.translations[key].replace(\"#\", value.toString())\n : config.translations[key]\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type\n */\nexport type ComponentType =\n | \"announce\" /* Announcement bar */\n | \"container\" /* Container */\n | \"content\" /* Content */\n | \"dialog\" /* Dialog */\n | \"header\" /* Header */\n | \"header-title\" /* Header title */\n | \"header-topic\" /* Header topic */\n | \"main\" /* Main area */\n | \"outdated\" /* Version warning */\n | \"palette\" /* Color palette */\n | \"search\" /* Search */\n | \"search-query\" /* Search input */\n | \"search-result\" /* Search results */\n | \"search-share\" /* Search sharing */\n | \"search-suggest\" /* Search suggestions */\n | \"sidebar\" /* Sidebar */\n | \"skip\" /* Skip link */\n | \"source\" /* Repository information */\n | \"tabs\" /* Navigation tabs */\n | \"toc\" /* Table of contents */\n | \"top\" /* Back-to-top button */\n\n/**\n * Component\n *\n * @template T - Component type\n * @template U - Reference type\n */\nexport type Component<\n T extends {} = {},\n U extends HTMLElement = HTMLElement\n> =\n T & {\n ref: U /* Component reference */\n }\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type map\n */\ninterface ComponentTypeMap {\n \"announce\": HTMLElement /* Announcement bar */\n \"container\": HTMLElement /* Container */\n \"content\": HTMLElement /* Content */\n \"dialog\": HTMLElement /* Dialog */\n \"header\": HTMLElement /* Header */\n \"header-title\": HTMLElement /* Header title */\n \"header-topic\": HTMLElement /* Header topic */\n \"main\": HTMLElement /* Main area */\n \"outdated\": HTMLElement /* Version warning */\n \"palette\": HTMLElement /* Color palette */\n \"search\": HTMLElement /* Search */\n \"search-query\": HTMLInputElement /* Search input */\n \"search-result\": HTMLElement /* Search results */\n \"search-share\": HTMLAnchorElement /* Search sharing */\n \"search-suggest\": HTMLElement /* Search suggestions */\n \"sidebar\": HTMLElement /* Sidebar */\n \"skip\": HTMLAnchorElement /* Skip link */\n \"source\": HTMLAnchorElement /* Repository information */\n \"tabs\": HTMLElement /* Navigation tabs */\n \"toc\": HTMLElement /* Table of contents */\n \"top\": HTMLAnchorElement /* Back-to-top button */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the element for a given component or throw a reference error\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getComponentElement(\n type: T, node: ParentNode = document\n): ComponentTypeMap[T] {\n return getElement(`[data-md-component=${type}]`, node)\n}\n\n/**\n * Retrieve all elements for a given component\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getComponentElements(\n type: T, node: ParentNode = document\n): ComponentTypeMap[T][] {\n return getElements(`[data-md-component=${type}]`, node)\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n map,\n mergeWith,\n switchMap,\n take,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n getElementContentSize,\n watchElementSize,\n watchElementVisibility\n} from \"~/browser\"\nimport { renderClipboardButton } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n Annotation,\n mountAnnotationList\n} from \"../../annotation\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Code block\n */\nexport interface CodeBlock {\n scrollable: boolean /* Code block overflows */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for Clipboard.js integration\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find candidate list element directly following a code block\n *\n * @param el - Code block element\n *\n * @returns List element or nothing\n */\nfunction findCandidateList(el: HTMLElement): HTMLElement | undefined {\n if (el.nextElementSibling) {\n const sibling = el.nextElementSibling as HTMLElement\n if (sibling.tagName === \"OL\")\n return sibling\n\n /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n else if (sibling.tagName === \"P\" && !sibling.children.length)\n return findCandidateList(sibling)\n }\n\n /* Everything else */\n return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch code block\n *\n * This function monitors size changes of the viewport, as well as switches of\n * content tabs with embedded code blocks, as both may trigger overflow.\n *\n * @param el - Code block element\n *\n * @returns Code block observable\n */\nexport function watchCodeBlock(\n el: HTMLElement\n): Observable {\n return watchElementSize(el)\n .pipe(\n map(({ width }) => {\n const content = getElementContentSize(el)\n return {\n scrollable: content.width > width\n }\n }),\n distinctUntilKeyChanged(\"scrollable\")\n )\n}\n\n/**\n * Mount code block\n *\n * This function ensures that an overflowing code block is focusable through\n * keyboard, so it can be scrolled without a mouse to improve on accessibility.\n * Furthermore, if code annotations are enabled, they are mounted if and only\n * if the code block is currently visible, e.g., not in a hidden content tab.\n *\n * @param el - Code block element\n * @param options - Options\n *\n * @returns Code block and annotation component observable\n */\nexport function mountCodeBlock(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const { matches: hover } = matchMedia(\"(hover)\")\n\n /* Defer mounting of code block - see https://bit.ly/3vHVoVD */\n const factory$ = defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ scrollable }) => {\n if (scrollable && hover)\n el.setAttribute(\"tabindex\", \"0\")\n else\n el.removeAttribute(\"tabindex\")\n })\n\n /* Render button for Clipboard.js integration */\n if (ClipboardJS.isSupported()) {\n const parent = el.closest(\"pre\")!\n parent.id = `__code_${++sequence}`\n parent.insertBefore(\n renderClipboardButton(parent.id),\n el\n )\n }\n\n /* Handle code annotations */\n const container = el.closest(\".highlight\")\n if (container instanceof HTMLElement) {\n const list = findCandidateList(container)\n\n /* Mount code annotations, if enabled */\n if (typeof list !== \"undefined\" && (\n container.classList.contains(\"annotate\") ||\n feature(\"content.code.annotate\")\n )) {\n const annotations$ = mountAnnotationList(list, el, options)\n\n /* Create and return component */\n return watchCodeBlock(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state })),\n mergeWith(\n watchElementSize(container)\n .pipe(\n takeUntil(push$.pipe(takeLast(1))),\n map(({ width, height }) => width && height),\n distinctUntilChanged(),\n switchMap(active => active ? annotations$ : EMPTY)\n )\n )\n )\n }\n }\n\n /* Create and return component */\n return watchCodeBlock(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n\n /* Mount code block on first sight */\n return watchElementVisibility(el)\n .pipe(\n filter(visible => visible),\n take(1),\n switchMap(() => factory$)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render an empty annotation\n *\n * @param id - Annotation identifier\n *\n * @returns Element\n */\nexport function renderAnnotation(id: number): HTMLElement {\n return (\n \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a 'copy-to-clipboard' button\n *\n * @param id - Unique identifier\n *\n * @returns Element\n */\nexport function renderClipboardButton(id: string): HTMLElement {\n return (\n code`}\n >\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { feature, translation } from \"~/_\"\nimport {\n SearchDocument,\n SearchMetadata,\n SearchResultItem\n} from \"~/integrations/search\"\nimport { h, truncate } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Render flag\n */\nconst enum Flag {\n TEASER = 1, /* Render teaser */\n PARENT = 2 /* Render as parent */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper function\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search document\n *\n * @param document - Search document\n * @param flag - Render flags\n *\n * @returns Element\n */\nfunction renderSearchDocument(\n document: SearchDocument & SearchMetadata, flag: Flag\n): HTMLElement {\n const parent = flag & Flag.PARENT\n const teaser = flag & Flag.TEASER\n\n /* Render missing query terms */\n const missing = Object.keys(document.terms)\n .filter(key => !document.terms[key])\n .reduce((list, key) => [\n ...list, {key}, \" \"\n ], [])\n .slice(0, -1)\n\n /* Assemble query string for highlighting */\n const url = new URL(document.location)\n if (feature(\"search.highlight\"))\n url.searchParams.set(\"h\", Object.entries(document.terms)\n .filter(([, match]) => match)\n .reduce((highlight, [value]) => `${highlight} ${value}`.trim(), \"\")\n )\n\n /* Render article or section, depending on flags */\n return (\n \n \n {parent > 0 &&
}\n

{document.title}

\n {teaser > 0 && document.text.length > 0 &&\n

\n {truncate(document.text, 320)}\n

\n }\n {document.tags && document.tags.map(tag => (\n {tag}\n ))}\n {teaser > 0 && missing.length > 0 &&\n

\n {translation(\"search.result.term.missing\")}: {...missing}\n

\n }\n \n \n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search result\n *\n * @param result - Search result\n *\n * @returns Element\n */\nexport function renderSearchResultItem(\n result: SearchResultItem\n): HTMLElement {\n const threshold = result[0].score\n const docs = [...result]\n\n /* Find and extract parent article */\n const parent = docs.findIndex(doc => !doc.location.includes(\"#\"))\n const [article] = docs.splice(parent, 1)\n\n /* Determine last index above threshold */\n let index = docs.findIndex(doc => doc.score < threshold)\n if (index === -1)\n index = docs.length\n\n /* Partition sections */\n const best = docs.slice(0, index)\n const more = docs.slice(index)\n\n /* Render children */\n const children = [\n renderSearchDocument(article, Flag.PARENT | +(!parent && index === 0)),\n ...best.map(section => renderSearchDocument(section, Flag.TEASER)),\n ...more.length ? [\n
\n \n {more.length > 0 && more.length === 1\n ? translation(\"search.result.more.one\")\n : translation(\"search.result.more.other\", more.length)\n }\n \n {...more.map(section => renderSearchDocument(section, Flag.TEASER))}\n
\n ] : []\n ]\n\n /* Render search result */\n return (\n
  • \n {children}\n
    • \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SourceFacts } from \"~/components\"\nimport { h, round } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render repository facts\n *\n * @param facts - Repository facts\n *\n * @returns Element\n */\nexport function renderSourceFacts(facts: SourceFacts): HTMLElement {\n return (\n
      \n {Object.entries(facts).map(([key, value]) => (\n
    • \n {typeof value === \"number\" ? round(value) : value}\n
      • \n ))}\n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tabbed control type\n */\ntype TabbedControlType =\n | \"prev\"\n | \"next\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render control for content tabs\n *\n * @param type - Control type\n *\n * @returns Element\n */\nexport function renderTabbedControl(\n type: TabbedControlType\n): HTMLElement {\n const classes = `tabbed-control tabbed-control--${type}`\n return (\n \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a table inside a wrapper to improve scrolling on mobile\n *\n * @param table - Table element\n *\n * @returns Element\n */\nexport function renderTable(table: HTMLElement): HTMLElement {\n return (\n
    \n
    \n {table}\n
    \n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { configuration, translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Version\n */\nexport interface Version {\n version: string /* Version identifier */\n title: string /* Version title */\n aliases: string[] /* Version aliases */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version\n *\n * @param version - Version\n *\n * @returns Element\n */\nfunction renderVersion(version: Version): HTMLElement {\n const config = configuration()\n\n /* Ensure trailing slash, see https://bit.ly/3rL5u3f */\n const url = new URL(`../${version.version}/`, config.base)\n return (\n
  • \n \n {version.title}\n \n
    • \n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version selector\n *\n * @param versions - Versions\n * @param active - Active version\n *\n * @returns Element\n */\nexport function renderVersionSelector(\n versions: Version[], active: Version\n): HTMLElement {\n return (\n
    \n \n {active.title}\n \n
      \n {versions.map(renderVersion)}\n
    \n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n animationFrameScheduler,\n combineLatest,\n defer,\n finalize,\n fromEvent,\n map,\n switchMap,\n take,\n takeLast,\n takeUntil,\n tap,\n throttleTime\n} from \"rxjs\"\n\nimport {\n ElementOffset,\n getElement,\n getElementSize,\n watchElementContentOffset,\n watchElementFocus,\n watchElementOffset,\n watchElementVisibility\n} from \"~/browser\"\n\nimport { Component } from \"../../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Annotation\n */\nexport interface Annotation {\n active: boolean /* Annotation is active */\n offset: ElementOffset /* Annotation offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation observable\n */\nexport function watchAnnotation(\n el: HTMLElement, container: HTMLElement\n): Observable {\n const offset$ = defer(() => combineLatest([\n watchElementOffset(el),\n watchElementContentOffset(container)\n ]))\n .pipe(\n map(([{ x, y }, scroll]) => {\n const { width } = getElementSize(el)\n return ({\n x: x - scroll.x + width / 2,\n y: y - scroll.y\n })\n })\n )\n\n /* Actively watch annotation on focus */\n return watchElementFocus(el)\n .pipe(\n switchMap(active => offset$\n .pipe(\n map(offset => ({ active, offset })),\n take(+!active || Infinity)\n )\n )\n )\n}\n\n/**\n * Mount annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotation(\n el: HTMLElement, container: HTMLElement\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe({\n\n /* Handle emission */\n next({ offset }) {\n el.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n el.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-tooltip-x\")\n el.style.removeProperty(\"--md-tooltip-y\")\n }\n })\n\n /* Start animation only when annotation is visible */\n const done$ = push$.pipe(takeLast(1))\n watchElementVisibility(el)\n .pipe(\n takeUntil(done$)\n )\n .subscribe(visible => {\n el.toggleAttribute(\"data-md-visible\", visible)\n })\n\n /* Track relative origin of tooltip */\n push$\n .pipe(\n throttleTime(500, animationFrameScheduler),\n map(() => container.getBoundingClientRect()),\n map(({ x }) => x)\n )\n .subscribe({\n\n /* Handle emission */\n next(origin) {\n if (origin)\n el.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n else\n el.style.removeProperty(\"--md-tooltip-0\")\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-tooltip-0\")\n }\n })\n\n /* Close open annotation on click */\n const index = getElement(\":scope > :last-child\", el)\n const blur$ = fromEvent(index, \"mousedown\", { once: true })\n push$\n .pipe(\n switchMap(({ active }) => active ? blur$ : EMPTY),\n tap(ev => ev.preventDefault())\n )\n .subscribe(() => el.blur())\n\n /* Create and return component */\n return watchAnnotation(el, container)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n finalize,\n merge,\n share,\n takeLast,\n takeUntil\n} from \"rxjs\"\n\nimport {\n getElement,\n getElements,\n getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n Annotation,\n mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find all annotation markers in the given code block\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findAnnotationMarkers(container: HTMLElement): Text[] {\n const markers: Text[] = []\n for (const comment of getElements(\".c, .c1, .cm\", container)) {\n let match: RegExpExecArray | null\n\n /* Split text at marker and add to list */\n let text = comment.firstChild as Text\n if (text instanceof Text)\n while ((match = /\\((\\d+)\\)/.exec(text.textContent!))) {\n const marker = text.splitText(match.index)\n text = marker.splitText(match[0].length)\n markers.push(marker)\n }\n }\n return markers\n}\n\n/**\n * Swap the child nodes of two elements\n *\n * @param source - Source element\n * @param target - Target element\n */\nfunction swap(source: HTMLElement, target: HTMLElement): void {\n target.append(...Array.from(source.childNodes))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation list\n *\n * This function analyzes the containing code block and checks for markers\n * referring to elements in the given annotation list. If no markers are found,\n * the list is left untouched. Otherwise, list elements are rendered as\n * annotations inside the code block.\n *\n * @param el - Annotation list element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationList(\n el: HTMLElement, container: HTMLElement, { print$ }: MountOptions\n): Observable> {\n\n /* Find and replace all markers with empty annotations */\n const annotations = new Map()\n for (const marker of findAnnotationMarkers(container)) {\n const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n if (getOptionalElement(`li:nth-child(${id})`, el)) {\n annotations.set(+id, renderAnnotation(+id))\n marker.replaceWith(annotations.get(+id)!)\n }\n }\n\n /* Keep list if there are no annotations to render */\n if (annotations.size === 0)\n return EMPTY\n\n /* Create and return component */\n return defer(() => {\n const done$ = new Subject()\n\n /* Handle print mode - see https://bit.ly/3rgPdpt */\n print$\n .pipe(\n takeUntil(done$.pipe(takeLast(1)))\n )\n .subscribe(active => {\n el.hidden = !active\n\n /* Show annotations in code block or list (print) */\n for (const [id, annotation] of annotations) {\n const inner = getElement(\".md-typeset\", annotation)\n const child = getElement(`li:nth-child(${id})`, el)\n if (!active)\n swap(child, inner)\n else\n swap(inner, child)\n }\n })\n\n /* Create and return component */\n return merge(...[...annotations]\n .map(([, annotation]) => (\n mountAnnotation(annotation, container)\n ))\n )\n .pipe(\n finalize(() => done$.complete()),\n share()\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n map,\n of,\n shareReplay,\n tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: Observable\n\n/**\n * Global sequence number for diagrams\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch Mermaid script\n *\n * @returns Mermaid scripts observable\n */\nfunction fetchScripts(): Observable {\n return typeof mermaid === \"undefined\" || mermaid instanceof Element\n ? watchScript(\"https://unpkg.com/mermaid@9.0.1/dist/mermaid.min.js\")\n : of(undefined)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount Mermaid diagram\n *\n * @param el - Code block element\n *\n * @returns Mermaid diagram component observable\n */\nexport function mountMermaid(\n el: HTMLElement\n): Observable> {\n el.classList.remove(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n mermaid$ ||= fetchScripts()\n .pipe(\n tap(() => mermaid.initialize({\n startOnLoad: false,\n themeCSS\n })),\n map(() => undefined),\n shareReplay(1)\n )\n\n /* Render diagram */\n mermaid$.subscribe(() => {\n el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n const id = `__mermaid_${sequence++}`\n const host = h(\"div\", { class: \"mermaid\" })\n mermaid.mermaidAPI.render(id, el.textContent, (svg: string) => {\n\n /* Create a shadow root and inject diagram */\n const shadow = host.attachShadow({ mode: \"closed\" })\n shadow.innerHTML = svg\n\n /* Replace code block with diagram */\n el.replaceWith(host)\n })\n })\n\n /* Create and return component */\n return mermaid$\n .pipe(\n map(() => ({ ref: el }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n filter,\n finalize,\n map,\n merge,\n tap\n} from \"rxjs\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Details\n */\nexport interface Details {\n action: \"open\" | \"close\" /* Details state */\n reveal?: boolean /* Details is revealed */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch details\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details observable\n */\nexport function watchDetails(\n el: HTMLDetailsElement, { target$, print$ }: WatchOptions\n): Observable
    {\n let open = true\n return merge(\n\n /* Open and focus details on location target */\n target$\n .pipe(\n map(target => target.closest(\"details:not([open])\")!),\n filter(details => el === details),\n map(() => ({\n action: \"open\", reveal: true\n }) as Details)\n ),\n\n /* Open details on print and close afterwards */\n print$\n .pipe(\n filter(active => active || !open),\n tap(() => open = el.open),\n map(active => ({\n action: active ? \"open\" : \"close\"\n }) as Details)\n )\n )\n}\n\n/**\n * Mount details\n *\n * This function ensures that `details` tags are opened on anchor jumps and\n * prior to printing, so the whole content of the page is visible.\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details component observable\n */\nexport function mountDetails(\n el: HTMLDetailsElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject
    ()\n push$.subscribe(({ action, reveal }) => {\n if (action === \"open\")\n el.setAttribute(\"open\", \"\")\n else\n el.removeAttribute(\"open\")\n if (reveal)\n el.scrollIntoView()\n })\n\n /* Create and return component */\n return watchDetails(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, of } from \"rxjs\"\n\nimport { renderTable } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Data table\n */\nexport interface DataTable {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Sentinel for replacement\n */\nconst sentinel = h(\"table\")\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount data table\n *\n * This function wraps a data table in another scrollable container, so it can\n * be smoothly scrolled on smaller screen sizes and won't break the layout.\n *\n * @param el - Data table element\n *\n * @returns Data table component observable\n */\nexport function mountDataTable(\n el: HTMLElement\n): Observable> {\n el.replaceWith(sentinel)\n sentinel.replaceWith(renderTable(el))\n\n /* Create and return component */\n return of({ ref: el })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n asyncScheduler,\n auditTime,\n combineLatest,\n defer,\n finalize,\n fromEvent,\n map,\n merge,\n skip,\n startWith,\n subscribeOn,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n getElement,\n getElementContentOffset,\n getElementContentSize,\n getElementOffset,\n getElementSize,\n getElements,\n watchElementContentOffset,\n watchElementSize\n} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content tabs\n */\nexport interface ContentTabs {\n active: HTMLLabelElement /* Active tab label */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n el: HTMLElement\n): Observable {\n const inputs = getElements(\":scope > input\", el)\n const initial = inputs.find(input => input.checked) || inputs[0]\n return merge(...inputs.map(input => fromEvent(input, \"change\")\n .pipe(\n map(() => getElement(`label[for=\"${input.id}\"]`))\n )\n ))\n .pipe(\n startWith(getElement(`label[for=\"${initial.id}\"]`)),\n map(active => ({ active }))\n )\n}\n\n/**\n * Mount content tabs\n *\n * This function scrolls the active tab into view. While this functionality is\n * provided by browsers as part of `scrollInfoView`, browsers will always also\n * scroll the vertical axis, which we do not want. Thus, we decided to provide\n * this functionality ourselves.\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs component observable\n */\nexport function mountContentTabs(\n el: HTMLElement\n): Observable> {\n\n /* Render content tab previous button for pagination */\n const prev = renderTabbedControl(\"prev\")\n el.append(prev)\n\n /* Render content tab next button for pagination */\n const next = renderTabbedControl(\"next\")\n el.append(next)\n\n /* Mount component on subscription */\n const container = getElement(\".tabbed-labels\", el)\n return defer(() => {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n combineLatest([push$, watchElementSize(el)])\n .pipe(\n auditTime(1, animationFrameScheduler),\n takeUntil(done$)\n )\n .subscribe({\n\n /* Handle emission */\n next([{ active }, size]) {\n const offset = getElementOffset(active)\n const { width } = getElementSize(active)\n\n /* Set tab indicator offset and width */\n el.style.setProperty(\"--md-indicator-x\", `${offset.x}px`)\n el.style.setProperty(\"--md-indicator-width\", `${width}px`)\n\n /* Scroll container to active content tab */\n const content = getElementContentOffset(container)\n if (\n offset.x < content.x ||\n offset.x + width > content.x + size.width\n )\n container.scrollTo({\n left: Math.max(0, offset.x - 16),\n behavior: \"smooth\"\n })\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-indicator-x\")\n el.style.removeProperty(\"--md-indicator-width\")\n }\n })\n\n /* Hide content tab buttons on borders */\n combineLatest([\n watchElementContentOffset(container),\n watchElementSize(container)\n ])\n .pipe(\n takeUntil(done$)\n )\n .subscribe(([offset, size]) => {\n const content = getElementContentSize(container)\n prev.hidden = offset.x < 16\n next.hidden = offset.x > content.width - size.width - 16\n })\n\n /* Paginate content tab container on click */\n merge(\n fromEvent(prev, \"click\").pipe(map(() => -1)),\n fromEvent(next, \"click\").pipe(map(() => +1))\n )\n .pipe(\n takeUntil(done$)\n )\n .subscribe(direction => {\n const { width } = getElementSize(container)\n container.scrollBy({\n left: width * direction,\n behavior: \"smooth\"\n })\n })\n\n /* Set up linking of content tabs, if enabled */\n if (feature(\"content.tabs.link\"))\n push$.pipe(skip(1))\n .subscribe(({ active }) => {\n const tab = active.innerText.trim()\n for (const set of getElements(\"[data-tabs]\"))\n for (const input of getElements(\n \":scope > input\", set\n )) {\n const label = getElement(`label[for=\"${input.id}\"]`)\n if (label.innerText.trim() === tab) {\n input.click()\n break\n }\n }\n\n /* Persist active tabs in local storage */\n const tabs = __md_get(\"__tabs\") || []\n __md_set(\"__tabs\", [...new Set([tab, ...tabs])])\n })\n\n /* Create and return component */\n return watchContentTabs(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n .pipe(\n subscribeOn(asyncScheduler)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, merge } from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Annotation } from \"../annotation\"\nimport {\n CodeBlock,\n Mermaid,\n mountCodeBlock,\n mountMermaid\n} from \"../code\"\nimport {\n Details,\n mountDetails\n} from \"../details\"\nimport {\n DataTable,\n mountDataTable\n} from \"../table\"\nimport {\n ContentTabs,\n mountContentTabs\n} from \"../tabs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content\n */\nexport type Content =\n | Annotation\n | ContentTabs\n | CodeBlock\n | Mermaid\n | DataTable\n | Details\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount content\n *\n * This function mounts all components that are found in the content of the\n * actual article, including code blocks, data tables and details.\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Content component observable\n */\nexport function mountContent(\n el: HTMLElement, { target$, print$ }: MountOptions\n): Observable> {\n return merge(\n\n /* Code blocks */\n ...getElements(\"pre:not(.mermaid) > code\", el)\n .map(child => mountCodeBlock(child, { print$ })),\n\n /* Mermaid diagrams */\n ...getElements(\"pre.mermaid\", el)\n .map(child => mountMermaid(child)),\n\n /* Data tables */\n ...getElements(\"table:not([class])\", el)\n .map(child => mountDataTable(child)),\n\n /* Details */\n ...getElements(\"details\", el)\n .map(child => mountDetails(child, { target$, print$ })),\n\n /* Content tabs */\n ...getElements(\"[data-tabs]\", el)\n .map(child => mountContentTabs(child))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n delay,\n finalize,\n map,\n merge,\n of,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dialog\n */\nexport interface Dialog {\n message: string /* Dialog message */\n active: boolean /* Dialog is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n alert$: Subject /* Alert subject */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n alert$: Subject /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch dialog\n *\n * @param _el - Dialog element\n * @param options - Options\n *\n * @returns Dialog observable\n */\nexport function watchDialog(\n _el: HTMLElement, { alert$ }: WatchOptions\n): Observable {\n return alert$\n .pipe(\n switchMap(message => merge(\n of(true),\n of(false).pipe(delay(2000))\n )\n .pipe(\n map(active => ({ message, active }))\n )\n )\n )\n}\n\n/**\n * Mount dialog\n *\n * This function reveals the dialog in the right corner when a new alert is\n * emitted through the subject that is passed as part of the options.\n *\n * @param el - Dialog element\n * @param options - Options\n *\n * @returns Dialog component observable\n */\nexport function mountDialog(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const inner = getElement(\".md-typeset\", el)\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ message, active }) => {\n el.classList.toggle(\"md-dialog--active\", active)\n inner.textContent = message\n })\n\n /* Create and return component */\n return watchDialog(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatest,\n combineLatestWith,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n map,\n of,\n shareReplay,\n startWith,\n switchMap,\n takeLast,\n takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n watchElementSize,\n watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface Header {\n height: number /* Header visible height */\n hidden: boolean /* Header is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute whether the header is hidden\n *\n * If the user scrolls past a certain threshold, the header can be hidden when\n * scrolling down, and shown when scrolling up.\n *\n * @param options - Options\n *\n * @returns Toggle observable\n */\nfunction isHidden({ viewport$ }: WatchOptions): Observable {\n if (!feature(\"header.autohide\"))\n return of(false)\n\n /* Compute direction and turning point */\n const direction$ = viewport$\n .pipe(\n map(({ offset: { y } }) => y),\n bufferCount(2, 1),\n map(([a, b]) => [a < b, b] as const),\n distinctUntilKeyChanged(0)\n )\n\n /* Compute whether header should be hidden */\n const hidden$ = combineLatest([viewport$, direction$])\n .pipe(\n filter(([{ offset }, [, y]]) => Math.abs(y - offset.y) > 100),\n map(([, [direction]]) => direction),\n distinctUntilChanged()\n )\n\n /* Compute threshold for hiding */\n const search$ = watchToggle(\"search\")\n return combineLatest([viewport$, search$])\n .pipe(\n map(([{ offset }, search]) => offset.y > 400 && !search),\n distinctUntilChanged(),\n switchMap(active => active ? hidden$ : of(false)),\n startWith(false)\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header observable\n */\nexport function watchHeader(\n el: HTMLElement, options: WatchOptions\n): Observable
    {\n return defer(() => combineLatest([\n watchElementSize(el),\n isHidden(options)\n ]))\n .pipe(\n map(([{ height }, hidden]) => ({\n height,\n hidden\n })),\n distinctUntilChanged((a, b) => (\n a.height === b.height &&\n a.hidden === b.hidden\n )),\n shareReplay(1)\n )\n}\n\n/**\n * Mount header\n *\n * This function manages the different states of the header, i.e. whether it's\n * hidden or rendered with a shadow. This depends heavily on the main area.\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header component observable\n */\nexport function mountHeader(\n el: HTMLElement, { header$, main$ }: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject
    ()\n const done$ = push$.pipe(takeLast(1))\n push$\n .pipe(\n distinctUntilKeyChanged(\"active\"),\n combineLatestWith(header$)\n )\n .subscribe(([{ active }, { hidden }]) => {\n el.classList.toggle(\"md-header--shadow\", active && !hidden)\n el.hidden = hidden\n })\n\n /* Link to main area */\n main$.subscribe(push$)\n\n /* Create and return component */\n return header$\n .pipe(\n takeUntil(done$),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n distinctUntilKeyChanged,\n finalize,\n map,\n tap\n} from \"rxjs\"\n\nimport {\n Viewport,\n getElementSize,\n getOptionalElement,\n watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Header } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface HeaderTitle {\n active: boolean /* Header title is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header title\n *\n * @param el - Heading element\n * @param options - Options\n *\n * @returns Header title observable\n */\nexport function watchHeaderTitle(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n return watchViewportAt(el, { viewport$, header$ })\n .pipe(\n map(({ offset: { y } }) => {\n const { height } = getElementSize(el)\n return {\n active: y >= height\n }\n }),\n distinctUntilKeyChanged(\"active\")\n )\n}\n\n/**\n * Mount header title\n *\n * This function swaps the header title from the site title to the title of the\n * current page when the user scrolls past the first headline.\n *\n * @param el - Header title element\n * @param options - Options\n *\n * @returns Header title component observable\n */\nexport function mountHeaderTitle(\n el: HTMLElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ active }) => {\n el.classList.toggle(\"md-header__title--active\", active)\n })\n\n /* Obtain headline, if any */\n const heading = getOptionalElement(\"article h1\")\n if (typeof heading === \"undefined\")\n return EMPTY\n\n /* Create and return component */\n return watchHeaderTitle(heading, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n map,\n switchMap\n} from \"rxjs\"\n\nimport {\n Viewport,\n watchElementSize\n} from \"~/browser\"\n\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Main area\n */\nexport interface Main {\n offset: number /* Main area top offset */\n height: number /* Main area visible height */\n active: boolean /* Main area is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch main area\n *\n * This function returns an observable that computes the visual parameters of\n * the main area which depends on the viewport vertical offset and height, as\n * well as the height of the header element, if the header is fixed.\n *\n * @param el - Main area element\n * @param options - Options\n *\n * @returns Main area observable\n */\nexport function watchMain(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable
    {\n\n /* Compute necessary adjustment for header */\n const adjust$ = header$\n .pipe(\n map(({ height }) => height),\n distinctUntilChanged()\n )\n\n /* Compute the main area's top and bottom borders */\n const border$ = adjust$\n .pipe(\n switchMap(() => watchElementSize(el)\n .pipe(\n map(({ height }) => ({\n top: el.offsetTop,\n bottom: el.offsetTop + height\n })),\n distinctUntilKeyChanged(\"bottom\")\n )\n )\n )\n\n /* Compute the main area's offset, visible height and if we scrolled past */\n return combineLatest([adjust$, border$, viewport$])\n .pipe(\n map(([header, { top, bottom }, { offset: { y }, size: { height } }]) => {\n height = Math.max(0, height\n - Math.max(0, top - y, header)\n - Math.max(0, height + y - bottom)\n )\n return {\n offset: top - header,\n height,\n active: top - header <= y\n }\n }),\n distinctUntilChanged((a, b) => (\n a.offset === b.offset &&\n a.height === b.height &&\n a.active === b.active\n ))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n asyncScheduler,\n defer,\n finalize,\n fromEvent,\n map,\n mergeMap,\n observeOn,\n of,\n shareReplay,\n startWith,\n tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Palette colors\n */\nexport interface PaletteColor {\n scheme?: string /* Color scheme */\n primary?: string /* Primary color */\n accent?: string /* Accent color */\n}\n\n/**\n * Palette\n */\nexport interface Palette {\n index: number /* Palette index */\n color: PaletteColor /* Palette colors */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch color palette\n *\n * @param inputs - Color palette element\n *\n * @returns Color palette observable\n */\nexport function watchPalette(\n inputs: HTMLInputElement[]\n): Observable {\n const current = __md_get(\"__palette\") || {\n index: inputs.findIndex(input => matchMedia(\n input.getAttribute(\"data-md-color-media\")!\n ).matches)\n }\n\n /* Emit changes in color palette */\n return of(...inputs)\n .pipe(\n mergeMap(input => fromEvent(input, \"change\")\n .pipe(\n map(() => input)\n )\n ),\n startWith(inputs[Math.max(0, current.index)]),\n map(input => ({\n index: inputs.indexOf(input),\n color: {\n scheme: input.getAttribute(\"data-md-color-scheme\"),\n primary: input.getAttribute(\"data-md-color-primary\"),\n accent: input.getAttribute(\"data-md-color-accent\")\n }\n } as Palette)),\n shareReplay(1)\n )\n}\n\n/**\n * Mount color palette\n *\n * @param el - Color palette element\n *\n * @returns Color palette component observable\n */\nexport function mountPalette(\n el: HTMLElement\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(palette => {\n document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n /* Set color palette */\n for (const [key, value] of Object.entries(palette.color))\n document.body.setAttribute(`data-md-color-${key}`, value)\n\n /* Toggle visibility */\n for (let index = 0; index < inputs.length; index++) {\n const label = inputs[index].nextElementSibling\n if (label instanceof HTMLElement)\n label.hidden = palette.index !== index\n }\n\n /* Persist preference in local storage */\n __md_set(\"__palette\", palette)\n })\n\n /* Revert transition durations after color switch */\n push$.pipe(observeOn(asyncScheduler))\n .subscribe(() => {\n document.body.removeAttribute(\"data-md-color-switching\")\n })\n\n /* Create and return component */\n const inputs = getElements(\"input\", el)\n return watchPalette(inputs)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n Observable,\n Subject,\n map,\n tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n alert$: Subject /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Extract text to copy\n *\n * @param el - HTML element\n *\n * @returns Extracted text\n */\nfunction extract(el: HTMLElement): string {\n el.setAttribute(\"data-md-copying\", \"\")\n const text = el.innerText\n el.removeAttribute(\"data-md-copying\")\n return text\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up Clipboard.js integration\n *\n * @param options - Options\n */\nexport function setupClipboardJS(\n { alert$ }: SetupOptions\n): void {\n if (ClipboardJS.isSupported()) {\n new Observable(subscriber => {\n new ClipboardJS(\"[data-clipboard-target], [data-clipboard-text]\", {\n text: el => (\n el.getAttribute(\"data-clipboard-text\")! ||\n extract(getElement(\n el.getAttribute(\"data-clipboard-target\")!\n ))\n )\n })\n .on(\"success\", ev => subscriber.next(ev))\n })\n .pipe(\n tap(ev => {\n const trigger = ev.trigger as HTMLElement\n trigger.focus()\n }),\n map(() => translation(\"clipboard.copied\"))\n )\n .subscribe(alert$)\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map,\n of,\n tap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport { getElements, requestXML } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = string[]\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Preprocess a list of URLs\n *\n * This function replaces the `site_url` in the sitemap with the actual base\n * URL, to allow instant loading to work in occasions like Netlify previews.\n *\n * @param urls - URLs\n *\n * @returns URL path parts\n */\nfunction preprocess(urls: Sitemap): Sitemap {\n if (urls.length < 2)\n return [\"\"]\n\n /* Take the first two URLs and remove everything after the last slash */\n const [root, next] = [...urls]\n .sort((a, b) => a.length - b.length)\n .map(url => url.replace(/[^/]+$/, \"\"))\n\n /* Compute common prefix */\n let index = 0\n if (root === next)\n index = root.length\n else\n while (root.charCodeAt(index) === next.charCodeAt(index))\n index++\n\n /* Remove common prefix and return in original order */\n return urls.map(url => url.replace(root.slice(0, index), \"\"))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base?: URL): Observable {\n const cached = __md_get(\"__sitemap\", sessionStorage, base)\n if (cached) {\n return of(cached)\n } else {\n const config = configuration()\n return requestXML(new URL(\"sitemap.xml\", base || config.base))\n .pipe(\n map(sitemap => preprocess(getElements(\"loc\", sitemap)\n .map(node => node.textContent!)\n )),\n catchError(() => EMPTY), // @todo refactor instant loading\n defaultIfEmpty([]),\n tap(sitemap => __md_set(\"__sitemap\", sitemap, sessionStorage, base))\n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n bufferCount,\n catchError,\n concatMap,\n debounceTime,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n fromEvent,\n map,\n merge,\n of,\n sample,\n share,\n skip,\n skipUntil,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n Viewport,\n ViewportOffset,\n getElements,\n getOptionalElement,\n request,\n setLocation,\n setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\nimport { h } from \"~/utilities\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * History state\n */\nexport interface HistoryState {\n url: URL /* State URL */\n offset?: ViewportOffset /* State viewport offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n document$: Subject /* Document subject */\n location$: Subject /* Location subject */\n viewport$: Observable /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up instant loading\n *\n * When fetching, theoretically, we could use `responseType: \"document\"`, but\n * since all MkDocs links are relative, we need to make sure that the current\n * location matches the document we just loaded. Otherwise any relative links\n * in the document could use the old location.\n *\n * This is the reason why we need to synchronize history events and the process\n * of fetching the document for navigation changes (except `popstate` events):\n *\n * 1. Fetch document via `XMLHTTPRequest`\n * 2. Set new location via `history.pushState`\n * 3. Parse and emit fetched document\n *\n * For `popstate` events, we must not use `history.pushState`, or the forward\n * history will be irreversibly overwritten. In case the request fails, the\n * location change is dispatched regularly.\n *\n * @param options - Options\n */\nexport function setupInstantLoading(\n { document$, location$, viewport$ }: SetupOptions\n): void {\n const config = configuration()\n if (location.protocol === \"file:\")\n return\n\n /* Disable automatic scroll restoration */\n if (\"scrollRestoration\" in history) {\n history.scrollRestoration = \"manual\"\n\n /* Hack: ensure that reloads restore viewport offset */\n fromEvent(window, \"beforeunload\")\n .subscribe(() => {\n history.scrollRestoration = \"auto\"\n })\n }\n\n /* Hack: ensure absolute favicon link to omit 404s when switching */\n const favicon = getOptionalElement(\"link[rel=icon]\")\n if (typeof favicon !== \"undefined\")\n favicon.href = favicon.href\n\n /* Intercept internal navigation */\n const push$ = fetchSitemap()\n .pipe(\n map(paths => paths.map(path => `${new URL(path, config.base)}`)),\n switchMap(urls => fromEvent(document.body, \"click\")\n .pipe(\n filter(ev => !ev.metaKey && !ev.ctrlKey),\n switchMap(ev => {\n if (ev.target instanceof Element) {\n const el = ev.target.closest(\"a\")\n if (el && !el.target) {\n const url = new URL(el.href)\n\n /* Canonicalize URL */\n url.search = \"\"\n url.hash = \"\"\n\n /* Check if URL should be intercepted */\n if (\n url.pathname !== location.pathname &&\n urls.includes(url.toString())\n ) {\n ev.preventDefault()\n return of({\n url: new URL(el.href)\n })\n }\n }\n }\n return NEVER\n })\n )\n ),\n share()\n )\n\n /* Intercept history back and forward */\n const pop$ = fromEvent(window, \"popstate\")\n .pipe(\n filter(ev => ev.state !== null),\n map(ev => ({\n url: new URL(location.href),\n offset: ev.state\n })),\n share()\n )\n\n /* Emit location change */\n merge(push$, pop$)\n .pipe(\n distinctUntilChanged((a, b) => a.url.href === b.url.href),\n map(({ url }) => url)\n )\n .subscribe(location$)\n\n /* Fetch document via `XMLHTTPRequest` */\n const response$ = location$\n .pipe(\n distinctUntilKeyChanged(\"pathname\"),\n switchMap(url => request(url.href)\n .pipe(\n catchError(() => {\n setLocation(url)\n return NEVER\n })\n )\n ),\n share()\n )\n\n /* Set new location via `history.pushState` */\n push$\n .pipe(\n sample(response$)\n )\n .subscribe(({ url }) => {\n history.pushState({}, \"\", `${url}`)\n })\n\n /* Parse and emit fetched document */\n const dom = new DOMParser()\n response$\n .pipe(\n switchMap(res => res.text()),\n map(res => dom.parseFromString(res, \"text/html\"))\n )\n .subscribe(document$)\n\n /* Replace meta tags and components */\n document$\n .pipe(\n skip(1)\n )\n .subscribe(replacement => {\n for (const selector of [\n\n /* Meta tags */\n \"title\",\n \"link[rel=canonical]\",\n \"meta[name=author]\",\n \"meta[name=description]\",\n\n /* Components */\n \"[data-md-component=announce]\",\n \"[data-md-component=container]\",\n \"[data-md-component=header-topic]\",\n \"[data-md-component=outdated]\",\n \"[data-md-component=logo]\",\n \"[data-md-component=skip]\",\n ...feature(\"navigation.tabs.sticky\")\n ? [\"[data-md-component=tabs]\"]\n : []\n ]) {\n const source = getOptionalElement(selector)\n const target = getOptionalElement(selector, replacement)\n if (\n typeof source !== \"undefined\" &&\n typeof target !== \"undefined\"\n ) {\n source.replaceWith(target)\n }\n }\n })\n\n /* Re-evaluate scripts */\n document$\n .pipe(\n skip(1),\n map(() => getComponentElement(\"container\")),\n switchMap(el => getElements(\"script\", el)),\n concatMap(el => {\n const script = h(\"script\")\n if (el.src) {\n for (const name of el.getAttributeNames())\n script.setAttribute(name, el.getAttribute(name)!)\n el.replaceWith(script)\n\n /* Complete when script is loaded */\n return new Observable(observer => {\n script.onload = () => observer.complete()\n })\n\n /* Complete immediately */\n } else {\n script.textContent = el.textContent\n el.replaceWith(script)\n return EMPTY\n }\n })\n )\n .subscribe()\n\n /* Emit history state change */\n merge(push$, pop$)\n .pipe(\n sample(document$)\n )\n .subscribe(({ url, offset }) => {\n if (url.hash && !offset) {\n setLocationHash(url.hash)\n } else {\n window.scrollTo(0, offset?.y || 0)\n }\n })\n\n /* Debounce update of viewport offset */\n viewport$\n .pipe(\n skipUntil(push$),\n debounceTime(250),\n distinctUntilKeyChanged(\"offset\")\n )\n .subscribe(({ offset }) => {\n history.replaceState(offset, \"\")\n })\n\n /* Set viewport offset from history */\n merge(push$, pop$)\n .pipe(\n bufferCount(2, 1),\n filter(([a, b]) => a.url.pathname === b.url.pathname),\n map(([, state]) => state)\n )\n .subscribe(({ offset }) => {\n window.scrollTo(0, offset?.y || 0)\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n parent?: SearchIndexDocument /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n docs: SearchIndexDocument[]\n): SearchDocumentMap {\n const documents = new Map()\n const parents = new Set()\n for (const doc of docs) {\n const [path, hash] = doc.location.split(\"#\")\n\n /* Extract location, title and tags */\n const location = doc.location\n const title = doc.title\n const tags = doc.tags\n\n /* Escape and cleanup text */\n const text = escapeHTML(doc.text)\n .replace(/\\s+(?=[,.:;!?])/g, \"\")\n .replace(/\\s+/g, \" \")\n\n /* Handle section */\n if (hash) {\n const parent = documents.get(path)!\n\n /* Ignore first section, override article */\n if (!parents.has(parent)) {\n parent.title = doc.title\n parent.text = text\n\n /* Remember that we processed the article */\n parents.add(parent)\n\n /* Add subsequent section */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n parent\n })\n }\n\n /* Add article */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n ...tags && { tags }\n })\n }\n }\n return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n const separator = new RegExp(config.separator, \"img\")\n const highlight = (_: unknown, data: string, term: string) => {\n return `${data}${term}`\n }\n\n /* Return factory function */\n return (query: string) => {\n query = query\n .replace(/[\\s*+\\-:~^]+/g, \" \")\n .trim()\n\n /* Create search term match expression */\n const match = new RegExp(`(^|${config.separator})(${\n query\n .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n .replace(separator, \"|\")\n })`, \"img\")\n\n /* Highlight string value */\n return value => (\n escape\n ? escapeHTML(value)\n : value\n )\n .replace(match, highlight)\n .replace(/<\\/mark>(\\s+)]*>/img, \"$1\")\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search transformation function\n *\n * @param value - Query value\n *\n * @returns Transformed query value\n */\nexport type SearchTransformFn = (value: string) => string\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Search for terms in quotation marks and prepend a `+` modifier to denote\n * that the resulting document must contain all terms, converting the query\n * to an `AND` query (as opposed to the default `OR` behavior). While users\n * may expect terms enclosed in quotation marks to map to span queries, i.e.\n * for which order is important, Lunr.js doesn't support them, so the best\n * we can do is to convert the terms to an `AND` query.\n *\n * 2. Replace control characters which are not located at the beginning of the\n * query or preceded by white space, or are not followed by a non-whitespace\n * character or are at the end of the query string. Furthermore, filter\n * unmatched quotation marks.\n *\n * 3. Trim excess whitespace from left and right.\n *\n * @param query - Query value\n *\n * @returns Transformed query value\n */\nexport function defaultTransform(query: string): string {\n return query\n .split(/\"([^\"]+)\"/g) /* => 1 */\n .map((terms, index) => index & 1\n ? terms.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n : terms\n )\n .join(\"\")\n .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\") /* => 2 */\n .trim() /* => 3 */\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SearchIndex, SearchResult } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search message type\n */\nexport const enum SearchMessageType {\n SETUP, /* Search index setup */\n READY, /* Search index ready */\n QUERY, /* Search query */\n RESULT /* Search results */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message containing the data necessary to setup the search index\n */\nexport interface SearchSetupMessage {\n type: SearchMessageType.SETUP /* Message type */\n data: SearchIndex /* Message data */\n}\n\n/**\n * Message indicating the search index is ready\n */\nexport interface SearchReadyMessage {\n type: SearchMessageType.READY /* Message type */\n}\n\n/**\n * Message containing a search query\n */\nexport interface SearchQueryMessage {\n type: SearchMessageType.QUERY /* Message type */\n data: string /* Message data */\n}\n\n/**\n * Message containing results for a search query\n */\nexport interface SearchResultMessage {\n type: SearchMessageType.RESULT /* Message type */\n data: SearchResult /* Message data */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message exchanged with the search worker\n */\nexport type SearchMessage =\n | SearchSetupMessage\n | SearchReadyMessage\n | SearchQueryMessage\n | SearchResultMessage\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Type guard for search setup messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchSetupMessage(\n message: SearchMessage\n): message is SearchSetupMessage {\n return message.type === SearchMessageType.SETUP\n}\n\n/**\n * Type guard for search ready messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchReadyMessage(\n message: SearchMessage\n): message is SearchReadyMessage {\n return message.type === SearchMessageType.READY\n}\n\n/**\n * Type guard for search query messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchQueryMessage(\n message: SearchMessage\n): message is SearchQueryMessage {\n return message.type === SearchMessageType.QUERY\n}\n\n/**\n * Type guard for search result messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchResultMessage(\n message: SearchMessage\n): message is SearchResultMessage {\n return message.type === SearchMessageType.RESULT\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n ObservableInput,\n Subject,\n from,\n map,\n share\n} from \"rxjs\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport { WorkerHandler, watchWorker } from \"~/browser\"\n\nimport { SearchIndex } from \"../../_\"\nimport {\n SearchOptions,\n SearchPipeline\n} from \"../../options\"\nimport {\n SearchMessage,\n SearchMessageType,\n SearchSetupMessage,\n isSearchResultMessage\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search worker\n */\nexport type SearchWorker = WorkerHandler\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search index\n *\n * @param data - Search index\n *\n * @returns Search index\n */\nfunction setupSearchIndex({ config, docs }: SearchIndex): SearchIndex {\n\n /* Override default language with value from translation */\n if (config.lang.length === 1 && config.lang[0] === \"en\")\n config.lang = [\n translation(\"search.config.lang\")\n ]\n\n /* Override default separator with value from translation */\n if (config.separator === \"[\\\\s\\\\-]+\")\n config.separator = translation(\"search.config.separator\")\n\n /* Set pipeline from translation */\n const pipeline = translation(\"search.config.pipeline\")\n .split(/\\s*,\\s*/)\n .filter(Boolean) as SearchPipeline\n\n /* Determine search options */\n const options: SearchOptions = {\n pipeline,\n suggestions: feature(\"search.suggest\")\n }\n\n /* Return search index after defaulting */\n return { config, docs, options }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search worker\n *\n * This function creates a web worker to set up and query the search index,\n * which is done using Lunr.js. The index must be passed as an observable to\n * enable hacks like _localsearch_ via search index embedding as JSON.\n *\n * @param url - Worker URL\n * @param index - Search index observable input\n *\n * @returns Search worker\n */\nexport function setupSearchWorker(\n url: string, index: ObservableInput\n): SearchWorker {\n const config = configuration()\n const worker = new Worker(url)\n\n /* Create communication channels and resolve relative links */\n const tx$ = new Subject()\n const rx$ = watchWorker(worker, { tx$ })\n .pipe(\n map(message => {\n if (isSearchResultMessage(message)) {\n for (const result of message.data.items)\n for (const document of result)\n document.location = `${new URL(document.location, config.base)}`\n }\n return message\n }),\n share()\n )\n\n /* Set up search index */\n from(index)\n .pipe(\n map(data => ({\n type: SearchMessageType.SETUP,\n data: setupSearchIndex(data)\n } as SearchSetupMessage))\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Return search worker */\n return { tx$, rx$ }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Subject,\n catchError,\n combineLatest,\n filter,\n fromEvent,\n map,\n of,\n switchMap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n getElement,\n getLocation,\n requestJSON,\n setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n Version,\n renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n document$: Subject /* Document subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up version selector\n *\n * @param options - Options\n */\nexport function setupVersionSelector(\n { document$ }: SetupOptions\n): void {\n const config = configuration()\n const versions$ = requestJSON(\n new URL(\"../versions.json\", config.base)\n )\n .pipe(\n catchError(() => EMPTY) // @todo refactor instant loading\n )\n\n /* Determine current version */\n const current$ = versions$\n .pipe(\n map(versions => {\n const [, current] = config.base.match(/([^/]+)\\/?$/)!\n return versions.find(({ version, aliases }) => (\n version === current || aliases.includes(current)\n )) || versions[0]\n })\n )\n\n /* Intercept inter-version navigation */\n versions$\n .pipe(\n map(versions => new Map(versions.map(version => [\n `${new URL(`../${version.version}/`, config.base)}`,\n version\n ]))),\n switchMap(urls => fromEvent(document.body, \"click\")\n .pipe(\n filter(ev => !ev.metaKey && !ev.ctrlKey),\n withLatestFrom(current$),\n switchMap(([ev, current]) => {\n if (ev.target instanceof Element) {\n const el = ev.target.closest(\"a\")\n if (el && !el.target && urls.has(el.href)) {\n const url = el.href\n // This is a temporary hack to detect if a version inside the\n // version selector or on another part of the site was clicked.\n // If we're inside the version selector, we definitely want to\n // find the same page, as we might have different deployments\n // due to aliases. However, if we're outside the version\n // selector, we must abort here, because we might otherwise\n // interfere with instant loading. We need to refactor this\n // at some point together with instant loading.\n //\n // See https://github.com/squidfunk/mkdocs-material/issues/4012\n if (!ev.target.closest(\".md-version\")) {\n const version = urls.get(url)!\n if (version === current)\n return EMPTY\n }\n ev.preventDefault()\n return of(url)\n }\n }\n return EMPTY\n }),\n switchMap(url => {\n const { version } = urls.get(url)!\n return fetchSitemap(new URL(url))\n .pipe(\n map(sitemap => {\n const location = getLocation()\n const path = location.href.replace(config.base, \"\")\n return sitemap.includes(path)\n ? new URL(`../${version}/${path}`, config.base)\n : new URL(url)\n })\n )\n })\n )\n )\n )\n .subscribe(url => setLocation(url))\n\n /* Render version selector and warning */\n combineLatest([versions$, current$])\n .subscribe(([versions, current]) => {\n const topic = getElement(\".md-header__topic\")\n topic.appendChild(renderVersionSelector(versions, current))\n })\n\n /* Integrate outdated version banner with instant loading */\n document$.pipe(switchMap(() => current$))\n .subscribe(current => {\n\n /* Check if version state was already determined */\n let outdated = __md_get(\"__outdated\", sessionStorage)\n if (outdated === null) {\n const latest = config.version?.default || \"latest\"\n outdated = !current.aliases.includes(latest)\n\n /* Persist version state in session storage */\n __md_set(\"__outdated\", outdated, sessionStorage)\n }\n\n /* Unhide outdated version banner */\n if (outdated)\n for (const warning of getComponentElements(\"outdated\"))\n warning.hidden = false\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n combineLatest,\n delay,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n share,\n shareReplay,\n startWith,\n take,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n getLocation,\n setToggle,\n watchElementFocus,\n watchToggle\n} from \"~/browser\"\nimport {\n SearchMessageType,\n SearchQueryMessage,\n SearchWorker,\n defaultTransform,\n isSearchReadyMessage\n} from \"~/integrations\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query\n */\nexport interface SearchQuery {\n value: string /* Query value */\n focus: boolean /* Query focus */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch search query\n *\n * Note that the focus event which triggers re-reading the current query value\n * is delayed by `1ms` so the input's empty state is allowed to propagate.\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query observable\n */\nexport function watchSearchQuery(\n el: HTMLInputElement, { rx$ }: SearchWorker\n): Observable {\n const fn = __search?.transform || defaultTransform\n\n /* Immediately show search dialog */\n const { searchParams } = getLocation()\n if (searchParams.has(\"q\"))\n setToggle(\"search\", true)\n\n /* Intercept query parameter (deep link) */\n const param$ = rx$\n .pipe(\n filter(isSearchReadyMessage),\n take(1),\n map(() => searchParams.get(\"q\") || \"\")\n )\n\n /* Remove query parameter when search is closed */\n watchToggle(\"search\")\n .pipe(\n filter(active => !active),\n take(1)\n )\n .subscribe(() => {\n const url = new URL(location.href)\n url.searchParams.delete(\"q\")\n history.replaceState({}, \"\", `${url}`)\n })\n\n /* Set query from parameter */\n param$.subscribe(value => { // TODO: not ideal - find a better way\n if (value) {\n el.value = value\n el.focus()\n }\n })\n\n /* Intercept focus and input events */\n const focus$ = watchElementFocus(el)\n const value$ = merge(\n fromEvent(el, \"keyup\"),\n fromEvent(el, \"focus\").pipe(delay(1)),\n param$\n )\n .pipe(\n map(() => fn(el.value)),\n startWith(\"\"),\n distinctUntilChanged(),\n )\n\n /* Combine into single observable */\n return combineLatest([value$, focus$])\n .pipe(\n map(([value, focus]) => ({ value, focus })),\n shareReplay(1)\n )\n}\n\n/**\n * Mount search query\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query component observable\n */\nexport function mountSearchQuery(\n el: HTMLInputElement, { tx$, rx$ }: SearchWorker\n): Observable> {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n\n /* Handle value changes */\n push$\n .pipe(\n distinctUntilKeyChanged(\"value\"),\n map(({ value }): SearchQueryMessage => ({\n type: SearchMessageType.QUERY,\n data: value\n }))\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Handle focus changes */\n push$\n .pipe(\n distinctUntilKeyChanged(\"focus\")\n )\n .subscribe(({ focus }) => {\n if (focus) {\n setToggle(\"search\", focus)\n el.placeholder = \"\"\n } else {\n el.placeholder = translation(\"search.placeholder\")\n }\n })\n\n /* Handle reset */\n fromEvent(el.form!, \"reset\")\n .pipe(\n takeUntil(done$)\n )\n .subscribe(() => el.focus())\n\n /* Create and return component */\n return watchSearchQuery(el, { tx$, rx$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state })),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n filter,\n finalize,\n map,\n merge,\n of,\n skipUntil,\n switchMap,\n take,\n tap,\n withLatestFrom,\n zipWith\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n getElement,\n watchElementBoundary\n} from \"~/browser\"\nimport {\n SearchResult,\n SearchWorker,\n isSearchReadyMessage,\n isSearchResultMessage\n} from \"~/integrations\"\nimport { renderSearchResultItem } from \"~/templates\"\nimport { round } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n query$: Observable /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search result list\n *\n * This function performs a lazy rendering of the search results, depending on\n * the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchResult(\n el: HTMLElement, { rx$ }: SearchWorker, { query$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n const boundary$ = watchElementBoundary(el.parentElement!)\n .pipe(\n filter(Boolean)\n )\n\n /* Retrieve nested components */\n const meta = getElement(\":scope > :first-child\", el)\n const list = getElement(\":scope > :last-child\", el)\n\n /* Wait until search is ready */\n const ready$ = rx$\n .pipe(\n filter(isSearchReadyMessage),\n take(1)\n )\n\n /* Update search result metadata */\n push$\n .pipe(\n withLatestFrom(query$),\n skipUntil(ready$)\n )\n .subscribe(([{ items }, { value }]) => {\n if (value) {\n switch (items.length) {\n\n /* No results */\n case 0:\n meta.textContent = translation(\"search.result.none\")\n break\n\n /* One result */\n case 1:\n meta.textContent = translation(\"search.result.one\")\n break\n\n /* Multiple result */\n default:\n meta.textContent = translation(\n \"search.result.other\",\n round(items.length)\n )\n }\n } else {\n meta.textContent = translation(\"search.result.placeholder\")\n }\n })\n\n /* Update search result list */\n push$\n .pipe(\n tap(() => list.innerHTML = \"\"),\n switchMap(({ items }) => merge(\n of(...items.slice(0, 10)),\n of(...items.slice(10))\n .pipe(\n bufferCount(4),\n zipWith(boundary$),\n switchMap(([chunk]) => chunk)\n )\n ))\n )\n .subscribe(result => list.appendChild(\n renderSearchResultItem(result)\n ))\n\n /* Filter search result message */\n const result$ = rx$\n .pipe(\n filter(isSearchResultMessage),\n map(({ data }) => data)\n )\n\n /* Create and return component */\n return result$\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n finalize,\n fromEvent,\n map,\n tap\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search sharing\n */\nexport interface SearchShare {\n url: URL /* Deep link for sharing */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n query$: Observable /* Search query observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n query$: Observable /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search sharing\n *\n * @param _el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing observable\n */\nexport function watchSearchShare(\n _el: HTMLElement, { query$ }: WatchOptions\n): Observable {\n return query$\n .pipe(\n map(({ value }) => {\n const url = getLocation()\n url.hash = \"\"\n url.searchParams.delete(\"h\")\n url.searchParams.set(\"q\", value)\n return { url }\n })\n )\n}\n\n/**\n * Mount search sharing\n *\n * @param el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing component observable\n */\nexport function mountSearchShare(\n el: HTMLAnchorElement, options: MountOptions\n): Observable> {\n const push$ = new Subject()\n push$.subscribe(({ url }) => {\n el.setAttribute(\"data-clipboard-text\", el.href)\n el.href = `${url}`\n })\n\n /* Prevent following of link */\n fromEvent(el, \"click\")\n .subscribe(ev => ev.preventDefault())\n\n /* Create and return component */\n return watchSearchShare(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n asyncScheduler,\n combineLatestWith,\n distinctUntilChanged,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n observeOn,\n tap\n} from \"rxjs\"\n\nimport { Keyboard } from \"~/browser\"\nimport {\n SearchResult,\n SearchWorker,\n isSearchResultMessage\n} from \"~/integrations\"\n\nimport { Component, getComponentElement } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search suggestions\n */\nexport interface SearchSuggest {}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n keyboard$: Observable /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search suggestions\n *\n * This function will perform a lazy rendering of the search results, depending\n * on the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchSuggest(\n el: HTMLElement, { rx$ }: SearchWorker, { keyboard$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n\n /* Retrieve query component and track all changes */\n const query = getComponentElement(\"search-query\")\n const query$ = merge(\n fromEvent(query, \"keydown\"),\n fromEvent(query, \"focus\")\n )\n .pipe(\n observeOn(asyncScheduler),\n map(() => query.value),\n distinctUntilChanged(),\n )\n\n /* Update search suggestions */\n push$\n .pipe(\n combineLatestWith(query$),\n map(([{ suggestions }, value]) => {\n const words = value.split(/([\\s-]+)/)\n if (suggestions?.length && words[words.length - 1]) {\n const last = suggestions[suggestions.length - 1]\n if (last.startsWith(words[words.length - 1]))\n words[words.length - 1] = last\n } else {\n words.length = 0\n }\n return words\n })\n )\n .subscribe(words => el.innerHTML = words\n .join(\"\")\n .replace(/\\s/g, \" \")\n )\n\n /* Set up search keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"search\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Right arrow: accept current suggestion */\n case \"ArrowRight\":\n if (\n el.innerText.length &&\n query.selectionStart === query.value.length\n )\n query.value = el.innerText\n break\n }\n })\n\n /* Filter search result message */\n const result$ = rx$\n .pipe(\n filter(isSearchResultMessage),\n map(({ data }) => data)\n )\n\n /* Create and return component */\n return result$\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(() => ({ ref: el }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n NEVER,\n Observable,\n ObservableInput,\n filter,\n merge,\n mergeWith,\n sample,\n take\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n Keyboard,\n getActiveElement,\n getElements,\n setToggle\n} from \"~/browser\"\nimport {\n SearchIndex,\n SearchResult,\n isSearchQueryMessage,\n isSearchReadyMessage,\n setupSearchWorker\n} from \"~/integrations\"\n\nimport {\n Component,\n getComponentElement,\n getComponentElements\n} from \"../../_\"\nimport {\n SearchQuery,\n mountSearchQuery\n} from \"../query\"\nimport { mountSearchResult } from \"../result\"\nimport {\n SearchShare,\n mountSearchShare\n} from \"../share\"\nimport {\n SearchSuggest,\n mountSearchSuggest\n} from \"../suggest\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search\n */\nexport type Search =\n | SearchQuery\n | SearchResult\n | SearchShare\n | SearchSuggest\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n index$: ObservableInput /* Search index observable */\n keyboard$: Observable /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search\n *\n * This function sets up the search functionality, including the underlying\n * web worker and all keyboard bindings.\n *\n * @param el - Search element\n * @param options - Options\n *\n * @returns Search component observable\n */\nexport function mountSearch(\n el: HTMLElement, { index$, keyboard$ }: MountOptions\n): Observable> {\n const config = configuration()\n try {\n const url = __search?.worker || config.search\n const worker = setupSearchWorker(url, index$)\n\n /* Retrieve query and result components */\n const query = getComponentElement(\"search-query\", el)\n const result = getComponentElement(\"search-result\", el)\n\n /* Re-emit query when search is ready */\n const { tx$, rx$ } = worker\n tx$\n .pipe(\n filter(isSearchQueryMessage),\n sample(rx$.pipe(filter(isSearchReadyMessage))),\n take(1)\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Set up search keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"search\")\n )\n .subscribe(key => {\n const active = getActiveElement()\n switch (key.type) {\n\n /* Enter: go to first (best) result */\n case \"Enter\":\n if (active === query) {\n const anchors = new Map()\n for (const anchor of getElements(\n \":first-child [href]\", result\n )) {\n const article = anchor.firstElementChild!\n anchors.set(anchor, parseFloat(\n article.getAttribute(\"data-md-score\")!\n ))\n }\n\n /* Go to result with highest score, if any */\n if (anchors.size) {\n const [[best]] = [...anchors].sort(([, a], [, b]) => b - a)\n best.click()\n }\n\n /* Otherwise omit form submission */\n key.claim()\n }\n break\n\n /* Escape or Tab: close search */\n case \"Escape\":\n case \"Tab\":\n setToggle(\"search\", false)\n query.blur()\n break\n\n /* Vertical arrows: select previous or next search result */\n case \"ArrowUp\":\n case \"ArrowDown\":\n if (typeof active === \"undefined\") {\n query.focus()\n } else {\n const els = [query, ...getElements(\n \":not(details) > [href], summary, details[open] [href]\",\n result\n )]\n const i = Math.max(0, (\n Math.max(0, els.indexOf(active)) + els.length + (\n key.type === \"ArrowUp\" ? -1 : +1\n )\n ) % els.length)\n els[i].focus()\n }\n\n /* Prevent scrolling of page */\n key.claim()\n break\n\n /* All other keys: hand to search query */\n default:\n if (query !== getActiveElement())\n query.focus()\n }\n })\n\n /* Set up global keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\"),\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Open search and select query */\n case \"f\":\n case \"s\":\n case \"/\":\n query.focus()\n query.select()\n\n /* Prevent scrolling of page */\n key.claim()\n break\n }\n })\n\n /* Create and return component */\n const query$ = mountSearchQuery(query, worker)\n const result$ = mountSearchResult(result, worker, { query$ })\n return merge(query$, result$)\n .pipe(\n mergeWith(\n\n /* Search sharing */\n ...getComponentElements(\"search-share\", el)\n .map(child => mountSearchShare(child, { query$ })),\n\n /* Search suggestions */\n ...getComponentElements(\"search-suggest\", el)\n .map(child => mountSearchSuggest(child, worker, { keyboard$ }))\n )\n )\n\n /* Gracefully handle broken search */\n } catch (err) {\n el.hidden = true\n return NEVER\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n ObservableInput,\n combineLatest,\n filter,\n map,\n startWith\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\nimport {\n SearchIndex,\n setupSearchHighlighter\n} from \"~/integrations\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlighting\n */\nexport interface SearchHighlight {\n nodes: Map /* Map of replacements */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n index$: ObservableInput /* Search index observable */\n location$: Observable /* Location observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search highlighting\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Search highlighting component observable\n */\nexport function mountSearchHiglight(\n el: HTMLElement, { index$, location$ }: MountOptions\n): Observable> {\n return combineLatest([\n index$,\n location$\n .pipe(\n startWith(getLocation()),\n filter(url => !!url.searchParams.get(\"h\"))\n )\n ])\n .pipe(\n map(([index, url]) => setupSearchHighlighter(index.config, true)(\n url.searchParams.get(\"h\")!\n )),\n map(fn => {\n const nodes = new Map()\n\n /* Traverse text nodes and collect matches */\n const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n for (let node = it.nextNode(); node; node = it.nextNode()) {\n if (node.parentElement?.offsetHeight) {\n const original = node.textContent!\n const replaced = fn(original)\n if (replaced.length > original.length)\n nodes.set(node as ChildNode, replaced)\n }\n }\n\n /* Replace original nodes with matches */\n for (const [node, text] of nodes) {\n const { childNodes } = h(\"span\", null, text)\n node.replaceWith(...Array.from(childNodes))\n }\n\n /* Return component */\n return { ref: el, nodes }\n })\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n auditTime,\n combineLatest,\n defer,\n distinctUntilChanged,\n finalize,\n map,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n Viewport,\n getElement,\n getElementOffset\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sidebar\n */\nexport interface Sidebar {\n height: number /* Sidebar height */\n locked: boolean /* Sidebar is locked */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n main$: Observable
    /* Main area observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch sidebar\n *\n * This function returns an observable that computes the visual parameters of\n * the sidebar which depends on the vertical viewport offset, as well as the\n * height of the main area. When the page is scrolled beyond the header, the\n * sidebar is locked and fills the remaining space.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar observable\n */\nexport function watchSidebar(\n el: HTMLElement, { viewport$, main$ }: WatchOptions\n): Observable {\n const parent = el.parentElement!\n const adjust =\n parent.offsetTop -\n parent.parentElement!.offsetTop\n\n /* Compute the sidebar's available height and if it should be locked */\n return combineLatest([main$, viewport$])\n .pipe(\n map(([{ offset, height }, { offset: { y } }]) => {\n height = height\n + Math.min(adjust, Math.max(0, y - offset))\n - adjust\n return {\n height,\n locked: y >= offset + adjust\n }\n }),\n distinctUntilChanged((a, b) => (\n a.height === b.height &&\n a.locked === b.locked\n ))\n )\n}\n\n/**\n * Mount sidebar\n *\n * This function doesn't set the height of the actual sidebar, but of its first\n * child \u2013 the `.md-sidebar__scrollwrap` element in order to mitigiate jittery\n * sidebars when the footer is scrolled into view. At some point we switched\n * from `absolute` / `fixed` positioning to `sticky` positioning, significantly\n * reducing jitter in some browsers (respectively Firefox and Safari) when\n * scrolling from the top. However, top-aligned sticky positioning means that\n * the sidebar snaps to the bottom when the end of the container is reached.\n * This is what leads to the mentioned jitter, as the sidebar's height may be\n * updated too slowly.\n *\n * This behaviour can be mitigiated by setting the height of the sidebar to `0`\n * while preserving the padding, and the height on its first element.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar component observable\n */\nexport function mountSidebar(\n el: HTMLElement, { header$, ...options }: MountOptions\n): Observable> {\n const inner = getElement(\".md-sidebar__scrollwrap\", el)\n const { y } = getElementOffset(inner)\n return defer(() => {\n const push$ = new Subject()\n push$\n .pipe(\n auditTime(0, animationFrameScheduler),\n withLatestFrom(header$)\n )\n .subscribe({\n\n /* Handle emission */\n next([{ height }, { height: offset }]) {\n inner.style.height = `${height - 2 * y}px`\n el.style.top = `${offset}px`\n },\n\n /* Handle complete */\n complete() {\n inner.style.height = \"\"\n el.style.top = \"\"\n }\n })\n\n /* Create and return component */\n return watchSidebar(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Repo, User } from \"github-types\"\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map,\n zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitHub release (partial)\n */\ninterface Release {\n tag_name: string /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitHub repository facts\n *\n * @param user - GitHub user or organization\n * @param repo - GitHub repository\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitHub(\n user: string, repo?: string\n): Observable {\n if (typeof repo !== \"undefined\") {\n const url = `https://api.github.com/repos/${user}/${repo}`\n return zip(\n\n /* Fetch version */\n requestJSON(`${url}/releases/latest`)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(release => ({\n version: release.tag_name\n })),\n defaultIfEmpty({})\n ),\n\n /* Fetch stars and forks */\n requestJSON(url)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(info => ({\n stars: info.stargazers_count,\n forks: info.forks_count\n })),\n defaultIfEmpty({})\n )\n )\n .pipe(\n map(([release, info]) => ({ ...release, ...info }))\n )\n\n /* User or organization */\n } else {\n const url = `https://api.github.com/users/${user}`\n return requestJSON(url)\n .pipe(\n map(info => ({\n repositories: info.public_repos\n })),\n defaultIfEmpty({})\n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ProjectSchema } from \"gitlab\"\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitLab repository facts\n *\n * @param base - GitLab base\n * @param project - GitLab project\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitLab(\n base: string, project: string\n): Observable {\n const url = `https://${base}/api/v4/projects/${encodeURIComponent(project)}`\n return requestJSON(url)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(({ star_count, forks_count }) => ({\n stars: star_count,\n forks: forks_count\n })),\n defaultIfEmpty({})\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable } from \"rxjs\"\n\nimport { fetchSourceFactsFromGitHub } from \"../github\"\nimport { fetchSourceFactsFromGitLab } from \"../gitlab\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository facts for repositories\n */\nexport interface RepositoryFacts {\n stars?: number /* Number of stars */\n forks?: number /* Number of forks */\n version?: string /* Latest version */\n}\n\n/**\n * Repository facts for organizations\n */\nexport interface OrganizationFacts {\n repositories?: number /* Number of repositories */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Repository facts\n */\nexport type SourceFacts =\n | RepositoryFacts\n | OrganizationFacts\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch repository facts\n *\n * @param url - Repository URL\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFacts(\n url: string\n): Observable {\n const [type] = url.match(/(git(?:hub|lab))/i) || []\n switch (type.toLowerCase()) {\n\n /* GitHub repository */\n case \"github\":\n const [, user, repo] = url.match(/^.+github\\.com\\/([^/]+)\\/?([^/]+)?/i)!\n return fetchSourceFactsFromGitHub(user, repo)\n\n /* GitLab repository */\n case \"gitlab\":\n const [, base, slug] = url.match(/^.+?([^/]*gitlab[^/]+)\\/(.+?)\\/?$/i)!\n return fetchSourceFactsFromGitLab(base, slug)\n\n /* Everything else */\n default:\n return EMPTY\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n catchError,\n defer,\n filter,\n finalize,\n map,\n of,\n shareReplay,\n tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\nimport { renderSourceFacts } from \"~/templates\"\n\nimport { Component } from \"../../_\"\nimport {\n SourceFacts,\n fetchSourceFacts\n} from \"../facts\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information\n */\nexport interface Source {\n facts: SourceFacts /* Repository facts */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information observable\n */\nlet fetch$: Observable\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch repository information\n *\n * This function tries to read the repository facts from session storage, and\n * if unsuccessful, fetches them from the underlying provider.\n *\n * @param el - Repository information element\n *\n * @returns Repository information observable\n */\nexport function watchSource(\n el: HTMLAnchorElement\n): Observable {\n return fetch$ ||= defer(() => {\n const cached = __md_get(\"__source\", sessionStorage)\n if (cached)\n return of(cached)\n else\n return fetchSourceFacts(el.href)\n .pipe(\n tap(facts => __md_set(\"__source\", facts, sessionStorage))\n )\n })\n .pipe(\n catchError(() => EMPTY),\n filter(facts => Object.keys(facts).length > 0),\n map(facts => ({ facts })),\n shareReplay(1)\n )\n}\n\n/**\n * Mount repository information\n *\n * @param el - Repository information element\n *\n * @returns Repository information component observable\n */\nexport function mountSource(\n el: HTMLAnchorElement\n): Observable> {\n const inner = getElement(\":scope > :last-child\", el)\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ facts }) => {\n inner.appendChild(renderSourceFacts(facts))\n inner.classList.add(\"md-source__repository--active\")\n })\n\n /* Create and return component */\n return watchSource(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n distinctUntilKeyChanged,\n finalize,\n map,\n of,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n watchElementSize,\n watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Navigation tabs\n */\nexport interface Tabs {\n hidden: boolean /* Navigation tabs are hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch navigation tabs\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs observable\n */\nexport function watchTabs(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n return watchElementSize(document.body)\n .pipe(\n switchMap(() => watchViewportAt(el, { header$, viewport$ })),\n map(({ offset: { y } }) => {\n return {\n hidden: y >= 10\n }\n }),\n distinctUntilKeyChanged(\"hidden\")\n )\n}\n\n/**\n * Mount navigation tabs\n *\n * This function hides the navigation tabs when scrolling past the threshold\n * and makes them reappear in a nice CSS animation when scrolling back up.\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs component observable\n */\nexport function mountTabs(\n el: HTMLElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe({\n\n /* Handle emission */\n next({ hidden }) {\n el.hidden = hidden\n },\n\n /* Handle complete */\n complete() {\n el.hidden = false\n }\n })\n\n /* Create and return component */\n return (\n feature(\"navigation.tabs.sticky\")\n ? of({ hidden: false })\n : watchTabs(el, options)\n )\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatestWith,\n debounceTime,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n finalize,\n map,\n of,\n repeat,\n scan,\n share,\n skip,\n startWith,\n switchMap,\n takeLast,\n takeUntil,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n getElement,\n getElements,\n getLocation,\n getOptionalElement,\n watchElementSize\n} from \"~/browser\"\n\nimport {\n Component,\n getComponentElement\n} from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Table of contents\n */\nexport interface TableOfContents {\n prev: HTMLAnchorElement[][] /* Anchors (previous) */\n next: HTMLAnchorElement[][] /* Anchors (next) */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch table of contents\n *\n * This is effectively a scroll spy implementation which will account for the\n * fixed header and automatically re-calculate anchor offsets when the viewport\n * is resized. The returned observable will only emit if the table of contents\n * needs to be repainted.\n *\n * This implementation tracks an anchor element's entire path starting from its\n * level up to the top-most anchor element, e.g. `[h3, h2, h1]`. Although the\n * Material theme currently doesn't make use of this information, it enables\n * the styling of the entire hierarchy through customization.\n *\n * Note that the current anchor is the last item of the `prev` anchor list.\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents observable\n */\nexport function watchTableOfContents(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n const table = new Map()\n\n /* Compute anchor-to-target mapping */\n const anchors = getElements(\"[href^=\\\\#]\", el)\n for (const anchor of anchors) {\n const id = decodeURIComponent(anchor.hash.substring(1))\n const target = getOptionalElement(`[id=\"${id}\"]`)\n if (typeof target !== \"undefined\")\n table.set(anchor, target)\n }\n\n /* Compute necessary adjustment for header */\n const adjust$ = header$\n .pipe(\n distinctUntilKeyChanged(\"height\"),\n map(({ height }) => {\n const main = getComponentElement(\"main\")\n const grid = getElement(\":scope > :first-child\", main)\n return height + 0.8 * (\n grid.offsetTop -\n main.offsetTop\n )\n }),\n share()\n )\n\n /* Compute partition of previous and next anchors */\n const partition$ = watchElementSize(document.body)\n .pipe(\n distinctUntilKeyChanged(\"height\"),\n\n /* Build index to map anchor paths to vertical offsets */\n switchMap(body => defer(() => {\n let path: HTMLAnchorElement[] = []\n return of([...table].reduce((index, [anchor, target]) => {\n while (path.length) {\n const last = table.get(path[path.length - 1])!\n if (last.tagName >= target.tagName) {\n path.pop()\n } else {\n break\n }\n }\n\n /* If the current anchor is hidden, continue with its parent */\n let offset = target.offsetTop\n while (!offset && target.parentElement) {\n target = target.parentElement\n offset = target.offsetTop\n }\n\n /* Map reversed anchor path to vertical offset */\n return index.set(\n [...path = [...path, anchor]].reverse(),\n offset\n )\n }, new Map()))\n })\n .pipe(\n\n /* Sort index by vertical offset (see https://bit.ly/30z6QSO) */\n map(index => new Map([...index].sort(([, a], [, b]) => a - b))),\n combineLatestWith(adjust$),\n\n /* Re-compute partition when viewport offset changes */\n switchMap(([index, adjust]) => viewport$\n .pipe(\n scan(([prev, next], { offset: { y }, size }) => {\n const last = y + size.height >= Math.floor(body.height)\n\n /* Look forward */\n while (next.length) {\n const [, offset] = next[0]\n if (offset - adjust < y || last) {\n prev = [...prev, next.shift()!]\n } else {\n break\n }\n }\n\n /* Look backward */\n while (prev.length) {\n const [, offset] = prev[prev.length - 1]\n if (offset - adjust >= y && !last) {\n next = [prev.pop()!, ...next]\n } else {\n break\n }\n }\n\n /* Return partition */\n return [prev, next]\n }, [[], [...index]]),\n distinctUntilChanged((a, b) => (\n a[0] === b[0] &&\n a[1] === b[1]\n ))\n )\n )\n )\n )\n )\n\n /* Compute and return anchor list migrations */\n return partition$\n .pipe(\n map(([prev, next]) => ({\n prev: prev.map(([path]) => path),\n next: next.map(([path]) => path)\n })),\n\n /* Extract anchor list migrations */\n startWith({ prev: [], next: [] }),\n bufferCount(2, 1),\n map(([a, b]) => {\n\n /* Moving down */\n if (a.prev.length < b.prev.length) {\n return {\n prev: b.prev.slice(Math.max(0, a.prev.length - 1), b.prev.length),\n next: []\n }\n\n /* Moving up */\n } else {\n return {\n prev: b.prev.slice(-1),\n next: b.next.slice(0, b.next.length - a.next.length)\n }\n }\n })\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount table of contents\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents component observable\n */\nexport function mountTableOfContents(\n el: HTMLElement, { viewport$, header$, target$ }: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n push$.subscribe(({ prev, next }) => {\n\n /* Look forward */\n for (const [anchor] of next) {\n anchor.classList.remove(\"md-nav__link--passed\")\n anchor.classList.remove(\"md-nav__link--active\")\n }\n\n /* Look backward */\n for (const [index, [anchor]] of prev.entries()) {\n anchor.classList.add(\"md-nav__link--passed\")\n anchor.classList.toggle(\n \"md-nav__link--active\",\n index === prev.length - 1\n )\n }\n })\n\n /* Set up anchor tracking, if enabled */\n if (feature(\"navigation.tracking\"))\n viewport$\n .pipe(\n takeUntil(done$),\n distinctUntilKeyChanged(\"offset\"),\n debounceTime(250),\n skip(1),\n takeUntil(target$.pipe(skip(1))),\n repeat({ delay: 250 }),\n withLatestFrom(push$)\n )\n .subscribe(([, { prev }]) => {\n const url = getLocation()\n\n /* Set hash fragment to active anchor */\n const anchor = prev[prev.length - 1]\n if (anchor && anchor.length) {\n const [active] = anchor\n const { hash } = new URL(active.href)\n if (url.hash !== hash) {\n url.hash = hash\n history.replaceState({}, \"\", `${url}`)\n }\n\n /* Reset anchor when at the top */\n } else {\n url.hash = \"\"\n history.replaceState({}, \"\", `${url}`)\n }\n })\n\n /* Create and return component */\n return watchTableOfContents(el, { viewport$, header$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatest,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n endWith,\n finalize,\n map,\n repeat,\n skip,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { Viewport } from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Back-to-top button\n */\nexport interface BackToTop {\n hidden: boolean /* Back-to-top button is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n main$: Observable
    /* Main area observable */\n target$: Observable /* Location target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch back-to-top\n *\n * @param _el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top observable\n */\nexport function watchBackToTop(\n _el: HTMLElement, { viewport$, main$, target$ }: WatchOptions\n): Observable {\n\n /* Compute direction */\n const direction$ = viewport$\n .pipe(\n map(({ offset: { y } }) => y),\n bufferCount(2, 1),\n map(([a, b]) => a > b && b > 0),\n distinctUntilChanged()\n )\n\n /* Compute whether main area is active */\n const active$ = main$\n .pipe(\n map(({ active }) => active)\n )\n\n /* Compute threshold for hiding */\n return combineLatest([active$, direction$])\n .pipe(\n map(([active, direction]) => !(active && direction)),\n distinctUntilChanged(),\n takeUntil(target$.pipe(skip(1))),\n endWith(true),\n repeat({ delay: 250 }),\n map(hidden => ({ hidden }))\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount back-to-top\n *\n * @param el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top component observable\n */\nexport function mountBackToTop(\n el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n push$.subscribe({\n\n /* Handle emission */\n next({ hidden }) {\n el.hidden = hidden\n if (hidden) {\n el.setAttribute(\"tabindex\", \"-1\")\n el.blur()\n } else {\n el.removeAttribute(\"tabindex\")\n }\n },\n\n /* Handle complete */\n complete() {\n el.style.top = \"\"\n el.hidden = true\n el.removeAttribute(\"tabindex\")\n }\n })\n\n /* Watch header height */\n header$\n .pipe(\n takeUntil(done$),\n distinctUntilKeyChanged(\"height\")\n )\n .subscribe(({ height }) => {\n el.style.top = `${height + 16}px`\n })\n\n /* Create and return component */\n return watchBackToTop(el, { viewport$, main$, target$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n mergeMap,\n switchMap,\n takeWhile,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n document$: Observable /* Document observable */\n tablet$: Observable /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch indeterminate checkboxes\n *\n * This function replaces the indeterminate \"pseudo state\" with the actual\n * indeterminate state, which is used to keep navigation always expanded.\n *\n * @param options - Options\n */\nexport function patchIndeterminate(\n { document$, tablet$ }: PatchOptions\n): void {\n document$\n .pipe(\n switchMap(() => getElements(\n // @todo `data-md-state` is deprecated and removed in v9\n \".md-toggle--indeterminate, [data-md-state=indeterminate]\"\n )),\n tap(el => {\n el.indeterminate = true\n el.checked = false\n }),\n mergeMap(el => fromEvent(el, \"change\")\n .pipe(\n takeWhile(() => el.classList.contains(\"md-toggle--indeterminate\")),\n map(() => el)\n )\n ),\n withLatestFrom(tablet$)\n )\n .subscribe(([el, tablet]) => {\n el.classList.remove(\"md-toggle--indeterminate\")\n if (tablet)\n el.checked = false\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n mergeMap,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n document$: Observable /* Document observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether the given device is an Apple device\n *\n * @returns Test result\n */\nfunction isAppleDevice(): boolean {\n return /(iPad|iPhone|iPod)/.test(navigator.userAgent)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch all elements with `data-md-scrollfix` attributes\n *\n * This is a year-old patch which ensures that overflow scrolling works at the\n * top and bottom of containers on iOS by ensuring a `1px` scroll offset upon\n * the start of a touch event.\n *\n * @see https://bit.ly/2SCtAOO - Original source\n *\n * @param options - Options\n */\nexport function patchScrollfix(\n { document$ }: PatchOptions\n): void {\n document$\n .pipe(\n switchMap(() => getElements(\"[data-md-scrollfix]\")),\n tap(el => el.removeAttribute(\"data-md-scrollfix\")),\n filter(isAppleDevice),\n mergeMap(el => fromEvent(el, \"touchstart\")\n .pipe(\n map(() => el)\n )\n )\n )\n .subscribe(el => {\n const top = el.scrollTop\n\n /* We're at the top of the container */\n if (top === 0) {\n el.scrollTop = 1\n\n /* We're at the bottom of the container */\n } else if (top + el.offsetHeight === el.scrollHeight) {\n el.scrollTop = top - 1\n }\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n delay,\n map,\n of,\n switchMap,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n Viewport,\n watchToggle\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n viewport$: Observable /* Viewport observable */\n tablet$: Observable /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch the document body to lock when search is open\n *\n * For mobile and tablet viewports, the search is rendered full screen, which\n * leads to scroll leaking when at the top or bottom of the search result. This\n * function locks the body when the search is in full screen mode, and restores\n * the scroll position when leaving.\n *\n * @param options - Options\n */\nexport function patchScrolllock(\n { viewport$, tablet$ }: PatchOptions\n): void {\n combineLatest([watchToggle(\"search\"), tablet$])\n .pipe(\n map(([active, tablet]) => active && !tablet),\n switchMap(active => of(active)\n .pipe(\n delay(active ? 400 : 100)\n )\n ),\n withLatestFrom(viewport$)\n )\n .subscribe(([active, { offset: { y }}]) => {\n if (active) {\n document.body.setAttribute(\"data-md-scrolllock\", \"\")\n document.body.style.top = `-${y}px`\n } else {\n const value = -1 * parseInt(document.body.style.top, 10)\n document.body.removeAttribute(\"data-md-scrolllock\")\n document.body.style.top = \"\"\n if (value)\n window.scrollTo(0, value)\n }\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n Object.entries = function (obj: object) {\n const data: [string, string][] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push([key, obj[key]])\n\n /* Return entries */\n return data\n }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n Object.values = function (obj: object) {\n const data: string[] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n 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"src/assets/javascripts/components/search/_/index.ts", "src/assets/javascripts/components/search/highlight/index.ts", "src/assets/javascripts/components/sidebar/index.ts", "src/assets/javascripts/components/source/facts/github/index.ts", "src/assets/javascripts/components/source/facts/gitlab/index.ts", "src/assets/javascripts/components/source/facts/_/index.ts", "src/assets/javascripts/components/source/_/index.ts", "src/assets/javascripts/components/tabs/index.ts", "src/assets/javascripts/components/toc/index.ts", "src/assets/javascripts/components/top/index.ts", "src/assets/javascripts/patches/indeterminate/index.ts", "src/assets/javascripts/patches/scrollfix/index.ts", "src/assets/javascripts/patches/scrolllock/index.ts", "src/assets/javascripts/polyfills/index.ts"], + "sourceRoot": "../../../..", + "sourcesContent": ["(function (global, factory) {\n typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n typeof define === 'function' && define.amd ? define(factory) :\n (factory());\n}(this, (function () { 'use strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "(function(global) {\r\n /**\r\n * Polyfill URLSearchParams\r\n *\r\n * Inspired from : https://github.com/WebReflection/url-search-params/blob/master/src/url-search-params.js\r\n */\r\n\r\n var checkIfIteratorIsSupported = function() {\r\n try {\r\n return !!Symbol.iterator;\r\n } catch (error) {\r\n return false;\r\n }\r\n };\r\n\r\n\r\n var iteratorSupported = checkIfIteratorIsSupported();\r\n\r\n var createIterator = function(items) {\r\n var iterator = {\r\n next: function() {\r\n var value = items.shift();\r\n return { done: value === void 0, value: value };\r\n }\r\n };\r\n\r\n if (iteratorSupported) {\r\n iterator[Symbol.iterator] = function() {\r\n return iterator;\r\n };\r\n }\r\n\r\n return iterator;\r\n };\r\n\r\n /**\r\n * Search param name and values should be encoded according to https://url.spec.whatwg.org/#urlencoded-serializing\r\n * encodeURIComponent() produces the same result except encoding spaces as `%20` instead of `+`.\r\n */\r\n var serializeParam = function(value) {\r\n return encodeURIComponent(value).replace(/%20/g, '+');\r\n };\r\n\r\n var deserializeParam = function(value) {\r\n return decodeURIComponent(String(value).replace(/\\+/g, ' '));\r\n };\r\n\r\n var polyfillURLSearchParams = function() {\r\n\r\n var URLSearchParams = function(searchString) {\r\n Object.defineProperty(this, '_entries', { writable: true, value: {} });\r\n var typeofSearchString = typeof searchString;\r\n\r\n if (typeofSearchString === 'undefined') {\r\n // do nothing\r\n } else if (typeofSearchString === 'string') {\r\n if (searchString !== '') {\r\n this._fromString(searchString);\r\n }\r\n } else if (searchString instanceof URLSearchParams) {\r\n var _this = this;\r\n searchString.forEach(function(value, name) {\r\n _this.append(name, value);\r\n });\r\n } else if ((searchString !== null) && (typeofSearchString === 'object')) {\r\n if (Object.prototype.toString.call(searchString) === '[object Array]') {\r\n for (var i = 0; i < searchString.length; i++) {\r\n var entry = searchString[i];\r\n if ((Object.prototype.toString.call(entry) === '[object Array]') || (entry.length !== 2)) {\r\n this.append(entry[0], entry[1]);\r\n } else {\r\n throw new TypeError('Expected [string, any] as entry at index ' + i + ' of URLSearchParams\\'s input');\r\n }\r\n }\r\n } else {\r\n for (var key in searchString) {\r\n if (searchString.hasOwnProperty(key)) {\r\n this.append(key, searchString[key]);\r\n }\r\n }\r\n }\r\n } else {\r\n throw new TypeError('Unsupported input\\'s type for URLSearchParams');\r\n }\r\n };\r\n\r\n var proto = URLSearchParams.prototype;\r\n\r\n proto.append = function(name, value) {\r\n if (name in this._entries) {\r\n this._entries[name].push(String(value));\r\n } else {\r\n this._entries[name] = [String(value)];\r\n }\r\n };\r\n\r\n proto.delete = function(name) {\r\n delete this._entries[name];\r\n };\r\n\r\n proto.get = function(name) {\r\n return (name in this._entries) ? this._entries[name][0] : null;\r\n };\r\n\r\n proto.getAll = function(name) {\r\n return (name in this._entries) ? this._entries[name].slice(0) : [];\r\n };\r\n\r\n proto.has = function(name) {\r\n return (name in this._entries);\r\n };\r\n\r\n proto.set = function(name, value) {\r\n this._entries[name] = [String(value)];\r\n };\r\n\r\n proto.forEach = function(callback, thisArg) {\r\n var entries;\r\n for (var name in this._entries) {\r\n if (this._entries.hasOwnProperty(name)) {\r\n entries = this._entries[name];\r\n for (var i = 0; i < entries.length; i++) {\r\n callback.call(thisArg, entries[i], name, this);\r\n }\r\n }\r\n }\r\n };\r\n\r\n proto.keys = function() {\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push(name);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n proto.values = function() {\r\n var items = [];\r\n this.forEach(function(value) {\r\n items.push(value);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n proto.entries = function() {\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push([name, value]);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n if (iteratorSupported) {\r\n proto[Symbol.iterator] = proto.entries;\r\n }\r\n\r\n proto.toString = function() {\r\n var searchArray = [];\r\n this.forEach(function(value, name) {\r\n searchArray.push(serializeParam(name) + '=' + serializeParam(value));\r\n });\r\n return searchArray.join('&');\r\n };\r\n\r\n\r\n global.URLSearchParams = URLSearchParams;\r\n };\r\n\r\n var checkIfURLSearchParamsSupported = function() {\r\n try {\r\n var URLSearchParams = global.URLSearchParams;\r\n\r\n return (\r\n (new URLSearchParams('?a=1').toString() === 'a=1') &&\r\n (typeof URLSearchParams.prototype.set === 'function') &&\r\n (typeof URLSearchParams.prototype.entries === 'function')\r\n );\r\n } catch (e) {\r\n return false;\r\n }\r\n };\r\n\r\n if (!checkIfURLSearchParamsSupported()) {\r\n polyfillURLSearchParams();\r\n }\r\n\r\n var proto = global.URLSearchParams.prototype;\r\n\r\n if (typeof proto.sort !== 'function') {\r\n proto.sort = function() {\r\n var _this = this;\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push([name, value]);\r\n if (!_this._entries) {\r\n _this.delete(name);\r\n }\r\n });\r\n items.sort(function(a, b) {\r\n if (a[0] < b[0]) {\r\n return -1;\r\n } else if (a[0] > b[0]) {\r\n return +1;\r\n } else {\r\n return 0;\r\n }\r\n });\r\n if (_this._entries) { // force reset because IE keeps keys index\r\n _this._entries = {};\r\n }\r\n for (var i = 0; i < items.length; i++) {\r\n this.append(items[i][0], items[i][1]);\r\n }\r\n };\r\n }\r\n\r\n if (typeof proto._fromString !== 'function') {\r\n Object.defineProperty(proto, '_fromString', {\r\n enumerable: false,\r\n configurable: false,\r\n writable: false,\r\n value: function(searchString) {\r\n if (this._entries) {\r\n this._entries = {};\r\n } else {\r\n var keys = [];\r\n this.forEach(function(value, name) {\r\n keys.push(name);\r\n });\r\n for (var i = 0; i < keys.length; i++) {\r\n this.delete(keys[i]);\r\n }\r\n }\r\n\r\n searchString = searchString.replace(/^\\?/, '');\r\n var attributes = searchString.split('&');\r\n var attribute;\r\n for (var i = 0; i < attributes.length; i++) {\r\n attribute = attributes[i].split('=');\r\n this.append(\r\n deserializeParam(attribute[0]),\r\n (attribute.length > 1) ? deserializeParam(attribute[1]) : ''\r\n );\r\n }\r\n }\r\n });\r\n }\r\n\r\n // HTMLAnchorElement\r\n\r\n})(\r\n (typeof global !== 'undefined') ? global\r\n : ((typeof window !== 'undefined') ? window\r\n : ((typeof self !== 'undefined') ? self : this))\r\n);\r\n\r\n(function(global) {\r\n /**\r\n * Polyfill URL\r\n *\r\n * Inspired from : https://github.com/arv/DOM-URL-Polyfill/blob/master/src/url.js\r\n */\r\n\r\n var checkIfURLIsSupported = function() {\r\n try {\r\n var u = new global.URL('b', 'http://a');\r\n u.pathname = 'c d';\r\n return (u.href === 'http://a/c%20d') && u.searchParams;\r\n } catch (e) {\r\n return false;\r\n }\r\n };\r\n\r\n\r\n var polyfillURL = function() {\r\n var _URL = global.URL;\r\n\r\n var URL = function(url, base) {\r\n if (typeof url !== 'string') url = String(url);\r\n if (base && typeof base !== 'string') base = String(base);\r\n\r\n // Only create another document if the base is different from current location.\r\n var doc = document, baseElement;\r\n if (base && (global.location === void 0 || base !== global.location.href)) {\r\n base = base.toLowerCase();\r\n doc = document.implementation.createHTMLDocument('');\r\n baseElement = doc.createElement('base');\r\n baseElement.href = base;\r\n doc.head.appendChild(baseElement);\r\n try {\r\n if (baseElement.href.indexOf(base) !== 0) throw new Error(baseElement.href);\r\n } catch (err) {\r\n throw new Error('URL unable to set base ' + base + ' due to ' + err);\r\n }\r\n }\r\n\r\n var anchorElement = doc.createElement('a');\r\n anchorElement.href = url;\r\n if (baseElement) {\r\n doc.body.appendChild(anchorElement);\r\n anchorElement.href = anchorElement.href; // force href to refresh\r\n }\r\n\r\n var inputElement = doc.createElement('input');\r\n inputElement.type = 'url';\r\n inputElement.value = url;\r\n\r\n if (anchorElement.protocol === ':' || !/:/.test(anchorElement.href) || (!inputElement.checkValidity() && !base)) {\r\n throw new TypeError('Invalid URL');\r\n }\r\n\r\n Object.defineProperty(this, '_anchorElement', {\r\n value: anchorElement\r\n });\r\n\r\n\r\n // create a linked searchParams which reflect its changes on URL\r\n var searchParams = new global.URLSearchParams(this.search);\r\n var enableSearchUpdate = true;\r\n var enableSearchParamsUpdate = true;\r\n var _this = this;\r\n ['append', 'delete', 'set'].forEach(function(methodName) {\r\n var method = searchParams[methodName];\r\n searchParams[methodName] = function() {\r\n method.apply(searchParams, arguments);\r\n if (enableSearchUpdate) {\r\n enableSearchParamsUpdate = false;\r\n _this.search = searchParams.toString();\r\n enableSearchParamsUpdate = true;\r\n }\r\n };\r\n });\r\n\r\n Object.defineProperty(this, 'searchParams', {\r\n value: searchParams,\r\n enumerable: true\r\n });\r\n\r\n var search = void 0;\r\n Object.defineProperty(this, '_updateSearchParams', {\r\n enumerable: false,\r\n configurable: false,\r\n writable: false,\r\n value: function() {\r\n if (this.search !== search) {\r\n search = this.search;\r\n if (enableSearchParamsUpdate) {\r\n enableSearchUpdate = false;\r\n this.searchParams._fromString(this.search);\r\n enableSearchUpdate = true;\r\n }\r\n }\r\n }\r\n });\r\n };\r\n\r\n var proto = URL.prototype;\r\n\r\n var linkURLWithAnchorAttribute = function(attributeName) {\r\n Object.defineProperty(proto, attributeName, {\r\n get: function() {\r\n return this._anchorElement[attributeName];\r\n },\r\n set: function(value) {\r\n this._anchorElement[attributeName] = value;\r\n },\r\n enumerable: true\r\n });\r\n };\r\n\r\n ['hash', 'host', 'hostname', 'port', 'protocol']\r\n .forEach(function(attributeName) {\r\n linkURLWithAnchorAttribute(attributeName);\r\n });\r\n\r\n Object.defineProperty(proto, 'search', {\r\n get: function() {\r\n return this._anchorElement['search'];\r\n },\r\n set: function(value) {\r\n this._anchorElement['search'] = value;\r\n this._updateSearchParams();\r\n },\r\n enumerable: true\r\n });\r\n\r\n Object.defineProperties(proto, {\r\n\r\n 'toString': {\r\n get: function() {\r\n var _this = this;\r\n return function() {\r\n return _this.href;\r\n };\r\n }\r\n },\r\n\r\n 'href': {\r\n get: function() {\r\n return this._anchorElement.href.replace(/\\?$/, '');\r\n },\r\n set: function(value) {\r\n this._anchorElement.href = value;\r\n this._updateSearchParams();\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'pathname': {\r\n get: function() {\r\n return this._anchorElement.pathname.replace(/(^\\/?)/, '/');\r\n },\r\n set: function(value) {\r\n this._anchorElement.pathname = value;\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'origin': {\r\n get: function() {\r\n // get expected port from protocol\r\n var expectedPort = { 'http:': 80, 'https:': 443, 'ftp:': 21 }[this._anchorElement.protocol];\r\n // add port to origin if, expected port is different than actual port\r\n // and it is not empty f.e http://foo:8080\r\n // 8080 != 80 && 8080 != ''\r\n var addPortToOrigin = this._anchorElement.port != expectedPort &&\r\n this._anchorElement.port !== '';\r\n\r\n return this._anchorElement.protocol +\r\n '//' +\r\n this._anchorElement.hostname +\r\n (addPortToOrigin ? (':' + this._anchorElement.port) : '');\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'password': { // TODO\r\n get: function() {\r\n return '';\r\n },\r\n set: function(value) {\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'username': { // TODO\r\n get: function() {\r\n return '';\r\n },\r\n set: function(value) {\r\n },\r\n enumerable: true\r\n },\r\n });\r\n\r\n URL.createObjectURL = function(blob) {\r\n return _URL.createObjectURL.apply(_URL, arguments);\r\n };\r\n\r\n URL.revokeObjectURL = function(url) {\r\n return _URL.revokeObjectURL.apply(_URL, arguments);\r\n };\r\n\r\n global.URL = URL;\r\n\r\n };\r\n\r\n if (!checkIfURLIsSupported()) {\r\n polyfillURL();\r\n }\r\n\r\n if ((global.location !== void 0) && !('origin' in global.location)) {\r\n var getOrigin = function() {\r\n return global.location.protocol + '//' + global.location.hostname + (global.location.port ? 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'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "Array.prototype.flat||Object.defineProperty(Array.prototype,\"flat\",{configurable:!0,value:function r(){var t=isNaN(arguments[0])?1:Number(arguments[0]);return t?Array.prototype.reduce.call(this,function(a,e){return Array.isArray(e)?a.push.apply(a,r.call(e,t-1)):a.push(e),a},[]):Array.prototype.slice.call(this)},writable:!0}),Array.prototype.flatMap||Object.defineProperty(Array.prototype,\"flatMap\",{configurable:!0,value:function(r){return Array.prototype.map.apply(this,arguments).flat()},writable:!0})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"array-flat-polyfill\"\nimport \"focus-visible\"\nimport \"unfetch/polyfill\"\nimport \"url-polyfill\"\n\nimport {\n EMPTY,\n NEVER,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getOptionalElement,\n requestJSON,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountAnnounce,\n mountBackToTop,\n mountConsent,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantLoading,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget()\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? __search?.index || requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up instant loading, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantLoading({ document$, location$, viewport$ })\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"[href][rel=prev]\")\n if (typeof prev !== \"undefined\")\n prev.click()\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"[href][rel=next]\")\n if (typeof next !== \"undefined\")\n next.click()\n break\n }\n })\n\n/* Set up patches */\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Consent */\n ...getComponentElements(\"consent\")\n .map(el => mountConsent(el, { target$ })),\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Announcement bar */\n ...getComponentElements(\"announce\")\n .map(el => mountAnnounce(el)),\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, { viewport$, header$, target$ })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.component$ = component$ /* Component observable */\n", "self.fetch||(self.fetch=function(e,n){return n=n||{},new Promise(function(t,s){var r=new XMLHttpRequest,o=[],u=[],i={},a=function(){return{ok:2==(r.status/100|0),statusText:r.statusText,status:r.status,url:r.responseURL,text:function(){return Promise.resolve(r.responseText)},json:function(){return Promise.resolve(r.responseText).then(JSON.parse)},blob:function(){return Promise.resolve(new Blob([r.response]))},clone:a,headers:{keys:function(){return o},entries:function(){return u},get:function(e){return i[e.toLowerCase()]},has:function(e){return e.toLowerCase()in i}}}};for(var c in r.open(n.method||\"get\",e,!0),r.onload=function(){r.getAllResponseHeaders().replace(/^(.*?):[^\\S\\n]*([\\s\\S]*?)$/gm,function(e,n,t){o.push(n=n.toLowerCase()),u.push([n,t]),i[n]=i[n]?i[n]+\",\"+t:t}),t(a())},r.onerror=s,r.withCredentials=\"include\"==n.credentials,n.headers)r.setRequestHeader(c,n.headers[c]);r.send(n.body||null)})});\n", "import tslib from '../tslib.js';\r\nconst {\r\n __extends,\r\n __assign,\r\n __rest,\r\n __decorate,\r\n __param,\r\n __metadata,\r\n __awaiter,\r\n __generator,\r\n __exportStar,\r\n __createBinding,\r\n __values,\r\n __read,\r\n __spread,\r\n __spreadArrays,\r\n __spreadArray,\r\n __await,\r\n __asyncGenerator,\r\n __asyncDelegator,\r\n __asyncValues,\r\n __makeTemplateObject,\r\n __importStar,\r\n __importDefault,\r\n __classPrivateFieldGet,\r\n __classPrivateFieldSet,\r\n} = tslib;\r\nexport {\r\n __extends,\r\n __assign,\r\n __rest,\r\n __decorate,\r\n __param,\r\n __metadata,\r\n __awaiter,\r\n __generator,\r\n __exportStar,\r\n __createBinding,\r\n __values,\r\n __read,\r\n __spread,\r\n __spreadArrays,\r\n __spreadArray,\r\n __await,\r\n __asyncGenerator,\r\n __asyncDelegator,\r\n __asyncValues,\r\n __makeTemplateObject,\r\n __importStar,\r\n __importDefault,\r\n __classPrivateFieldGet,\r\n __classPrivateFieldSet,\r\n};\r\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n ReplaySubject,\n Subject,\n fromEvent\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch document\n *\n * Documents are implemented as subjects, so all downstream observables are\n * automatically updated when a new document is emitted.\n *\n * @returns Document subject\n */\nexport function watchDocument(): Subject {\n const document$ = new ReplaySubject(1)\n fromEvent(document, \"DOMContentLoaded\", { once: true })\n .subscribe(() => document$.next(document))\n\n /* Return document */\n return document$\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements(\n selector: string, node?: ParentNode\n): T[]\n\nexport function getElements(\n selector: string, node: ParentNode = document\n): T[] {\n return Array.from(node.querySelectorAll(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement(\n selector: string, node?: ParentNode\n): T\n\nexport function getElement(\n selector: string, node: ParentNode = document\n): T {\n const el = getOptionalElement(selector, node)\n if (typeof el === \"undefined\")\n throw new ReferenceError(\n `Missing element: expected \"${selector}\" to be present`\n )\n\n /* Return element */\n return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement(\n selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement(\n selector: string, node: ParentNode = document\n): T | undefined {\n return node.querySelector(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n return document.activeElement instanceof HTMLElement\n ? document.activeElement || undefined\n : undefined\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n debounceTime,\n distinctUntilChanged,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * Previously, this function used `focus` and `blur` events to determine whether\n * an element is focused, but this doesn't work if there are focusable elements\n * within the elements itself. A better solutions are `focusin` and `focusout`\n * events, which bubble up the tree and allow for more fine-grained control.\n *\n * `debounceTime` is necessary, because when a focus change happens inside an\n * element, the observable would first emit `false` and then `true` again.\n *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(document.body, \"focusin\"),\n fromEvent(document.body, \"focusout\")\n )\n .pipe(\n debounceTime(1),\n map(() => {\n const active = getActiveElement()\n return typeof active !== \"undefined\"\n ? el.contains(active)\n : false\n }),\n startWith(el === getActiveElement()),\n distinctUntilChanged()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n animationFrameScheduler,\n auditTime,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementOffset {\n x: number /* Horizontal offset */\n y: number /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffset(\n el: HTMLElement\n): ElementOffset {\n return {\n x: el.offsetLeft,\n y: el.offsetTop\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffset(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(window, \"load\"),\n fromEvent(window, \"resize\")\n )\n .pipe(\n auditTime(0, animationFrameScheduler),\n map(() => getElementOffset(el)),\n startWith(getElementOffset(el))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n animationFrameScheduler,\n auditTime,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\nimport { ElementOffset } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content offset (= scroll offset)\n *\n * @param el - Element\n *\n * @returns Element content offset\n */\nexport function getElementContentOffset(\n el: HTMLElement\n): ElementOffset {\n return {\n x: el.scrollLeft,\n y: el.scrollTop\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element content offset\n *\n * @param el - Element\n *\n * @returns Element content offset observable\n */\nexport function watchElementContentOffset(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(el, \"scroll\"),\n fromEvent(window, \"resize\")\n )\n .pipe(\n auditTime(0, animationFrameScheduler),\n map(() => getElementContentOffset(el)),\n startWith(getElementContentOffset(el))\n )\n}\n", "/**\r\n * A collection of shims that provide minimal functionality of the ES6 collections.\r\n *\r\n * These implementations are not meant to be used outside of the ResizeObserver\r\n * modules as they cover only a limited range of use cases.\r\n */\r\n/* eslint-disable require-jsdoc, valid-jsdoc */\r\nvar MapShim = (function () {\r\n if (typeof Map !== 'undefined') {\r\n return Map;\r\n }\r\n /**\r\n * Returns index in provided array that matches the specified key.\r\n *\r\n * @param {Array} arr\r\n * @param {*} key\r\n * @returns {number}\r\n */\r\n function getIndex(arr, key) {\r\n var result = -1;\r\n arr.some(function (entry, index) {\r\n if (entry[0] === key) {\r\n result = index;\r\n return true;\r\n }\r\n return false;\r\n });\r\n return result;\r\n }\r\n return /** @class */ (function () {\r\n function class_1() {\r\n this.__entries__ = [];\r\n }\r\n Object.defineProperty(class_1.prototype, \"size\", {\r\n /**\r\n * @returns {boolean}\r\n */\r\n get: function () {\r\n return this.__entries__.length;\r\n },\r\n enumerable: true,\r\n configurable: true\r\n });\r\n /**\r\n * @param {*} key\r\n * @returns {*}\r\n */\r\n class_1.prototype.get = function (key) {\r\n var index = getIndex(this.__entries__, key);\r\n var entry = this.__entries__[index];\r\n return entry && entry[1];\r\n };\r\n /**\r\n * @param {*} key\r\n * @param {*} value\r\n * @returns {void}\r\n */\r\n class_1.prototype.set = function (key, value) {\r\n var index = getIndex(this.__entries__, key);\r\n if (~index) {\r\n this.__entries__[index][1] = value;\r\n }\r\n else {\r\n this.__entries__.push([key, value]);\r\n }\r\n };\r\n /**\r\n * @param {*} key\r\n * @returns {void}\r\n */\r\n class_1.prototype.delete = function (key) {\r\n var entries = this.__entries__;\r\n var index = getIndex(entries, key);\r\n if (~index) {\r\n entries.splice(index, 1);\r\n }\r\n };\r\n /**\r\n * @param {*} key\r\n * @returns {void}\r\n */\r\n class_1.prototype.has = function (key) {\r\n return !!~getIndex(this.__entries__, key);\r\n };\r\n /**\r\n * @returns {void}\r\n */\r\n class_1.prototype.clear = function () {\r\n this.__entries__.splice(0);\r\n };\r\n /**\r\n * @param {Function} callback\r\n * @param {*} [ctx=null]\r\n * @returns {void}\r\n */\r\n class_1.prototype.forEach = function (callback, ctx) {\r\n if (ctx === void 0) { ctx = null; }\r\n for (var _i = 0, _a = this.__entries__; _i < _a.length; _i++) {\r\n var entry = _a[_i];\r\n callback.call(ctx, entry[1], entry[0]);\r\n }\r\n };\r\n return class_1;\r\n }());\r\n})();\n\n/**\r\n * Detects whether window and document objects are available in current environment.\r\n */\r\nvar isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined' && window.document === document;\n\n// Returns global object of a current environment.\r\nvar global$1 = (function () {\r\n if (typeof global !== 'undefined' && global.Math === Math) {\r\n return global;\r\n }\r\n if (typeof self !== 'undefined' && self.Math === Math) {\r\n return self;\r\n }\r\n if (typeof window !== 'undefined' && window.Math === Math) {\r\n return window;\r\n }\r\n // eslint-disable-next-line no-new-func\r\n return Function('return this')();\r\n})();\n\n/**\r\n * A shim for the requestAnimationFrame which falls back to the setTimeout if\r\n * first one is not supported.\r\n *\r\n * @returns {number} Requests' identifier.\r\n */\r\nvar requestAnimationFrame$1 = (function () {\r\n if (typeof requestAnimationFrame === 'function') {\r\n // It's required to use a bounded function because IE sometimes throws\r\n // an \"Invalid calling object\" error if rAF is invoked without the global\r\n // object on the left hand side.\r\n return requestAnimationFrame.bind(global$1);\r\n }\r\n return function (callback) { return setTimeout(function () { return callback(Date.now()); }, 1000 / 60); };\r\n})();\n\n// Defines minimum timeout before adding a trailing call.\r\nvar trailingTimeout = 2;\r\n/**\r\n * Creates a wrapper function which ensures that provided callback will be\r\n * invoked only once during the specified delay period.\r\n *\r\n * @param {Function} callback - Function to be invoked after the delay period.\r\n * @param {number} delay - Delay after which to invoke callback.\r\n * @returns {Function}\r\n */\r\nfunction throttle (callback, delay) {\r\n var leadingCall = false, trailingCall = false, lastCallTime = 0;\r\n /**\r\n * Invokes the original callback function and schedules new invocation if\r\n * the \"proxy\" was called during current request.\r\n *\r\n * @returns {void}\r\n */\r\n function resolvePending() {\r\n if (leadingCall) {\r\n leadingCall = false;\r\n callback();\r\n }\r\n if (trailingCall) {\r\n proxy();\r\n }\r\n }\r\n /**\r\n * Callback invoked after the specified delay. It will further postpone\r\n * invocation of the original function delegating it to the\r\n * requestAnimationFrame.\r\n *\r\n * @returns {void}\r\n */\r\n function timeoutCallback() {\r\n requestAnimationFrame$1(resolvePending);\r\n }\r\n /**\r\n * Schedules invocation of the original function.\r\n *\r\n * @returns {void}\r\n */\r\n function proxy() {\r\n var timeStamp = Date.now();\r\n if (leadingCall) {\r\n // Reject immediately following calls.\r\n if (timeStamp - lastCallTime < trailingTimeout) {\r\n return;\r\n }\r\n // Schedule new call to be in invoked when the pending one is resolved.\r\n // This is important for \"transitions\" which never actually start\r\n // immediately so there is a chance that we might miss one if change\r\n // happens amids the pending invocation.\r\n trailingCall = true;\r\n }\r\n else {\r\n leadingCall = true;\r\n trailingCall = false;\r\n setTimeout(timeoutCallback, delay);\r\n }\r\n lastCallTime = timeStamp;\r\n }\r\n return proxy;\r\n}\n\n// Minimum delay before invoking the update of observers.\r\nvar REFRESH_DELAY = 20;\r\n// A list of substrings of CSS properties used to find transition events that\r\n// might affect dimensions of observed elements.\r\nvar transitionKeys = ['top', 'right', 'bottom', 'left', 'width', 'height', 'size', 'weight'];\r\n// Check if MutationObserver is available.\r\nvar mutationObserverSupported = typeof MutationObserver !== 'undefined';\r\n/**\r\n * Singleton controller class which handles updates of ResizeObserver instances.\r\n */\r\nvar ResizeObserverController = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserverController.\r\n *\r\n * @private\r\n */\r\n function ResizeObserverController() {\r\n /**\r\n * Indicates whether DOM listeners have been added.\r\n *\r\n * @private {boolean}\r\n */\r\n this.connected_ = false;\r\n /**\r\n * Tells that controller has subscribed for Mutation Events.\r\n *\r\n * @private {boolean}\r\n */\r\n this.mutationEventsAdded_ = false;\r\n /**\r\n * Keeps reference to the instance of MutationObserver.\r\n *\r\n * @private {MutationObserver}\r\n */\r\n this.mutationsObserver_ = null;\r\n /**\r\n * A list of connected observers.\r\n *\r\n * @private {Array}\r\n */\r\n this.observers_ = [];\r\n this.onTransitionEnd_ = this.onTransitionEnd_.bind(this);\r\n this.refresh = throttle(this.refresh.bind(this), REFRESH_DELAY);\r\n }\r\n /**\r\n * Adds observer to observers list.\r\n *\r\n * @param {ResizeObserverSPI} observer - Observer to be added.\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.addObserver = function (observer) {\r\n if (!~this.observers_.indexOf(observer)) {\r\n this.observers_.push(observer);\r\n }\r\n // Add listeners if they haven't been added yet.\r\n if (!this.connected_) {\r\n this.connect_();\r\n }\r\n };\r\n /**\r\n * Removes observer from observers list.\r\n *\r\n * @param {ResizeObserverSPI} observer - Observer to be removed.\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.removeObserver = function (observer) {\r\n var observers = this.observers_;\r\n var index = observers.indexOf(observer);\r\n // Remove observer if it's present in registry.\r\n if (~index) {\r\n observers.splice(index, 1);\r\n }\r\n // Remove listeners if controller has no connected observers.\r\n if (!observers.length && this.connected_) {\r\n this.disconnect_();\r\n }\r\n };\r\n /**\r\n * Invokes the update of observers. It will continue running updates insofar\r\n * it detects changes.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.refresh = function () {\r\n var changesDetected = this.updateObservers_();\r\n // Continue running updates if changes have been detected as there might\r\n // be future ones caused by CSS transitions.\r\n if (changesDetected) {\r\n this.refresh();\r\n }\r\n };\r\n /**\r\n * Updates every observer from observers list and notifies them of queued\r\n * entries.\r\n *\r\n * @private\r\n * @returns {boolean} Returns \"true\" if any observer has detected changes in\r\n * dimensions of it's elements.\r\n */\r\n ResizeObserverController.prototype.updateObservers_ = function () {\r\n // Collect observers that have active observations.\r\n var activeObservers = this.observers_.filter(function (observer) {\r\n return observer.gatherActive(), observer.hasActive();\r\n });\r\n // Deliver notifications in a separate cycle in order to avoid any\r\n // collisions between observers, e.g. when multiple instances of\r\n // ResizeObserver are tracking the same element and the callback of one\r\n // of them changes content dimensions of the observed target. Sometimes\r\n // this may result in notifications being blocked for the rest of observers.\r\n activeObservers.forEach(function (observer) { return observer.broadcastActive(); });\r\n return activeObservers.length > 0;\r\n };\r\n /**\r\n * Initializes DOM listeners.\r\n *\r\n * @private\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.connect_ = function () {\r\n // Do nothing if running in a non-browser environment or if listeners\r\n // have been already added.\r\n if (!isBrowser || this.connected_) {\r\n return;\r\n }\r\n // Subscription to the \"Transitionend\" event is used as a workaround for\r\n // delayed transitions. This way it's possible to capture at least the\r\n // final state of an element.\r\n document.addEventListener('transitionend', this.onTransitionEnd_);\r\n window.addEventListener('resize', this.refresh);\r\n if (mutationObserverSupported) {\r\n this.mutationsObserver_ = new MutationObserver(this.refresh);\r\n this.mutationsObserver_.observe(document, {\r\n attributes: true,\r\n childList: true,\r\n characterData: true,\r\n subtree: true\r\n });\r\n }\r\n else {\r\n document.addEventListener('DOMSubtreeModified', this.refresh);\r\n this.mutationEventsAdded_ = true;\r\n }\r\n this.connected_ = true;\r\n };\r\n /**\r\n * Removes DOM listeners.\r\n *\r\n * @private\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.disconnect_ = function () {\r\n // Do nothing if running in a non-browser environment or if listeners\r\n // have been already removed.\r\n if (!isBrowser || !this.connected_) {\r\n return;\r\n }\r\n document.removeEventListener('transitionend', this.onTransitionEnd_);\r\n window.removeEventListener('resize', this.refresh);\r\n if (this.mutationsObserver_) {\r\n this.mutationsObserver_.disconnect();\r\n }\r\n if (this.mutationEventsAdded_) {\r\n document.removeEventListener('DOMSubtreeModified', this.refresh);\r\n }\r\n this.mutationsObserver_ = null;\r\n this.mutationEventsAdded_ = false;\r\n this.connected_ = false;\r\n };\r\n /**\r\n * \"Transitionend\" event handler.\r\n *\r\n * @private\r\n * @param {TransitionEvent} event\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.onTransitionEnd_ = function (_a) {\r\n var _b = _a.propertyName, propertyName = _b === void 0 ? '' : _b;\r\n // Detect whether transition may affect dimensions of an element.\r\n var isReflowProperty = transitionKeys.some(function (key) {\r\n return !!~propertyName.indexOf(key);\r\n });\r\n if (isReflowProperty) {\r\n this.refresh();\r\n }\r\n };\r\n /**\r\n * Returns instance of the ResizeObserverController.\r\n *\r\n * @returns {ResizeObserverController}\r\n */\r\n ResizeObserverController.getInstance = function () {\r\n if (!this.instance_) {\r\n this.instance_ = new ResizeObserverController();\r\n }\r\n return this.instance_;\r\n };\r\n /**\r\n * Holds reference to the controller's instance.\r\n *\r\n * @private {ResizeObserverController}\r\n */\r\n ResizeObserverController.instance_ = null;\r\n return ResizeObserverController;\r\n}());\n\n/**\r\n * Defines non-writable/enumerable properties of the provided target object.\r\n *\r\n * @param {Object} target - Object for which to define properties.\r\n * @param {Object} props - Properties to be defined.\r\n * @returns {Object} Target object.\r\n */\r\nvar defineConfigurable = (function (target, props) {\r\n for (var _i = 0, _a = Object.keys(props); _i < _a.length; _i++) {\r\n var key = _a[_i];\r\n Object.defineProperty(target, key, {\r\n value: props[key],\r\n enumerable: false,\r\n writable: false,\r\n configurable: true\r\n });\r\n }\r\n return target;\r\n});\n\n/**\r\n * Returns the global object associated with provided element.\r\n *\r\n * @param {Object} target\r\n * @returns {Object}\r\n */\r\nvar getWindowOf = (function (target) {\r\n // Assume that the element is an instance of Node, which means that it\r\n // has the \"ownerDocument\" property from which we can retrieve a\r\n // corresponding global object.\r\n var ownerGlobal = target && target.ownerDocument && target.ownerDocument.defaultView;\r\n // Return the local global object if it's not possible extract one from\r\n // provided element.\r\n return ownerGlobal || global$1;\r\n});\n\n// Placeholder of an empty content rectangle.\r\nvar emptyRect = createRectInit(0, 0, 0, 0);\r\n/**\r\n * Converts provided string to a number.\r\n *\r\n * @param {number|string} value\r\n * @returns {number}\r\n */\r\nfunction toFloat(value) {\r\n return parseFloat(value) || 0;\r\n}\r\n/**\r\n * Extracts borders size from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @param {...string} positions - Borders positions (top, right, ...)\r\n * @returns {number}\r\n */\r\nfunction getBordersSize(styles) {\r\n var positions = [];\r\n for (var _i = 1; _i < arguments.length; _i++) {\r\n positions[_i - 1] = arguments[_i];\r\n }\r\n return positions.reduce(function (size, position) {\r\n var value = styles['border-' + position + '-width'];\r\n return size + toFloat(value);\r\n }, 0);\r\n}\r\n/**\r\n * Extracts paddings sizes from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @returns {Object} Paddings box.\r\n */\r\nfunction getPaddings(styles) {\r\n var positions = ['top', 'right', 'bottom', 'left'];\r\n var paddings = {};\r\n for (var _i = 0, positions_1 = positions; _i < positions_1.length; _i++) {\r\n var position = positions_1[_i];\r\n var value = styles['padding-' + position];\r\n paddings[position] = toFloat(value);\r\n }\r\n return paddings;\r\n}\r\n/**\r\n * Calculates content rectangle of provided SVG element.\r\n *\r\n * @param {SVGGraphicsElement} target - Element content rectangle of which needs\r\n * to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getSVGContentRect(target) {\r\n var bbox = target.getBBox();\r\n return createRectInit(0, 0, bbox.width, bbox.height);\r\n}\r\n/**\r\n * Calculates content rectangle of provided HTMLElement.\r\n *\r\n * @param {HTMLElement} target - Element for which to calculate the content rectangle.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getHTMLElementContentRect(target) {\r\n // Client width & height properties can't be\r\n // used exclusively as they provide rounded values.\r\n var clientWidth = target.clientWidth, clientHeight = target.clientHeight;\r\n // By this condition we can catch all non-replaced inline, hidden and\r\n // detached elements. Though elements with width & height properties less\r\n // than 0.5 will be discarded as well.\r\n //\r\n // Without it we would need to implement separate methods for each of\r\n // those cases and it's not possible to perform a precise and performance\r\n // effective test for hidden elements. E.g. even jQuery's ':visible' filter\r\n // gives wrong results for elements with width & height less than 0.5.\r\n if (!clientWidth && !clientHeight) {\r\n return emptyRect;\r\n }\r\n var styles = getWindowOf(target).getComputedStyle(target);\r\n var paddings = getPaddings(styles);\r\n var horizPad = paddings.left + paddings.right;\r\n var vertPad = paddings.top + paddings.bottom;\r\n // Computed styles of width & height are being used because they are the\r\n // only dimensions available to JS that contain non-rounded values. It could\r\n // be possible to utilize the getBoundingClientRect if only it's data wasn't\r\n // affected by CSS transformations let alone paddings, borders and scroll bars.\r\n var width = toFloat(styles.width), height = toFloat(styles.height);\r\n // Width & height include paddings and borders when the 'border-box' box\r\n // model is applied (except for IE).\r\n if (styles.boxSizing === 'border-box') {\r\n // Following conditions are required to handle Internet Explorer which\r\n // doesn't include paddings and borders to computed CSS dimensions.\r\n //\r\n // We can say that if CSS dimensions + paddings are equal to the \"client\"\r\n // properties then it's either IE, and thus we don't need to subtract\r\n // anything, or an element merely doesn't have paddings/borders styles.\r\n if (Math.round(width + horizPad) !== clientWidth) {\r\n width -= getBordersSize(styles, 'left', 'right') + horizPad;\r\n }\r\n if (Math.round(height + vertPad) !== clientHeight) {\r\n height -= getBordersSize(styles, 'top', 'bottom') + vertPad;\r\n }\r\n }\r\n // Following steps can't be applied to the document's root element as its\r\n // client[Width/Height] properties represent viewport area of the window.\r\n // Besides, it's as well not necessary as the itself neither has\r\n // rendered scroll bars nor it can be clipped.\r\n if (!isDocumentElement(target)) {\r\n // In some browsers (only in Firefox, actually) CSS width & height\r\n // include scroll bars size which can be removed at this step as scroll\r\n // bars are the only difference between rounded dimensions + paddings\r\n // and \"client\" properties, though that is not always true in Chrome.\r\n var vertScrollbar = Math.round(width + horizPad) - clientWidth;\r\n var horizScrollbar = Math.round(height + vertPad) - clientHeight;\r\n // Chrome has a rather weird rounding of \"client\" properties.\r\n // E.g. for an element with content width of 314.2px it sometimes gives\r\n // the client width of 315px and for the width of 314.7px it may give\r\n // 314px. And it doesn't happen all the time. So just ignore this delta\r\n // as a non-relevant.\r\n if (Math.abs(vertScrollbar) !== 1) {\r\n width -= vertScrollbar;\r\n }\r\n if (Math.abs(horizScrollbar) !== 1) {\r\n height -= horizScrollbar;\r\n }\r\n }\r\n return createRectInit(paddings.left, paddings.top, width, height);\r\n}\r\n/**\r\n * Checks whether provided element is an instance of the SVGGraphicsElement.\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nvar isSVGGraphicsElement = (function () {\r\n // Some browsers, namely IE and Edge, don't have the SVGGraphicsElement\r\n // interface.\r\n if (typeof SVGGraphicsElement !== 'undefined') {\r\n return function (target) { return target instanceof getWindowOf(target).SVGGraphicsElement; };\r\n }\r\n // If it's so, then check that element is at least an instance of the\r\n // SVGElement and that it has the \"getBBox\" method.\r\n // eslint-disable-next-line no-extra-parens\r\n return function (target) { return (target instanceof getWindowOf(target).SVGElement &&\r\n typeof target.getBBox === 'function'); };\r\n})();\r\n/**\r\n * Checks whether provided element is a document element ().\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nfunction isDocumentElement(target) {\r\n return target === getWindowOf(target).document.documentElement;\r\n}\r\n/**\r\n * Calculates an appropriate content rectangle for provided html or svg element.\r\n *\r\n * @param {Element} target - Element content rectangle of which needs to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getContentRect(target) {\r\n if (!isBrowser) {\r\n return emptyRect;\r\n }\r\n if (isSVGGraphicsElement(target)) {\r\n return getSVGContentRect(target);\r\n }\r\n return getHTMLElementContentRect(target);\r\n}\r\n/**\r\n * Creates rectangle with an interface of the DOMRectReadOnly.\r\n * Spec: https://drafts.fxtf.org/geometry/#domrectreadonly\r\n *\r\n * @param {DOMRectInit} rectInit - Object with rectangle's x/y coordinates and dimensions.\r\n * @returns {DOMRectReadOnly}\r\n */\r\nfunction createReadOnlyRect(_a) {\r\n var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n // If DOMRectReadOnly is available use it as a prototype for the rectangle.\r\n var Constr = typeof DOMRectReadOnly !== 'undefined' ? DOMRectReadOnly : Object;\r\n var rect = Object.create(Constr.prototype);\r\n // Rectangle's properties are not writable and non-enumerable.\r\n defineConfigurable(rect, {\r\n x: x, y: y, width: width, height: height,\r\n top: y,\r\n right: x + width,\r\n bottom: height + y,\r\n left: x\r\n });\r\n return rect;\r\n}\r\n/**\r\n * Creates DOMRectInit object based on the provided dimensions and the x/y coordinates.\r\n * Spec: https://drafts.fxtf.org/geometry/#dictdef-domrectinit\r\n *\r\n * @param {number} x - X coordinate.\r\n * @param {number} y - Y coordinate.\r\n * @param {number} width - Rectangle's width.\r\n * @param {number} height - Rectangle's height.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction createRectInit(x, y, width, height) {\r\n return { x: x, y: y, width: width, height: height };\r\n}\n\n/**\r\n * Class that is responsible for computations of the content rectangle of\r\n * provided DOM element and for keeping track of it's changes.\r\n */\r\nvar ResizeObservation = /** @class */ (function () {\r\n /**\r\n * Creates an instance of ResizeObservation.\r\n *\r\n * @param {Element} target - Element to be observed.\r\n */\r\n function ResizeObservation(target) {\r\n /**\r\n * Broadcasted width of content rectangle.\r\n *\r\n * @type {number}\r\n */\r\n this.broadcastWidth = 0;\r\n /**\r\n * Broadcasted height of content rectangle.\r\n *\r\n * @type {number}\r\n */\r\n this.broadcastHeight = 0;\r\n /**\r\n * Reference to the last observed content rectangle.\r\n *\r\n * @private {DOMRectInit}\r\n */\r\n this.contentRect_ = createRectInit(0, 0, 0, 0);\r\n this.target = target;\r\n }\r\n /**\r\n * Updates content rectangle and tells whether it's width or height properties\r\n * have changed since the last broadcast.\r\n *\r\n * @returns {boolean}\r\n */\r\n ResizeObservation.prototype.isActive = function () {\r\n var rect = getContentRect(this.target);\r\n this.contentRect_ = rect;\r\n return (rect.width !== this.broadcastWidth ||\r\n rect.height !== this.broadcastHeight);\r\n };\r\n /**\r\n * Updates 'broadcastWidth' and 'broadcastHeight' properties with a data\r\n * from the corresponding properties of the last observed content rectangle.\r\n *\r\n * @returns {DOMRectInit} Last observed content rectangle.\r\n */\r\n ResizeObservation.prototype.broadcastRect = function () {\r\n var rect = this.contentRect_;\r\n this.broadcastWidth = rect.width;\r\n this.broadcastHeight = rect.height;\r\n return rect;\r\n };\r\n return ResizeObservation;\r\n}());\n\nvar ResizeObserverEntry = /** @class */ (function () {\r\n /**\r\n * Creates an instance of ResizeObserverEntry.\r\n *\r\n * @param {Element} target - Element that is being observed.\r\n * @param {DOMRectInit} rectInit - Data of the element's content rectangle.\r\n */\r\n function ResizeObserverEntry(target, rectInit) {\r\n var contentRect = createReadOnlyRect(rectInit);\r\n // According to the specification following properties are not writable\r\n // and are also not enumerable in the native implementation.\r\n //\r\n // Property accessors are not being used as they'd require to define a\r\n // private WeakMap storage which may cause memory leaks in browsers that\r\n // don't support this type of collections.\r\n defineConfigurable(this, { target: target, contentRect: contentRect });\r\n }\r\n return ResizeObserverEntry;\r\n}());\n\nvar ResizeObserverSPI = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserver.\r\n *\r\n * @param {ResizeObserverCallback} callback - Callback function that is invoked\r\n * when one of the observed elements changes it's content dimensions.\r\n * @param {ResizeObserverController} controller - Controller instance which\r\n * is responsible for the updates of observer.\r\n * @param {ResizeObserver} callbackCtx - Reference to the public\r\n * ResizeObserver instance which will be passed to callback function.\r\n */\r\n function ResizeObserverSPI(callback, controller, callbackCtx) {\r\n /**\r\n * Collection of resize observations that have detected changes in dimensions\r\n * of elements.\r\n *\r\n * @private {Array}\r\n */\r\n this.activeObservations_ = [];\r\n /**\r\n * Registry of the ResizeObservation instances.\r\n *\r\n * @private {Map}\r\n */\r\n this.observations_ = new MapShim();\r\n if (typeof callback !== 'function') {\r\n throw new TypeError('The callback provided as parameter 1 is not a function.');\r\n }\r\n this.callback_ = callback;\r\n this.controller_ = controller;\r\n this.callbackCtx_ = callbackCtx;\r\n }\r\n /**\r\n * Starts observing provided element.\r\n *\r\n * @param {Element} target - Element to be observed.\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.observe = function (target) {\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n // Do nothing if current environment doesn't have the Element interface.\r\n if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n return;\r\n }\r\n if (!(target instanceof getWindowOf(target).Element)) {\r\n throw new TypeError('parameter 1 is not of type \"Element\".');\r\n }\r\n var observations = this.observations_;\r\n // Do nothing if element is already being observed.\r\n if (observations.has(target)) {\r\n return;\r\n }\r\n observations.set(target, new ResizeObservation(target));\r\n this.controller_.addObserver(this);\r\n // Force the update of observations.\r\n this.controller_.refresh();\r\n };\r\n /**\r\n * Stops observing provided element.\r\n *\r\n * @param {Element} target - Element to stop observing.\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.unobserve = function (target) {\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n // Do nothing if current environment doesn't have the Element interface.\r\n if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n return;\r\n }\r\n if (!(target instanceof getWindowOf(target).Element)) {\r\n throw new TypeError('parameter 1 is not of type \"Element\".');\r\n }\r\n var observations = this.observations_;\r\n // Do nothing if element is not being observed.\r\n if (!observations.has(target)) {\r\n return;\r\n }\r\n observations.delete(target);\r\n if (!observations.size) {\r\n this.controller_.removeObserver(this);\r\n }\r\n };\r\n /**\r\n * Stops observing all elements.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.disconnect = function () {\r\n this.clearActive();\r\n this.observations_.clear();\r\n this.controller_.removeObserver(this);\r\n };\r\n /**\r\n * Collects observation instances the associated element of which has changed\r\n * it's content rectangle.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.gatherActive = function () {\r\n var _this = this;\r\n this.clearActive();\r\n this.observations_.forEach(function (observation) {\r\n if (observation.isActive()) {\r\n _this.activeObservations_.push(observation);\r\n }\r\n });\r\n };\r\n /**\r\n * Invokes initial callback function with a list of ResizeObserverEntry\r\n * instances collected from active resize observations.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.broadcastActive = function () {\r\n // Do nothing if observer doesn't have active observations.\r\n if (!this.hasActive()) {\r\n return;\r\n }\r\n var ctx = this.callbackCtx_;\r\n // Create ResizeObserverEntry instance for every active observation.\r\n var entries = this.activeObservations_.map(function (observation) {\r\n return new ResizeObserverEntry(observation.target, observation.broadcastRect());\r\n });\r\n this.callback_.call(ctx, entries, ctx);\r\n this.clearActive();\r\n };\r\n /**\r\n * Clears the collection of active observations.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.clearActive = function () {\r\n this.activeObservations_.splice(0);\r\n };\r\n /**\r\n * Tells whether observer has active observations.\r\n *\r\n * @returns {boolean}\r\n */\r\n ResizeObserverSPI.prototype.hasActive = function () {\r\n return this.activeObservations_.length > 0;\r\n };\r\n return ResizeObserverSPI;\r\n}());\n\n// Registry of internal observers. If WeakMap is not available use current shim\r\n// for the Map collection as it has all required methods and because WeakMap\r\n// can't be fully polyfilled anyway.\r\nvar observers = typeof WeakMap !== 'undefined' ? new WeakMap() : new MapShim();\r\n/**\r\n * ResizeObserver API. Encapsulates the ResizeObserver SPI implementation\r\n * exposing only those methods and properties that are defined in the spec.\r\n */\r\nvar ResizeObserver = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserver.\r\n *\r\n * @param {ResizeObserverCallback} callback - Callback that is invoked when\r\n * dimensions of the observed elements change.\r\n */\r\n function ResizeObserver(callback) {\r\n if (!(this instanceof ResizeObserver)) {\r\n throw new TypeError('Cannot call a class as a function.');\r\n }\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n var controller = ResizeObserverController.getInstance();\r\n var observer = new ResizeObserverSPI(callback, controller, this);\r\n observers.set(this, observer);\r\n }\r\n return ResizeObserver;\r\n}());\r\n// Expose public methods of ResizeObserver.\r\n[\r\n 'observe',\r\n 'unobserve',\r\n 'disconnect'\r\n].forEach(function (method) {\r\n ResizeObserver.prototype[method] = function () {\r\n var _a;\r\n return (_a = observers.get(this))[method].apply(_a, arguments);\r\n };\r\n});\n\nvar index = (function () {\r\n // Export existing implementation if available.\r\n if (typeof global$1.ResizeObserver !== 'undefined') {\r\n return global$1.ResizeObserver;\r\n }\r\n return ResizeObserver;\r\n})();\n\nexport default index;\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ResizeObserver from \"resize-observer-polyfill\"\nimport {\n NEVER,\n Observable,\n Subject,\n defer,\n filter,\n finalize,\n map,\n merge,\n of,\n shareReplay,\n startWith,\n switchMap,\n tap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementSize {\n width: number /* Element width */\n height: number /* Element height */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Resize observer entry subject\n */\nconst entry$ = new Subject()\n\n/**\n * Resize observer observable\n *\n * This observable will create a `ResizeObserver` on the first subscription\n * and will automatically terminate it when there are no more subscribers.\n * It's quite important to centralize observation in a single `ResizeObserver`,\n * as the performance difference can be quite dramatic, as the link shows.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n new ResizeObserver(entries => {\n for (const entry of entries)\n entry$.next(entry)\n })\n))\n .pipe(\n switchMap(observer => merge(NEVER, of(observer))\n .pipe(\n finalize(() => observer.disconnect())\n )\n ),\n shareReplay(1)\n )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n el: HTMLElement\n): ElementSize {\n return {\n width: el.offsetWidth,\n height: el.offsetHeight\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n el: HTMLElement\n): Observable {\n return observer$\n .pipe(\n tap(observer => observer.observe(el)),\n switchMap(observer => entry$\n .pipe(\n filter(({ target }) => target === el),\n finalize(() => observer.unobserve(el)),\n map(() => getElementSize(el))\n )\n ),\n startWith(getElementSize(el))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ElementSize } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content size (= scroll width and height)\n *\n * @param el - Element\n *\n * @returns Element content size\n */\nexport function getElementContentSize(\n el: HTMLElement\n): ElementSize {\n return {\n width: el.scrollWidth,\n height: el.scrollHeight\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n NEVER,\n Observable,\n Subject,\n defer,\n distinctUntilChanged,\n filter,\n finalize,\n map,\n merge,\n of,\n shareReplay,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport {\n getElementContentSize,\n getElementSize,\n watchElementContentOffset\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Intersection observer entry subject\n */\nconst entry$ = new Subject()\n\n/**\n * Intersection observer observable\n *\n * This observable will create an `IntersectionObserver` on first subscription\n * and will automatically terminate it when there are no more subscribers.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n new IntersectionObserver(entries => {\n for (const entry of entries)\n entry$.next(entry)\n }, {\n threshold: 0\n })\n))\n .pipe(\n switchMap(observer => merge(NEVER, of(observer))\n .pipe(\n finalize(() => observer.disconnect())\n )\n ),\n shareReplay(1)\n )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element visibility\n *\n * @param el - Element\n *\n * @returns Element visibility observable\n */\nexport function watchElementVisibility(\n el: HTMLElement\n): Observable {\n return observer$\n .pipe(\n tap(observer => observer.observe(el)),\n switchMap(observer => entry$\n .pipe(\n filter(({ target }) => target === el),\n finalize(() => observer.unobserve(el)),\n map(({ isIntersecting }) => isIntersecting)\n )\n )\n )\n}\n\n/**\n * Watch element boundary\n *\n * This function returns an observable which emits whether the bottom content\n * boundary (= scroll offset) of an element is within a certain threshold.\n *\n * @param el - Element\n * @param threshold - Threshold\n *\n * @returns Element boundary observable\n */\nexport function watchElementBoundary(\n el: HTMLElement, threshold = 16\n): Observable {\n return watchElementContentOffset(el)\n .pipe(\n map(({ y }) => {\n const visible = getElementSize(el)\n const content = getElementContentSize(el)\n return y >= (\n content.height - visible.height - threshold\n )\n }),\n distinctUntilChanged()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n startWith\n} from \"rxjs\"\n\nimport { getElement } from \"../element\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle\n */\nexport type Toggle =\n | \"drawer\" /* Toggle for drawer */\n | \"search\" /* Toggle for search */\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle map\n */\nconst toggles: Record = {\n drawer: getElement(\"[data-md-toggle=drawer]\"),\n search: getElement(\"[data-md-toggle=search]\")\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the value of a toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value\n */\nexport function getToggle(name: Toggle): boolean {\n return toggles[name].checked\n}\n\n/**\n * Set toggle\n *\n * Simulating a click event seems to be the most cross-browser compatible way\n * of changing the value while also emitting a `change` event. Before, Material\n * used `CustomEvent` to programmatically change the value of a toggle, but this\n * is a much simpler and cleaner solution which doesn't require a polyfill.\n *\n * @param name - Toggle\n * @param value - Toggle value\n */\nexport function setToggle(name: Toggle, value: boolean): void {\n if (toggles[name].checked !== value)\n toggles[name].click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value observable\n */\nexport function watchToggle(name: Toggle): Observable {\n const el = toggles[name]\n return fromEvent(el, \"change\")\n .pipe(\n map(() => el.checked),\n startWith(el.checked)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n share\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../element\"\nimport { getToggle } from \"../toggle\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Keyboard mode\n */\nexport type KeyboardMode =\n | \"global\" /* Global */\n | \"search\" /* Search is open */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Keyboard\n */\nexport interface Keyboard {\n mode: KeyboardMode /* Keyboard mode */\n type: string /* Key type */\n claim(): void /* Key claim */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether an element may receive keyboard input\n *\n * @param el - Element\n * @param type - Key type\n *\n * @returns Test result\n */\nfunction isSusceptibleToKeyboard(\n el: HTMLElement, type: string\n): boolean {\n switch (el.constructor) {\n\n /* Input elements */\n case HTMLInputElement:\n /* @ts-expect-error - omit unnecessary type cast */\n if (el.type === \"radio\")\n return /^Arrow/.test(type)\n else\n return true\n\n /* Select element and textarea */\n case HTMLSelectElement:\n case HTMLTextAreaElement:\n return true\n\n /* Everything else */\n default:\n return el.isContentEditable\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch keyboard\n *\n * @returns Keyboard observable\n */\nexport function watchKeyboard(): Observable {\n return fromEvent(window, \"keydown\")\n .pipe(\n filter(ev => !(ev.metaKey || ev.ctrlKey)),\n map(ev => ({\n mode: getToggle(\"search\") ? \"search\" : \"global\",\n type: ev.key,\n claim() {\n ev.preventDefault()\n ev.stopPropagation()\n }\n } as Keyboard)),\n filter(({ mode, type }) => {\n if (mode === \"global\") {\n const active = getActiveElement()\n if (typeof active !== \"undefined\")\n return !isSusceptibleToKeyboard(active, type)\n }\n return true\n }),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Subject } from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location\n *\n * This function returns a `URL` object (and not `Location`) to normalize the\n * typings across the application. Furthermore, locations need to be tracked\n * without setting them and `Location` is a singleton which represents the\n * current location.\n *\n * @returns URL\n */\nexport function getLocation(): URL {\n return new URL(location.href)\n}\n\n/**\n * Set location\n *\n * @param url - URL to change to\n */\nexport function setLocation(url: URL): void {\n location.href = url.href\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location\n *\n * @returns Location subject\n */\nexport function watchLocation(): Subject {\n return new Subject()\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { JSX as JSXInternal } from \"preact\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * HTML attributes\n */\ntype Attributes =\n & JSXInternal.HTMLAttributes\n & JSXInternal.SVGAttributes\n & Record\n\n/**\n * Child element\n */\ntype Child =\n | HTMLElement\n | Text\n | string\n | number\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Append a child node to an element\n *\n * @param el - Element\n * @param child - Child node(s)\n */\nfunction appendChild(el: HTMLElement, child: Child | Child[]): void {\n\n /* Handle primitive types (including raw HTML) */\n if (typeof child === \"string\" || typeof child === \"number\") {\n el.innerHTML += child.toString()\n\n /* Handle nodes */\n } else if (child instanceof Node) {\n el.appendChild(child)\n\n /* Handle nested children */\n } else if (Array.isArray(child)) {\n for (const node of child)\n appendChild(el, node)\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * JSX factory\n *\n * @template T - Element type\n *\n * @param tag - HTML tag\n * @param attributes - HTML attributes\n * @param children - Child elements\n *\n * @returns Element\n */\nexport function h(\n tag: T, attributes?: Attributes | null, ...children: Child[]\n): HTMLElementTagNameMap[T]\n\nexport function h(\n tag: string, attributes?: Attributes | null, ...children: Child[]\n): T\n\nexport function h(\n tag: string, attributes?: Attributes | null, ...children: Child[]\n): T {\n const el = document.createElement(tag)\n\n /* Set attributes, if any */\n if (attributes)\n for (const attr of Object.keys(attributes)) {\n if (typeof attributes[attr] === \"undefined\")\n continue\n\n /* Set default attribute or boolean */\n if (typeof attributes[attr] !== \"boolean\")\n el.setAttribute(attr, attributes[attr])\n else\n el.setAttribute(attr, \"\")\n }\n\n /* Append child nodes */\n for (const child of children)\n appendChild(el, child)\n\n /* Return element */\n return el as T\n}\n\n/* ----------------------------------------------------------------------------\n * Namespace\n * ------------------------------------------------------------------------- */\n\nexport declare namespace h {\n namespace JSX {\n type Element = HTMLElement\n type IntrinsicElements = JSXInternal.IntrinsicElements\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Truncate a string after the given number of characters\n *\n * This is not a very reasonable approach, since the summaries kind of suck.\n * It would be better to create something more intelligent, highlighting the\n * search occurrences and making a better summary out of it, but this note was\n * written three years ago, so who knows if we'll ever fix it.\n *\n * @param value - Value to be truncated\n * @param n - Number of characters\n *\n * @returns Truncated value\n */\nexport function truncate(value: string, n: number): string {\n let i = n\n if (value.length > i) {\n while (value[i] !== \" \" && --i > 0) { /* keep eating */ }\n return `${value.substring(0, i)}...`\n }\n return value\n}\n\n/**\n * Round a number for display with repository facts\n *\n * This is a reverse-engineered version of GitHub's weird rounding algorithm\n * for stars, forks and all other numbers. While all numbers below `1,000` are\n * returned as-is, bigger numbers are converted to fixed numbers:\n *\n * - `1,049` => `1k`\n * - `1,050` => `1.1k`\n * - `1,949` => `1.9k`\n * - `1,950` => `2k`\n *\n * @param value - Original value\n *\n * @returns Rounded value\n */\nexport function round(value: number): string {\n if (value > 999) {\n const digits = +((value - 950) % 1000 > 99)\n return `${((value + 0.000001) / 1000).toFixed(digits)}k`\n } else {\n return value.toString()\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n shareReplay,\n startWith\n} from \"rxjs\"\n\nimport { getOptionalElement } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location hash\n *\n * @returns Location hash\n */\nexport function getLocationHash(): string {\n return location.hash.substring(1)\n}\n\n/**\n * Set location hash\n *\n * Setting a new fragment identifier via `location.hash` will have no effect\n * if the value doesn't change. When a new fragment identifier is set, we want\n * the browser to target the respective element at all times, which is why we\n * use this dirty little trick.\n *\n * @param hash - Location hash\n */\nexport function setLocationHash(hash: string): void {\n const el = h(\"a\", { href: hash })\n el.addEventListener(\"click\", ev => ev.stopPropagation())\n el.click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location hash\n *\n * @returns Location hash observable\n */\nexport function watchLocationHash(): Observable {\n return fromEvent(window, \"hashchange\")\n .pipe(\n map(getLocationHash),\n startWith(getLocationHash()),\n filter(hash => hash.length > 0),\n shareReplay(1)\n )\n}\n\n/**\n * Watch location target\n *\n * @returns Location target observable\n */\nexport function watchLocationTarget(): Observable {\n return watchLocationHash()\n .pipe(\n map(id => getOptionalElement(`[id=\"${id}\"]`)!),\n filter(el => typeof el !== \"undefined\")\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n fromEvent,\n fromEventPattern,\n map,\n merge,\n startWith,\n switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch media query\n *\n * Note that although `MediaQueryList.addListener` is deprecated we have to\n * use it, because it's the only way to ensure proper downward compatibility.\n *\n * @see https://bit.ly/3dUBH2m - GitHub issue\n *\n * @param query - Media query\n *\n * @returns Media observable\n */\nexport function watchMedia(query: string): Observable {\n const media = matchMedia(query)\n return fromEventPattern(next => (\n media.addListener(() => next(media.matches))\n ))\n .pipe(\n startWith(media.matches)\n )\n}\n\n/**\n * Watch print mode\n *\n * @returns Print observable\n */\nexport function watchPrint(): Observable {\n const media = matchMedia(\"print\")\n return merge(\n fromEvent(window, \"beforeprint\").pipe(map(() => true)),\n fromEvent(window, \"afterprint\").pipe(map(() => false))\n )\n .pipe(\n startWith(media.matches)\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Toggle an observable with a media observable\n *\n * @template T - Data type\n *\n * @param query$ - Media observable\n * @param factory - Observable factory\n *\n * @returns Toggled observable\n */\nexport function at(\n query$: Observable, factory: () => Observable\n): Observable {\n return query$\n .pipe(\n switchMap(active => active ? factory() : EMPTY)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n catchError,\n from,\n map,\n of,\n shareReplay,\n switchMap,\n throwError\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * If the request fails (e.g. when dispatched from `file://` locations), the\n * observable will complete without emitting a value.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Response observable\n */\nexport function request(\n url: URL | string, options: RequestInit = { credentials: \"same-origin\" }\n): Observable {\n return from(fetch(`${url}`, options))\n .pipe(\n catchError(() => EMPTY),\n switchMap(res => res.status !== 200\n ? throwError(() => new Error(res.statusText))\n : of(res)\n )\n )\n}\n\n/**\n * Fetch JSON from the given URL\n *\n * @template T - Data type\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestJSON(\n url: URL | string, options?: RequestInit\n): Observable {\n return request(url, options)\n .pipe(\n switchMap(res => res.json()),\n shareReplay(1)\n )\n}\n\n/**\n * Fetch XML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestXML(\n url: URL | string, options?: RequestInit\n): Observable {\n const dom = new DOMParser()\n return request(url, options)\n .pipe(\n switchMap(res => res.text()),\n map(res => dom.parseFromString(res, \"text/xml\")),\n shareReplay(1)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n defer,\n finalize,\n fromEvent,\n map,\n merge,\n switchMap,\n take,\n throwError\n} from \"rxjs\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create and load a `script` element\n *\n * This function returns an observable that will emit when the script was\n * successfully loaded, or throw an error if it didn't.\n *\n * @param src - Script URL\n *\n * @returns Script observable\n */\nexport function watchScript(src: string): Observable {\n const script = h(\"script\", { src })\n return defer(() => {\n document.head.appendChild(script)\n return merge(\n fromEvent(script, \"load\"),\n fromEvent(script, \"error\")\n .pipe(\n switchMap(() => (\n throwError(() => new ReferenceError(`Invalid script: ${src}`))\n ))\n )\n )\n .pipe(\n map(() => undefined),\n finalize(() => document.head.removeChild(script)),\n take(1)\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport offset\n */\nexport interface ViewportOffset {\n x: number /* Horizontal offset */\n y: number /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport offset\n *\n * On iOS Safari, viewport offset can be negative due to overflow scrolling.\n * As this may induce strange behaviors downstream, we'll just limit it to 0.\n *\n * @returns Viewport offset\n */\nexport function getViewportOffset(): ViewportOffset {\n return {\n x: Math.max(0, scrollX),\n y: Math.max(0, scrollY)\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport offset\n *\n * @returns Viewport offset observable\n */\nexport function watchViewportOffset(): Observable {\n return merge(\n fromEvent(window, \"scroll\", { passive: true }),\n fromEvent(window, \"resize\", { passive: true })\n )\n .pipe(\n map(getViewportOffset),\n startWith(getViewportOffset())\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport size\n */\nexport interface ViewportSize {\n width: number /* Viewport width */\n height: number /* Viewport height */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport size\n *\n * @returns Viewport size\n */\nexport function getViewportSize(): ViewportSize {\n return {\n width: innerWidth,\n height: innerHeight\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport size\n *\n * @returns Viewport size observable\n */\nexport function watchViewportSize(): Observable {\n return fromEvent(window, \"resize\", { passive: true })\n .pipe(\n map(getViewportSize),\n startWith(getViewportSize())\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n map,\n shareReplay\n} from \"rxjs\"\n\nimport {\n ViewportOffset,\n watchViewportOffset\n} from \"../offset\"\nimport {\n ViewportSize,\n watchViewportSize\n} from \"../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport\n */\nexport interface Viewport {\n offset: ViewportOffset /* Viewport offset */\n size: ViewportSize /* Viewport size */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport\n *\n * @returns Viewport observable\n */\nexport function watchViewport(): Observable {\n return combineLatest([\n watchViewportOffset(),\n watchViewportSize()\n ])\n .pipe(\n map(([offset, size]) => ({ offset, size })),\n shareReplay(1)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n distinctUntilKeyChanged,\n map\n} from \"rxjs\"\n\nimport { Header } from \"~/components\"\n\nimport { getElementOffset } from \"../../element\"\nimport { Viewport } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport relative to element\n *\n * @param el - Element\n * @param options - Options\n *\n * @returns Viewport observable\n */\nexport function watchViewportAt(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n const size$ = viewport$\n .pipe(\n distinctUntilKeyChanged(\"size\")\n )\n\n /* Compute element offset */\n const offset$ = combineLatest([size$, header$])\n .pipe(\n map(() => getElementOffset(el))\n )\n\n /* Compute relative viewport, return hot observable */\n return combineLatest([header$, viewport$, offset$])\n .pipe(\n map(([{ height }, { offset, size }, { x, y }]) => ({\n offset: {\n x: offset.x - x,\n y: offset.y - y + height\n },\n size\n }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n fromEvent,\n map,\n share,\n switchMap,\n tap,\n throttle\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Worker message\n */\nexport interface WorkerMessage {\n type: unknown /* Message type */\n data?: unknown /* Message data */\n}\n\n/**\n * Worker handler\n *\n * @template T - Message type\n */\nexport interface WorkerHandler<\n T extends WorkerMessage\n> {\n tx$: Subject /* Message transmission subject */\n rx$: Observable /* Message receive observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n *\n * @template T - Worker message type\n */\ninterface WatchOptions {\n tx$: Observable /* Message transmission observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch a web worker\n *\n * This function returns an observable that sends all values emitted by the\n * message observable to the web worker. Web worker communication is expected\n * to be bidirectional (request-response) and synchronous. Messages that are\n * emitted during a pending request are throttled, the last one is emitted.\n *\n * @param worker - Web worker\n * @param options - Options\n *\n * @returns Worker message observable\n */\nexport function watchWorker(\n worker: Worker, { tx$ }: WatchOptions\n): Observable {\n\n /* Intercept messages from worker-like objects */\n const rx$ = fromEvent(worker, \"message\")\n .pipe(\n map(({ data }) => data as T)\n )\n\n /* Send and receive messages, return hot observable */\n return tx$\n .pipe(\n throttle(() => rx$, { leading: true, trailing: true }),\n tap(message => worker.postMessage(message)),\n switchMap(() => rx$),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getLocation } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Feature flag\n */\nexport type Flag =\n | \"announce.dismiss\" /* Dismissable announcement bar */\n | \"content.code.annotate\" /* Code annotations */\n | \"content.tabs.link\" /* Link content tabs */\n | \"header.autohide\" /* Hide header */\n | \"navigation.expand\" /* Automatic expansion */\n | \"navigation.indexes\" /* Section pages */\n | \"navigation.instant\" /* Instant loading */\n | \"navigation.sections\" /* Section navigation */\n | \"navigation.tabs\" /* Tabs navigation */\n | \"navigation.tabs.sticky\" /* Tabs navigation (sticky) */\n | \"navigation.top\" /* Back-to-top button */\n | \"navigation.tracking\" /* Anchor tracking */\n | \"search.highlight\" /* Search highlighting */\n | \"search.share\" /* Search sharing */\n | \"search.suggest\" /* Search suggestions */\n | \"toc.integrate\" /* Integrated table of contents */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Translation\n */\nexport type Translation =\n | \"clipboard.copy\" /* Copy to clipboard */\n | \"clipboard.copied\" /* Copied to clipboard */\n | \"search.config.lang\" /* Search language */\n | \"search.config.pipeline\" /* Search pipeline */\n | \"search.config.separator\" /* Search separator */\n | \"search.placeholder\" /* Search */\n | \"search.result.placeholder\" /* Type to start searching */\n | \"search.result.none\" /* No matching documents */\n | \"search.result.one\" /* 1 matching document */\n | \"search.result.other\" /* # matching documents */\n | \"search.result.more.one\" /* 1 more on this page */\n | \"search.result.more.other\" /* # more on this page */\n | \"search.result.term.missing\" /* Missing */\n | \"select.version.title\" /* Version selector */\n\n/**\n * Translations\n */\nexport type Translations = Record\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Versioning\n */\nexport interface Versioning {\n provider: \"mike\" /* Version provider */\n default?: string /* Default version */\n}\n\n/**\n * Configuration\n */\nexport interface Config {\n base: string /* Base URL */\n features: Flag[] /* Feature flags */\n translations: Translations /* Translations */\n search: string /* Search worker URL */\n version?: Versioning /* Versioning */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration and make base URL absolute\n */\nconst script = getElement(\"#__config\")\nconst config: Config = JSON.parse(script.textContent!)\nconfig.base = `${new URL(config.base, getLocation())}`\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration\n *\n * @returns Global configuration\n */\nexport function configuration(): Config {\n return config\n}\n\n/**\n * Check whether a feature flag is enabled\n *\n * @param flag - Feature flag\n *\n * @returns Test result\n */\nexport function feature(flag: Flag): boolean {\n return config.features.includes(flag)\n}\n\n/**\n * Retrieve the translation for the given key\n *\n * @param key - Key to be translated\n * @param value - Positional value, if any\n *\n * @returns Translation\n */\nexport function translation(\n key: Translation, value?: string | number\n): string {\n return typeof value !== \"undefined\"\n ? config.translations[key].replace(\"#\", value.toString())\n : config.translations[key]\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type\n */\nexport type ComponentType =\n | \"announce\" /* Announcement bar */\n | \"container\" /* Container */\n | \"consent\" /* Consent */\n | \"content\" /* Content */\n | \"dialog\" /* Dialog */\n | \"header\" /* Header */\n | \"header-title\" /* Header title */\n | \"header-topic\" /* Header topic */\n | \"main\" /* Main area */\n | \"outdated\" /* Version warning */\n | \"palette\" /* Color palette */\n | \"search\" /* Search */\n | \"search-query\" /* Search input */\n | \"search-result\" /* Search results */\n | \"search-share\" /* Search sharing */\n | \"search-suggest\" /* Search suggestions */\n | \"sidebar\" /* Sidebar */\n | \"skip\" /* Skip link */\n | \"source\" /* Repository information */\n | \"tabs\" /* Navigation tabs */\n | \"toc\" /* Table of contents */\n | \"top\" /* Back-to-top button */\n\n/**\n * Component\n *\n * @template T - Component type\n * @template U - Reference type\n */\nexport type Component<\n T extends {} = {},\n U extends HTMLElement = HTMLElement\n> =\n T & {\n ref: U /* Component reference */\n }\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type map\n */\ninterface ComponentTypeMap {\n \"announce\": HTMLElement /* Announcement bar */\n \"container\": HTMLElement /* Container */\n \"consent\": HTMLElement /* Consent */\n \"content\": HTMLElement /* Content */\n \"dialog\": HTMLElement /* Dialog */\n \"header\": HTMLElement /* Header */\n \"header-title\": HTMLElement /* Header title */\n \"header-topic\": HTMLElement /* Header topic */\n \"main\": HTMLElement /* Main area */\n \"outdated\": HTMLElement /* Version warning */\n \"palette\": HTMLElement /* Color palette */\n \"search\": HTMLElement /* Search */\n \"search-query\": HTMLInputElement /* Search input */\n \"search-result\": HTMLElement /* Search results */\n \"search-share\": HTMLAnchorElement /* Search sharing */\n \"search-suggest\": HTMLElement /* Search suggestions */\n \"sidebar\": HTMLElement /* Sidebar */\n \"skip\": HTMLAnchorElement /* Skip link */\n \"source\": HTMLAnchorElement /* Repository information */\n \"tabs\": HTMLElement /* Navigation tabs */\n \"toc\": HTMLElement /* Table of contents */\n \"top\": HTMLAnchorElement /* Back-to-top button */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the element for a given component or throw a reference error\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getComponentElement(\n type: T, node: ParentNode = document\n): ComponentTypeMap[T] {\n return getElement(`[data-md-component=${type}]`, node)\n}\n\n/**\n * Retrieve all elements for a given component\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getComponentElements(\n type: T, node: ParentNode = document\n): ComponentTypeMap[T][] {\n return getElements(`[data-md-component=${type}]`, node)\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n finalize,\n fromEvent,\n map,\n startWith,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Announcement bar\n */\nexport interface Announce {\n hash: number /* Content hash */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar observable\n */\nexport function watchAnnounce(\n el: HTMLElement\n): Observable {\n const button = getElement(\".md-typeset > :first-child\", el)\n return fromEvent(button, \"click\", { once: true })\n .pipe(\n map(() => getElement(\".md-typeset\", el)),\n map(content => ({ hash: __md_hash(content.innerHTML) }))\n )\n}\n\n/**\n * Mount announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar component observable\n */\nexport function mountAnnounce(\n el: HTMLElement\n): Observable> {\n if (!feature(\"announce.dismiss\") || !el.childElementCount)\n return EMPTY\n\n /* Mount component on subscription */\n return defer(() => {\n const push$ = new Subject()\n push$\n .pipe(\n startWith({ hash: __md_get(\"__announce\") })\n )\n .subscribe(({ hash }) => {\n if (hash && hash === (__md_get(\"__announce\") ?? hash)) {\n el.hidden = true\n\n /* Persist preference in local storage */\n __md_set(\"__announce\", hash)\n }\n })\n\n /* Create and return component */\n return watchAnnounce(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n finalize,\n map,\n tap\n} from \"rxjs\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Consent\n */\nexport interface Consent {\n hidden: boolean /* Consent is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n target$: Observable /* Target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent observable\n */\nexport function watchConsent(\n el: HTMLElement, { target$ }: WatchOptions\n): Observable {\n return target$\n .pipe(\n map(target => ({ hidden: target !== el }))\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent component observable\n */\nexport function mountConsent(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const internal$ = new Subject()\n internal$.subscribe(({ hidden }) => {\n el.hidden = hidden\n })\n\n /* Create and return component */\n return watchConsent(el, options)\n .pipe(\n tap(state => internal$.next(state)),\n finalize(() => internal$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n map,\n mergeWith,\n switchMap,\n take,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n getElementContentSize,\n watchElementSize,\n watchElementVisibility\n} from \"~/browser\"\nimport { renderClipboardButton } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n Annotation,\n mountAnnotationList\n} from \"../../annotation\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Code block\n */\nexport interface CodeBlock {\n scrollable: boolean /* Code block overflows */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for Clipboard.js integration\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find candidate list element directly following a code block\n *\n * @param el - Code block element\n *\n * @returns List element or nothing\n */\nfunction findCandidateList(el: HTMLElement): HTMLElement | undefined {\n if (el.nextElementSibling) {\n const sibling = el.nextElementSibling as HTMLElement\n if (sibling.tagName === \"OL\")\n return sibling\n\n /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n else if (sibling.tagName === \"P\" && !sibling.children.length)\n return findCandidateList(sibling)\n }\n\n /* Everything else */\n return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch code block\n *\n * This function monitors size changes of the viewport, as well as switches of\n * content tabs with embedded code blocks, as both may trigger overflow.\n *\n * @param el - Code block element\n *\n * @returns Code block observable\n */\nexport function watchCodeBlock(\n el: HTMLElement\n): Observable {\n return watchElementSize(el)\n .pipe(\n map(({ width }) => {\n const content = getElementContentSize(el)\n return {\n scrollable: content.width > width\n }\n }),\n distinctUntilKeyChanged(\"scrollable\")\n )\n}\n\n/**\n * Mount code block\n *\n * This function ensures that an overflowing code block is focusable through\n * keyboard, so it can be scrolled without a mouse to improve on accessibility.\n * Furthermore, if code annotations are enabled, they are mounted if and only\n * if the code block is currently visible, e.g., not in a hidden content tab.\n *\n * @param el - Code block element\n * @param options - Options\n *\n * @returns Code block and annotation component observable\n */\nexport function mountCodeBlock(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const { matches: hover } = matchMedia(\"(hover)\")\n\n /* Defer mounting of code block - see https://bit.ly/3vHVoVD */\n const factory$ = defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ scrollable }) => {\n if (scrollable && hover)\n el.setAttribute(\"tabindex\", \"0\")\n else\n el.removeAttribute(\"tabindex\")\n })\n\n /* Render button for Clipboard.js integration */\n if (ClipboardJS.isSupported()) {\n const parent = el.closest(\"pre\")!\n parent.id = `__code_${++sequence}`\n parent.insertBefore(\n renderClipboardButton(parent.id),\n el\n )\n }\n\n /* Handle code annotations */\n const container = el.closest(\".highlight\")\n if (container instanceof HTMLElement) {\n const list = findCandidateList(container)\n\n /* Mount code annotations, if enabled */\n if (typeof list !== \"undefined\" && (\n container.classList.contains(\"annotate\") ||\n feature(\"content.code.annotate\")\n )) {\n const annotations$ = mountAnnotationList(list, el, options)\n\n /* Create and return component */\n return watchCodeBlock(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state })),\n mergeWith(\n watchElementSize(container)\n .pipe(\n takeUntil(push$.pipe(takeLast(1))),\n map(({ width, height }) => width && height),\n distinctUntilChanged(),\n switchMap(active => active ? annotations$ : EMPTY)\n )\n )\n )\n }\n }\n\n /* Create and return component */\n return watchCodeBlock(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n\n /* Mount code block on first sight */\n return watchElementVisibility(el)\n .pipe(\n filter(visible => visible),\n take(1),\n switchMap(() => factory$)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render an empty annotation\n *\n * @param id - Annotation identifier\n *\n * @returns Element\n */\nexport function renderAnnotation(id: number): HTMLElement {\n return (\n \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a 'copy-to-clipboard' button\n *\n * @param id - Unique identifier\n *\n * @returns Element\n */\nexport function renderClipboardButton(id: string): HTMLElement {\n return (\n code`}\n >\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { feature, translation } from \"~/_\"\nimport {\n SearchDocument,\n SearchMetadata,\n SearchResultItem\n} from \"~/integrations/search\"\nimport { h, truncate } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Render flag\n */\nconst enum Flag {\n TEASER = 1, /* Render teaser */\n PARENT = 2 /* Render as parent */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper function\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search document\n *\n * @param document - Search document\n * @param flag - Render flags\n *\n * @returns Element\n */\nfunction renderSearchDocument(\n document: SearchDocument & SearchMetadata, flag: Flag\n): HTMLElement {\n const parent = flag & Flag.PARENT\n const teaser = flag & Flag.TEASER\n\n /* Render missing query terms */\n const missing = Object.keys(document.terms)\n .filter(key => !document.terms[key])\n .reduce((list, key) => [\n ...list, {key}, \" \"\n ], [])\n .slice(0, -1)\n\n /* Assemble query string for highlighting */\n const url = new URL(document.location)\n if (feature(\"search.highlight\"))\n url.searchParams.set(\"h\", Object.entries(document.terms)\n .filter(([, match]) => match)\n .reduce((highlight, [value]) => `${highlight} ${value}`.trim(), \"\")\n )\n\n /* Render article or section, depending on flags */\n return (\n \n \n {parent > 0 &&
    }\n

    {document.title}

    \n {teaser > 0 && document.text.length > 0 &&\n

    \n {truncate(document.text, 320)}\n

    \n }\n {document.tags && document.tags.map(tag => (\n {tag}\n ))}\n {teaser > 0 && missing.length > 0 &&\n

    \n {translation(\"search.result.term.missing\")}: {...missing}\n

    \n }\n \n     \n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search result\n *\n * @param result - Search result\n *\n * @returns Element\n */\nexport function renderSearchResultItem(\n result: SearchResultItem\n): HTMLElement {\n const threshold = result[0].score\n const docs = [...result]\n\n /* Find and extract parent article */\n const parent = docs.findIndex(doc => !doc.location.includes(\"#\"))\n const [article] = docs.splice(parent, 1)\n\n /* Determine last index above threshold */\n let index = docs.findIndex(doc => doc.score < threshold)\n if (index === -1)\n index = docs.length\n\n /* Partition sections */\n const best = docs.slice(0, index)\n const more = docs.slice(index)\n\n /* Render children */\n const children = [\n renderSearchDocument(article, Flag.PARENT | +(!parent && index === 0)),\n ...best.map(section => renderSearchDocument(section, Flag.TEASER)),\n ...more.length ? [\n
    \n \n {more.length > 0 && more.length === 1\n ? translation(\"search.result.more.one\")\n : translation(\"search.result.more.other\", more.length)\n }\n \n {...more.map(section => renderSearchDocument(section, Flag.TEASER))}\n
    \n ] : []\n ]\n\n /* Render search result */\n return (\n
  • \n {children}\n
    • \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SourceFacts } from \"~/components\"\nimport { h, round } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render repository facts\n *\n * @param facts - Repository facts\n *\n * @returns Element\n */\nexport function renderSourceFacts(facts: SourceFacts): HTMLElement {\n return (\n
      \n {Object.entries(facts).map(([key, value]) => (\n
    • \n {typeof value === \"number\" ? round(value) : value}\n
      • \n ))}\n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tabbed control type\n */\ntype TabbedControlType =\n | \"prev\"\n | \"next\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render control for content tabs\n *\n * @param type - Control type\n *\n * @returns Element\n */\nexport function renderTabbedControl(\n type: TabbedControlType\n): HTMLElement {\n const classes = `tabbed-control tabbed-control--${type}`\n return (\n \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a table inside a wrapper to improve scrolling on mobile\n *\n * @param table - Table element\n *\n * @returns Element\n */\nexport function renderTable(table: HTMLElement): HTMLElement {\n return (\n
    \n
    \n {table}\n
    \n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { configuration, translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Version\n */\nexport interface Version {\n version: string /* Version identifier */\n title: string /* Version title */\n aliases: string[] /* Version aliases */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version\n *\n * @param version - Version\n *\n * @returns Element\n */\nfunction renderVersion(version: Version): HTMLElement {\n const config = configuration()\n\n /* Ensure trailing slash, see https://bit.ly/3rL5u3f */\n const url = new URL(`../${version.version}/`, config.base)\n return (\n
  • \n \n {version.title}\n \n
    • \n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version selector\n *\n * @param versions - Versions\n * @param active - Active version\n *\n * @returns Element\n */\nexport function renderVersionSelector(\n versions: Version[], active: Version\n): HTMLElement {\n return (\n
    \n \n {active.title}\n \n
      \n {versions.map(renderVersion)}\n
    \n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n animationFrameScheduler,\n combineLatest,\n defer,\n finalize,\n fromEvent,\n map,\n switchMap,\n take,\n takeLast,\n takeUntil,\n tap,\n throttleTime\n} from \"rxjs\"\n\nimport {\n ElementOffset,\n getElement,\n getElementSize,\n watchElementContentOffset,\n watchElementFocus,\n watchElementOffset,\n watchElementVisibility\n} from \"~/browser\"\n\nimport { Component } from \"../../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Annotation\n */\nexport interface Annotation {\n active: boolean /* Annotation is active */\n offset: ElementOffset /* Annotation offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation observable\n */\nexport function watchAnnotation(\n el: HTMLElement, container: HTMLElement\n): Observable {\n const offset$ = defer(() => combineLatest([\n watchElementOffset(el),\n watchElementContentOffset(container)\n ]))\n .pipe(\n map(([{ x, y }, scroll]) => {\n const { width } = getElementSize(el)\n return ({\n x: x - scroll.x + width / 2,\n y: y - scroll.y\n })\n })\n )\n\n /* Actively watch annotation on focus */\n return watchElementFocus(el)\n .pipe(\n switchMap(active => offset$\n .pipe(\n map(offset => ({ active, offset })),\n take(+!active || Infinity)\n )\n )\n )\n}\n\n/**\n * Mount annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotation(\n el: HTMLElement, container: HTMLElement\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe({\n\n /* Handle emission */\n next({ offset }) {\n el.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n el.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-tooltip-x\")\n el.style.removeProperty(\"--md-tooltip-y\")\n }\n })\n\n /* Start animation only when annotation is visible */\n const done$ = push$.pipe(takeLast(1))\n watchElementVisibility(el)\n .pipe(\n takeUntil(done$)\n )\n .subscribe(visible => {\n el.toggleAttribute(\"data-md-visible\", visible)\n })\n\n /* Track relative origin of tooltip */\n push$\n .pipe(\n throttleTime(500, animationFrameScheduler),\n map(() => container.getBoundingClientRect()),\n map(({ x }) => x)\n )\n .subscribe({\n\n /* Handle emission */\n next(origin) {\n if (origin)\n el.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n else\n el.style.removeProperty(\"--md-tooltip-0\")\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-tooltip-0\")\n }\n })\n\n /* Close open annotation on click */\n const index = getElement(\":scope > :last-child\", el)\n const blur$ = fromEvent(index, \"mousedown\", { once: true })\n push$\n .pipe(\n switchMap(({ active }) => active ? blur$ : EMPTY),\n tap(ev => ev.preventDefault())\n )\n .subscribe(() => el.blur())\n\n /* Create and return component */\n return watchAnnotation(el, container)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n finalize,\n merge,\n share,\n takeLast,\n takeUntil\n} from \"rxjs\"\n\nimport {\n getElement,\n getElements,\n getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n Annotation,\n mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find all annotation markers in the given code block\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findAnnotationMarkers(container: HTMLElement): Text[] {\n const markers: Text[] = []\n for (const comment of getElements(\".c, .c1, .cm\", container)) {\n let match: RegExpExecArray | null\n\n /* Split text at marker and add to list */\n let text = comment.firstChild as Text\n if (text instanceof Text)\n while ((match = /\\((\\d+)\\)/.exec(text.textContent!))) {\n const marker = text.splitText(match.index)\n text = marker.splitText(match[0].length)\n markers.push(marker)\n }\n }\n return markers\n}\n\n/**\n * Swap the child nodes of two elements\n *\n * @param source - Source element\n * @param target - Target element\n */\nfunction swap(source: HTMLElement, target: HTMLElement): void {\n target.append(...Array.from(source.childNodes))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation list\n *\n * This function analyzes the containing code block and checks for markers\n * referring to elements in the given annotation list. If no markers are found,\n * the list is left untouched. Otherwise, list elements are rendered as\n * annotations inside the code block.\n *\n * @param el - Annotation list element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationList(\n el: HTMLElement, container: HTMLElement, { print$ }: MountOptions\n): Observable> {\n\n /* Find and replace all markers with empty annotations */\n const annotations = new Map()\n for (const marker of findAnnotationMarkers(container)) {\n const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n if (getOptionalElement(`li:nth-child(${id})`, el)) {\n annotations.set(+id, renderAnnotation(+id))\n marker.replaceWith(annotations.get(+id)!)\n }\n }\n\n /* Keep list if there are no annotations to render */\n if (annotations.size === 0)\n return EMPTY\n\n /* Create and return component */\n return defer(() => {\n const done$ = new Subject()\n\n /* Handle print mode - see https://bit.ly/3rgPdpt */\n print$\n .pipe(\n takeUntil(done$.pipe(takeLast(1)))\n )\n .subscribe(active => {\n el.hidden = !active\n\n /* Show annotations in code block or list (print) */\n for (const [id, annotation] of annotations) {\n const inner = getElement(\".md-typeset\", annotation)\n const child = getElement(`li:nth-child(${id})`, el)\n if (!active)\n swap(child, inner)\n else\n swap(inner, child)\n }\n })\n\n /* Create and return component */\n return merge(...[...annotations]\n .map(([, annotation]) => (\n mountAnnotation(annotation, container)\n ))\n )\n .pipe(\n finalize(() => done$.complete()),\n share()\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n map,\n of,\n shareReplay,\n tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: Observable\n\n/**\n * Global sequence number for diagrams\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch Mermaid script\n *\n * @returns Mermaid scripts observable\n */\nfunction fetchScripts(): Observable {\n return typeof mermaid === \"undefined\" || mermaid instanceof Element\n ? watchScript(\"https://unpkg.com/mermaid@9.0.1/dist/mermaid.min.js\")\n : of(undefined)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount Mermaid diagram\n *\n * @param el - Code block element\n *\n * @returns Mermaid diagram component observable\n */\nexport function mountMermaid(\n el: HTMLElement\n): Observable> {\n el.classList.remove(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n mermaid$ ||= fetchScripts()\n .pipe(\n tap(() => mermaid.initialize({\n startOnLoad: false,\n themeCSS\n })),\n map(() => undefined),\n shareReplay(1)\n )\n\n /* Render diagram */\n mermaid$.subscribe(() => {\n el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n const id = `__mermaid_${sequence++}`\n const host = h(\"div\", { class: \"mermaid\" })\n mermaid.mermaidAPI.render(id, el.textContent, (svg: string) => {\n\n /* Create a shadow root and inject diagram */\n const shadow = host.attachShadow({ mode: \"closed\" })\n shadow.innerHTML = svg\n\n /* Replace code block with diagram */\n el.replaceWith(host)\n })\n })\n\n /* Create and return component */\n return mermaid$\n .pipe(\n map(() => ({ ref: el }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n filter,\n finalize,\n map,\n merge,\n tap\n} from \"rxjs\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Details\n */\nexport interface Details {\n action: \"open\" | \"close\" /* Details state */\n reveal?: boolean /* Details is revealed */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch details\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details observable\n */\nexport function watchDetails(\n el: HTMLDetailsElement, { target$, print$ }: WatchOptions\n): Observable
    {\n let open = true\n return merge(\n\n /* Open and focus details on location target */\n target$\n .pipe(\n map(target => target.closest(\"details:not([open])\")!),\n filter(details => el === details),\n map(() => ({\n action: \"open\", reveal: true\n }) as Details)\n ),\n\n /* Open details on print and close afterwards */\n print$\n .pipe(\n filter(active => active || !open),\n tap(() => open = el.open),\n map(active => ({\n action: active ? \"open\" : \"close\"\n }) as Details)\n )\n )\n}\n\n/**\n * Mount details\n *\n * This function ensures that `details` tags are opened on anchor jumps and\n * prior to printing, so the whole content of the page is visible.\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details component observable\n */\nexport function mountDetails(\n el: HTMLDetailsElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject
    ()\n push$.subscribe(({ action, reveal }) => {\n if (action === \"open\")\n el.setAttribute(\"open\", \"\")\n else\n el.removeAttribute(\"open\")\n if (reveal)\n el.scrollIntoView()\n })\n\n /* Create and return component */\n return watchDetails(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, of } from \"rxjs\"\n\nimport { renderTable } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Data table\n */\nexport interface DataTable {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Sentinel for replacement\n */\nconst sentinel = h(\"table\")\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount data table\n *\n * This function wraps a data table in another scrollable container, so it can\n * be smoothly scrolled on smaller screen sizes and won't break the layout.\n *\n * @param el - Data table element\n *\n * @returns Data table component observable\n */\nexport function mountDataTable(\n el: HTMLElement\n): Observable> {\n el.replaceWith(sentinel)\n sentinel.replaceWith(renderTable(el))\n\n /* Create and return component */\n return of({ ref: el })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n asyncScheduler,\n auditTime,\n combineLatest,\n defer,\n finalize,\n fromEvent,\n map,\n merge,\n skip,\n startWith,\n subscribeOn,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n getElement,\n getElementContentOffset,\n getElementContentSize,\n getElementOffset,\n getElementSize,\n getElements,\n watchElementContentOffset,\n watchElementSize\n} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content tabs\n */\nexport interface ContentTabs {\n active: HTMLLabelElement /* Active tab label */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n el: HTMLElement\n): Observable {\n const inputs = getElements(\":scope > input\", el)\n const initial = inputs.find(input => input.checked) || inputs[0]\n return merge(...inputs.map(input => fromEvent(input, \"change\")\n .pipe(\n map(() => getElement(`label[for=\"${input.id}\"]`))\n )\n ))\n .pipe(\n startWith(getElement(`label[for=\"${initial.id}\"]`)),\n map(active => ({ active }))\n )\n}\n\n/**\n * Mount content tabs\n *\n * This function scrolls the active tab into view. While this functionality is\n * provided by browsers as part of `scrollInfoView`, browsers will always also\n * scroll the vertical axis, which we do not want. Thus, we decided to provide\n * this functionality ourselves.\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs component observable\n */\nexport function mountContentTabs(\n el: HTMLElement\n): Observable> {\n\n /* Render content tab previous button for pagination */\n const prev = renderTabbedControl(\"prev\")\n el.append(prev)\n\n /* Render content tab next button for pagination */\n const next = renderTabbedControl(\"next\")\n el.append(next)\n\n /* Mount component on subscription */\n const container = getElement(\".tabbed-labels\", el)\n return defer(() => {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n combineLatest([push$, watchElementSize(el)])\n .pipe(\n auditTime(1, animationFrameScheduler),\n takeUntil(done$)\n )\n .subscribe({\n\n /* Handle emission */\n next([{ active }, size]) {\n const offset = getElementOffset(active)\n const { width } = getElementSize(active)\n\n /* Set tab indicator offset and width */\n el.style.setProperty(\"--md-indicator-x\", `${offset.x}px`)\n el.style.setProperty(\"--md-indicator-width\", `${width}px`)\n\n /* Scroll container to active content tab */\n const content = getElementContentOffset(container)\n if (\n offset.x < content.x ||\n offset.x + width > content.x + size.width\n )\n container.scrollTo({\n left: Math.max(0, offset.x - 16),\n behavior: \"smooth\"\n })\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-indicator-x\")\n el.style.removeProperty(\"--md-indicator-width\")\n }\n })\n\n /* Hide content tab buttons on borders */\n combineLatest([\n watchElementContentOffset(container),\n watchElementSize(container)\n ])\n .pipe(\n takeUntil(done$)\n )\n .subscribe(([offset, size]) => {\n const content = getElementContentSize(container)\n prev.hidden = offset.x < 16\n next.hidden = offset.x > content.width - size.width - 16\n })\n\n /* Paginate content tab container on click */\n merge(\n fromEvent(prev, \"click\").pipe(map(() => -1)),\n fromEvent(next, \"click\").pipe(map(() => +1))\n )\n .pipe(\n takeUntil(done$)\n )\n .subscribe(direction => {\n const { width } = getElementSize(container)\n container.scrollBy({\n left: width * direction,\n behavior: \"smooth\"\n })\n })\n\n /* Set up linking of content tabs, if enabled */\n if (feature(\"content.tabs.link\"))\n push$.pipe(skip(1))\n .subscribe(({ active }) => {\n const tab = active.innerText.trim()\n for (const set of getElements(\"[data-tabs]\"))\n for (const input of getElements(\n \":scope > input\", set\n )) {\n const label = getElement(`label[for=\"${input.id}\"]`)\n if (label.innerText.trim() === tab) {\n input.click()\n break\n }\n }\n\n /* Persist active tabs in local storage */\n const tabs = __md_get(\"__tabs\") || []\n __md_set(\"__tabs\", [...new Set([tab, ...tabs])])\n })\n\n /* Create and return component */\n return watchContentTabs(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n .pipe(\n subscribeOn(asyncScheduler)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, merge } from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Annotation } from \"../annotation\"\nimport {\n CodeBlock,\n Mermaid,\n mountCodeBlock,\n mountMermaid\n} from \"../code\"\nimport {\n Details,\n mountDetails\n} from \"../details\"\nimport {\n DataTable,\n mountDataTable\n} from \"../table\"\nimport {\n ContentTabs,\n mountContentTabs\n} from \"../tabs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content\n */\nexport type Content =\n | Annotation\n | ContentTabs\n | CodeBlock\n | Mermaid\n | DataTable\n | Details\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount content\n *\n * This function mounts all components that are found in the content of the\n * actual article, including code blocks, data tables and details.\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Content component observable\n */\nexport function mountContent(\n el: HTMLElement, { target$, print$ }: MountOptions\n): Observable> {\n return merge(\n\n /* Code blocks */\n ...getElements(\"pre:not(.mermaid) > code\", el)\n .map(child => mountCodeBlock(child, { print$ })),\n\n /* Mermaid diagrams */\n ...getElements(\"pre.mermaid\", el)\n .map(child => mountMermaid(child)),\n\n /* Data tables */\n ...getElements(\"table:not([class])\", el)\n .map(child => mountDataTable(child)),\n\n /* Details */\n ...getElements(\"details\", el)\n .map(child => mountDetails(child, { target$, print$ })),\n\n /* Content tabs */\n ...getElements(\"[data-tabs]\", el)\n .map(child => mountContentTabs(child))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n delay,\n finalize,\n map,\n merge,\n of,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dialog\n */\nexport interface Dialog {\n message: string /* Dialog message */\n active: boolean /* Dialog is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n alert$: Subject /* Alert subject */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n alert$: Subject /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch dialog\n *\n * @param _el - Dialog element\n * @param options - Options\n *\n * @returns Dialog observable\n */\nexport function watchDialog(\n _el: HTMLElement, { alert$ }: WatchOptions\n): Observable {\n return alert$\n .pipe(\n switchMap(message => merge(\n of(true),\n of(false).pipe(delay(2000))\n )\n .pipe(\n map(active => ({ message, active }))\n )\n )\n )\n}\n\n/**\n * Mount dialog\n *\n * This function reveals the dialog in the right corner when a new alert is\n * emitted through the subject that is passed as part of the options.\n *\n * @param el - Dialog element\n * @param options - Options\n *\n * @returns Dialog component observable\n */\nexport function mountDialog(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const inner = getElement(\".md-typeset\", el)\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ message, active }) => {\n el.classList.toggle(\"md-dialog--active\", active)\n inner.textContent = message\n })\n\n /* Create and return component */\n return watchDialog(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatest,\n combineLatestWith,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n map,\n of,\n shareReplay,\n startWith,\n switchMap,\n takeLast,\n takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n watchElementSize,\n watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface Header {\n height: number /* Header visible height */\n hidden: boolean /* Header is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute whether the header is hidden\n *\n * If the user scrolls past a certain threshold, the header can be hidden when\n * scrolling down, and shown when scrolling up.\n *\n * @param options - Options\n *\n * @returns Toggle observable\n */\nfunction isHidden({ viewport$ }: WatchOptions): Observable {\n if (!feature(\"header.autohide\"))\n return of(false)\n\n /* Compute direction and turning point */\n const direction$ = viewport$\n .pipe(\n map(({ offset: { y } }) => y),\n bufferCount(2, 1),\n map(([a, b]) => [a < b, b] as const),\n distinctUntilKeyChanged(0)\n )\n\n /* Compute whether header should be hidden */\n const hidden$ = combineLatest([viewport$, direction$])\n .pipe(\n filter(([{ offset }, [, y]]) => Math.abs(y - offset.y) > 100),\n map(([, [direction]]) => direction),\n distinctUntilChanged()\n )\n\n /* Compute threshold for hiding */\n const search$ = watchToggle(\"search\")\n return combineLatest([viewport$, search$])\n .pipe(\n map(([{ offset }, search]) => offset.y > 400 && !search),\n distinctUntilChanged(),\n switchMap(active => active ? hidden$ : of(false)),\n startWith(false)\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header observable\n */\nexport function watchHeader(\n el: HTMLElement, options: WatchOptions\n): Observable
    {\n return defer(() => combineLatest([\n watchElementSize(el),\n isHidden(options)\n ]))\n .pipe(\n map(([{ height }, hidden]) => ({\n height,\n hidden\n })),\n distinctUntilChanged((a, b) => (\n a.height === b.height &&\n a.hidden === b.hidden\n )),\n shareReplay(1)\n )\n}\n\n/**\n * Mount header\n *\n * This function manages the different states of the header, i.e. whether it's\n * hidden or rendered with a shadow. This depends heavily on the main area.\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header component observable\n */\nexport function mountHeader(\n el: HTMLElement, { header$, main$ }: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject
    ()\n const done$ = push$.pipe(takeLast(1))\n push$\n .pipe(\n distinctUntilKeyChanged(\"active\"),\n combineLatestWith(header$)\n )\n .subscribe(([{ active }, { hidden }]) => {\n el.classList.toggle(\"md-header--shadow\", active && !hidden)\n el.hidden = hidden\n })\n\n /* Link to main area */\n main$.subscribe(push$)\n\n /* Create and return component */\n return header$\n .pipe(\n takeUntil(done$),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n distinctUntilKeyChanged,\n finalize,\n map,\n tap\n} from \"rxjs\"\n\nimport {\n Viewport,\n getElementSize,\n getOptionalElement,\n watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Header } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface HeaderTitle {\n active: boolean /* Header title is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header title\n *\n * @param el - Heading element\n * @param options - Options\n *\n * @returns Header title observable\n */\nexport function watchHeaderTitle(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n return watchViewportAt(el, { viewport$, header$ })\n .pipe(\n map(({ offset: { y } }) => {\n const { height } = getElementSize(el)\n return {\n active: y >= height\n }\n }),\n distinctUntilKeyChanged(\"active\")\n )\n}\n\n/**\n * Mount header title\n *\n * This function swaps the header title from the site title to the title of the\n * current page when the user scrolls past the first headline.\n *\n * @param el - Header title element\n * @param options - Options\n *\n * @returns Header title component observable\n */\nexport function mountHeaderTitle(\n el: HTMLElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ active }) => {\n el.classList.toggle(\"md-header__title--active\", active)\n })\n\n /* Obtain headline, if any */\n const heading = getOptionalElement(\"article h1\")\n if (typeof heading === \"undefined\")\n return EMPTY\n\n /* Create and return component */\n return watchHeaderTitle(heading, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n map,\n switchMap\n} from \"rxjs\"\n\nimport {\n Viewport,\n watchElementSize\n} from \"~/browser\"\n\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Main area\n */\nexport interface Main {\n offset: number /* Main area top offset */\n height: number /* Main area visible height */\n active: boolean /* Main area is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch main area\n *\n * This function returns an observable that computes the visual parameters of\n * the main area which depends on the viewport vertical offset and height, as\n * well as the height of the header element, if the header is fixed.\n *\n * @param el - Main area element\n * @param options - Options\n *\n * @returns Main area observable\n */\nexport function watchMain(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable
    {\n\n /* Compute necessary adjustment for header */\n const adjust$ = header$\n .pipe(\n map(({ height }) => height),\n distinctUntilChanged()\n )\n\n /* Compute the main area's top and bottom borders */\n const border$ = adjust$\n .pipe(\n switchMap(() => watchElementSize(el)\n .pipe(\n map(({ height }) => ({\n top: el.offsetTop,\n bottom: el.offsetTop + height\n })),\n distinctUntilKeyChanged(\"bottom\")\n )\n )\n )\n\n /* Compute the main area's offset, visible height and if we scrolled past */\n return combineLatest([adjust$, border$, viewport$])\n .pipe(\n map(([header, { top, bottom }, { offset: { y }, size: { height } }]) => {\n height = Math.max(0, height\n - Math.max(0, top - y, header)\n - Math.max(0, height + y - bottom)\n )\n return {\n offset: top - header,\n height,\n active: top - header <= y\n }\n }),\n distinctUntilChanged((a, b) => (\n a.offset === b.offset &&\n a.height === b.height &&\n a.active === b.active\n ))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n asyncScheduler,\n defer,\n finalize,\n fromEvent,\n map,\n mergeMap,\n observeOn,\n of,\n shareReplay,\n startWith,\n tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Palette colors\n */\nexport interface PaletteColor {\n scheme?: string /* Color scheme */\n primary?: string /* Primary color */\n accent?: string /* Accent color */\n}\n\n/**\n * Palette\n */\nexport interface Palette {\n index: number /* Palette index */\n color: PaletteColor /* Palette colors */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch color palette\n *\n * @param inputs - Color palette element\n *\n * @returns Color palette observable\n */\nexport function watchPalette(\n inputs: HTMLInputElement[]\n): Observable {\n const current = __md_get(\"__palette\") || {\n index: inputs.findIndex(input => matchMedia(\n input.getAttribute(\"data-md-color-media\")!\n ).matches)\n }\n\n /* Emit changes in color palette */\n return of(...inputs)\n .pipe(\n mergeMap(input => fromEvent(input, \"change\")\n .pipe(\n map(() => input)\n )\n ),\n startWith(inputs[Math.max(0, current.index)]),\n map(input => ({\n index: inputs.indexOf(input),\n color: {\n scheme: input.getAttribute(\"data-md-color-scheme\"),\n primary: input.getAttribute(\"data-md-color-primary\"),\n accent: input.getAttribute(\"data-md-color-accent\")\n }\n } as Palette)),\n shareReplay(1)\n )\n}\n\n/**\n * Mount color palette\n *\n * @param el - Color palette element\n *\n * @returns Color palette component observable\n */\nexport function mountPalette(\n el: HTMLElement\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(palette => {\n document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n /* Set color palette */\n for (const [key, value] of Object.entries(palette.color))\n document.body.setAttribute(`data-md-color-${key}`, value)\n\n /* Toggle visibility */\n for (let index = 0; index < inputs.length; index++) {\n const label = inputs[index].nextElementSibling\n if (label instanceof HTMLElement)\n label.hidden = palette.index !== index\n }\n\n /* Persist preference in local storage */\n __md_set(\"__palette\", palette)\n })\n\n /* Revert transition durations after color switch */\n push$.pipe(observeOn(asyncScheduler))\n .subscribe(() => {\n document.body.removeAttribute(\"data-md-color-switching\")\n })\n\n /* Create and return component */\n const inputs = getElements(\"input\", el)\n return watchPalette(inputs)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n Observable,\n Subject,\n map,\n tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n alert$: Subject /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Extract text to copy\n *\n * @param el - HTML element\n *\n * @returns Extracted text\n */\nfunction extract(el: HTMLElement): string {\n el.setAttribute(\"data-md-copying\", \"\")\n const text = el.innerText\n el.removeAttribute(\"data-md-copying\")\n return text\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up Clipboard.js integration\n *\n * @param options - Options\n */\nexport function setupClipboardJS(\n { alert$ }: SetupOptions\n): void {\n if (ClipboardJS.isSupported()) {\n new Observable(subscriber => {\n new ClipboardJS(\"[data-clipboard-target], [data-clipboard-text]\", {\n text: el => (\n el.getAttribute(\"data-clipboard-text\")! ||\n extract(getElement(\n el.getAttribute(\"data-clipboard-target\")!\n ))\n )\n })\n .on(\"success\", ev => subscriber.next(ev))\n })\n .pipe(\n tap(ev => {\n const trigger = ev.trigger as HTMLElement\n trigger.focus()\n }),\n map(() => translation(\"clipboard.copied\"))\n )\n .subscribe(alert$)\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map,\n of,\n tap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport { getElements, requestXML } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = string[]\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Preprocess a list of URLs\n *\n * This function replaces the `site_url` in the sitemap with the actual base\n * URL, to allow instant loading to work in occasions like Netlify previews.\n *\n * @param urls - URLs\n *\n * @returns URL path parts\n */\nfunction preprocess(urls: Sitemap): Sitemap {\n if (urls.length < 2)\n return [\"\"]\n\n /* Take the first two URLs and remove everything after the last slash */\n const [root, next] = [...urls]\n .sort((a, b) => a.length - b.length)\n .map(url => url.replace(/[^/]+$/, \"\"))\n\n /* Compute common prefix */\n let index = 0\n if (root === next)\n index = root.length\n else\n while (root.charCodeAt(index) === next.charCodeAt(index))\n index++\n\n /* Remove common prefix and return in original order */\n return urls.map(url => url.replace(root.slice(0, index), \"\"))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base?: URL): Observable {\n const cached = __md_get(\"__sitemap\", sessionStorage, base)\n if (cached) {\n return of(cached)\n } else {\n const config = configuration()\n return requestXML(new URL(\"sitemap.xml\", base || config.base))\n .pipe(\n map(sitemap => preprocess(getElements(\"loc\", sitemap)\n .map(node => node.textContent!)\n )),\n catchError(() => EMPTY), // @todo refactor instant loading\n defaultIfEmpty([]),\n tap(sitemap => __md_set(\"__sitemap\", sitemap, sessionStorage, base))\n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n bufferCount,\n catchError,\n concatMap,\n debounceTime,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n fromEvent,\n map,\n merge,\n of,\n sample,\n share,\n skip,\n skipUntil,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n Viewport,\n ViewportOffset,\n getElements,\n getOptionalElement,\n request,\n setLocation,\n setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\nimport { h } from \"~/utilities\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * History state\n */\nexport interface HistoryState {\n url: URL /* State URL */\n offset?: ViewportOffset /* State viewport offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n document$: Subject /* Document subject */\n location$: Subject /* Location subject */\n viewport$: Observable /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up instant loading\n *\n * When fetching, theoretically, we could use `responseType: \"document\"`, but\n * since all MkDocs links are relative, we need to make sure that the current\n * location matches the document we just loaded. Otherwise any relative links\n * in the document could use the old location.\n *\n * This is the reason why we need to synchronize history events and the process\n * of fetching the document for navigation changes (except `popstate` events):\n *\n * 1. Fetch document via `XMLHTTPRequest`\n * 2. Set new location via `history.pushState`\n * 3. Parse and emit fetched document\n *\n * For `popstate` events, we must not use `history.pushState`, or the forward\n * history will be irreversibly overwritten. In case the request fails, the\n * location change is dispatched regularly.\n *\n * @param options - Options\n */\nexport function setupInstantLoading(\n { document$, location$, viewport$ }: SetupOptions\n): void {\n const config = configuration()\n if (location.protocol === \"file:\")\n return\n\n /* Disable automatic scroll restoration */\n if (\"scrollRestoration\" in history) {\n history.scrollRestoration = \"manual\"\n\n /* Hack: ensure that reloads restore viewport offset */\n fromEvent(window, \"beforeunload\")\n .subscribe(() => {\n history.scrollRestoration = \"auto\"\n })\n }\n\n /* Hack: ensure absolute favicon link to omit 404s when switching */\n const favicon = getOptionalElement(\"link[rel=icon]\")\n if (typeof favicon !== \"undefined\")\n favicon.href = favicon.href\n\n /* Intercept internal navigation */\n const push$ = fetchSitemap()\n .pipe(\n map(paths => paths.map(path => `${new URL(path, config.base)}`)),\n switchMap(urls => fromEvent(document.body, \"click\")\n .pipe(\n filter(ev => !ev.metaKey && !ev.ctrlKey),\n switchMap(ev => {\n if (ev.target instanceof Element) {\n const el = ev.target.closest(\"a\")\n if (el && !el.target) {\n const url = new URL(el.href)\n\n /* Canonicalize URL */\n url.search = \"\"\n url.hash = \"\"\n\n /* Check if URL should be intercepted */\n if (\n url.pathname !== location.pathname &&\n urls.includes(url.toString())\n ) {\n ev.preventDefault()\n return of({\n url: new URL(el.href)\n })\n }\n }\n }\n return NEVER\n })\n )\n ),\n share()\n )\n\n /* Intercept history back and forward */\n const pop$ = fromEvent(window, \"popstate\")\n .pipe(\n filter(ev => ev.state !== null),\n map(ev => ({\n url: new URL(location.href),\n offset: ev.state\n })),\n share()\n )\n\n /* Emit location change */\n merge(push$, pop$)\n .pipe(\n distinctUntilChanged((a, b) => a.url.href === b.url.href),\n map(({ url }) => url)\n )\n .subscribe(location$)\n\n /* Fetch document via `XMLHTTPRequest` */\n const response$ = location$\n .pipe(\n distinctUntilKeyChanged(\"pathname\"),\n switchMap(url => request(url.href)\n .pipe(\n catchError(() => {\n setLocation(url)\n return NEVER\n })\n )\n ),\n share()\n )\n\n /* Set new location via `history.pushState` */\n push$\n .pipe(\n sample(response$)\n )\n .subscribe(({ url }) => {\n history.pushState({}, \"\", `${url}`)\n })\n\n /* Parse and emit fetched document */\n const dom = new DOMParser()\n response$\n .pipe(\n switchMap(res => res.text()),\n map(res => dom.parseFromString(res, \"text/html\"))\n )\n .subscribe(document$)\n\n /* Replace meta tags and components */\n document$\n .pipe(\n skip(1)\n )\n .subscribe(replacement => {\n for (const selector of [\n\n /* Meta tags */\n \"title\",\n \"link[rel=canonical]\",\n \"meta[name=author]\",\n \"meta[name=description]\",\n\n /* Components */\n \"[data-md-component=announce]\",\n \"[data-md-component=container]\",\n \"[data-md-component=header-topic]\",\n \"[data-md-component=outdated]\",\n \"[data-md-component=logo]\",\n \"[data-md-component=skip]\",\n ...feature(\"navigation.tabs.sticky\")\n ? [\"[data-md-component=tabs]\"]\n : []\n ]) {\n const source = getOptionalElement(selector)\n const target = getOptionalElement(selector, replacement)\n if (\n typeof source !== \"undefined\" &&\n typeof target !== \"undefined\"\n ) {\n source.replaceWith(target)\n }\n }\n })\n\n /* Re-evaluate scripts */\n document$\n .pipe(\n skip(1),\n map(() => getComponentElement(\"container\")),\n switchMap(el => getElements(\"script\", el)),\n concatMap(el => {\n const script = h(\"script\")\n if (el.src) {\n for (const name of el.getAttributeNames())\n script.setAttribute(name, el.getAttribute(name)!)\n el.replaceWith(script)\n\n /* Complete when script is loaded */\n return new Observable(observer => {\n script.onload = () => observer.complete()\n })\n\n /* Complete immediately */\n } else {\n script.textContent = el.textContent\n el.replaceWith(script)\n return EMPTY\n }\n })\n )\n .subscribe()\n\n /* Emit history state change */\n merge(push$, pop$)\n .pipe(\n sample(document$)\n )\n .subscribe(({ url, offset }) => {\n if (url.hash && !offset) {\n setLocationHash(url.hash)\n } else {\n window.scrollTo(0, offset?.y || 0)\n }\n })\n\n /* Debounce update of viewport offset */\n viewport$\n .pipe(\n skipUntil(push$),\n debounceTime(250),\n distinctUntilKeyChanged(\"offset\")\n )\n .subscribe(({ offset }) => {\n history.replaceState(offset, \"\")\n })\n\n /* Set viewport offset from history */\n merge(push$, pop$)\n .pipe(\n bufferCount(2, 1),\n filter(([a, b]) => a.url.pathname === b.url.pathname),\n map(([, state]) => state)\n )\n .subscribe(({ offset }) => {\n window.scrollTo(0, offset?.y || 0)\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n parent?: SearchIndexDocument /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n docs: SearchIndexDocument[]\n): SearchDocumentMap {\n const documents = new Map()\n const parents = new Set()\n for (const doc of docs) {\n const [path, hash] = doc.location.split(\"#\")\n\n /* Extract location, title and tags */\n const location = doc.location\n const title = doc.title\n const tags = doc.tags\n\n /* Escape and cleanup text */\n const text = escapeHTML(doc.text)\n .replace(/\\s+(?=[,.:;!?])/g, \"\")\n .replace(/\\s+/g, \" \")\n\n /* Handle section */\n if (hash) {\n const parent = documents.get(path)!\n\n /* Ignore first section, override article */\n if (!parents.has(parent)) {\n parent.title = doc.title\n parent.text = text\n\n /* Remember that we processed the article */\n parents.add(parent)\n\n /* Add subsequent section */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n parent\n })\n }\n\n /* Add article */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n ...tags && { tags }\n })\n }\n }\n return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n const separator = new RegExp(config.separator, \"img\")\n const highlight = (_: unknown, data: string, term: string) => {\n return `${data}${term}`\n }\n\n /* Return factory function */\n return (query: string) => {\n query = query\n .replace(/[\\s*+\\-:~^]+/g, \" \")\n .trim()\n\n /* Create search term match expression */\n const match = new RegExp(`(^|${config.separator})(${\n query\n .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n .replace(separator, \"|\")\n })`, \"img\")\n\n /* Highlight string value */\n return value => (\n escape\n ? escapeHTML(value)\n : value\n )\n .replace(match, highlight)\n .replace(/<\\/mark>(\\s+)]*>/img, \"$1\")\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search transformation function\n *\n * @param value - Query value\n *\n * @returns Transformed query value\n */\nexport type SearchTransformFn = (value: string) => string\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Search for terms in quotation marks and prepend a `+` modifier to denote\n * that the resulting document must contain all terms, converting the query\n * to an `AND` query (as opposed to the default `OR` behavior). While users\n * may expect terms enclosed in quotation marks to map to span queries, i.e.\n * for which order is important, Lunr.js doesn't support them, so the best\n * we can do is to convert the terms to an `AND` query.\n *\n * 2. Replace control characters which are not located at the beginning of the\n * query or preceded by white space, or are not followed by a non-whitespace\n * character or are at the end of the query string. Furthermore, filter\n * unmatched quotation marks.\n *\n * 3. Trim excess whitespace from left and right.\n *\n * @param query - Query value\n *\n * @returns Transformed query value\n */\nexport function defaultTransform(query: string): string {\n return query\n .split(/\"([^\"]+)\"/g) /* => 1 */\n .map((terms, index) => index & 1\n ? terms.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n : terms\n )\n .join(\"\")\n .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\") /* => 2 */\n .trim() /* => 3 */\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SearchIndex, SearchResult } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search message type\n */\nexport const enum SearchMessageType {\n SETUP, /* Search index setup */\n READY, /* Search index ready */\n QUERY, /* Search query */\n RESULT /* Search results */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message containing the data necessary to setup the search index\n */\nexport interface SearchSetupMessage {\n type: SearchMessageType.SETUP /* Message type */\n data: SearchIndex /* Message data */\n}\n\n/**\n * Message indicating the search index is ready\n */\nexport interface SearchReadyMessage {\n type: SearchMessageType.READY /* Message type */\n}\n\n/**\n * Message containing a search query\n */\nexport interface SearchQueryMessage {\n type: SearchMessageType.QUERY /* Message type */\n data: string /* Message data */\n}\n\n/**\n * Message containing results for a search query\n */\nexport interface SearchResultMessage {\n type: SearchMessageType.RESULT /* Message type */\n data: SearchResult /* Message data */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message exchanged with the search worker\n */\nexport type SearchMessage =\n | SearchSetupMessage\n | SearchReadyMessage\n | SearchQueryMessage\n | SearchResultMessage\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Type guard for search setup messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchSetupMessage(\n message: SearchMessage\n): message is SearchSetupMessage {\n return message.type === SearchMessageType.SETUP\n}\n\n/**\n * Type guard for search ready messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchReadyMessage(\n message: SearchMessage\n): message is SearchReadyMessage {\n return message.type === SearchMessageType.READY\n}\n\n/**\n * Type guard for search query messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchQueryMessage(\n message: SearchMessage\n): message is SearchQueryMessage {\n return message.type === SearchMessageType.QUERY\n}\n\n/**\n * Type guard for search result messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchResultMessage(\n message: SearchMessage\n): message is SearchResultMessage {\n return message.type === SearchMessageType.RESULT\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n ObservableInput,\n Subject,\n from,\n map,\n share\n} from \"rxjs\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport { WorkerHandler, watchWorker } from \"~/browser\"\n\nimport { SearchIndex } from \"../../_\"\nimport {\n SearchOptions,\n SearchPipeline\n} from \"../../options\"\nimport {\n SearchMessage,\n SearchMessageType,\n SearchSetupMessage,\n isSearchResultMessage\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search worker\n */\nexport type SearchWorker = WorkerHandler\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search index\n *\n * @param data - Search index\n *\n * @returns Search index\n */\nfunction setupSearchIndex({ config, docs }: SearchIndex): SearchIndex {\n\n /* Override default language with value from translation */\n if (config.lang.length === 1 && config.lang[0] === \"en\")\n config.lang = [\n translation(\"search.config.lang\")\n ]\n\n /* Override default separator with value from translation */\n if (config.separator === \"[\\\\s\\\\-]+\")\n config.separator = translation(\"search.config.separator\")\n\n /* Set pipeline from translation */\n const pipeline = translation(\"search.config.pipeline\")\n .split(/\\s*,\\s*/)\n .filter(Boolean) as SearchPipeline\n\n /* Determine search options */\n const options: SearchOptions = {\n pipeline,\n suggestions: feature(\"search.suggest\")\n }\n\n /* Return search index after defaulting */\n return { config, docs, options }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search worker\n *\n * This function creates a web worker to set up and query the search index,\n * which is done using Lunr.js. The index must be passed as an observable to\n * enable hacks like _localsearch_ via search index embedding as JSON.\n *\n * @param url - Worker URL\n * @param index - Search index observable input\n *\n * @returns Search worker\n */\nexport function setupSearchWorker(\n url: string, index: ObservableInput\n): SearchWorker {\n const config = configuration()\n const worker = new Worker(url)\n\n /* Create communication channels and resolve relative links */\n const tx$ = new Subject()\n const rx$ = watchWorker(worker, { tx$ })\n .pipe(\n map(message => {\n if (isSearchResultMessage(message)) {\n for (const result of message.data.items)\n for (const document of result)\n document.location = `${new URL(document.location, config.base)}`\n }\n return message\n }),\n share()\n )\n\n /* Set up search index */\n from(index)\n .pipe(\n map(data => ({\n type: SearchMessageType.SETUP,\n data: setupSearchIndex(data)\n } as SearchSetupMessage))\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Return search worker */\n return { tx$, rx$ }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Subject,\n catchError,\n combineLatest,\n filter,\n fromEvent,\n map,\n of,\n switchMap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n getElement,\n getLocation,\n requestJSON,\n setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n Version,\n renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n document$: Subject /* Document subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up version selector\n *\n * @param options - Options\n */\nexport function setupVersionSelector(\n { document$ }: SetupOptions\n): void {\n const config = configuration()\n const versions$ = requestJSON(\n new URL(\"../versions.json\", config.base)\n )\n .pipe(\n catchError(() => EMPTY) // @todo refactor instant loading\n )\n\n /* Determine current version */\n const current$ = versions$\n .pipe(\n map(versions => {\n const [, current] = config.base.match(/([^/]+)\\/?$/)!\n return versions.find(({ version, aliases }) => (\n version === current || aliases.includes(current)\n )) || versions[0]\n })\n )\n\n /* Intercept inter-version navigation */\n versions$\n .pipe(\n map(versions => new Map(versions.map(version => [\n `${new URL(`../${version.version}/`, config.base)}`,\n version\n ]))),\n switchMap(urls => fromEvent(document.body, \"click\")\n .pipe(\n filter(ev => !ev.metaKey && !ev.ctrlKey),\n withLatestFrom(current$),\n switchMap(([ev, current]) => {\n if (ev.target instanceof Element) {\n const el = ev.target.closest(\"a\")\n if (el && !el.target && urls.has(el.href)) {\n const url = el.href\n // This is a temporary hack to detect if a version inside the\n // version selector or on another part of the site was clicked.\n // If we're inside the version selector, we definitely want to\n // find the same page, as we might have different deployments\n // due to aliases. However, if we're outside the version\n // selector, we must abort here, because we might otherwise\n // interfere with instant loading. We need to refactor this\n // at some point together with instant loading.\n //\n // See https://github.com/squidfunk/mkdocs-material/issues/4012\n if (!ev.target.closest(\".md-version\")) {\n const version = urls.get(url)!\n if (version === current)\n return EMPTY\n }\n ev.preventDefault()\n return of(url)\n }\n }\n return EMPTY\n }),\n switchMap(url => {\n const { version } = urls.get(url)!\n return fetchSitemap(new URL(url))\n .pipe(\n map(sitemap => {\n const location = getLocation()\n const path = location.href.replace(config.base, \"\")\n return sitemap.includes(path)\n ? new URL(`../${version}/${path}`, config.base)\n : new URL(url)\n })\n )\n })\n )\n )\n )\n .subscribe(url => setLocation(url))\n\n /* Render version selector and warning */\n combineLatest([versions$, current$])\n .subscribe(([versions, current]) => {\n const topic = getElement(\".md-header__topic\")\n topic.appendChild(renderVersionSelector(versions, current))\n })\n\n /* Integrate outdated version banner with instant loading */\n document$.pipe(switchMap(() => current$))\n .subscribe(current => {\n\n /* Check if version state was already determined */\n let outdated = __md_get(\"__outdated\", sessionStorage)\n if (outdated === null) {\n const latest = config.version?.default || \"latest\"\n outdated = !current.aliases.includes(latest)\n\n /* Persist version state in session storage */\n __md_set(\"__outdated\", outdated, sessionStorage)\n }\n\n /* Unhide outdated version banner */\n if (outdated)\n for (const warning of getComponentElements(\"outdated\"))\n warning.hidden = false\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n combineLatest,\n delay,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n share,\n shareReplay,\n startWith,\n take,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n getLocation,\n setToggle,\n watchElementFocus,\n watchToggle\n} from \"~/browser\"\nimport {\n SearchMessageType,\n SearchQueryMessage,\n SearchWorker,\n defaultTransform,\n isSearchReadyMessage\n} from \"~/integrations\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query\n */\nexport interface SearchQuery {\n value: string /* Query value */\n focus: boolean /* Query focus */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch search query\n *\n * Note that the focus event which triggers re-reading the current query value\n * is delayed by `1ms` so the input's empty state is allowed to propagate.\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query observable\n */\nexport function watchSearchQuery(\n el: HTMLInputElement, { rx$ }: SearchWorker\n): Observable {\n const fn = __search?.transform || defaultTransform\n\n /* Immediately show search dialog */\n const { searchParams } = getLocation()\n if (searchParams.has(\"q\"))\n setToggle(\"search\", true)\n\n /* Intercept query parameter (deep link) */\n const param$ = rx$\n .pipe(\n filter(isSearchReadyMessage),\n take(1),\n map(() => searchParams.get(\"q\") || \"\")\n )\n\n /* Remove query parameter when search is closed */\n watchToggle(\"search\")\n .pipe(\n filter(active => !active),\n take(1)\n )\n .subscribe(() => {\n const url = new URL(location.href)\n url.searchParams.delete(\"q\")\n history.replaceState({}, \"\", `${url}`)\n })\n\n /* Set query from parameter */\n param$.subscribe(value => { // TODO: not ideal - find a better way\n if (value) {\n el.value = value\n el.focus()\n }\n })\n\n /* Intercept focus and input events */\n const focus$ = watchElementFocus(el)\n const value$ = merge(\n fromEvent(el, \"keyup\"),\n fromEvent(el, \"focus\").pipe(delay(1)),\n param$\n )\n .pipe(\n map(() => fn(el.value)),\n startWith(\"\"),\n distinctUntilChanged(),\n )\n\n /* Combine into single observable */\n return combineLatest([value$, focus$])\n .pipe(\n map(([value, focus]) => ({ value, focus })),\n shareReplay(1)\n )\n}\n\n/**\n * Mount search query\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query component observable\n */\nexport function mountSearchQuery(\n el: HTMLInputElement, { tx$, rx$ }: SearchWorker\n): Observable> {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n\n /* Handle value changes */\n push$\n .pipe(\n distinctUntilKeyChanged(\"value\"),\n map(({ value }): SearchQueryMessage => ({\n type: SearchMessageType.QUERY,\n data: value\n }))\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Handle focus changes */\n push$\n .pipe(\n distinctUntilKeyChanged(\"focus\")\n )\n .subscribe(({ focus }) => {\n if (focus) {\n setToggle(\"search\", focus)\n el.placeholder = \"\"\n } else {\n el.placeholder = translation(\"search.placeholder\")\n }\n })\n\n /* Handle reset */\n fromEvent(el.form!, \"reset\")\n .pipe(\n takeUntil(done$)\n )\n .subscribe(() => el.focus())\n\n /* Create and return component */\n return watchSearchQuery(el, { tx$, rx$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state })),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n filter,\n finalize,\n map,\n merge,\n of,\n skipUntil,\n switchMap,\n take,\n tap,\n withLatestFrom,\n zipWith\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n getElement,\n watchElementBoundary\n} from \"~/browser\"\nimport {\n SearchResult,\n SearchWorker,\n isSearchReadyMessage,\n isSearchResultMessage\n} from \"~/integrations\"\nimport { renderSearchResultItem } from \"~/templates\"\nimport { round } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n query$: Observable /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search result list\n *\n * This function performs a lazy rendering of the search results, depending on\n * the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchResult(\n el: HTMLElement, { rx$ }: SearchWorker, { query$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n const boundary$ = watchElementBoundary(el.parentElement!)\n .pipe(\n filter(Boolean)\n )\n\n /* Retrieve nested components */\n const meta = getElement(\":scope > :first-child\", el)\n const list = getElement(\":scope > :last-child\", el)\n\n /* Wait until search is ready */\n const ready$ = rx$\n .pipe(\n filter(isSearchReadyMessage),\n take(1)\n )\n\n /* Update search result metadata */\n push$\n .pipe(\n withLatestFrom(query$),\n skipUntil(ready$)\n )\n .subscribe(([{ items }, { value }]) => {\n if (value) {\n switch (items.length) {\n\n /* No results */\n case 0:\n meta.textContent = translation(\"search.result.none\")\n break\n\n /* One result */\n case 1:\n meta.textContent = translation(\"search.result.one\")\n break\n\n /* Multiple result */\n default:\n meta.textContent = translation(\n \"search.result.other\",\n round(items.length)\n )\n }\n } else {\n meta.textContent = translation(\"search.result.placeholder\")\n }\n })\n\n /* Update search result list */\n push$\n .pipe(\n tap(() => list.innerHTML = \"\"),\n switchMap(({ items }) => merge(\n of(...items.slice(0, 10)),\n of(...items.slice(10))\n .pipe(\n bufferCount(4),\n zipWith(boundary$),\n switchMap(([chunk]) => chunk)\n )\n ))\n )\n .subscribe(result => list.appendChild(\n renderSearchResultItem(result)\n ))\n\n /* Filter search result message */\n const result$ = rx$\n .pipe(\n filter(isSearchResultMessage),\n map(({ data }) => data)\n )\n\n /* Create and return component */\n return result$\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n finalize,\n fromEvent,\n map,\n tap\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search sharing\n */\nexport interface SearchShare {\n url: URL /* Deep link for sharing */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n query$: Observable /* Search query observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n query$: Observable /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search sharing\n *\n * @param _el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing observable\n */\nexport function watchSearchShare(\n _el: HTMLElement, { query$ }: WatchOptions\n): Observable {\n return query$\n .pipe(\n map(({ value }) => {\n const url = getLocation()\n url.hash = \"\"\n url.searchParams.delete(\"h\")\n url.searchParams.set(\"q\", value)\n return { url }\n })\n )\n}\n\n/**\n * Mount search sharing\n *\n * @param el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing component observable\n */\nexport function mountSearchShare(\n el: HTMLAnchorElement, options: MountOptions\n): Observable> {\n const push$ = new Subject()\n push$.subscribe(({ url }) => {\n el.setAttribute(\"data-clipboard-text\", el.href)\n el.href = `${url}`\n })\n\n /* Prevent following of link */\n fromEvent(el, \"click\")\n .subscribe(ev => ev.preventDefault())\n\n /* Create and return component */\n return watchSearchShare(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n asyncScheduler,\n combineLatestWith,\n distinctUntilChanged,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n observeOn,\n tap\n} from \"rxjs\"\n\nimport { Keyboard } from \"~/browser\"\nimport {\n SearchResult,\n SearchWorker,\n isSearchResultMessage\n} from \"~/integrations\"\n\nimport { Component, getComponentElement } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search suggestions\n */\nexport interface SearchSuggest {}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n keyboard$: Observable /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search suggestions\n *\n * This function will perform a lazy rendering of the search results, depending\n * on the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchSuggest(\n el: HTMLElement, { rx$ }: SearchWorker, { keyboard$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n\n /* Retrieve query component and track all changes */\n const query = getComponentElement(\"search-query\")\n const query$ = merge(\n fromEvent(query, \"keydown\"),\n fromEvent(query, \"focus\")\n )\n .pipe(\n observeOn(asyncScheduler),\n map(() => query.value),\n distinctUntilChanged(),\n )\n\n /* Update search suggestions */\n push$\n .pipe(\n combineLatestWith(query$),\n map(([{ suggestions }, value]) => {\n const words = value.split(/([\\s-]+)/)\n if (suggestions?.length && words[words.length - 1]) {\n const last = suggestions[suggestions.length - 1]\n if (last.startsWith(words[words.length - 1]))\n words[words.length - 1] = last\n } else {\n words.length = 0\n }\n return words\n })\n )\n .subscribe(words => el.innerHTML = words\n .join(\"\")\n .replace(/\\s/g, \" \")\n )\n\n /* Set up search keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"search\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Right arrow: accept current suggestion */\n case \"ArrowRight\":\n if (\n el.innerText.length &&\n query.selectionStart === query.value.length\n )\n query.value = el.innerText\n break\n }\n })\n\n /* Filter search result message */\n const result$ = rx$\n .pipe(\n filter(isSearchResultMessage),\n map(({ data }) => data)\n )\n\n /* Create and return component */\n return result$\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(() => ({ ref: el }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n NEVER,\n Observable,\n ObservableInput,\n filter,\n merge,\n mergeWith,\n sample,\n take\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n Keyboard,\n getActiveElement,\n getElements,\n setToggle\n} from \"~/browser\"\nimport {\n SearchIndex,\n SearchResult,\n isSearchQueryMessage,\n isSearchReadyMessage,\n setupSearchWorker\n} from \"~/integrations\"\n\nimport {\n Component,\n getComponentElement,\n getComponentElements\n} from \"../../_\"\nimport {\n SearchQuery,\n mountSearchQuery\n} from \"../query\"\nimport { mountSearchResult } from \"../result\"\nimport {\n SearchShare,\n mountSearchShare\n} from \"../share\"\nimport {\n SearchSuggest,\n mountSearchSuggest\n} from \"../suggest\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search\n */\nexport type Search =\n | SearchQuery\n | SearchResult\n | SearchShare\n | SearchSuggest\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n index$: ObservableInput /* Search index observable */\n keyboard$: Observable /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search\n *\n * This function sets up the search functionality, including the underlying\n * web worker and all keyboard bindings.\n *\n * @param el - Search element\n * @param options - Options\n *\n * @returns Search component observable\n */\nexport function mountSearch(\n el: HTMLElement, { index$, keyboard$ }: MountOptions\n): Observable> {\n const config = configuration()\n try {\n const url = __search?.worker || config.search\n const worker = setupSearchWorker(url, index$)\n\n /* Retrieve query and result components */\n const query = getComponentElement(\"search-query\", el)\n const result = getComponentElement(\"search-result\", el)\n\n /* Re-emit query when search is ready */\n const { tx$, rx$ } = worker\n tx$\n .pipe(\n filter(isSearchQueryMessage),\n sample(rx$.pipe(filter(isSearchReadyMessage))),\n take(1)\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Set up search keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"search\")\n )\n .subscribe(key => {\n const active = getActiveElement()\n switch (key.type) {\n\n /* Enter: go to first (best) result */\n case \"Enter\":\n if (active === query) {\n const anchors = new Map()\n for (const anchor of getElements(\n \":first-child [href]\", result\n )) {\n const article = anchor.firstElementChild!\n anchors.set(anchor, parseFloat(\n article.getAttribute(\"data-md-score\")!\n ))\n }\n\n /* Go to result with highest score, if any */\n if (anchors.size) {\n const [[best]] = [...anchors].sort(([, a], [, b]) => b - a)\n best.click()\n }\n\n /* Otherwise omit form submission */\n key.claim()\n }\n break\n\n /* Escape or Tab: close search */\n case \"Escape\":\n case \"Tab\":\n setToggle(\"search\", false)\n query.blur()\n break\n\n /* Vertical arrows: select previous or next search result */\n case \"ArrowUp\":\n case \"ArrowDown\":\n if (typeof active === \"undefined\") {\n query.focus()\n } else {\n const els = [query, ...getElements(\n \":not(details) > [href], summary, details[open] [href]\",\n result\n )]\n const i = Math.max(0, (\n Math.max(0, els.indexOf(active)) + els.length + (\n key.type === \"ArrowUp\" ? -1 : +1\n )\n ) % els.length)\n els[i].focus()\n }\n\n /* Prevent scrolling of page */\n key.claim()\n break\n\n /* All other keys: hand to search query */\n default:\n if (query !== getActiveElement())\n query.focus()\n }\n })\n\n /* Set up global keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\"),\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Open search and select query */\n case \"f\":\n case \"s\":\n case \"/\":\n query.focus()\n query.select()\n\n /* Prevent scrolling of page */\n key.claim()\n break\n }\n })\n\n /* Create and return component */\n const query$ = mountSearchQuery(query, worker)\n const result$ = mountSearchResult(result, worker, { query$ })\n return merge(query$, result$)\n .pipe(\n mergeWith(\n\n /* Search sharing */\n ...getComponentElements(\"search-share\", el)\n .map(child => mountSearchShare(child, { query$ })),\n\n /* Search suggestions */\n ...getComponentElements(\"search-suggest\", el)\n .map(child => mountSearchSuggest(child, worker, { keyboard$ }))\n )\n )\n\n /* Gracefully handle broken search */\n } catch (err) {\n el.hidden = true\n return NEVER\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n ObservableInput,\n combineLatest,\n filter,\n map,\n startWith\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\nimport {\n SearchIndex,\n setupSearchHighlighter\n} from \"~/integrations\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlighting\n */\nexport interface SearchHighlight {\n nodes: Map /* Map of replacements */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n index$: ObservableInput /* Search index observable */\n location$: Observable /* Location observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search highlighting\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Search highlighting component observable\n */\nexport function mountSearchHiglight(\n el: HTMLElement, { index$, location$ }: MountOptions\n): Observable> {\n return combineLatest([\n index$,\n location$\n .pipe(\n startWith(getLocation()),\n filter(url => !!url.searchParams.get(\"h\"))\n )\n ])\n .pipe(\n map(([index, url]) => setupSearchHighlighter(index.config, true)(\n url.searchParams.get(\"h\")!\n )),\n map(fn => {\n const nodes = new Map()\n\n /* Traverse text nodes and collect matches */\n const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n for (let node = it.nextNode(); node; node = it.nextNode()) {\n if (node.parentElement?.offsetHeight) {\n const original = node.textContent!\n const replaced = fn(original)\n if (replaced.length > original.length)\n nodes.set(node as ChildNode, replaced)\n }\n }\n\n /* Replace original nodes with matches */\n for (const [node, text] of nodes) {\n const { childNodes } = h(\"span\", null, text)\n node.replaceWith(...Array.from(childNodes))\n }\n\n /* Return component */\n return { ref: el, nodes }\n })\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n auditTime,\n combineLatest,\n defer,\n distinctUntilChanged,\n finalize,\n map,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n Viewport,\n getElement,\n getElementOffset\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sidebar\n */\nexport interface Sidebar {\n height: number /* Sidebar height */\n locked: boolean /* Sidebar is locked */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n main$: Observable
    /* Main area observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch sidebar\n *\n * This function returns an observable that computes the visual parameters of\n * the sidebar which depends on the vertical viewport offset, as well as the\n * height of the main area. When the page is scrolled beyond the header, the\n * sidebar is locked and fills the remaining space.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar observable\n */\nexport function watchSidebar(\n el: HTMLElement, { viewport$, main$ }: WatchOptions\n): Observable {\n const parent = el.parentElement!\n const adjust =\n parent.offsetTop -\n parent.parentElement!.offsetTop\n\n /* Compute the sidebar's available height and if it should be locked */\n return combineLatest([main$, viewport$])\n .pipe(\n map(([{ offset, height }, { offset: { y } }]) => {\n height = height\n + Math.min(adjust, Math.max(0, y - offset))\n - adjust\n return {\n height,\n locked: y >= offset + adjust\n }\n }),\n distinctUntilChanged((a, b) => (\n a.height === b.height &&\n a.locked === b.locked\n ))\n )\n}\n\n/**\n * Mount sidebar\n *\n * This function doesn't set the height of the actual sidebar, but of its first\n * child \u2013 the `.md-sidebar__scrollwrap` element in order to mitigiate jittery\n * sidebars when the footer is scrolled into view. At some point we switched\n * from `absolute` / `fixed` positioning to `sticky` positioning, significantly\n * reducing jitter in some browsers (respectively Firefox and Safari) when\n * scrolling from the top. However, top-aligned sticky positioning means that\n * the sidebar snaps to the bottom when the end of the container is reached.\n * This is what leads to the mentioned jitter, as the sidebar's height may be\n * updated too slowly.\n *\n * This behaviour can be mitigiated by setting the height of the sidebar to `0`\n * while preserving the padding, and the height on its first element.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar component observable\n */\nexport function mountSidebar(\n el: HTMLElement, { header$, ...options }: MountOptions\n): Observable> {\n const inner = getElement(\".md-sidebar__scrollwrap\", el)\n const { y } = getElementOffset(inner)\n return defer(() => {\n const push$ = new Subject()\n push$\n .pipe(\n auditTime(0, animationFrameScheduler),\n withLatestFrom(header$)\n )\n .subscribe({\n\n /* Handle emission */\n next([{ height }, { height: offset }]) {\n inner.style.height = `${height - 2 * y}px`\n el.style.top = `${offset}px`\n },\n\n /* Handle complete */\n complete() {\n inner.style.height = \"\"\n el.style.top = \"\"\n }\n })\n\n /* Create and return component */\n return watchSidebar(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Repo, User } from \"github-types\"\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map,\n zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitHub release (partial)\n */\ninterface Release {\n tag_name: string /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitHub repository facts\n *\n * @param user - GitHub user or organization\n * @param repo - GitHub repository\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitHub(\n user: string, repo?: string\n): Observable {\n if (typeof repo !== \"undefined\") {\n const url = `https://api.github.com/repos/${user}/${repo}`\n return zip(\n\n /* Fetch version */\n requestJSON(`${url}/releases/latest`)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(release => ({\n version: release.tag_name\n })),\n defaultIfEmpty({})\n ),\n\n /* Fetch stars and forks */\n requestJSON(url)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(info => ({\n stars: info.stargazers_count,\n forks: info.forks_count\n })),\n defaultIfEmpty({})\n )\n )\n .pipe(\n map(([release, info]) => ({ ...release, ...info }))\n )\n\n /* User or organization */\n } else {\n const url = `https://api.github.com/users/${user}`\n return requestJSON(url)\n .pipe(\n map(info => ({\n repositories: info.public_repos\n })),\n defaultIfEmpty({})\n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ProjectSchema } from \"gitlab\"\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitLab repository facts\n *\n * @param base - GitLab base\n * @param project - GitLab project\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitLab(\n base: string, project: string\n): Observable {\n const url = `https://${base}/api/v4/projects/${encodeURIComponent(project)}`\n return requestJSON(url)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(({ star_count, forks_count }) => ({\n stars: star_count,\n forks: forks_count\n })),\n defaultIfEmpty({})\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable } from \"rxjs\"\n\nimport { fetchSourceFactsFromGitHub } from \"../github\"\nimport { fetchSourceFactsFromGitLab } from \"../gitlab\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository facts for repositories\n */\nexport interface RepositoryFacts {\n stars?: number /* Number of stars */\n forks?: number /* Number of forks */\n version?: string /* Latest version */\n}\n\n/**\n * Repository facts for organizations\n */\nexport interface OrganizationFacts {\n repositories?: number /* Number of repositories */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Repository facts\n */\nexport type SourceFacts =\n | RepositoryFacts\n | OrganizationFacts\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch repository facts\n *\n * @param url - Repository URL\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFacts(\n url: string\n): Observable {\n const [type] = url.match(/(git(?:hub|lab))/i) || []\n switch (type.toLowerCase()) {\n\n /* GitHub repository */\n case \"github\":\n const [, user, repo] = url.match(/^.+github\\.com\\/([^/]+)\\/?([^/]+)?/i)!\n return fetchSourceFactsFromGitHub(user, repo)\n\n /* GitLab repository */\n case \"gitlab\":\n const [, base, slug] = url.match(/^.+?([^/]*gitlab[^/]+)\\/(.+?)\\/?$/i)!\n return fetchSourceFactsFromGitLab(base, slug)\n\n /* Everything else */\n default:\n return EMPTY\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n catchError,\n defer,\n filter,\n finalize,\n map,\n of,\n shareReplay,\n tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\nimport { renderSourceFacts } from \"~/templates\"\n\nimport { Component } from \"../../_\"\nimport {\n SourceFacts,\n fetchSourceFacts\n} from \"../facts\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information\n */\nexport interface Source {\n facts: SourceFacts /* Repository facts */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information observable\n */\nlet fetch$: Observable\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch repository information\n *\n * This function tries to read the repository facts from session storage, and\n * if unsuccessful, fetches them from the underlying provider.\n *\n * @param el - Repository information element\n *\n * @returns Repository information observable\n */\nexport function watchSource(\n el: HTMLAnchorElement\n): Observable {\n return fetch$ ||= defer(() => {\n const cached = __md_get(\"__source\", sessionStorage)\n if (cached)\n return of(cached)\n else\n return fetchSourceFacts(el.href)\n .pipe(\n tap(facts => __md_set(\"__source\", facts, sessionStorage))\n )\n })\n .pipe(\n catchError(() => EMPTY),\n filter(facts => Object.keys(facts).length > 0),\n map(facts => ({ facts })),\n shareReplay(1)\n )\n}\n\n/**\n * Mount repository information\n *\n * @param el - Repository information element\n *\n * @returns Repository information component observable\n */\nexport function mountSource(\n el: HTMLAnchorElement\n): Observable> {\n const inner = getElement(\":scope > :last-child\", el)\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ facts }) => {\n inner.appendChild(renderSourceFacts(facts))\n inner.classList.add(\"md-source__repository--active\")\n })\n\n /* Create and return component */\n return watchSource(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n distinctUntilKeyChanged,\n finalize,\n map,\n of,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n watchElementSize,\n watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Navigation tabs\n */\nexport interface Tabs {\n hidden: boolean /* Navigation tabs are hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch navigation tabs\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs observable\n */\nexport function watchTabs(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n return watchElementSize(document.body)\n .pipe(\n switchMap(() => watchViewportAt(el, { header$, viewport$ })),\n map(({ offset: { y } }) => {\n return {\n hidden: y >= 10\n }\n }),\n distinctUntilKeyChanged(\"hidden\")\n )\n}\n\n/**\n * Mount navigation tabs\n *\n * This function hides the navigation tabs when scrolling past the threshold\n * and makes them reappear in a nice CSS animation when scrolling back up.\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs component observable\n */\nexport function mountTabs(\n el: HTMLElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe({\n\n /* Handle emission */\n next({ hidden }) {\n el.hidden = hidden\n },\n\n /* Handle complete */\n complete() {\n el.hidden = false\n }\n })\n\n /* Create and return component */\n return (\n feature(\"navigation.tabs.sticky\")\n ? of({ hidden: false })\n : watchTabs(el, options)\n )\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatestWith,\n debounceTime,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n finalize,\n map,\n of,\n repeat,\n scan,\n share,\n skip,\n startWith,\n switchMap,\n takeLast,\n takeUntil,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n getElement,\n getElements,\n getLocation,\n getOptionalElement,\n watchElementSize\n} from \"~/browser\"\n\nimport {\n Component,\n getComponentElement\n} from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Table of contents\n */\nexport interface TableOfContents {\n prev: HTMLAnchorElement[][] /* Anchors (previous) */\n next: HTMLAnchorElement[][] /* Anchors (next) */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch table of contents\n *\n * This is effectively a scroll spy implementation which will account for the\n * fixed header and automatically re-calculate anchor offsets when the viewport\n * is resized. The returned observable will only emit if the table of contents\n * needs to be repainted.\n *\n * This implementation tracks an anchor element's entire path starting from its\n * level up to the top-most anchor element, e.g. `[h3, h2, h1]`. Although the\n * Material theme currently doesn't make use of this information, it enables\n * the styling of the entire hierarchy through customization.\n *\n * Note that the current anchor is the last item of the `prev` anchor list.\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents observable\n */\nexport function watchTableOfContents(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n const table = new Map()\n\n /* Compute anchor-to-target mapping */\n const anchors = getElements(\"[href^=\\\\#]\", el)\n for (const anchor of anchors) {\n const id = decodeURIComponent(anchor.hash.substring(1))\n const target = getOptionalElement(`[id=\"${id}\"]`)\n if (typeof target !== \"undefined\")\n table.set(anchor, target)\n }\n\n /* Compute necessary adjustment for header */\n const adjust$ = header$\n .pipe(\n distinctUntilKeyChanged(\"height\"),\n map(({ height }) => {\n const main = getComponentElement(\"main\")\n const grid = getElement(\":scope > :first-child\", main)\n return height + 0.8 * (\n grid.offsetTop -\n main.offsetTop\n )\n }),\n share()\n )\n\n /* Compute partition of previous and next anchors */\n const partition$ = watchElementSize(document.body)\n .pipe(\n distinctUntilKeyChanged(\"height\"),\n\n /* Build index to map anchor paths to vertical offsets */\n switchMap(body => defer(() => {\n let path: HTMLAnchorElement[] = []\n return of([...table].reduce((index, [anchor, target]) => {\n while (path.length) {\n const last = table.get(path[path.length - 1])!\n if (last.tagName >= target.tagName) {\n path.pop()\n } else {\n break\n }\n }\n\n /* If the current anchor is hidden, continue with its parent */\n let offset = target.offsetTop\n while (!offset && target.parentElement) {\n target = target.parentElement\n offset = target.offsetTop\n }\n\n /* Map reversed anchor path to vertical offset */\n return index.set(\n [...path = [...path, anchor]].reverse(),\n offset\n )\n }, new Map()))\n })\n .pipe(\n\n /* Sort index by vertical offset (see https://bit.ly/30z6QSO) */\n map(index => new Map([...index].sort(([, a], [, b]) => a - b))),\n combineLatestWith(adjust$),\n\n /* Re-compute partition when viewport offset changes */\n switchMap(([index, adjust]) => viewport$\n .pipe(\n scan(([prev, next], { offset: { y }, size }) => {\n const last = y + size.height >= Math.floor(body.height)\n\n /* Look forward */\n while (next.length) {\n const [, offset] = next[0]\n if (offset - adjust < y || last) {\n prev = [...prev, next.shift()!]\n } else {\n break\n }\n }\n\n /* Look backward */\n while (prev.length) {\n const [, offset] = prev[prev.length - 1]\n if (offset - adjust >= y && !last) {\n next = [prev.pop()!, ...next]\n } else {\n break\n }\n }\n\n /* Return partition */\n return [prev, next]\n }, [[], [...index]]),\n distinctUntilChanged((a, b) => (\n a[0] === b[0] &&\n a[1] === b[1]\n ))\n )\n )\n )\n )\n )\n\n /* Compute and return anchor list migrations */\n return partition$\n .pipe(\n map(([prev, next]) => ({\n prev: prev.map(([path]) => path),\n next: next.map(([path]) => path)\n })),\n\n /* Extract anchor list migrations */\n startWith({ prev: [], next: [] }),\n bufferCount(2, 1),\n map(([a, b]) => {\n\n /* Moving down */\n if (a.prev.length < b.prev.length) {\n return {\n prev: b.prev.slice(Math.max(0, a.prev.length - 1), b.prev.length),\n next: []\n }\n\n /* Moving up */\n } else {\n return {\n prev: b.prev.slice(-1),\n next: b.next.slice(0, b.next.length - a.next.length)\n }\n }\n })\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount table of contents\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents component observable\n */\nexport function mountTableOfContents(\n el: HTMLElement, { viewport$, header$, target$ }: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n push$.subscribe(({ prev, next }) => {\n\n /* Look forward */\n for (const [anchor] of next) {\n anchor.classList.remove(\"md-nav__link--passed\")\n anchor.classList.remove(\"md-nav__link--active\")\n }\n\n /* Look backward */\n for (const [index, [anchor]] of prev.entries()) {\n anchor.classList.add(\"md-nav__link--passed\")\n anchor.classList.toggle(\n \"md-nav__link--active\",\n index === prev.length - 1\n )\n }\n })\n\n /* Set up anchor tracking, if enabled */\n if (feature(\"navigation.tracking\"))\n viewport$\n .pipe(\n takeUntil(done$),\n distinctUntilKeyChanged(\"offset\"),\n debounceTime(250),\n skip(1),\n takeUntil(target$.pipe(skip(1))),\n repeat({ delay: 250 }),\n withLatestFrom(push$)\n )\n .subscribe(([, { prev }]) => {\n const url = getLocation()\n\n /* Set hash fragment to active anchor */\n const anchor = prev[prev.length - 1]\n if (anchor && anchor.length) {\n const [active] = anchor\n const { hash } = new URL(active.href)\n if (url.hash !== hash) {\n url.hash = hash\n history.replaceState({}, \"\", `${url}`)\n }\n\n /* Reset anchor when at the top */\n } else {\n url.hash = \"\"\n history.replaceState({}, \"\", `${url}`)\n }\n })\n\n /* Create and return component */\n return watchTableOfContents(el, { viewport$, header$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatest,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n endWith,\n finalize,\n map,\n repeat,\n skip,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { Viewport } from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Back-to-top button\n */\nexport interface BackToTop {\n hidden: boolean /* Back-to-top button is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n main$: Observable
    /* Main area observable */\n target$: Observable /* Location target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch back-to-top\n *\n * @param _el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top observable\n */\nexport function watchBackToTop(\n _el: HTMLElement, { viewport$, main$, target$ }: WatchOptions\n): Observable {\n\n /* Compute direction */\n const direction$ = viewport$\n .pipe(\n map(({ offset: { y } }) => y),\n bufferCount(2, 1),\n map(([a, b]) => a > b && b > 0),\n distinctUntilChanged()\n )\n\n /* Compute whether main area is active */\n const active$ = main$\n .pipe(\n map(({ active }) => active)\n )\n\n /* Compute threshold for hiding */\n return combineLatest([active$, direction$])\n .pipe(\n map(([active, direction]) => !(active && direction)),\n distinctUntilChanged(),\n takeUntil(target$.pipe(skip(1))),\n endWith(true),\n repeat({ delay: 250 }),\n map(hidden => ({ hidden }))\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount back-to-top\n *\n * @param el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top component observable\n */\nexport function mountBackToTop(\n el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n push$.subscribe({\n\n /* Handle emission */\n next({ hidden }) {\n el.hidden = hidden\n if (hidden) {\n el.setAttribute(\"tabindex\", \"-1\")\n el.blur()\n } else {\n el.removeAttribute(\"tabindex\")\n }\n },\n\n /* Handle complete */\n complete() {\n el.style.top = \"\"\n el.hidden = true\n el.removeAttribute(\"tabindex\")\n }\n })\n\n /* Watch header height */\n header$\n .pipe(\n takeUntil(done$),\n distinctUntilKeyChanged(\"height\")\n )\n .subscribe(({ height }) => {\n el.style.top = `${height + 16}px`\n })\n\n /* Create and return component */\n return watchBackToTop(el, { viewport$, main$, target$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n mergeMap,\n switchMap,\n takeWhile,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n document$: Observable /* Document observable */\n tablet$: Observable /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch indeterminate checkboxes\n *\n * This function replaces the indeterminate \"pseudo state\" with the actual\n * indeterminate state, which is used to keep navigation always expanded.\n *\n * @param options - Options\n */\nexport function patchIndeterminate(\n { document$, tablet$ }: PatchOptions\n): void {\n document$\n .pipe(\n switchMap(() => getElements(\n // @todo `data-md-state` is deprecated and removed in v9\n \".md-toggle--indeterminate, [data-md-state=indeterminate]\"\n )),\n tap(el => {\n el.indeterminate = true\n el.checked = false\n }),\n mergeMap(el => fromEvent(el, \"change\")\n .pipe(\n takeWhile(() => el.classList.contains(\"md-toggle--indeterminate\")),\n map(() => el)\n )\n ),\n withLatestFrom(tablet$)\n )\n .subscribe(([el, tablet]) => {\n el.classList.remove(\"md-toggle--indeterminate\")\n if (tablet)\n el.checked = false\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n mergeMap,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n document$: Observable /* Document observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether the given device is an Apple device\n *\n * @returns Test result\n */\nfunction isAppleDevice(): boolean {\n return /(iPad|iPhone|iPod)/.test(navigator.userAgent)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch all elements with `data-md-scrollfix` attributes\n *\n * This is a year-old patch which ensures that overflow scrolling works at the\n * top and bottom of containers on iOS by ensuring a `1px` scroll offset upon\n * the start of a touch event.\n *\n * @see https://bit.ly/2SCtAOO - Original source\n *\n * @param options - Options\n */\nexport function patchScrollfix(\n { document$ }: PatchOptions\n): void {\n document$\n .pipe(\n switchMap(() => getElements(\"[data-md-scrollfix]\")),\n tap(el => el.removeAttribute(\"data-md-scrollfix\")),\n filter(isAppleDevice),\n mergeMap(el => fromEvent(el, \"touchstart\")\n .pipe(\n map(() => el)\n )\n )\n )\n .subscribe(el => {\n const top = el.scrollTop\n\n /* We're at the top of the container */\n if (top === 0) {\n el.scrollTop = 1\n\n /* We're at the bottom of the container */\n } else if (top + el.offsetHeight === el.scrollHeight) {\n el.scrollTop = top - 1\n }\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n delay,\n map,\n of,\n switchMap,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n Viewport,\n watchToggle\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n viewport$: Observable /* Viewport observable */\n tablet$: Observable /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch the document body to lock when search is open\n *\n * For mobile and tablet viewports, the search is rendered full screen, which\n * leads to scroll leaking when at the top or bottom of the search result. This\n * function locks the body when the search is in full screen mode, and restores\n * the scroll position when leaving.\n *\n * @param options - Options\n */\nexport function patchScrolllock(\n { viewport$, tablet$ }: PatchOptions\n): void {\n combineLatest([watchToggle(\"search\"), tablet$])\n .pipe(\n map(([active, tablet]) => active && !tablet),\n switchMap(active => of(active)\n .pipe(\n delay(active ? 400 : 100)\n )\n ),\n withLatestFrom(viewport$)\n )\n .subscribe(([active, { offset: { y }}]) => {\n if (active) {\n document.body.setAttribute(\"data-md-scrolllock\", \"\")\n document.body.style.top = `-${y}px`\n } else {\n const value = -1 * parseInt(document.body.style.top, 10)\n document.body.removeAttribute(\"data-md-scrolllock\")\n document.body.style.top = \"\"\n if (value)\n window.scrollTo(0, value)\n }\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n Object.entries = function (obj: object) {\n const data: [string, string][] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push([key, obj[key]])\n\n /* Return entries */\n return data\n }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n Object.values = function (obj: object) {\n const data: string[] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n 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"best", "more", "children", "section", "renderSourceFacts", "facts", "h", "key", "value", "round", "renderTabbedControl", "type", "classes", "h", "renderTable", "table", "h", "renderVersion", "version", "config", "configuration", "url", "h", "renderVersionSelector", "versions", "active", "translation", "watchAnnotation", "el", "container", "offset$", "defer", "combineLatest", "watchElementOffset", "watchElementContentOffset", "map", "x", "y", "scroll", "width", "getElementSize", "watchElementFocus", "switchMap", "active", "offset", "take", "mountAnnotation", "push$", "Subject", "done$", "takeLast", "watchElementVisibility", "takeUntil", "visible", "throttleTime", "animationFrameScheduler", "origin", "index", "getElement", "blur$", "fromEvent", "EMPTY", "tap", "ev", "state", "finalize", "__spreadValues", "findAnnotationMarkers", "container", "markers", "comment", "getElements", "match", "text", "marker", "swap", "source", "target", "mountAnnotationList", "el", "print$", "annotations", "id", "getOptionalElement", "renderAnnotation", "EMPTY", "defer", "done$", "Subject", "takeUntil", "takeLast", "active", "annotation", "inner", "getElement", "child", "merge", "mountAnnotation", "finalize", "share", "sequence", "findCandidateList", "el", "sibling", "watchCodeBlock", "watchElementSize", "map", "width", "getElementContentSize", "distinctUntilKeyChanged", "mountCodeBlock", "options", "hover", "factory$", "defer", "push$", "Subject", "scrollable", "ClipboardJS", "parent", "renderClipboardButton", "container", "list", "feature", "annotations$", "mountAnnotationList", "tap", "state", "finalize", "__spreadValues", "mergeWith", "takeUntil", "takeLast", "height", "distinctUntilChanged", "switchMap", "active", "EMPTY", "watchElementVisibility", "filter", "visible", "take", "mermaid$", "sequence", "fetchScripts", "watchScript", "of", "mountMermaid", "el", "tap", "mermaid_default", "map", "shareReplay", "id", "host", "h", "svg", "shadow", "watchDetails", "el", "target$", "print$", "open", "merge", "map", "target", "filter", "details", "active", "tap", "mountDetails", "options", "defer", "push$", "Subject", "action", "reveal", "state", "finalize", "__spreadValues", "sentinel", "h", "mountDataTable", "el", "renderTable", "of", "watchContentTabs", "el", "inputs", "getElements", "initial", "input", "merge", "fromEvent", "map", "getElement", "startWith", "active", "mountContentTabs", "prev", "renderTabbedControl", "next", "container", "defer", "push$", "Subject", "done$", "takeLast", "combineLatest", "watchElementSize", "auditTime", "animationFrameScheduler", "takeUntil", "size", "offset", "getElementOffset", "width", "getElementSize", "content", "getElementContentOffset", "watchElementContentOffset", "getElementContentSize", "direction", "feature", "skip", "tab", "set", "tabs", "tap", "state", "finalize", "__spreadValues", "subscribeOn", "asyncScheduler", "mountContent", "el", "target$", "print$", "merge", "getElements", "child", "mountCodeBlock", "mountMermaid", 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"heading", "getOptionalElement", "EMPTY", "tap", "state", "finalize", "__spreadValues", "watchMain", "el", "viewport$", "header$", "adjust$", "map", "height", "distinctUntilChanged", "border$", "switchMap", "watchElementSize", "distinctUntilKeyChanged", "combineLatest", "header", "top", "bottom", "y", "a", "b", "watchPalette", "inputs", "current", "input", "of", "mergeMap", "fromEvent", "map", "startWith", "shareReplay", "mountPalette", "el", "defer", "push$", "Subject", "palette", "key", "value", "index", "label", "observeOn", "asyncScheduler", "getElements", "tap", "state", "finalize", "__spreadValues", "import_clipboard", "extract", "el", "text", "setupClipboardJS", "alert$", "ClipboardJS", "Observable", "subscriber", "getElement", "ev", "tap", "map", "translation", "preprocess", "urls", "root", "next", "a", "b", "url", "index", "fetchSitemap", "base", "cached", "of", "config", "configuration", "requestXML", "map", "sitemap", "getElements", "node", "catchError", "EMPTY", "defaultIfEmpty", "tap", "setupInstantLoading", "document$", "location$", "viewport$", "config", "configuration", "fromEvent", "favicon", "getOptionalElement", "push$", "fetchSitemap", "map", "paths", "path", "switchMap", "urls", "filter", "ev", "el", "url", "of", "NEVER", "share", "pop$", "merge", "distinctUntilChanged", "a", "b", "response$", "distinctUntilKeyChanged", "request", "catchError", "setLocation", "sample", "dom", "res", "skip", "replacement", "selector", "feature", "source", "target", "getComponentElement", "getElements", "concatMap", "script", "h", "name", "Observable", "observer", "EMPTY", "offset", "setLocationHash", "skipUntil", "debounceTime", "bufferCount", "state", "import_escape_html", "import_escape_html", "setupSearchHighlighter", "config", "escape", "separator", "highlight", "_", "data", "term", "query", "match", "value", "escapeHTML", "defaultTransform", "query", "terms", "index", "isSearchReadyMessage", "message", "isSearchQueryMessage", "isSearchResultMessage", "setupSearchIndex", "config", "docs", "translation", "options", "feature", "setupSearchWorker", "url", "index", "configuration", "worker", "tx$", "Subject", "rx$", "watchWorker", "map", "message", "isSearchResultMessage", "result", "document", "share", "from", "data", "setupVersionSelector", "document$", "config", "configuration", "versions$", "requestJSON", "catchError", "EMPTY", "current$", "map", "versions", "current", "version", "aliases", "switchMap", "urls", "fromEvent", "filter", "ev", "withLatestFrom", "el", "url", "of", "fetchSitemap", "sitemap", "path", "getLocation", "setLocation", "combineLatest", "getElement", "renderVersionSelector", "_a", "outdated", "latest", "warning", "getComponentElements", "watchSearchQuery", "el", "rx$", "fn", "defaultTransform", "searchParams", "getLocation", "setToggle", "param$", "filter", "isSearchReadyMessage", "take", "map", "watchToggle", "active", "url", "value", "focus$", "watchElementFocus", "value$", "merge", 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"data", "key", "x", "y", "nodes", "parent", "i", "node", "document$", "watchDocument", "location$", "watchLocation", "target$", "watchLocationTarget", "keyboard$", "watchKeyboard", "viewport$", "watchViewport", "tablet$", "watchMedia", "screen$", "print$", "watchPrint", "config", "configuration", "index$", "requestJSON", "NEVER", "alert$", "Subject", "setupClipboardJS", "feature", "setupInstantLoading", "_a", "setupVersionSelector", "merge", "delay", "setToggle", "filter", "mode", "key", "prev", "getOptionalElement", "next", "patchIndeterminate", "patchScrollfix", "patchScrolllock", "header$", "watchHeader", "getComponentElement", "main$", "map", "switchMap", "el", "watchMain", "shareReplay", "control$", "getComponentElements", "mountConsent", "mountDialog", "mountHeader", "mountPalette", "mountSearch", "mountSource", "content$", "defer", "mountAnnounce", "mountContent", "mountSearchHiglight", "EMPTY", "mountHeaderTitle", "at", "mountSidebar", "mountTabs", "mountTableOfContents", "mountBackToTop", "component$", "mergeWith"] +} diff --git a/assets/javascripts/workers/search.b97dbffb.min.js b/assets/javascripts/workers/search.ecf98df9.min.js similarity index 99% rename from assets/javascripts/workers/search.b97dbffb.min.js rename to assets/javascripts/workers/search.ecf98df9.min.js index 8201c5e..f8c7399 100644 --- a/assets/javascripts/workers/search.b97dbffb.min.js +++ b/assets/javascripts/workers/search.ecf98df9.min.js @@ -44,5 +44,5 @@ * Copyright(c) 2015 Tiancheng "Timothy" Gu * MIT Licensed */var Le=/["'&<>]/;ne.exports=we;function we(t){var e=""+t,r=Le.exec(e);if(!r)return e;var n,i="",s=0,o=0;for(s=r.index;s=0;r--){let n=t[r];typeof n!="object"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?e.insertBefore(this.previousSibling,n):e.replaceChild(n,this)}}}));var ie=H(q());function se(t){let e=new Map,r=new Set;for(let n of t){let[i,s]=n.location.split("#"),o=n.location,a=n.title,u=n.tags,c=(0,ie.default)(n.text).replace(/\s+(?=[,.:;!?])/g,"").replace(/\s+/g," ");if(s){let h=e.get(i);r.has(h)?e.set(o,{location:o,title:a,text:c,parent:h}):(h.title=n.title,h.text=c,r.add(h))}else e.set(o,M({location:o,title:a,text:c},u&&{tags:u}))}return e}var oe=H(q());function ae(t,e){let r=new RegExp(t.separator,"img"),n=(i,s,o)=>`${s}${o}`;return i=>{i=i.replace(/[\s*+\-:~^]+/g," ").trim();let s=new RegExp(`(^|${t.separator})(${i.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return o=>(e?(0,oe.default)(o):o).replace(s,n).replace(/<\/mark>(\s+)]*>/img,"$1")}}function ue(t){let e=new lunr.Query(["title","text"]);return new lunr.QueryParser(t,e).parse(),e.clauses}function ce(t,e){var i;let r=new Set(t),n={};for(let s=0;s!n.has(i)))]}var U=class{constructor({config:e,docs:r,options:n}){this.options=n,this.documents=se(r),this.highlight=ae(e,!1),lunr.tokenizer.separator=new RegExp(e.separator),this.index=lunr(function(){e.lang.length===1&&e.lang[0]!=="en"?this.use(lunr[e.lang[0]]):e.lang.length>1&&this.use(lunr.multiLanguage(...e.lang));let i=Ee(["trimmer","stopWordFilter","stemmer"],n.pipeline);for(let s of e.lang.map(o=>o==="en"?lunr:lunr[o]))for(let o of i)this.pipeline.remove(s[o]),this.searchPipeline.remove(s[o]);this.ref("location"),this.field("title",{boost:1e3}),this.field("text"),this.field("tags",{boost:1e6,extractor:s=>{let{tags:o=[]}=s;return o.reduce((a,u)=>[...a,...lunr.tokenizer(u)],[])}});for(let s of r)this.add(s,{boost:s.boost})})}search(e){if(e)try{let r=this.highlight(e),n=ue(e).filter(o=>o.presence!==lunr.Query.presence.PROHIBITED),i=this.index.search(`${e}*`).reduce((o,{ref:a,score:u,matchData:c})=>{let h=this.documents.get(a);if(typeof h!="undefined"){let{location:y,title:g,text:b,tags:m,parent:Q}=h,p=ce(n,Object.keys(c.metadata)),d=+!Q+ +Object.values(p).every(w=>w);o.push(Z(M({location:y,title:r(g),text:r(b)},m&&{tags:m.map(r)}),{score:u*(1+d),terms:p}))}return o},[]).sort((o,a)=>a.score-o.score).reduce((o,a)=>{let u=this.documents.get(a.location);if(typeof u!="undefined"){let c="parent"in u?u.parent.location:u.location;o.set(c,[...o.get(c)||[],a])}return o},new Map),s;if(this.options.suggestions){let o=this.index.query(a=>{for(let u of n)a.term(u.term,{fields:["title"],presence:lunr.Query.presence.REQUIRED,wildcard:lunr.Query.wildcard.TRAILING})});s=o.length?Object.keys(o[0].matchData.metadata):[]}return M({items:[...i.values()]},typeof s!="undefined"&&{suggestions:s})}catch(r){console.warn(`Invalid query: ${e} \u2013 see https://bit.ly/2s3ChXG`)}return{items:[]}}};var Y;function ke(t){return z(this,null,function*(){let e="../lunr";if(typeof parent!="undefined"&&"IFrameWorker"in parent){let n=document.querySelector("script[src]"),[i]=n.src.split("/worker");e=e.replace("..",i)}let r=[];for(let n of t.lang){switch(n){case"ja":r.push(`${e}/tinyseg.js`);break;case"hi":case"th":r.push(`${e}/wordcut.js`);break}n!=="en"&&r.push(`${e}/min/lunr.${n}.min.js`)}t.lang.length>1&&r.push(`${e}/min/lunr.multi.min.js`),r.length&&(yield importScripts(`${e}/min/lunr.stemmer.support.min.js`,...r))})}function Te(t){return z(this,null,function*(){switch(t.type){case 0:return yield ke(t.data.config),Y=new U(t.data),{type:1};case 2:return{type:3,data:Y?Y.search(t.data):{items:[]}};default:throw new TypeError("Invalid message type")}})}self.lunr=le.default;addEventListener("message",t=>z(void 0,null,function*(){postMessage(yield Te(t.data))}));})(); -//# sourceMappingURL=search.b97dbffb.min.js.map +//# sourceMappingURL=search.ecf98df9.min.js.map diff --git a/assets/javascripts/workers/search.b97dbffb.min.js.map b/assets/javascripts/workers/search.ecf98df9.min.js.map similarity index 74% rename from assets/javascripts/workers/search.b97dbffb.min.js.map rename to assets/javascripts/workers/search.ecf98df9.min.js.map index 5764b1b..eada3e6 100644 --- a/assets/javascripts/workers/search.b97dbffb.min.js.map +++ b/assets/javascripts/workers/search.ecf98df9.min.js.map @@ -3,6 +3,6 @@ "sources": ["node_modules/lunr/lunr.js", "node_modules/escape-html/index.js", "src/assets/javascripts/integrations/search/worker/main/index.ts", "src/assets/javascripts/polyfills/index.ts", "src/assets/javascripts/integrations/search/document/index.ts", "src/assets/javascripts/integrations/search/highlighter/index.ts", "src/assets/javascripts/integrations/search/query/_/index.ts", "src/assets/javascripts/integrations/search/_/index.ts"], "sourceRoot": "../../../..", "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n * this.field('title')\n * this.field('body')\n * this.ref('id')\n *\n * documents.forEach(function (doc) {\n * this.add(doc)\n * }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n var builder = new lunr.Builder\n\n builder.pipeline.add(\n lunr.trimmer,\n lunr.stopWordFilter,\n lunr.stemmer\n )\n\n builder.searchPipeline.add(\n lunr.stemmer\n )\n\n config.call(builder, builder)\n return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n /* eslint-disable no-console */\n return function (message) {\n if (global.console && console.warn) {\n console.warn(message)\n }\n }\n /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n if (obj === void 0 || obj === null) {\n return \"\"\n } else {\n return obj.toString()\n }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n if (obj === null || obj === undefined) {\n return obj\n }\n\n var clone = Object.create(null),\n keys = Object.keys(obj)\n\n for (var i = 0; i < keys.length; i++) {\n var key = keys[i],\n val = obj[key]\n\n if (Array.isArray(val)) {\n clone[key] = val.slice()\n continue\n }\n\n if (typeof val === 'string' ||\n typeof val === 'number' ||\n typeof val === 'boolean') {\n clone[key] = val\n continue\n }\n\n throw new TypeError(\"clone is not deep and does not support nested objects\")\n }\n\n return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n this.docRef = docRef\n this.fieldName = fieldName\n this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n var n = s.indexOf(lunr.FieldRef.joiner)\n\n if (n === -1) {\n throw \"malformed field ref string\"\n }\n\n var fieldRef = s.slice(0, n),\n docRef = s.slice(n + 1)\n\n return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n if (this._stringValue == undefined) {\n this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n }\n\n return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n this.elements = Object.create(null)\n\n if (elements) {\n this.length = elements.length\n\n for (var i = 0; i < this.length; i++) {\n this.elements[elements[i]] = true\n }\n } else {\n this.length = 0\n }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n intersect: function (other) {\n return other\n },\n\n union: function () {\n return this\n },\n\n contains: function () {\n return true\n }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n intersect: function () {\n return this\n },\n\n union: function (other) {\n return other\n },\n\n contains: function () {\n return false\n }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n var a, b, elements, intersection = []\n\n if (other === lunr.Set.complete) {\n return this\n }\n\n if (other === lunr.Set.empty) {\n return other\n }\n\n if (this.length < other.length) {\n a = this\n b = other\n } else {\n a = other\n b = this\n }\n\n elements = Object.keys(a.elements)\n\n for (var i = 0; i < elements.length; i++) {\n var element = elements[i]\n if (element in b.elements) {\n intersection.push(element)\n }\n }\n\n return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n if (other === lunr.Set.complete) {\n return lunr.Set.complete\n }\n\n if (other === lunr.Set.empty) {\n return this\n }\n\n return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n var documentsWithTerm = 0\n\n for (var fieldName in posting) {\n if (fieldName == '_index') continue // Ignore the term index, its not a field\n documentsWithTerm += Object.keys(posting[fieldName]).length\n }\n\n var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n this.str = str || \"\"\n this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n * return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n this.str = fn(this.str, this.metadata)\n return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n fn = fn || function (s) { return s }\n return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n if (obj == null || obj == undefined) {\n return []\n }\n\n if (Array.isArray(obj)) {\n return obj.map(function (t) {\n return new lunr.Token(\n lunr.utils.asString(t).toLowerCase(),\n lunr.utils.clone(metadata)\n )\n })\n }\n\n var str = obj.toString().toLowerCase(),\n len = str.length,\n tokens = []\n\n for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n var char = str.charAt(sliceEnd),\n sliceLength = sliceEnd - sliceStart\n\n if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n if (sliceLength > 0) {\n var tokenMetadata = lunr.utils.clone(metadata) || {}\n tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n tokenMetadata[\"index\"] = tokens.length\n\n tokens.push(\n new lunr.Token (\n str.slice(sliceStart, sliceEnd),\n tokenMetadata\n )\n )\n }\n\n sliceStart = sliceEnd + 1\n }\n\n }\n\n return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n if (label in this.registeredFunctions) {\n lunr.utils.warn('Overwriting existing registered function: ' + label)\n }\n\n fn.label = label\n lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n if (!isRegistered) {\n lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n var pipeline = new lunr.Pipeline\n\n serialised.forEach(function (fnName) {\n var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n if (fn) {\n pipeline.add(fn)\n } else {\n throw new Error('Cannot load unregistered function: ' + fnName)\n }\n })\n\n return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n var fns = Array.prototype.slice.call(arguments)\n\n fns.forEach(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n this._stack.push(fn)\n }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n pos = pos + 1\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n var pos = this._stack.indexOf(fn)\n if (pos == -1) {\n return\n }\n\n this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n var stackLength = this._stack.length\n\n for (var i = 0; i < stackLength; i++) {\n var fn = this._stack[i]\n var memo = []\n\n for (var j = 0; j < tokens.length; j++) {\n var result = fn(tokens[j], j, tokens)\n\n if (result === null || result === void 0 || result === '') continue\n\n if (Array.isArray(result)) {\n for (var k = 0; k < result.length; k++) {\n memo.push(result[k])\n }\n } else {\n memo.push(result)\n }\n }\n\n tokens = memo\n }\n\n return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n var token = new lunr.Token (str, metadata)\n\n return this.run([token]).map(function (t) {\n return t.toString()\n })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n return this._stack.map(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n return fn.label\n })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n this._magnitude = 0\n this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n // For an empty vector the tuple can be inserted at the beginning\n if (this.elements.length == 0) {\n return 0\n }\n\n var start = 0,\n end = this.elements.length / 2,\n sliceLength = end - start,\n pivotPoint = Math.floor(sliceLength / 2),\n pivotIndex = this.elements[pivotPoint * 2]\n\n while (sliceLength > 1) {\n if (pivotIndex < index) {\n start = pivotPoint\n }\n\n if (pivotIndex > index) {\n end = pivotPoint\n }\n\n if (pivotIndex == index) {\n break\n }\n\n sliceLength = end - start\n pivotPoint = start + Math.floor(sliceLength / 2)\n pivotIndex = this.elements[pivotPoint * 2]\n }\n\n if (pivotIndex == index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex > index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex < index) {\n return (pivotPoint + 1) * 2\n }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n this.upsert(insertIdx, val, function () {\n throw \"duplicate index\"\n })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n this._magnitude = 0\n var position = this.positionForIndex(insertIdx)\n\n if (this.elements[position] == insertIdx) {\n this.elements[position + 1] = fn(this.elements[position + 1], val)\n } else {\n this.elements.splice(position, 0, insertIdx, val)\n }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n if (this._magnitude) return this._magnitude\n\n var sumOfSquares = 0,\n elementsLength = this.elements.length\n\n for (var i = 1; i < elementsLength; i += 2) {\n var val = this.elements[i]\n sumOfSquares += val * val\n }\n\n return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n var dotProduct = 0,\n a = this.elements, b = otherVector.elements,\n aLen = a.length, bLen = b.length,\n aVal = 0, bVal = 0,\n i = 0, j = 0\n\n while (i < aLen && j < bLen) {\n aVal = a[i], bVal = b[j]\n if (aVal < bVal) {\n i += 2\n } else if (aVal > bVal) {\n j += 2\n } else if (aVal == bVal) {\n dotProduct += a[i + 1] * b[j + 1]\n i += 2\n j += 2\n }\n }\n\n return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n var output = new Array (this.elements.length / 2)\n\n for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n output[j] = this.elements[i]\n }\n\n return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n var step2list = {\n \"ational\" : \"ate\",\n \"tional\" : \"tion\",\n \"enci\" : \"ence\",\n \"anci\" : \"ance\",\n \"izer\" : \"ize\",\n \"bli\" : \"ble\",\n \"alli\" : \"al\",\n \"entli\" : \"ent\",\n \"eli\" : \"e\",\n \"ousli\" : \"ous\",\n \"ization\" : \"ize\",\n \"ation\" : \"ate\",\n \"ator\" : \"ate\",\n \"alism\" : \"al\",\n \"iveness\" : \"ive\",\n \"fulness\" : \"ful\",\n \"ousness\" : \"ous\",\n \"aliti\" : \"al\",\n \"iviti\" : \"ive\",\n \"biliti\" : \"ble\",\n \"logi\" : \"log\"\n },\n\n step3list = {\n \"icate\" : \"ic\",\n \"ative\" : \"\",\n \"alize\" : \"al\",\n \"iciti\" : \"ic\",\n \"ical\" : \"ic\",\n \"ful\" : \"\",\n \"ness\" : \"\"\n },\n\n c = \"[^aeiou]\", // consonant\n v = \"[aeiouy]\", // vowel\n C = c + \"[^aeiouy]*\", // consonant sequence\n V = v + \"[aeiou]*\", // vowel sequence\n\n mgr0 = \"^(\" + C + \")?\" + V + C, // [C]VC... is m>0\n meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\", // [C]VC[V] is m=1\n mgr1 = \"^(\" + C + \")?\" + V + C + V + C, // [C]VCVC... is m>1\n s_v = \"^(\" + C + \")?\" + v; // vowel in stem\n\n var re_mgr0 = new RegExp(mgr0);\n var re_mgr1 = new RegExp(mgr1);\n var re_meq1 = new RegExp(meq1);\n var re_s_v = new RegExp(s_v);\n\n var re_1a = /^(.+?)(ss|i)es$/;\n var re2_1a = /^(.+?)([^s])s$/;\n var re_1b = /^(.+?)eed$/;\n var re2_1b = /^(.+?)(ed|ing)$/;\n var re_1b_2 = /.$/;\n var re2_1b_2 = /(at|bl|iz)$/;\n var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var re_1c = /^(.+?[^aeiou])y$/;\n var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n var re_5 = /^(.+?)e$/;\n var re_5_1 = /ll$/;\n var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var porterStemmer = function porterStemmer(w) {\n var stem,\n suffix,\n firstch,\n re,\n re2,\n re3,\n re4;\n\n if (w.length < 3) { return w; }\n\n firstch = w.substr(0,1);\n if (firstch == \"y\") {\n w = firstch.toUpperCase() + w.substr(1);\n }\n\n // Step 1a\n re = re_1a\n re2 = re2_1a;\n\n if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n // Step 1b\n re = re_1b;\n re2 = re2_1b;\n if (re.test(w)) {\n var fp = re.exec(w);\n re = re_mgr0;\n if (re.test(fp[1])) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1];\n re2 = re_s_v;\n if (re2.test(stem)) {\n w = stem;\n re2 = re2_1b_2;\n re3 = re3_1b_2;\n re4 = re4_1b_2;\n if (re2.test(w)) { w = w + \"e\"; }\n else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n else if (re4.test(w)) { w = w + \"e\"; }\n }\n }\n\n // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n re = re_1c;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n w = stem + \"i\";\n }\n\n // Step 2\n re = re_2;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step2list[suffix];\n }\n }\n\n // Step 3\n re = re_3;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step3list[suffix];\n }\n }\n\n // Step 4\n re = re_4;\n re2 = re2_4;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n if (re.test(stem)) {\n w = stem;\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1] + fp[2];\n re2 = re_mgr1;\n if (re2.test(stem)) {\n w = stem;\n }\n }\n\n // Step 5\n re = re_5;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n re2 = re_meq1;\n re3 = re3_5;\n if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n w = stem;\n }\n }\n\n re = re_5_1;\n re2 = re_mgr1;\n if (re.test(w) && re2.test(w)) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n\n // and turn initial Y back to y\n\n if (firstch == \"y\") {\n w = firstch.toLowerCase() + w.substr(1);\n }\n\n return w;\n };\n\n return function (token) {\n return token.update(porterStemmer);\n }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n var words = stopWords.reduce(function (memo, stopWord) {\n memo[stopWord] = stopWord\n return memo\n }, {})\n\n return function (token) {\n if (token && words[token.toString()] !== token.toString()) return token\n }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n 'a',\n 'able',\n 'about',\n 'across',\n 'after',\n 'all',\n 'almost',\n 'also',\n 'am',\n 'among',\n 'an',\n 'and',\n 'any',\n 'are',\n 'as',\n 'at',\n 'be',\n 'because',\n 'been',\n 'but',\n 'by',\n 'can',\n 'cannot',\n 'could',\n 'dear',\n 'did',\n 'do',\n 'does',\n 'either',\n 'else',\n 'ever',\n 'every',\n 'for',\n 'from',\n 'get',\n 'got',\n 'had',\n 'has',\n 'have',\n 'he',\n 'her',\n 'hers',\n 'him',\n 'his',\n 'how',\n 'however',\n 'i',\n 'if',\n 'in',\n 'into',\n 'is',\n 'it',\n 'its',\n 'just',\n 'least',\n 'let',\n 'like',\n 'likely',\n 'may',\n 'me',\n 'might',\n 'most',\n 'must',\n 'my',\n 'neither',\n 'no',\n 'nor',\n 'not',\n 'of',\n 'off',\n 'often',\n 'on',\n 'only',\n 'or',\n 'other',\n 'our',\n 'own',\n 'rather',\n 'said',\n 'say',\n 'says',\n 'she',\n 'should',\n 'since',\n 'so',\n 'some',\n 'than',\n 'that',\n 'the',\n 'their',\n 'them',\n 'then',\n 'there',\n 'these',\n 'they',\n 'this',\n 'tis',\n 'to',\n 'too',\n 'twas',\n 'us',\n 'wants',\n 'was',\n 'we',\n 'were',\n 'what',\n 'when',\n 'where',\n 'which',\n 'while',\n 'who',\n 'whom',\n 'why',\n 'will',\n 'with',\n 'would',\n 'yet',\n 'you',\n 'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n return token.update(function (s) {\n return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n this.final = false\n this.edges = {}\n this.id = lunr.TokenSet._nextId\n lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n var builder = new lunr.TokenSet.Builder\n\n for (var i = 0, len = arr.length; i < len; i++) {\n builder.insert(arr[i])\n }\n\n builder.finish()\n return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n if ('editDistance' in clause) {\n return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n } else {\n return lunr.TokenSet.fromString(clause.term)\n }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n var root = new lunr.TokenSet\n\n var stack = [{\n node: root,\n editsRemaining: editDistance,\n str: str\n }]\n\n while (stack.length) {\n var frame = stack.pop()\n\n // no edit\n if (frame.str.length > 0) {\n var char = frame.str.charAt(0),\n noEditNode\n\n if (char in frame.node.edges) {\n noEditNode = frame.node.edges[char]\n } else {\n noEditNode = new lunr.TokenSet\n frame.node.edges[char] = noEditNode\n }\n\n if (frame.str.length == 1) {\n noEditNode.final = true\n }\n\n stack.push({\n node: noEditNode,\n editsRemaining: frame.editsRemaining,\n str: frame.str.slice(1)\n })\n }\n\n if (frame.editsRemaining == 0) {\n continue\n }\n\n // insertion\n if (\"*\" in frame.node.edges) {\n var insertionNode = frame.node.edges[\"*\"]\n } else {\n var insertionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = insertionNode\n }\n\n if (frame.str.length == 0) {\n insertionNode.final = true\n }\n\n stack.push({\n node: insertionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str\n })\n\n // deletion\n // can only do a deletion if we have enough edits remaining\n // and if there are characters left to delete in the string\n if (frame.str.length > 1) {\n stack.push({\n node: frame.node,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // deletion\n // just removing the last character from the str\n if (frame.str.length == 1) {\n frame.node.final = true\n }\n\n // substitution\n // can only do a substitution if we have enough edits remaining\n // and if there are characters left to substitute\n if (frame.str.length >= 1) {\n if (\"*\" in frame.node.edges) {\n var substitutionNode = frame.node.edges[\"*\"]\n } else {\n var substitutionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = substitutionNode\n }\n\n if (frame.str.length == 1) {\n substitutionNode.final = true\n }\n\n stack.push({\n node: substitutionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // transposition\n // can only do a transposition if there are edits remaining\n // and there are enough characters to transpose\n if (frame.str.length > 1) {\n var charA = frame.str.charAt(0),\n charB = frame.str.charAt(1),\n transposeNode\n\n if (charB in frame.node.edges) {\n transposeNode = frame.node.edges[charB]\n } else {\n transposeNode = new lunr.TokenSet\n frame.node.edges[charB] = transposeNode\n }\n\n if (frame.str.length == 1) {\n transposeNode.final = true\n }\n\n stack.push({\n node: transposeNode,\n editsRemaining: frame.editsRemaining - 1,\n str: charA + frame.str.slice(2)\n })\n }\n }\n\n return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n var node = new lunr.TokenSet,\n root = node\n\n /*\n * Iterates through all characters within the passed string\n * appending a node for each character.\n *\n * When a wildcard character is found then a self\n * referencing edge is introduced to continually match\n * any number of any characters.\n */\n for (var i = 0, len = str.length; i < len; i++) {\n var char = str[i],\n final = (i == len - 1)\n\n if (char == \"*\") {\n node.edges[char] = node\n node.final = final\n\n } else {\n var next = new lunr.TokenSet\n next.final = final\n\n node.edges[char] = next\n node = next\n }\n }\n\n return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n var words = []\n\n var stack = [{\n prefix: \"\",\n node: this\n }]\n\n while (stack.length) {\n var frame = stack.pop(),\n edges = Object.keys(frame.node.edges),\n len = edges.length\n\n if (frame.node.final) {\n /* In Safari, at this point the prefix is sometimes corrupted, see:\n * https://github.com/olivernn/lunr.js/issues/279 Calling any\n * String.prototype method forces Safari to \"cast\" this string to what\n * it's supposed to be, fixing the bug. */\n frame.prefix.charAt(0)\n words.push(frame.prefix)\n }\n\n for (var i = 0; i < len; i++) {\n var edge = edges[i]\n\n stack.push({\n prefix: frame.prefix.concat(edge),\n node: frame.node.edges[edge]\n })\n }\n }\n\n return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n // NOTE: Using Object.keys here as this.edges is very likely\n // to enter 'hash-mode' with many keys being added\n //\n // avoiding a for-in loop here as it leads to the function\n // being de-optimised (at least in V8). From some simple\n // benchmarks the performance is comparable, but allowing\n // V8 to optimize may mean easy performance wins in the future.\n\n if (this._str) {\n return this._str\n }\n\n var str = this.final ? '1' : '0',\n labels = Object.keys(this.edges).sort(),\n len = labels.length\n\n for (var i = 0; i < len; i++) {\n var label = labels[i],\n node = this.edges[label]\n\n str = str + label + node.id\n }\n\n return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n var output = new lunr.TokenSet,\n frame = undefined\n\n var stack = [{\n qNode: b,\n output: output,\n node: this\n }]\n\n while (stack.length) {\n frame = stack.pop()\n\n // NOTE: As with the #toString method, we are using\n // Object.keys and a for loop instead of a for-in loop\n // as both of these objects enter 'hash' mode, causing\n // the function to be de-optimised in V8\n var qEdges = Object.keys(frame.qNode.edges),\n qLen = qEdges.length,\n nEdges = Object.keys(frame.node.edges),\n nLen = nEdges.length\n\n for (var q = 0; q < qLen; q++) {\n var qEdge = qEdges[q]\n\n for (var n = 0; n < nLen; n++) {\n var nEdge = nEdges[n]\n\n if (nEdge == qEdge || qEdge == '*') {\n var node = frame.node.edges[nEdge],\n qNode = frame.qNode.edges[qEdge],\n final = node.final && qNode.final,\n next = undefined\n\n if (nEdge in frame.output.edges) {\n // an edge already exists for this character\n // no need to create a new node, just set the finality\n // bit unless this node is already final\n next = frame.output.edges[nEdge]\n next.final = next.final || final\n\n } else {\n // no edge exists yet, must create one\n // set the finality bit and insert it\n // into the output\n next = new lunr.TokenSet\n next.final = final\n frame.output.edges[nEdge] = next\n }\n\n stack.push({\n qNode: qNode,\n output: next,\n node: node\n })\n }\n }\n }\n }\n\n return output\n}\nlunr.TokenSet.Builder = function () {\n this.previousWord = \"\"\n this.root = new lunr.TokenSet\n this.uncheckedNodes = []\n this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n var node,\n commonPrefix = 0\n\n if (word < this.previousWord) {\n throw new Error (\"Out of order word insertion\")\n }\n\n for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n if (word[i] != this.previousWord[i]) break\n commonPrefix++\n }\n\n this.minimize(commonPrefix)\n\n if (this.uncheckedNodes.length == 0) {\n node = this.root\n } else {\n node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n }\n\n for (var i = commonPrefix; i < word.length; i++) {\n var nextNode = new lunr.TokenSet,\n char = word[i]\n\n node.edges[char] = nextNode\n\n this.uncheckedNodes.push({\n parent: node,\n char: char,\n child: nextNode\n })\n\n node = nextNode\n }\n\n node.final = true\n this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n var node = this.uncheckedNodes[i],\n childKey = node.child.toString()\n\n if (childKey in this.minimizedNodes) {\n node.parent.edges[node.char] = this.minimizedNodes[childKey]\n } else {\n // Cache the key for this node since\n // we know it can't change anymore\n node.child._str = childKey\n\n this.minimizedNodes[childKey] = node.child\n }\n\n this.uncheckedNodes.pop()\n }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n this.invertedIndex = attrs.invertedIndex\n this.fieldVectors = attrs.fieldVectors\n this.tokenSet = attrs.tokenSet\n this.fields = attrs.fields\n this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example Simple single term query\n * hello\n * @example Multiple term query\n * hello world\n * @example term scoped to a field\n * title:hello\n * @example term with a boost of 10\n * hello^10\n * @example term with an edit distance of 2\n * hello~2\n * @example terms with presence modifiers\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first. For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n return this.query(function (query) {\n var parser = new lunr.QueryParser(queryString, query)\n parser.parse()\n })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n // for each query clause\n // * process terms\n // * expand terms from token set\n // * find matching documents and metadata\n // * get document vectors\n // * score documents\n\n var query = new lunr.Query(this.fields),\n matchingFields = Object.create(null),\n queryVectors = Object.create(null),\n termFieldCache = Object.create(null),\n requiredMatches = Object.create(null),\n prohibitedMatches = Object.create(null)\n\n /*\n * To support field level boosts a query vector is created per\n * field. An empty vector is eagerly created to support negated\n * queries.\n */\n for (var i = 0; i < this.fields.length; i++) {\n queryVectors[this.fields[i]] = new lunr.Vector\n }\n\n fn.call(query, query)\n\n for (var i = 0; i < query.clauses.length; i++) {\n /*\n * Unless the pipeline has been disabled for this term, which is\n * the case for terms with wildcards, we need to pass the clause\n * term through the search pipeline. A pipeline returns an array\n * of processed terms. Pipeline functions may expand the passed\n * term, which means we may end up performing multiple index lookups\n * for a single query term.\n */\n var clause = query.clauses[i],\n terms = null,\n clauseMatches = lunr.Set.empty\n\n if (clause.usePipeline) {\n terms = this.pipeline.runString(clause.term, {\n fields: clause.fields\n })\n } else {\n terms = [clause.term]\n }\n\n for (var m = 0; m < terms.length; m++) {\n var term = terms[m]\n\n /*\n * Each term returned from the pipeline needs to use the same query\n * clause object, e.g. the same boost and or edit distance. The\n * simplest way to do this is to re-use the clause object but mutate\n * its term property.\n */\n clause.term = term\n\n /*\n * From the term in the clause we create a token set which will then\n * be used to intersect the indexes token set to get a list of terms\n * to lookup in the inverted index\n */\n var termTokenSet = lunr.TokenSet.fromClause(clause),\n expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n /*\n * If a term marked as required does not exist in the tokenSet it is\n * impossible for the search to return any matches. We set all the field\n * scoped required matches set to empty and stop examining any further\n * clauses.\n */\n if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = lunr.Set.empty\n }\n\n break\n }\n\n for (var j = 0; j < expandedTerms.length; j++) {\n /*\n * For each term get the posting and termIndex, this is required for\n * building the query vector.\n */\n var expandedTerm = expandedTerms[j],\n posting = this.invertedIndex[expandedTerm],\n termIndex = posting._index\n\n for (var k = 0; k < clause.fields.length; k++) {\n /*\n * For each field that this query term is scoped by (by default\n * all fields are in scope) we need to get all the document refs\n * that have this term in that field.\n *\n * The posting is the entry in the invertedIndex for the matching\n * term from above.\n */\n var field = clause.fields[k],\n fieldPosting = posting[field],\n matchingDocumentRefs = Object.keys(fieldPosting),\n termField = expandedTerm + \"/\" + field,\n matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n /*\n * if the presence of this term is required ensure that the matching\n * documents are added to the set of required matches for this clause.\n *\n */\n if (clause.presence == lunr.Query.presence.REQUIRED) {\n clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n if (requiredMatches[field] === undefined) {\n requiredMatches[field] = lunr.Set.complete\n }\n }\n\n /*\n * if the presence of this term is prohibited ensure that the matching\n * documents are added to the set of prohibited matches for this field,\n * creating that set if it does not yet exist.\n */\n if (clause.presence == lunr.Query.presence.PROHIBITED) {\n if (prohibitedMatches[field] === undefined) {\n prohibitedMatches[field] = lunr.Set.empty\n }\n\n prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n /*\n * Prohibited matches should not be part of the query vector used for\n * similarity scoring and no metadata should be extracted so we continue\n * to the next field\n */\n continue\n }\n\n /*\n * The query field vector is populated using the termIndex found for\n * the term and a unit value with the appropriate boost applied.\n * Using upsert because there could already be an entry in the vector\n * for the term we are working with. In that case we just add the scores\n * together.\n */\n queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n /**\n * If we've already seen this term, field combo then we've already collected\n * the matching documents and metadata, no need to go through all that again\n */\n if (termFieldCache[termField]) {\n continue\n }\n\n for (var l = 0; l < matchingDocumentRefs.length; l++) {\n /*\n * All metadata for this term/field/document triple\n * are then extracted and collected into an instance\n * of lunr.MatchData ready to be returned in the query\n * results\n */\n var matchingDocumentRef = matchingDocumentRefs[l],\n matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n metadata = fieldPosting[matchingDocumentRef],\n fieldMatch\n\n if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n } else {\n fieldMatch.add(expandedTerm, field, metadata)\n }\n\n }\n\n termFieldCache[termField] = true\n }\n }\n }\n\n /**\n * If the presence was required we need to update the requiredMatches field sets.\n * We do this after all fields for the term have collected their matches because\n * the clause terms presence is required in _any_ of the fields not _all_ of the\n * fields.\n */\n if (clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n }\n }\n }\n\n /**\n * Need to combine the field scoped required and prohibited\n * matching documents into a global set of required and prohibited\n * matches\n */\n var allRequiredMatches = lunr.Set.complete,\n allProhibitedMatches = lunr.Set.empty\n\n for (var i = 0; i < this.fields.length; i++) {\n var field = this.fields[i]\n\n if (requiredMatches[field]) {\n allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n }\n\n if (prohibitedMatches[field]) {\n allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n }\n }\n\n var matchingFieldRefs = Object.keys(matchingFields),\n results = [],\n matches = Object.create(null)\n\n /*\n * If the query is negated (contains only prohibited terms)\n * we need to get _all_ fieldRefs currently existing in the\n * index. This is only done when we know that the query is\n * entirely prohibited terms to avoid any cost of getting all\n * fieldRefs unnecessarily.\n *\n * Additionally, blank MatchData must be created to correctly\n * populate the results.\n */\n if (query.isNegated()) {\n matchingFieldRefs = Object.keys(this.fieldVectors)\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n var matchingFieldRef = matchingFieldRefs[i]\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n matchingFields[matchingFieldRef] = new lunr.MatchData\n }\n }\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n /*\n * Currently we have document fields that match the query, but we\n * need to return documents. The matchData and scores are combined\n * from multiple fields belonging to the same document.\n *\n * Scores are calculated by field, using the query vectors created\n * above, and combined into a final document score using addition.\n */\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n docRef = fieldRef.docRef\n\n if (!allRequiredMatches.contains(docRef)) {\n continue\n }\n\n if (allProhibitedMatches.contains(docRef)) {\n continue\n }\n\n var fieldVector = this.fieldVectors[fieldRef],\n score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n docMatch\n\n if ((docMatch = matches[docRef]) !== undefined) {\n docMatch.score += score\n docMatch.matchData.combine(matchingFields[fieldRef])\n } else {\n var match = {\n ref: docRef,\n score: score,\n matchData: matchingFields[fieldRef]\n }\n matches[docRef] = match\n results.push(match)\n }\n }\n\n /*\n * Sort the results objects by score, highest first.\n */\n return results.sort(function (a, b) {\n return b.score - a.score\n })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n var invertedIndex = Object.keys(this.invertedIndex)\n .sort()\n .map(function (term) {\n return [term, this.invertedIndex[term]]\n }, this)\n\n var fieldVectors = Object.keys(this.fieldVectors)\n .map(function (ref) {\n return [ref, this.fieldVectors[ref].toJSON()]\n }, this)\n\n return {\n version: lunr.version,\n fields: this.fields,\n fieldVectors: fieldVectors,\n invertedIndex: invertedIndex,\n pipeline: this.pipeline.toJSON()\n }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n var attrs = {},\n fieldVectors = {},\n serializedVectors = serializedIndex.fieldVectors,\n invertedIndex = Object.create(null),\n serializedInvertedIndex = serializedIndex.invertedIndex,\n tokenSetBuilder = new lunr.TokenSet.Builder,\n pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n if (serializedIndex.version != lunr.version) {\n lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n }\n\n for (var i = 0; i < serializedVectors.length; i++) {\n var tuple = serializedVectors[i],\n ref = tuple[0],\n elements = tuple[1]\n\n fieldVectors[ref] = new lunr.Vector(elements)\n }\n\n for (var i = 0; i < serializedInvertedIndex.length; i++) {\n var tuple = serializedInvertedIndex[i],\n term = tuple[0],\n posting = tuple[1]\n\n tokenSetBuilder.insert(term)\n invertedIndex[term] = posting\n }\n\n tokenSetBuilder.finish()\n\n attrs.fields = serializedIndex.fields\n\n attrs.fieldVectors = fieldVectors\n attrs.invertedIndex = invertedIndex\n attrs.tokenSet = tokenSetBuilder.root\n attrs.pipeline = pipeline\n\n return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.\n * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n this._ref = \"id\"\n this._fields = Object.create(null)\n this._documents = Object.create(null)\n this.invertedIndex = Object.create(null)\n this.fieldTermFrequencies = {}\n this.fieldLengths = {}\n this.tokenizer = lunr.tokenizer\n this.pipeline = new lunr.Pipeline\n this.searchPipeline = new lunr.Pipeline\n this.documentCount = 0\n this._b = 0.75\n this._k1 = 1.2\n this.termIndex = 0\n this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example Extracting a nested field\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n if (/\\//.test(fieldName)) {\n throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n }\n\n this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n if (number < 0) {\n this._b = 0\n } else if (number > 1) {\n this._b = 1\n } else {\n this._b = number\n }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n var docRef = doc[this._ref],\n fields = Object.keys(this._fields)\n\n this._documents[docRef] = attributes || {}\n this.documentCount += 1\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i],\n extractor = this._fields[fieldName].extractor,\n field = extractor ? extractor(doc) : doc[fieldName],\n tokens = this.tokenizer(field, {\n fields: [fieldName]\n }),\n terms = this.pipeline.run(tokens),\n fieldRef = new lunr.FieldRef (docRef, fieldName),\n fieldTerms = Object.create(null)\n\n this.fieldTermFrequencies[fieldRef] = fieldTerms\n this.fieldLengths[fieldRef] = 0\n\n // store the length of this field for this document\n this.fieldLengths[fieldRef] += terms.length\n\n // calculate term frequencies for this field\n for (var j = 0; j < terms.length; j++) {\n var term = terms[j]\n\n if (fieldTerms[term] == undefined) {\n fieldTerms[term] = 0\n }\n\n fieldTerms[term] += 1\n\n // add to inverted index\n // create an initial posting if one doesn't exist\n if (this.invertedIndex[term] == undefined) {\n var posting = Object.create(null)\n posting[\"_index\"] = this.termIndex\n this.termIndex += 1\n\n for (var k = 0; k < fields.length; k++) {\n posting[fields[k]] = Object.create(null)\n }\n\n this.invertedIndex[term] = posting\n }\n\n // add an entry for this term/fieldName/docRef to the invertedIndex\n if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n }\n\n // store all whitelisted metadata about this token in the\n // inverted index\n for (var l = 0; l < this.metadataWhitelist.length; l++) {\n var metadataKey = this.metadataWhitelist[l],\n metadata = term.metadata[metadataKey]\n\n if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n }\n\n this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n }\n }\n\n }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n var fieldRefs = Object.keys(this.fieldLengths),\n numberOfFields = fieldRefs.length,\n accumulator = {},\n documentsWithField = {}\n\n for (var i = 0; i < numberOfFields; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n field = fieldRef.fieldName\n\n documentsWithField[field] || (documentsWithField[field] = 0)\n documentsWithField[field] += 1\n\n accumulator[field] || (accumulator[field] = 0)\n accumulator[field] += this.fieldLengths[fieldRef]\n }\n\n var fields = Object.keys(this._fields)\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i]\n accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n }\n\n this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n var fieldVectors = {},\n fieldRefs = Object.keys(this.fieldTermFrequencies),\n fieldRefsLength = fieldRefs.length,\n termIdfCache = Object.create(null)\n\n for (var i = 0; i < fieldRefsLength; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n fieldName = fieldRef.fieldName,\n fieldLength = this.fieldLengths[fieldRef],\n fieldVector = new lunr.Vector,\n termFrequencies = this.fieldTermFrequencies[fieldRef],\n terms = Object.keys(termFrequencies),\n termsLength = terms.length\n\n\n var fieldBoost = this._fields[fieldName].boost || 1,\n docBoost = this._documents[fieldRef.docRef].boost || 1\n\n for (var j = 0; j < termsLength; j++) {\n var term = terms[j],\n tf = termFrequencies[term],\n termIndex = this.invertedIndex[term]._index,\n idf, score, scoreWithPrecision\n\n if (termIdfCache[term] === undefined) {\n idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n termIdfCache[term] = idf\n } else {\n idf = termIdfCache[term]\n }\n\n score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n score *= fieldBoost\n score *= docBoost\n scoreWithPrecision = Math.round(score * 1000) / 1000\n // Converts 1.23456789 to 1.234.\n // Reducing the precision so that the vectors take up less\n // space when serialised. Doing it now so that they behave\n // the same before and after serialisation. Also, this is\n // the fastest approach to reducing a number's precision in\n // JavaScript.\n\n fieldVector.insert(termIndex, scoreWithPrecision)\n }\n\n fieldVectors[fieldRef] = fieldVector\n }\n\n this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n this.tokenSet = lunr.TokenSet.fromArray(\n Object.keys(this.invertedIndex).sort()\n )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n this.calculateAverageFieldLengths()\n this.createFieldVectors()\n this.createTokenSet()\n\n return new lunr.Index({\n invertedIndex: this.invertedIndex,\n fieldVectors: this.fieldVectors,\n tokenSet: this.tokenSet,\n fields: Object.keys(this._fields),\n pipeline: this.searchPipeline\n })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n var args = Array.prototype.slice.call(arguments, 1)\n args.unshift(this)\n fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n var clonedMetadata = Object.create(null),\n metadataKeys = Object.keys(metadata || {})\n\n // Cloning the metadata to prevent the original\n // being mutated during match data combination.\n // Metadata is kept in an array within the inverted\n // index so cloning the data can be done with\n // Array#slice\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n clonedMetadata[key] = metadata[key].slice()\n }\n\n this.metadata = Object.create(null)\n\n if (term !== undefined) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = clonedMetadata\n }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n var terms = Object.keys(otherMatchData.metadata)\n\n for (var i = 0; i < terms.length; i++) {\n var term = terms[i],\n fields = Object.keys(otherMatchData.metadata[term])\n\n if (this.metadata[term] == undefined) {\n this.metadata[term] = Object.create(null)\n }\n\n for (var j = 0; j < fields.length; j++) {\n var field = fields[j],\n keys = Object.keys(otherMatchData.metadata[term][field])\n\n if (this.metadata[term][field] == undefined) {\n this.metadata[term][field] = Object.create(null)\n }\n\n for (var k = 0; k < keys.length; k++) {\n var key = keys[k]\n\n if (this.metadata[term][field][key] == undefined) {\n this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n } else {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n }\n\n }\n }\n }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n if (!(term in this.metadata)) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = metadata\n return\n }\n\n if (!(field in this.metadata[term])) {\n this.metadata[term][field] = metadata\n return\n }\n\n var metadataKeys = Object.keys(metadata)\n\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n\n if (key in this.metadata[term][field]) {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n } else {\n this.metadata[term][field][key] = metadata[key]\n }\n }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n this.clauses = []\n this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with trailing wildcard\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example query term with leading and trailing wildcard\n * query.term('foo', {\n * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with required presence\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n /**\n * Term's presence in a document is optional, this is the default value.\n */\n OPTIONAL: 1,\n\n /**\n * Term's presence in a document is required, documents that do not contain\n * this term will not be returned.\n */\n REQUIRED: 2,\n\n /**\n * Term's presence in a document is prohibited, documents that do contain\n * this term will not be returned.\n */\n PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n if (!('fields' in clause)) {\n clause.fields = this.allFields\n }\n\n if (!('boost' in clause)) {\n clause.boost = 1\n }\n\n if (!('usePipeline' in clause)) {\n clause.usePipeline = true\n }\n\n if (!('wildcard' in clause)) {\n clause.wildcard = lunr.Query.wildcard.NONE\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n clause.term = \"*\" + clause.term\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n clause.term = \"\" + clause.term + \"*\"\n }\n\n if (!('presence' in clause)) {\n clause.presence = lunr.Query.presence.OPTIONAL\n }\n\n this.clauses.push(clause)\n\n return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n for (var i = 0; i < this.clauses.length; i++) {\n if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n return false\n }\n }\n\n return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example adding a single term to a query\n * query.term(\"foo\")\n * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard\n * query.term(\"foo\", {\n * fields: [\"title\"],\n * boost: 10,\n * wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example using lunr.tokenizer to convert a string to tokens before using them as terms\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n if (Array.isArray(term)) {\n term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n return this\n }\n\n var clause = options || {}\n clause.term = term.toString()\n\n this.clause(clause)\n\n return this\n}\nlunr.QueryParseError = function (message, start, end) {\n this.name = \"QueryParseError\"\n this.message = message\n this.start = start\n this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n this.lexemes = []\n this.str = str\n this.length = str.length\n this.pos = 0\n this.start = 0\n this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n var state = lunr.QueryLexer.lexText\n\n while (state) {\n state = state(this)\n }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n var subSlices = [],\n sliceStart = this.start,\n sliceEnd = this.pos\n\n for (var i = 0; i < this.escapeCharPositions.length; i++) {\n sliceEnd = this.escapeCharPositions[i]\n subSlices.push(this.str.slice(sliceStart, sliceEnd))\n sliceStart = sliceEnd + 1\n }\n\n subSlices.push(this.str.slice(sliceStart, this.pos))\n this.escapeCharPositions.length = 0\n\n return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n this.lexemes.push({\n type: type,\n str: this.sliceString(),\n start: this.start,\n end: this.pos\n })\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n this.escapeCharPositions.push(this.pos - 1)\n this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n if (this.pos >= this.length) {\n return lunr.QueryLexer.EOS\n }\n\n var char = this.str.charAt(this.pos)\n this.pos += 1\n return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n if (this.start == this.pos) {\n this.pos += 1\n }\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n var char, charCode\n\n do {\n char = this.next()\n charCode = char.charCodeAt(0)\n } while (charCode > 47 && charCode < 58)\n\n if (char != lunr.QueryLexer.EOS) {\n this.backup()\n }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.FIELD)\n lexer.ignore()\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n if (lexer.width() > 1) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.TERM)\n }\n\n lexer.ignore()\n\n if (lexer.more()) {\n return lunr.QueryLexer.lexText\n }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.BOOST)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n while (true) {\n var char = lexer.next()\n\n if (char == lunr.QueryLexer.EOS) {\n return lunr.QueryLexer.lexEOS\n }\n\n // Escape character is '\\'\n if (char.charCodeAt(0) == 92) {\n lexer.escapeCharacter()\n continue\n }\n\n if (char == \":\") {\n return lunr.QueryLexer.lexField\n }\n\n if (char == \"~\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexEditDistance\n }\n\n if (char == \"^\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexBoost\n }\n\n // \"+\" indicates term presence is required\n // checking for length to ensure that only\n // leading \"+\" are considered\n if (char == \"+\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n // \"-\" indicates term presence is prohibited\n // checking for length to ensure that only\n // leading \"-\" are considered\n if (char == \"-\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n if (char.match(lunr.QueryLexer.termSeparator)) {\n return lunr.QueryLexer.lexTerm\n }\n }\n}\n\nlunr.QueryParser = function (str, query) {\n this.lexer = new lunr.QueryLexer (str)\n this.query = query\n this.currentClause = {}\n this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n this.lexer.run()\n this.lexemes = this.lexer.lexemes\n\n var state = lunr.QueryParser.parseClause\n\n while (state) {\n state = state(this)\n }\n\n return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n var lexeme = this.peekLexeme()\n this.lexemeIdx += 1\n return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n var completedClause = this.currentClause\n this.query.clause(completedClause)\n this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n var lexeme = parser.peekLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.type) {\n case lunr.QueryLexer.PRESENCE:\n return lunr.QueryParser.parsePresence\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n if (lexeme.str.length >= 1) {\n errorMessage += \" with value '\" + lexeme.str + \"'\"\n }\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.str) {\n case \"-\":\n parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n break\n case \"+\":\n parser.currentClause.presence = lunr.Query.presence.REQUIRED\n break\n default:\n var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term or field, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.fields = [lexeme.str]\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n parser.currentClause.term = lexeme.str.toLowerCase()\n\n if (lexeme.str.indexOf(\"*\") != -1) {\n parser.currentClause.usePipeline = false\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var editDistance = parseInt(lexeme.str, 10)\n\n if (isNaN(editDistance)) {\n var errorMessage = \"edit distance must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.editDistance = editDistance\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var boost = parseInt(lexeme.str, 10)\n\n if (isNaN(boost)) {\n var errorMessage = \"boost must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.boost = boost\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\n /**\n * export the module via AMD, CommonJS or as a browser global\n * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n */\n ;(function (root, factory) {\n if (typeof define === 'function' && define.amd) {\n // AMD. Register as an anonymous module.\n define(factory)\n } else if (typeof exports === 'object') {\n /**\n * Node. Does not work with strict CommonJS, but\n * only CommonJS-like enviroments that support module.exports,\n * like Node.\n */\n module.exports = factory()\n } else {\n // Browser globals (root is window)\n root.lunr = factory()\n }\n }(this, function () {\n /**\n * Just return a value to define the module export.\n * This example returns an object, but the module\n * can return a function as the exported value.\n */\n return lunr\n }))\n})();\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport \"~/polyfills\"\n\nimport { Search, SearchIndexConfig } from \"../../_\"\nimport {\n SearchMessage,\n SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for usage with `iframe-worker` polyfill\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous polyfilled implementation. The\n * cool thing is that awaiting a non-Promise is a noop, so extending the type\n * definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n */\ndeclare global {\n function importScripts(...urls: string[]): Promise | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages, which are defined through the search index configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search index configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n config: SearchIndexConfig\n): Promise {\n let base = \"../lunr\"\n\n /* Detect `iframe-worker` and fix base URL */\n if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n const worker = document.querySelector(\"script[src]\")!\n const [path] = worker.src.split(\"/worker\")\n\n /* Prefix base with path */\n base = base.replace(\"..\", path)\n }\n\n /* Add scripts for languages */\n const scripts = []\n for (const lang of config.lang) {\n switch (lang) {\n\n /* Add segmenter for Japanese */\n case \"ja\":\n scripts.push(`${base}/tinyseg.js`)\n break\n\n /* Add segmenter for Hindi and Thai */\n case \"hi\":\n case \"th\":\n scripts.push(`${base}/wordcut.js`)\n break\n }\n\n /* Add language support */\n if (lang !== \"en\")\n scripts.push(`${base}/min/lunr.${lang}.min.js`)\n }\n\n /* Add multi-language support */\n if (config.lang.length > 1)\n scripts.push(`${base}/min/lunr.multi.min.js`)\n\n /* Load scripts synchronously */\n if (scripts.length)\n await importScripts(\n `${base}/min/lunr.stemmer.support.min.js`,\n ...scripts\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n message: SearchMessage\n): Promise {\n switch (message.type) {\n\n /* Search setup message */\n case SearchMessageType.SETUP:\n await setupSearchLanguages(message.data.config)\n index = new Search(message.data)\n return {\n type: SearchMessageType.READY\n }\n\n /* Search query message */\n case SearchMessageType.QUERY:\n return {\n type: SearchMessageType.RESULT,\n data: index ? index.search(message.data) : { items: [] }\n }\n\n /* All other messages */\n default:\n throw new TypeError(\"Invalid message type\")\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* @ts-expect-error - expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n Object.entries = function (obj: object) {\n const data: [string, string][] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push([key, obj[key]])\n\n /* Return entries */\n return data\n }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n Object.values = function (obj: object) {\n const data: string[] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push(obj[key])\n\n /* Return values */\n return data\n }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n /* Polyfill `Element.scrollTo` */\n if (!Element.prototype.scrollTo)\n Element.prototype.scrollTo = function (\n x?: ScrollToOptions | number, y?: number\n ): void {\n if (typeof x === \"object\") {\n this.scrollLeft = x.left!\n this.scrollTop = x.top!\n } else {\n this.scrollLeft = x!\n this.scrollTop = y!\n }\n }\n\n /* Polyfill `Element.replaceWith` */\n if (!Element.prototype.replaceWith)\n Element.prototype.replaceWith = function (\n ...nodes: Array\n ): void {\n const parent = this.parentNode\n if (parent) {\n if (nodes.length === 0)\n parent.removeChild(this)\n\n /* Replace children and create text nodes */\n for (let i = nodes.length - 1; i >= 0; i--) {\n let node = nodes[i]\n if (typeof node !== \"object\")\n node = document.createTextNode(node)\n else if (node.parentNode)\n node.parentNode.removeChild(node)\n\n /* Replace child or insert before previous sibling */\n if (!i)\n parent.replaceChild(node, this)\n else\n parent.insertBefore(this.previousSibling!, node)\n }\n }\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n parent?: SearchIndexDocument /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n docs: SearchIndexDocument[]\n): SearchDocumentMap {\n const documents = new Map()\n const parents = new Set()\n for (const doc of docs) {\n const [path, hash] = doc.location.split(\"#\")\n\n /* Extract location, title and tags */\n const location = doc.location\n const title = doc.title\n const tags = doc.tags\n\n /* Escape and cleanup text */\n const text = escapeHTML(doc.text)\n .replace(/\\s+(?=[,.:;!?])/g, \"\")\n .replace(/\\s+/g, \" \")\n\n /* Handle section */\n if (hash) {\n const parent = documents.get(path)!\n\n /* Ignore first section, override article */\n if (!parents.has(parent)) {\n parent.title = doc.title\n parent.text = text\n\n /* Remember that we processed the article */\n parents.add(parent)\n\n /* Add subsequent section */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n parent\n })\n }\n\n /* Add article */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n ...tags && { tags }\n })\n }\n }\n return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n const separator = new RegExp(config.separator, \"img\")\n const highlight = (_: unknown, data: string, term: string) => {\n return `${data}${term}`\n }\n\n /* Return factory function */\n return (query: string) => {\n query = query\n .replace(/[\\s*+\\-:~^]+/g, \" \")\n .trim()\n\n /* Create search term match expression */\n const match = new RegExp(`(^|${config.separator})(${\n query\n .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n .replace(separator, \"|\")\n })`, \"img\")\n\n /* Highlight string value */\n return value => (\n escape\n ? escapeHTML(value)\n : value\n )\n .replace(match, highlight)\n .replace(/<\\/mark>(\\s+)]*>/img, \"$1\")\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n presence: lunr.Query.presence /* Clause presence */\n term: string /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n value: string\n): SearchQueryClause[] {\n const query = new (lunr as any).Query([\"title\", \"text\"])\n const parser = new (lunr as any).QueryParser(value, query)\n\n /* Parse and return query clauses */\n parser.parse()\n return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n const clauses = new Set(query)\n\n /* Match query clauses against terms */\n const result: SearchQueryTerms = {}\n for (let t = 0; t < terms.length; t++)\n for (const clause of clauses)\n if (terms[t].startsWith(clause.term)) {\n result[clause.term] = true\n clauses.delete(clause)\n }\n\n /* Annotate unmatched non-stopword query clauses */\n for (const clause of clauses)\n if (lunr.stopWordFilter?.(clause.term as any))\n result[clause.term] = false\n\n /* Return query terms */\n return result\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n SearchDocument,\n SearchDocumentMap,\n setupSearchDocumentMap\n} from \"../document\"\nimport {\n SearchHighlightFactoryFn,\n setupSearchHighlighter\n} from \"../highlighter\"\nimport { SearchOptions } from \"../options\"\nimport {\n SearchQueryTerms,\n getSearchQueryTerms,\n parseSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index configuration\n */\nexport interface SearchIndexConfig {\n lang: string[] /* Search languages */\n separator: string /* Search separator */\n}\n\n/**\n * Search index document\n */\nexport interface SearchIndexDocument {\n location: string /* Document location */\n title: string /* Document title */\n text: string /* Document text */\n tags?: string[] /* Document tags */\n boost?: number /* Document boost */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n *\n * This interfaces describes the format of the `search_index.json` file which\n * is automatically built by the MkDocs search plugin.\n */\nexport interface SearchIndex {\n config: SearchIndexConfig /* Search index configuration */\n docs: SearchIndexDocument[] /* Search index documents */\n options: SearchOptions /* Search options */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search metadata\n */\nexport interface SearchMetadata {\n score: number /* Score (relevance) */\n terms: SearchQueryTerms /* Search query terms */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result document\n */\nexport type SearchResultDocument = SearchDocument & SearchMetadata\n\n/**\n * Search result item\n */\nexport type SearchResultItem = SearchResultDocument[]\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result\n */\nexport interface SearchResult {\n items: SearchResultItem[] /* Search result items */\n suggestions?: string[] /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n const [x, y] = [new Set(a), new Set(b)]\n return [\n ...new Set([...x].filter(value => !y.has(value)))\n ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n /**\n * Search document mapping\n *\n * A mapping of URLs (including hash fragments) to the actual articles and\n * sections of the documentation. The search document mapping must be created\n * regardless of whether the index was prebuilt or not, as Lunr.js itself\n * only stores the actual index.\n */\n protected documents: SearchDocumentMap\n\n /**\n * Search highlight factory function\n */\n protected highlight: SearchHighlightFactoryFn\n\n /**\n * The underlying Lunr.js search index\n */\n protected index: lunr.Index\n\n /**\n * Search options\n */\n protected options: SearchOptions\n\n /**\n * Create the search integration\n *\n * @param data - Search index\n */\n public constructor({ config, docs, options }: SearchIndex) {\n this.options = options\n\n /* Set up document map and highlighter factory */\n this.documents = setupSearchDocumentMap(docs)\n this.highlight = setupSearchHighlighter(config, false)\n\n /* Set separator for tokenizer */\n lunr.tokenizer.separator = new RegExp(config.separator)\n\n /* Create search index */\n this.index = lunr(function () {\n\n /* Set up multi-language support */\n if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n this.use((lunr as any)[config.lang[0]])\n } else if (config.lang.length > 1) {\n this.use((lunr as any).multiLanguage(...config.lang))\n }\n\n /* Compute functions to be removed from the pipeline */\n const fns = difference([\n \"trimmer\", \"stopWordFilter\", \"stemmer\"\n ], options.pipeline)\n\n /* Remove functions from the pipeline for registered languages */\n for (const lang of config.lang.map(language => (\n language === \"en\" ? lunr : (lunr as any)[language]\n ))) {\n for (const fn of fns) {\n this.pipeline.remove(lang[fn])\n this.searchPipeline.remove(lang[fn])\n }\n }\n\n /* Set up reference */\n this.ref(\"location\")\n\n /* Set up fields */\n this.field(\"title\", { boost: 1e3 })\n this.field(\"text\")\n this.field(\"tags\", { boost: 1e6, extractor: doc => {\n const { tags = [] } = doc as SearchDocument\n return tags.reduce((list, tag) => [\n ...list,\n ...lunr.tokenizer(tag)\n ], [] as lunr.Token[])\n } })\n\n /* Index documents */\n for (const doc of docs)\n this.add(doc, { boost: doc.boost })\n })\n }\n\n /**\n * Search for matching documents\n *\n * The search index which MkDocs provides is divided up into articles, which\n * contain the whole content of the individual pages, and sections, which only\n * contain the contents of the subsections obtained by breaking the individual\n * pages up at `h1` ... `h6`. As there may be many sections on different pages\n * with identical titles (for example within this very project, e.g. \"Usage\"\n * or \"Installation\"), they need to be put into the context of the containing\n * page. For this reason, section results are grouped within their respective\n * articles which are the top-level results that are returned.\n *\n * @param query - Query value\n *\n * @returns Search results\n */\n public search(query: string): SearchResult {\n if (query) {\n try {\n const highlight = this.highlight(query)\n\n /* Parse query to extract clauses for analysis */\n const clauses = parseSearchQuery(query)\n .filter(clause => (\n clause.presence !== lunr.Query.presence.PROHIBITED\n ))\n\n /* Perform search and post-process results */\n const groups = this.index.search(`${query}*`)\n\n /* Apply post-query boosts based on title and search query terms */\n .reduce((item, { ref, score, matchData }) => {\n const document = this.documents.get(ref)\n if (typeof document !== \"undefined\") {\n const { location, title, text, tags, parent } = document\n\n /* Compute and analyze search query terms */\n const terms = getSearchQueryTerms(\n clauses,\n Object.keys(matchData.metadata)\n )\n\n /* Highlight title and text and apply post-query boosts */\n const boost = +!parent + +Object.values(terms).every(t => t)\n item.push({\n location,\n title: highlight(title),\n text: highlight(text),\n ...tags && { tags: tags.map(highlight) },\n score: score * (1 + boost),\n terms\n })\n }\n return item\n }, [])\n\n /* Sort search results again after applying boosts */\n .sort((a, b) => b.score - a.score)\n\n /* Group search results by page */\n .reduce((items, result) => {\n const document = this.documents.get(result.location)\n if (typeof document !== \"undefined\") {\n const ref = \"parent\" in document\n ? document.parent!.location\n : document.location\n items.set(ref, [...items.get(ref) || [], result])\n }\n return items\n }, new Map())\n\n /* Generate search suggestions, if desired */\n let suggestions: string[] | undefined\n if (this.options.suggestions) {\n const titles = this.index.query(builder => {\n for (const clause of clauses)\n builder.term(clause.term, {\n fields: [\"title\"],\n presence: lunr.Query.presence.REQUIRED,\n wildcard: lunr.Query.wildcard.TRAILING\n })\n })\n\n /* Retrieve suggestions for best match */\n suggestions = titles.length\n ? 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    - +
    schmelczer/great-ai @@ -2484,6 +2484,9 @@ To check out the Dockerimage, go to
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    Examine data

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    Train model

    +

    Train model

    Deploy service

    Summary#

    @@ -1035,6 +1035,9 @@
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    - +
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    - +
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    - +
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    -

    : Download

    +

    : Download

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    + + + @@ -1112,25 +1115,25 @@     - + - + - + - +
    @@ -1143,10 +1146,11 @@
    - + + - + diff --git a/how-to-guides/configure-service/index.html b/how-to-guides/configure-service/index.html index 4059e48..658fe5a 100644 --- a/how-to-guides/configure-service/index.html +++ b/how-to-guides/configure-service/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -1067,6 +1067,9 @@
    + + + @@ -1130,25 +1133,25 @@     - + - + - + - +
    @@ -1161,10 +1164,11 @@
    - + + - + diff --git a/how-to-guides/create-service/index.html b/how-to-guides/create-service/index.html index 377f828..fec3d41 100644 --- a/how-to-guides/create-service/index.html +++ b/how-to-guides/create-service/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -1068,6 +1068,9 @@
    + + + @@ -1131,25 +1134,25 @@     - + - + - + - +
    @@ -1162,10 +1165,11 @@
    - + + - + diff --git a/how-to-guides/handle-training-data/index.html b/how-to-guides/handle-training-data/index.html index 8198fac..1b5e1aa 100644 --- a/how-to-guides/handle-training-data/index.html +++ b/how-to-guides/handle-training-data/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -974,6 +974,9 @@
    + + + @@ -1037,25 +1040,25 @@     - + - + - + - +
    @@ -1068,10 +1071,11 @@
    - + + - + diff --git a/how-to-guides/install/index.html b/how-to-guides/install/index.html index fea556f..c221818 100644 --- a/how-to-guides/install/index.html +++ b/how-to-guides/install/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -974,6 +974,9 @@
    + + + @@ -1037,25 +1040,25 @@     - + - + - + - +
    @@ -1068,10 +1071,11 @@
    - + + - + diff --git a/how-to-guides/large-file/index.html b/how-to-guides/large-file/index.html index 58bd1f7..70c673f 100644 --- a/how-to-guides/large-file/index.html +++ b/how-to-guides/large-file/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -1123,6 +1123,9 @@
    + + + @@ -1186,25 +1189,25 @@     - + - + - + - +
    @@ -1217,10 +1220,11 @@
    - + + - + diff --git a/how-to-guides/use-service/index.html b/how-to-guides/use-service/index.html index 4b730e0..2802c98 100644 --- a/how-to-guides/use-service/index.html +++ b/how-to-guides/use-service/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -1112,6 +1112,9 @@
    + + + @@ -1175,25 +1178,25 @@     - + - + - + - +
    @@ -1206,10 +1209,11 @@
    - + + - + diff --git a/index.html b/index.html index 302a10c..075f2f6 100644 --- a/index.html +++ b/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -1038,9 +1038,9 @@

    There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great.

    However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best practices, and thus, can meaningfully improve your deployment without requiring prohibitively large effort.

    -

    Find it on PyPI

    -

    Find it on DockerHub

    -

    Check out the tutorial

    +

    Find it on PyPI

    +

    Find it on DockerHub

    +

    Check out the tutorial

    Production use#

    GreatAI has been battle-tested on the core platform services of ScoutinScience.

    @@ -1058,6 +1058,9 @@
    + + + @@ -1106,25 +1109,25 @@ - + - + - + - +
    @@ -1137,10 +1140,11 @@
    - + + - + diff --git a/reference/index.html b/reference/index.html index 06dbd3d..4b186bc 100644 --- a/reference/index.html +++ b/reference/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -5182,6 +5182,9 @@ not advised for production use.

    + + + @@ -5245,25 +5248,25 @@ not advised for production use.

     - + - + - + - +
    @@ -5276,10 +5279,11 @@ not advised for production use.

    - + + - + diff --git a/reference/large-file/index.html b/reference/large-file/index.html index 81d1a88..1b2f604 100644 --- a/reference/large-file/index.html +++ b/reference/large-file/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -1866,8 +1866,7 @@ automatic cache-pruning.

    Source code in great_ai/large_file/large_file/large_file_base.py -
    Source code in great_ai/large_file/large_file/large_file_base.py - 
     26
- 27
+          
    351352353
-354
     27
  28
  29
  30
@@ -2194,335 +2193,336 @@ automatic cache-pruning.
    		
 
 
 
    class LargeFileBase(ABC):
-    """Base for LargeFile implementations with different backends.
-
-    Store large files remotely using the familiar API of `open()`. With built-in
-    versioning, pruning and local cache.
-
-    By default, files are stored in the ".cache" folder and the least recently used is
-    deleted after the overall size reaches 30 GBs.
-
-    Examples:
-        >>> LargeFileBase.cache_path = Path(".cache")
-
-        >>> LargeFileBase.max_cache_size = "30GB"
-
-    Attributes:
-        initialized: Tell whether `configure_credentials` or
-            `configure_credentials_from_file` has been already called.
-        cache_path: Storage location for cached files.
-        max_cache_size: Delete files until the folder at `cache_path` is smaller than
-            this value. Examples: "5 GB", "10MB", "0.3 TB". Set to `None` for no
-            automatic cache-pruning.
-    """
-
-    initialized = False
-    cache_path = Path(".cache")
-    max_cache_size: Optional[str] = "30GB"
-
-    def __init__(
-        self,
-        name: str,
-        mode: str = "r",
-        *,
-        buffering: int = -1,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = None,
-        newline: Optional[str] = None,
-        version: Optional[int] = None,
-        keep_last_n: Optional[int] = None,
-        cache_only_mode: bool = False,
-    ):
-        self._name = name
-        self._version = version
-        self._mode = mode
-        self._keep_last_n = keep_last_n
-        self._cache_only_mode = cache_only_mode
-
-        self._buffering = buffering
-        self._encoding = encoding
-
-        if errors is not None and sys.version_info[1] < 8:
-            raise RuntimeError(
-                "The `errors` kwarg is only supported in 3.8 <= Python versions."
-            )
-        self._errors = errors
-
-        self._newline = newline
-
-        LargeFileBase.cache_path.mkdir(parents=True, exist_ok=True)
-
-        self._find_instances()
-        self._check_mode_and_set_version()
-
-    @classmethod
-    def configure_credentials_from_file(
-        cls,
-        secrets: Union[Path, str, ConfigFile],
-    ) -> None:
-        """Load file and feed its content to `configure_credentials`.
-
-        Extra keys are ignored.
-        """
-
-        if not isinstance(secrets, ConfigFile):
-            secrets = ConfigFile(secrets)
-        cls.configure_credentials(**{k.lower(): v for k, v in secrets.items()})
-
-    @classmethod
-    def configure_credentials(cls, **kwargs: str) -> None:
-        """Configure required credentials for the LargeFile backend."""
-
-        cls.initialized = True
-
-    def __enter__(self) -> IO:
-        params = dict(
-            mode=self._mode,
-            buffering=self._buffering,
-            encoding=self._encoding,
-            newline=self._newline,
-            delete=False,
-            prefix="large_file-",
-        )
-
-        if sys.version_info[1] >= 8:
-            params["errors"] = self._errors
-
-        self._file: IO[Any] = (
-            tempfile.NamedTemporaryFile(**params)  # type: ignore
-            if "w" in self._mode
-            else open(
-                self.get(),
-                mode=self._mode,
-                buffering=self._buffering,
-                encoding=self._encoding,
-                newline=self._newline,
-                errors=self._errors,
-            )
-        )
-
-        return self._file
-
-    def __exit__(
-        self,
-        type: Optional[Type[BaseException]],
-        exc: Optional[BaseException],
-        traceback: Optional[TracebackType],
-    ) -> Literal[False]:
-        self._file.close()
-
-        if type is None:
-            if "w" in self._mode:
-                self.push(Path(self._file.name))
-                os.unlink(self._file.name)
-        else:
-            logger.exception("Could not finish operation.")
-
-        return False
-
-    @property
-    def version(self) -> int:
-        """Numeric version of the file proxied by this LargeFile instance."""
-
-        return cast(int, self._version)
-
-    @lru_cache(1)
-    def get(self, hide_progress: bool = False) -> Path:
-        """Return path to the proxy of a file (or directory).
-
-        If not available in the local cache, an attempt is made to download it.
-
-        Args:
-            hide_progress: Do not show a progress update after each 10% of progress.
-        """
-
-        remote_path = next(
-            i.remote_path for i in self._instances if i.version == self._version
-        )
-
-        destination = self.cache_path / self._local_name
-        if not destination.exists():
-            logger.info(f"File {self._local_name} does not exist locally")
-
-            with tempfile.TemporaryDirectory() as tmp:
-                local_root_path = Path(tmp)
-                tmp_file_archive = (
-                    local_root_path / f"{self._local_name}{ARCHIVE_EXTENSION}"
-                )
-                self._download(
-                    remote_path, tmp_file_archive, hide_progress=hide_progress
-                )
-
-                logger.info(f"Decompressing {self._local_name}")
-                shutil.unpack_archive(str(tmp_file_archive), tmp, COMPRESSION_ALGORITHM)
-                shutil.move(str(local_root_path / self._local_name), str(destination))
-        else:
-            logger.info(f"File {self._local_name} found in cache")
-
-        return destination
-
-    def push(self, path: Union[Path, str], hide_progress: bool = False) -> None:
-        """Upload a file (or directory) as a new version of `key`.
-
-        The file/directory is compressed before upload.
-
-        Args:
-            hide_progress: Do not show a progress update after each 10% of progress.
-        """
-
-        if isinstance(path, str):
-            path = Path(path)
-
-        with tempfile.TemporaryDirectory() as tmp:
-            if path.is_file():
-                logger.info(f"Copying file for {self._local_name}")
-                copy: Any = shutil.copy
-            else:
-                logger.info(f"Copying directory for {self._local_name}")
-                copy = shutil.copytree
-
-            try:
-                # Make local copy in the cache
-                shutil.rmtree(self.cache_path / self._local_name, ignore_errors=True)
-                copy(str(path), str(self.cache_path / self._local_name))
-            except shutil.SameFileError:
-                pass  # No worries
-
-            copy(str(path), str(Path(tmp) / self._local_name))
-
-            with tempfile.TemporaryDirectory() as tmp2:
-                # A directory has to be zipped and it cannot contain the output of the zipping
-                logger.info(f"Compressing {self._local_name}")
-                shutil.make_archive(
-                    str(Path(tmp2) / self._local_name),
-                    COMPRESSION_ALGORITHM,
-                    tmp,
-                )
-
-                file_to_be_uploaded = (
-                    Path(tmp2) / f"{self._local_name}{ARCHIVE_EXTENSION}"
-                )
-                self._upload(file_to_be_uploaded, hide_progress=hide_progress)
-
-        self.clean_up()
-
-    def delete(self) -> None:
-        """Delete all versions of the files under this `key`."""
-
-        self._keep_last_n = 0
-        self._delete_old_remote_versions()
-
-    @property
-    def versions_pretty(self) -> str:
-        """Formatted string of all available versions."""
-        return ", ".join((str(i.version) for i in self._instances))
-
-    def clean_up(self) -> None:
-        """Delete local and remote versions according to currently set cache and retention policy."""
-
-        self._delete_old_remote_versions()
-        self._prune_cache()
-
-    @property
-    def _local_name(self) -> str:
-        return f"{self._name}{CACHE_NAME_VERSION_SEPARATOR}{self.version}"
-
-    def _find_instances(self) -> None:
-        if self._cache_only_mode:
-            self._instances = self._find_instances_from_cache()
-        else:
-            self._instances = self._find_remote_instances()
-
-        self._instances = sorted(self._instances, key=lambda i: i.version)
-
-    def _find_instances_from_cache(self) -> List[DataInstance]:
-        logger.info(f"Fetching cached versions of {self._name}")
-
-        candidates = [
-            DataInstance(
-                name=CACHE_NAME_VERSION_SEPARATOR.join(
-                    f.name.split(CACHE_NAME_VERSION_SEPARATOR)[:-1]
-                ),
-                version=int(f.name.split(CACHE_NAME_VERSION_SEPARATOR)[-1]),
-                remote_path=f,
-            )
-            for f in self.cache_path.glob(
-                f"{self._name}{CACHE_NAME_VERSION_SEPARATOR}*"
-            )
-        ]
-
-        return [c for c in candidates if c.name == self._name]
-
-    def _check_mode_and_set_version(self) -> None:
-        if "+" in self._mode:
-            raise ValueError(
-                f"File mode `{self._mode}` is not allowed3, remove the `+`."
-            )
-
-        if "w" in self._mode:
-            if self._version is not None:
-                raise ValueError("Providing a version is not allowed in write mode.")
-
-            self._version = self._instances[-1].version + 1 if self._instances else 0
-
-        elif "r" in self._mode:
-            if not self._instances:
-                raise FileNotFoundError(
-                    f"File {self._name} not found. No versions are available."
-                )
-
-            if self._version is None:
-                self._version = self._instances[-1].version
-                logger.info(
-                    f"Latest version of {self._name} is {self._version} "
-                    + f"(from versions: {self.versions_pretty})"
-                )
-            elif self._version not in [i.version for i in self._instances]:
-                raise FileNotFoundError(
-                    f"File {self._name} not found with version {self._version}. "
-                    + f"(from versions: {self.versions_pretty})"
-                )
-        else:
-            raise ValueError("Unsupported file mode.")
-
-    def _prune_cache(self) -> None:
-        self.cache_path.mkdir(parents=True, exist_ok=True)
-
-        if self.max_cache_size is None:
-            return
-
-        allowed_size = human_readable_to_byte(self.max_cache_size)
-        assert allowed_size >= 0
-
-        least_recently_read = sorted(
-            [f for f in self.cache_path.glob("*")], key=lambda f: f.stat().st_atime
-        )
-
-        while sum(os.path.getsize(f) for f in least_recently_read) > allowed_size:
-            file = least_recently_read.pop(0)
-            logger.info(
-                f"Deleting file from cache to meet quota (max_cache_size={self.max_cache_size}): {file}"
-            )
-            os.unlink(file)
-
-    @abstractmethod
-    def _find_remote_instances(self) -> List[DataInstance]:
-        pass
-
-    @abstractmethod
-    def _download(
-        self, remote_path: Any, local_path: Path, hide_progress: bool
-    ) -> None:
-        pass
-
-    @abstractmethod
-    def _upload(self, local_path: Path, hide_progress: bool) -> None:
-        pass
-
-    @abstractmethod
-    def _delete_old_remote_versions(self) -> None:
-        pass
+354
+355
    class LargeFileBase(ABC):
+    """Base for LargeFile implementations with different backends.
+
+    Store large files remotely using the familiar API of `open()`. With built-in
+    versioning, pruning and local cache.
+
+    By default, files are stored in the ".cache" folder and the least recently used is
+    deleted after the overall size reaches 30 GBs.
+
+    Examples:
+        >>> LargeFileBase.cache_path = Path(".cache")
+
+        >>> LargeFileBase.max_cache_size = "30GB"
+
+    Attributes:
+        initialized: Tell whether `configure_credentials` or
+            `configure_credentials_from_file` has been already called.
+        cache_path: Storage location for cached files.
+        max_cache_size: Delete files until the folder at `cache_path` is smaller than
+            this value. Examples: "5 GB", "10MB", "0.3 TB". Set to `None` for no
+            automatic cache-pruning.
+    """
+
+    initialized = False
+    cache_path = Path(".cache")
+    max_cache_size: Optional[str] = "30GB"
+
+    def __init__(
+        self,
+        name: str,
+        mode: str = "r",
+        *,
+        buffering: int = -1,
+        encoding: Optional[str] = None,
+        errors: Optional[str] = None,
+        newline: Optional[str] = None,
+        version: Optional[int] = None,
+        keep_last_n: Optional[int] = None,
+        cache_only_mode: bool = False,
+    ):
+        self._name = re.sub(r"[^a-zA-Z0-9._-]+", "", name)
+        self._version = version
+        self._mode = mode
+        self._keep_last_n = keep_last_n
+        self._cache_only_mode = cache_only_mode
+
+        self._buffering = buffering
+        self._encoding = encoding
+
+        if errors is not None and sys.version_info[1] < 8:
+            raise RuntimeError(
+                "The `errors` kwarg is only supported in 3.8 <= Python versions."
+            )
+        self._errors = errors
+
+        self._newline = newline
+
+        LargeFileBase.cache_path.mkdir(parents=True, exist_ok=True)
+
+        self._find_instances()
+        self._check_mode_and_set_version()
+
+    @classmethod
+    def configure_credentials_from_file(
+        cls,
+        secrets: Union[Path, str, ConfigFile],
+    ) -> None:
+        """Load file and feed its content to `configure_credentials`.
+
+        Extra keys are ignored.
+        """
+
+        if not isinstance(secrets, ConfigFile):
+            secrets = ConfigFile(secrets)
+        cls.configure_credentials(**{k.lower(): v for k, v in secrets.items()})
+
+    @classmethod
+    def configure_credentials(cls, **kwargs: str) -> None:
+        """Configure required credentials for the LargeFile backend."""
+
+        cls.initialized = True
+
+    def __enter__(self) -> IO:
+        params = dict(
+            mode=self._mode,
+            buffering=self._buffering,
+            encoding=self._encoding,
+            newline=self._newline,
+            delete=False,
+            prefix="large_file-",
+        )
+
+        if sys.version_info[1] >= 8:
+            params["errors"] = self._errors
+
+        self._file: IO[Any] = (
+            tempfile.NamedTemporaryFile(**params)  # type: ignore
+            if "w" in self._mode
+            else open(
+                self.get(),
+                mode=self._mode,
+                buffering=self._buffering,
+                encoding=self._encoding,
+                newline=self._newline,
+                errors=self._errors,
+            )
+        )
+
+        return self._file
+
+    def __exit__(
+        self,
+        type: Optional[Type[BaseException]],
+        exc: Optional[BaseException],
+        traceback: Optional[TracebackType],
+    ) -> Literal[False]:
+        self._file.close()
+
+        if type is None:
+            if "w" in self._mode:
+                self.push(Path(self._file.name))
+                os.unlink(self._file.name)
+        else:
+            logger.exception("Could not finish operation.")
+
+        return False
+
+    @property
+    def version(self) -> int:
+        """Numeric version of the file proxied by this LargeFile instance."""
+
+        return cast(int, self._version)
+
+    @lru_cache(1)
+    def get(self, hide_progress: bool = False) -> Path:
+        """Return path to the proxy of a file (or directory).
+
+        If not available in the local cache, an attempt is made to download it.
+
+        Args:
+            hide_progress: Do not show a progress update after each 10% of progress.
+        """
+
+        remote_path = next(
+            i.remote_path for i in self._instances if i.version == self._version
+        )
+
+        destination = self.cache_path / self._local_name
+        if not destination.exists():
+            logger.info(f"File {self._local_name} does not exist locally")
+
+            with tempfile.TemporaryDirectory() as tmp:
+                local_root_path = Path(tmp)
+                tmp_file_archive = (
+                    local_root_path / f"{self._local_name}{ARCHIVE_EXTENSION}"
+                )
+                self._download(
+                    remote_path, tmp_file_archive, hide_progress=hide_progress
+                )
+
+                logger.info(f"Decompressing {self._local_name}")
+                shutil.unpack_archive(str(tmp_file_archive), tmp, COMPRESSION_ALGORITHM)
+                shutil.move(str(local_root_path / self._local_name), str(destination))
+        else:
+            logger.info(f"File {self._local_name} found in cache")
+
+        return destination
+
+    def push(self, path: Union[Path, str], hide_progress: bool = False) -> None:
+        """Upload a file (or directory) as a new version of `key`.
+
+        The file/directory is compressed before upload.
+
+        Args:
+            hide_progress: Do not show a progress update after each 10% of progress.
+        """
+
+        if isinstance(path, str):
+            path = Path(path)
+
+        with tempfile.TemporaryDirectory() as tmp:
+            if path.is_file():
+                logger.info(f"Copying file for {self._local_name}")
+                copy: Any = shutil.copy
+            else:
+                logger.info(f"Copying directory for {self._local_name}")
+                copy = shutil.copytree
+
+            try:
+                # Make local copy in the cache
+                shutil.rmtree(self.cache_path / self._local_name, ignore_errors=True)
+                copy(str(path), str(self.cache_path / self._local_name))
+            except shutil.SameFileError:
+                pass  # No worries
+
+            copy(str(path), str(Path(tmp) / self._local_name))
+
+            with tempfile.TemporaryDirectory() as tmp2:
+                # A directory has to be zipped and it cannot contain the output of the zipping
+                logger.info(f"Compressing {self._local_name}")
+                shutil.make_archive(
+                    str(Path(tmp2) / self._local_name),
+                    COMPRESSION_ALGORITHM,
+                    tmp,
+                )
+
+                file_to_be_uploaded = (
+                    Path(tmp2) / f"{self._local_name}{ARCHIVE_EXTENSION}"
+                )
+                self._upload(file_to_be_uploaded, hide_progress=hide_progress)
+
+        self.clean_up()
+
+    def delete(self) -> None:
+        """Delete all versions of the files under this `key`."""
+
+        self._keep_last_n = 0
+        self._delete_old_remote_versions()
+
+    @property
+    def versions_pretty(self) -> str:
+        """Formatted string of all available versions."""
+        return ", ".join((str(i.version) for i in self._instances))
+
+    def clean_up(self) -> None:
+        """Delete local and remote versions according to currently set cache and retention policy."""
+
+        self._delete_old_remote_versions()
+        self._prune_cache()
+
+    @property
+    def _local_name(self) -> str:
+        return f"{self._name}{CACHE_NAME_VERSION_SEPARATOR}{self.version}"
+
+    def _find_instances(self) -> None:
+        if self._cache_only_mode:
+            self._instances = self._find_instances_from_cache()
+        else:
+            self._instances = self._find_remote_instances()
+
+        self._instances = sorted(self._instances, key=lambda i: i.version)
+
+    def _find_instances_from_cache(self) -> List[DataInstance]:
+        logger.info(f"Fetching cached versions of {self._name}")
+
+        candidates = [
+            DataInstance(
+                name=CACHE_NAME_VERSION_SEPARATOR.join(
+                    f.name.split(CACHE_NAME_VERSION_SEPARATOR)[:-1]
+                ),
+                version=int(f.name.split(CACHE_NAME_VERSION_SEPARATOR)[-1]),
+                remote_path=f,
+            )
+            for f in self.cache_path.glob(
+                f"{self._name}{CACHE_NAME_VERSION_SEPARATOR}*"
+            )
+        ]
+
+        return [c for c in candidates if c.name == self._name]
+
+    def _check_mode_and_set_version(self) -> None:
+        if "+" in self._mode:
+            raise ValueError(
+                f"File mode `{self._mode}` is not allowed3, remove the `+`."
+            )
+
+        if "w" in self._mode:
+            if self._version is not None:
+                raise ValueError("Providing a version is not allowed in write mode.")
+
+            self._version = self._instances[-1].version + 1 if self._instances else 0
+
+        elif "r" in self._mode:
+            if not self._instances:
+                raise FileNotFoundError(
+                    f"File {self._name} not found. No versions are available."
+                )
+
+            if self._version is None:
+                self._version = self._instances[-1].version
+                logger.info(
+                    f"Latest version of {self._name} is {self._version} "
+                    + f"(from versions: {self.versions_pretty})"
+                )
+            elif self._version not in [i.version for i in self._instances]:
+                raise FileNotFoundError(
+                    f"File {self._name} not found with version {self._version}. "
+                    + f"(from versions: {self.versions_pretty})"
+                )
+        else:
+            raise ValueError("Unsupported file mode.")
+
+    def _prune_cache(self) -> None:
+        self.cache_path.mkdir(parents=True, exist_ok=True)
+
+        if self.max_cache_size is None:
+            return
+
+        allowed_size = human_readable_to_byte(self.max_cache_size)
+        assert allowed_size >= 0
+
+        least_recently_read = sorted(
+            [f for f in self.cache_path.glob("*")], key=lambda f: f.stat().st_atime
+        )
+
+        while sum(os.path.getsize(f) for f in least_recently_read) > allowed_size:
+            file = least_recently_read.pop(0)
+            logger.info(
+                f"Deleting file from cache to meet quota (max_cache_size={self.max_cache_size}): {file}"
+            )
+            os.unlink(file)
+
+    @abstractmethod
+    def _find_remote_instances(self) -> List[DataInstance]:
+        pass
+
+    @abstractmethod
+    def _download(
+        self, remote_path: Any, local_path: Path, hide_progress: bool
+    ) -> None:
+        pass
+
+    @abstractmethod
+    def _upload(self, local_path: Path, hide_progress: bool) -> None:
+        pass
+
+    @abstractmethod
+    def _delete_old_remote_versions(self) -> None:
+        pass
 
    
         
 
@@ -2554,15 +2554,15 @@ automatic cache-pruning.
    Source code in great_ai/large_file/large_file/large_file_base.py - 
    250
-251
+        
    251
 252
 253
-254
    def clean_up(self) -> None:
-    """Delete local and remote versions according to currently set cache and retention policy."""
-
-    self._delete_old_remote_versions()
-    self._prune_cache()
+254
+255
    def clean_up(self) -> None:
+    """Delete local and remote versions according to currently set cache and retention policy."""
+
+    self._delete_old_remote_versions()
+    self._prune_cache()
    @@ -2589,15 +2589,15 @@ automatic cache-pruning.
    Source code in great_ai/large_file/large_file/large_file_base.py - 
    102
-103
+        
    103
 104
 105
-106
    @classmethod
-def configure_credentials(cls, **kwargs: str) -> None:
-    """Configure required credentials for the LargeFile backend."""
-
-    cls.initialized = True
+106
+107
    @classmethod
+def configure_credentials(cls, **kwargs: str) -> None:
+    """Configure required credentials for the LargeFile backend."""
+
+    cls.initialized = True
    @@ -2625,8 +2625,7 @@ automatic cache-pruning.
    Source code in great_ai/large_file/large_file/large_file_base.py - 
     88
- 89
+        
     97 98 99
-100
     89
  90
  91
  92
@@ -2637,19 +2636,20 @@ automatic cache-pruning.
    		
 
 
 
    @classmethod
-def configure_credentials_from_file(
-    cls,
-    secrets: Union[Path, str, ConfigFile],
-) -> None:
-    """Load file and feed its content to `configure_credentials`.
-
-    Extra keys are ignored.
-    """
-
-    if not isinstance(secrets, ConfigFile):
-        secrets = ConfigFile(secrets)
-    cls.configure_credentials(**{k.lower(): v for k, v in secrets.items()})
+100
+101
    @classmethod
+def configure_credentials_from_file(
+    cls,
+    secrets: Union[Path, str, ConfigFile],
+) -> None:
+    """Load file and feed its content to `configure_credentials`.
+
+    Extra keys are ignored.
+    """
+
+    if not isinstance(secrets, ConfigFile):
+        secrets = ConfigFile(secrets)
+    cls.configure_credentials(**{k.lower(): v for k, v in secrets.items()})
    @@ -2672,15 +2672,15 @@ automatic cache-pruning.
    Source code in great_ai/large_file/large_file/large_file_base.py - 
    239
-240
+        
    240
 241
 242
-243
    def delete(self) -> None:
-    """Delete all versions of the files under this `key`."""
-
-    self._keep_last_n = 0
-    self._delete_old_remote_versions()
+243
+244
    def delete(self) -> None:
+    """Delete all versions of the files under this `key`."""
+
+    self._keep_last_n = 0
+    self._delete_old_remote_versions()
    @@ -2732,8 +2732,7 @@ automatic cache-pruning.
    Source code in great_ai/large_file/large_file/large_file_base.py - 
    159
-160
+        
    189190191
-192
    160
 161
 162
 163
@@ -2765,40 +2764,41 @@ automatic cache-pruning.
    		
 
 
 
    @lru_cache(1)
-def get(self, hide_progress: bool = False) -> Path:
-    """Return path to the proxy of a file (or directory).
-
-    If not available in the local cache, an attempt is made to download it.
-
-    Args:
-        hide_progress: Do not show a progress update after each 10% of progress.
-    """
-
-    remote_path = next(
-        i.remote_path for i in self._instances if i.version == self._version
-    )
-
-    destination = self.cache_path / self._local_name
-    if not destination.exists():
-        logger.info(f"File {self._local_name} does not exist locally")
-
-        with tempfile.TemporaryDirectory() as tmp:
-            local_root_path = Path(tmp)
-            tmp_file_archive = (
-                local_root_path / f"{self._local_name}{ARCHIVE_EXTENSION}"
-            )
-            self._download(
-                remote_path, tmp_file_archive, hide_progress=hide_progress
-            )
-
-            logger.info(f"Decompressing {self._local_name}")
-            shutil.unpack_archive(str(tmp_file_archive), tmp, COMPRESSION_ALGORITHM)
-            shutil.move(str(local_root_path / self._local_name), str(destination))
-    else:
-        logger.info(f"File {self._local_name} found in cache")
-
-    return destination
+192
+193
    @lru_cache(1)
+def get(self, hide_progress: bool = False) -> Path:
+    """Return path to the proxy of a file (or directory).
+
+    If not available in the local cache, an attempt is made to download it.
+
+    Args:
+        hide_progress: Do not show a progress update after each 10% of progress.
+    """
+
+    remote_path = next(
+        i.remote_path for i in self._instances if i.version == self._version
+    )
+
+    destination = self.cache_path / self._local_name
+    if not destination.exists():
+        logger.info(f"File {self._local_name} does not exist locally")
+
+        with tempfile.TemporaryDirectory() as tmp:
+            local_root_path = Path(tmp)
+            tmp_file_archive = (
+                local_root_path / f"{self._local_name}{ARCHIVE_EXTENSION}"
+            )
+            self._download(
+                remote_path, tmp_file_archive, hide_progress=hide_progress
+            )
+
+            logger.info(f"Decompressing {self._local_name}")
+            shutil.unpack_archive(str(tmp_file_archive), tmp, COMPRESSION_ALGORITHM)
+            shutil.move(str(local_root_path / self._local_name), str(destination))
+    else:
+        logger.info(f"File {self._local_name} found in cache")
+
+    return destination
    @@ -2846,8 +2846,7 @@ automatic cache-pruning.
    Source code in great_ai/large_file/large_file/large_file_base.py - 
    194
-195
+        
    234235236
-237
    195
 196
 197
 198
@@ -2889,50 +2888,51 @@ automatic cache-pruning.
    		
 
 
 
    def push(self, path: Union[Path, str], hide_progress: bool = False) -> None:
-    """Upload a file (or directory) as a new version of `key`.
-
-    The file/directory is compressed before upload.
-
-    Args:
-        hide_progress: Do not show a progress update after each 10% of progress.
-    """
-
-    if isinstance(path, str):
-        path = Path(path)
-
-    with tempfile.TemporaryDirectory() as tmp:
-        if path.is_file():
-            logger.info(f"Copying file for {self._local_name}")
-            copy: Any = shutil.copy
-        else:
-            logger.info(f"Copying directory for {self._local_name}")
-            copy = shutil.copytree
-
-        try:
-            # Make local copy in the cache
-            shutil.rmtree(self.cache_path / self._local_name, ignore_errors=True)
-            copy(str(path), str(self.cache_path / self._local_name))
-        except shutil.SameFileError:
-            pass  # No worries
-
-        copy(str(path), str(Path(tmp) / self._local_name))
-
-        with tempfile.TemporaryDirectory() as tmp2:
-            # A directory has to be zipped and it cannot contain the output of the zipping
-            logger.info(f"Compressing {self._local_name}")
-            shutil.make_archive(
-                str(Path(tmp2) / self._local_name),
-                COMPRESSION_ALGORITHM,
-                tmp,
-            )
-
-            file_to_be_uploaded = (
-                Path(tmp2) / f"{self._local_name}{ARCHIVE_EXTENSION}"
-            )
-            self._upload(file_to_be_uploaded, hide_progress=hide_progress)
-
-    self.clean_up()
+237
+238
    def push(self, path: Union[Path, str], hide_progress: bool = False) -> None:
+    """Upload a file (or directory) as a new version of `key`.
+
+    The file/directory is compressed before upload.
+
+    Args:
+        hide_progress: Do not show a progress update after each 10% of progress.
+    """
+
+    if isinstance(path, str):
+        path = Path(path)
+
+    with tempfile.TemporaryDirectory() as tmp:
+        if path.is_file():
+            logger.info(f"Copying file for {self._local_name}")
+            copy: Any = shutil.copy
+        else:
+            logger.info(f"Copying directory for {self._local_name}")
+            copy = shutil.copytree
+
+        try:
+            # Make local copy in the cache
+            shutil.rmtree(self.cache_path / self._local_name, ignore_errors=True)
+            copy(str(path), str(self.cache_path / self._local_name))
+        except shutil.SameFileError:
+            pass  # No worries
+
+        copy(str(path), str(Path(tmp) / self._local_name))
+
+        with tempfile.TemporaryDirectory() as tmp2:
+            # A directory has to be zipped and it cannot contain the output of the zipping
+            logger.info(f"Compressing {self._local_name}")
+            shutil.make_archive(
+                str(Path(tmp2) / self._local_name),
+                COMPRESSION_ALGORITHM,
+                tmp,
+            )
+
+            file_to_be_uploaded = (
+                Path(tmp2) / f"{self._local_name}{ARCHIVE_EXTENSION}"
+            )
+            self._upload(file_to_be_uploaded, hide_progress=hide_progress)
+
+    self.clean_up()
    @@ -2959,15 +2959,15 @@ automatic cache-pruning.
    Source code in great_ai/large_file/large_file/large_file_base.py - 
    153
-154
+        
    154
 155
 156
-157
    @property
-def version(self) -> int:
-    """Numeric version of the file proxied by this LargeFile instance."""
-
-    return cast(int, self._version)
+157
+158
    @property
+def version(self) -> int:
+    """Numeric version of the file proxied by this LargeFile instance."""
+
+    return cast(int, self._version)
    @@ -2994,13 +2994,13 @@ automatic cache-pruning.
    + + + @@ -3089,25 +3092,25 @@ automatic cache-pruning.

    - + - + - + - + @@ -3120,10 +3123,11 @@ automatic cache-pruning.

    - + + - + diff --git a/reference/utilities/index.html b/reference/utilities/index.html index dca5a18..be016b2 100644 --- a/reference/utilities/index.html +++ b/reference/utilities/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -4534,6 +4534,9 @@ the system and ignore_missing=False.

    + + + @@ -4597,25 +4600,25 @@ the system and ignore_missing=False.

     - + - + - + - +
    @@ -4628,10 +4631,11 @@ the system and ignore_missing=False.

    - + + - + diff --git a/reference/views/index.html b/reference/views/index.html index b0df417..c4560f8 100644 --- a/reference/views/index.html +++ b/reference/views/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -1517,6 +1517,9 @@ function, value of ENVIRONMENT, its split if has any, and either
    + + + @@ -1580,25 +1583,25 @@ function, value of ENVIRONMENT, its split if has any, and either     - + - + - + - +
    @@ -1611,10 +1614,11 @@ function, value of ENVIRONMENT, its split if has any, and either
    - + + - + diff --git a/search/search_index.json b/search/search_index.json index d8b85d3..f9246df 100644 --- a/search/search_index.json +++ b/search/search_index.json @@ -1 +1 @@ -{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Overview of GreatAI Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing unintended biases . GreatAI helps fixing this by allowing you to easily transform your prototype AI code into production-ready software . Case studies \"There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature.\" \u2014 John et al. \"Finally, we have found that existing tools to aid Machine Learning development do not address the specificities of different projects, and thus, are seldom adopted by teams.\" \u2014 Haakman et al. \"Because a mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code, it may be less costly to create a clean native solution rather than re-use a generic package.\" \u2014 Sculley et al. \"For example, practice 25 is very important for \u201cTraceability\", yet relatively weakly adopted. We expect that the results from this type of analysis can, in the future, provide useful guidance for practitioners in terms of aiding them to assess their rate of adoption for each practice and to create roadmaps for improving their processes. \u2014 Serban et al. Features # Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Graceful error handling Seamless support for both synchronous and asynchronous inference methods Easy integration with remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for online monitoring and analysing traces Active support for Python 3.7, 3.8, 3.9, and 3.10 Roadmap # Prometheus & Grafana integration Well-tested feature extraction code for non-NLP data Support for direct file input Support for PostgreSQL Hello world # pip install great-ai demo.py from great_ai import GreatAI @GreatAI . create #(1) def greeter ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your greeter function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, greeter could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either way, it is imperative to have continuous oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai demo.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation. Why is this GREAT? # GreatAI fits between the prototype and deployment phases of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, several best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust Why GreatAI? # There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub Check out the tutorial Production use # GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Overview"},{"location":"#features","text":"Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Graceful error handling Seamless support for both synchronous and asynchronous inference methods Easy integration with remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for online monitoring and analysing traces Active support for Python 3.7, 3.8, 3.9, and 3.10","title":"Features"},{"location":"#roadmap","text":"Prometheus & Grafana integration Well-tested feature extraction code for non-NLP data Support for direct file input Support for PostgreSQL","title":"Roadmap"},{"location":"#hello-world","text":"pip install great-ai demo.py from great_ai import GreatAI @GreatAI . create #(1) def greeter ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your greeter function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, greeter could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either way, it is imperative to have continuous oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai demo.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation.","title":"Hello world"},{"location":"#why-is-this-great","text":"GreatAI fits between the prototype and deployment phases of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, several best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust","title":"Why is this GREAT?"},{"location":"#why-greatai","text":"There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub Check out the tutorial","title":"Why GreatAI?"},{"location":"#production-use","text":"GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Production use"},{"location":"explanation/","text":"Explanation # A lot more details and discussion about the problem context and approaches of GreatAI along with its evaluation can be found in my thesis. : Download","title":"Explanation"},{"location":"explanation/#explanation","text":"A lot more details and discussion about the problem context and approaches of GreatAI along with its evaluation can be found in my thesis. : Download","title":"Explanation"},{"location":"examples/scibert/","text":"Summarising scientific publications from a tech-transfer perspective # This is a simplified example illustrating how great-ai is used in practice at ScoutinScience . The subpages show great-ai in action by going over the life-cycle of finetuning and deploying a BERT-based software service. Propriety data The purpose of this example is to show you different ways in which great-ai can assist you. The exact NLP task being solved is not central. Stemming from this, and from the difficult nature of obtaining appropriate training data, the propriety dataset used for the experiments is not shared. Objectives # You will see how the great_ai.utilities can integrate into your Data Science workflow. You will see how great_ai.large_file can be used to version and store your trained model. You will see how GreatAI should be used prepare your model for a robust and responsible deployment. You will see multiple ways of customising your deployment. Overview # One of the core features of the ScoutinScience platform is summarising research papers from a tech-transfer perspective. In short, extractive summarisation is preferred using a binary classifier trained on clients' judgement of sentence interestingness. Thus, documents are sentences and the expected output is a binary label showing whether a sentence is \"worthy\" of being in the tech-transfer summary. Providing an explanation for each decision is imperative since ScoutinScience embraces applying only explainable AI (XAI) methods wherever feasible. Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Examine data Train model Deploy service Summary # Data notebook # We load and analyse the data by calculating inter-rater reliability and checking the feasibility of using an AI-based approach by testing the accuracy of a trivial baseline method. Training notebook # We simply fine-tune SciBERT. After training and evaluating a model, it is exported using great_ai.save_model . For more info, checkout the configuration how-to page . Deployment notebook # We customise the GreatAI configuration, create custom cahcing for the model and implement an inference function that can be hardened by wrapping it in a GreatAI instance. We also extract the attention weights as a quasi-explanation. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Additional files # There are some other files required for deploying the notebook. For example, the config file for S3 and MongoDB or a Dockerfile for building a custom image. These are compiled and shown on a separate page.","title":"Summarising scientific publications from a tech-transfer perspective"},{"location":"examples/scibert/#summarising-scientific-publications-from-a-tech-transfer-perspective","text":"This is a simplified example illustrating how great-ai is used in practice at ScoutinScience . 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Examine data Train model Deploy service","title":"Overview"},{"location":"examples/scibert/#summary","text":"","title":"Summary"},{"location":"examples/scibert/#data-notebook","text":"We load and analyse the data by calculating inter-rater reliability and checking the feasibility of using an AI-based approach by testing the accuracy of a trivial baseline method.","title":"Data notebook"},{"location":"examples/scibert/#training-notebook","text":"We simply fine-tune SciBERT. After training and evaluating a model, it is exported using great_ai.save_model . For more info, checkout the configuration how-to page .","title":"Training notebook"},{"location":"examples/scibert/#deployment-notebook","text":"We customise the GreatAI configuration, create custom cahcing for the model and implement an inference function that can be hardened by wrapping it in a GreatAI instance. We also extract the attention weights as a quasi-explanation. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly.","title":"Deployment notebook"},{"location":"examples/scibert/#additional-files","text":"There are some other files required for deploying the notebook. For example, the config file for S3 and MongoDB or a Dockerfile for building a custom image. These are compiled and shown on a separate page.","title":"Additional files"},{"location":"examples/scibert/additional-files/","text":"Additional files in the repository # In order to give you a smooth experience while comprehending this example, all non-notebook files are presented on this page in one place. In reality, these files should be in your project's top-level directory. config.ini # config.ini ENVIRONMENT = DEVELOPMENT ENVIRONMENT = ENV:ENVIRONMENT MONGO_CONNECTION_STRING = ENV:MONGO_CONNECTION_STRING MONGO_DATABASE = highlights AWS_REGION_NAME = eu-west-2 AWS_ACCESS_KEY_ID = MY_DEFAULT_AWS_ACCESS_KEY_ID_FOR_DEVELOPMENT AWS_SECRET_ACCESS_KEY = MY_DEFAULT_AWS_SECRET_ACCESS_KEY_FOR_DEVELOPMENT LARGE_FILES_BUCKET_NAME = my-orgs-large-files AWS_REGION_NAME = ENV:AWS_REGION_NAME AWS_ACCESS_KEY_ID = ENV:AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY = ENV:AWS_SECRET_ACCESS_KEY LARGE_FILES_BUCKET_NAME = ENV:LARGE_FILES_BUCKET_NAME All necessary configuration which is read by great_ai.utilities.ConfigFile . This will resolve values starting with ENV: from your environment variables. requirements.txt # requirements.txt torch==1.12.0 transformers==4.20.1 numpy==1.23.0 Usually, it is recommended to pin (freeze) the library versions on which we depend. This file is referenced by the Dockerfile . Dockerfile # Dockerfile FROM schmelczera/great-ai:v0.1.6 COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt COPY . ./ RUN large-file --backend s3 --secrets s3.ini --cache scibert-highlights CMD [ \"deploy.ipynb\" ] This is used by the CD pipeline to create the production deployment of the service. .dockerignore # .dockerignore .cache .git data .gitignore .env .vscode .dockerignore Dockerfile .mypy_cache .gitignore **/__pycache__ **/.DS_Store README.md It is useful not to send, for example, the .cache folder used by LargeFile to the docker daemon; this will speed up your local build times substantially. .gitignore A very similar looking .gitignore file should also be present.","title":"Additional files in the repository"},{"location":"examples/scibert/additional-files/#additional-files-in-the-repository","text":"In order to give you a smooth experience while comprehending this example, all non-notebook files are presented on this page in one place. 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This will resolve values starting with ENV: from your environment variables.","title":"config.ini"},{"location":"examples/scibert/additional-files/#requirementstxt","text":"requirements.txt torch==1.12.0 transformers==4.20.1 numpy==1.23.0 Usually, it is recommended to pin (freeze) the library versions on which we depend. 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Explore data and feasibility of approach \u00b6 We had asked our clients and in-house experts to annotate sentences using a rigorous guideline. The aim is to decide on which sentences they would like to see in a summary for a paper. The results are in JSON format, each annotator has a separate file. Let's load them. In [1]: Copied! from pathlib import Path import json annotations = [] for p in Path ( \"data\" ) . glob ( \"*.json\" ): with open ( p , encoding = \"utf-8\" ) as f : print ( p ) annotations . append ( json . load ( f )) evaluations = { sentence : [ annotation [ sentence ] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation . keys () } } X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] from pathlib import Path import json annotations = [] for p in Path(\"data\").glob(\"*.json\"): with open(p, encoding=\"utf-8\") as f: print(p) annotations.append(json.load(f)) evaluations = { sentence: [ annotation[sentence] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation.keys() } } X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] data/evaluation-experiment-2-stage #1-sa6a0y.json data/evaluation-experiment-2-stage #1-2m6dmb.json Save the compiled and processed data for later use using LargeFileS3. In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , \"w\" , encoding = \"utf-8\" ) as f : json . dump (( X , y ), f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", \"w\", encoding=\"utf-8\") as f: json.dump((X, y), f) Copying file for summary-train-dataset-small-0 Compressing summary-train-dataset-small-0 Uploading summary-train-dataset-small-0 to S3 as summary-train-dataset-small/0 Uploading summary-train-dataset-small-0.tar.gz 0.04/0.04 MB (100.0%) Filter out sentences which don't have enough annotations. In [3]: Copied! y1 = [ e [ 0 ] for e in evaluations . values () if len ( e ) == 2 ] y2 = [ e [ 1 ] for e in evaluations . values () if len ( e ) == 2 ] y1 = [e[0] for e in evaluations.values() if len(e) == 2] y2 = [e[1] for e in evaluations.values() if len(e) == 2] Calculate Cohen's kappa . It's a bit low but the task itself is pretty subjective so it's not all that surprising. In [4]: Copied! import sklearn.metrics sklearn . metrics . cohen_kappa_score ( y1 , y2 ) import sklearn.metrics sklearn.metrics.cohen_kappa_score(y1, y2) Out[4]: 0.3546448712421808 Can we train anything on this data? Let's try with a trivial SVM. In [5]: Copied! X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] In [6]: Copied! from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True , min_df = 3 , max_df = 0.3 )), ( \"classifier\" , LinearSVC ()), ] ) # baseline model cross_val_score ( model , X , y , cv = 5 ) from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True, min_df=3, max_df=0.3)), (\"classifier\", LinearSVC()), ] ) # baseline model cross_val_score(model, X, y, cv=5) Out[6]: array([0.79, 0.75, 0.77, 0.69, 0.77]) The cross-validation shows promising accuracies. But accuracy isn't everything, therefore, we should investigate the accuracy metrics. In [7]: Copied! X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.2 , random_state = 0 ) model . fit ( X_train , y_train ) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) model.fit(X_train, y_train) Out[7]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) TfidfVectorizer TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True) LinearSVC LinearSVC() In [8]: Copied! y_predicted = model . predict ( X_test ) print ( sklearn . metrics . classification_report ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ] ) ) sklearn . metrics . ConfusionMatrixDisplay . from_predictions ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ], xticks_rotation = \"vertical\" , values_format = \".2f\" , ) None y_predicted = model.predict(X_test) print( sklearn.metrics.classification_report( [y > 0 for y in y_test], [y > 0 for y in y_predicted] ) ) sklearn.metrics.ConfusionMatrixDisplay.from_predictions( [y > 0 for y in y_test], [y > 0 for y in y_predicted], xticks_rotation=\"vertical\", values_format=\".2f\", ) None precision recall f1-score support False 0.78 0.78 0.78 51 True 0.78 0.78 0.78 49 accuracy 0.78 100 macro avg 0.78 0.78 0.78 100 weighted avg 0.78 0.78 0.78 100 We get an F1-score of 0.78 without any hyperparameter-optimisation; this task may be feasible to solve with AI. Next: Part 2","title":"Explore data and feasibility of approach"},{"location":"examples/scibert/data/#explore-data-and-feasibility-of-approach","text":"We had asked our clients and in-house experts to annotate sentences using a rigorous guideline. The aim is to decide on which sentences they would like to see in a summary for a paper. The results are in JSON format, each annotator has a separate file. Let's load them. In [1]: Copied! from pathlib import Path import json annotations = [] for p in Path ( \"data\" ) . glob ( \"*.json\" ): with open ( p , encoding = \"utf-8\" ) as f : print ( p ) annotations . append ( json . load ( f )) evaluations = { sentence : [ annotation [ sentence ] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation . keys () } } X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] from pathlib import Path import json annotations = [] for p in Path(\"data\").glob(\"*.json\"): with open(p, encoding=\"utf-8\") as f: print(p) annotations.append(json.load(f)) evaluations = { sentence: [ annotation[sentence] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation.keys() } } X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] data/evaluation-experiment-2-stage #1-sa6a0y.json data/evaluation-experiment-2-stage #1-2m6dmb.json Save the compiled and processed data for later use using LargeFileS3. In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , \"w\" , encoding = \"utf-8\" ) as f : json . dump (( X , y ), f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", \"w\", encoding=\"utf-8\") as f: json.dump((X, y), f) Copying file for summary-train-dataset-small-0 Compressing summary-train-dataset-small-0 Uploading summary-train-dataset-small-0 to S3 as summary-train-dataset-small/0 Uploading summary-train-dataset-small-0.tar.gz 0.04/0.04 MB (100.0%) Filter out sentences which don't have enough annotations. In [3]: Copied! y1 = [ e [ 0 ] for e in evaluations . values () if len ( e ) == 2 ] y2 = [ e [ 1 ] for e in evaluations . values () if len ( e ) == 2 ] y1 = [e[0] for e in evaluations.values() if len(e) == 2] y2 = [e[1] for e in evaluations.values() if len(e) == 2] Calculate Cohen's kappa . It's a bit low but the task itself is pretty subjective so it's not all that surprising. In [4]: Copied! import sklearn.metrics sklearn . metrics . cohen_kappa_score ( y1 , y2 ) import sklearn.metrics sklearn.metrics.cohen_kappa_score(y1, y2) Out[4]: 0.3546448712421808 Can we train anything on this data? Let's try with a trivial SVM. In [5]: Copied! X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] In [6]: Copied! from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True , min_df = 3 , max_df = 0.3 )), ( \"classifier\" , LinearSVC ()), ] ) # baseline model cross_val_score ( model , X , y , cv = 5 ) from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True, min_df=3, max_df=0.3)), (\"classifier\", LinearSVC()), ] ) # baseline model cross_val_score(model, X, y, cv=5) Out[6]: array([0.79, 0.75, 0.77, 0.69, 0.77]) The cross-validation shows promising accuracies. But accuracy isn't everything, therefore, we should investigate the accuracy metrics. In [7]: Copied! X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.2 , random_state = 0 ) model . fit ( X_train , y_train ) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) model.fit(X_train, y_train) Out[7]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) TfidfVectorizer TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True) LinearSVC LinearSVC() In [8]: Copied! y_predicted = model . predict ( X_test ) print ( sklearn . metrics . classification_report ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ] ) ) sklearn . metrics . ConfusionMatrixDisplay . from_predictions ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ], xticks_rotation = \"vertical\" , values_format = \".2f\" , ) None y_predicted = model.predict(X_test) print( sklearn.metrics.classification_report( [y > 0 for y in y_test], [y > 0 for y in y_predicted] ) ) sklearn.metrics.ConfusionMatrixDisplay.from_predictions( [y > 0 for y in y_test], [y > 0 for y in y_predicted], xticks_rotation=\"vertical\", values_format=\".2f\", ) None precision recall f1-score support False 0.78 0.78 0.78 51 True 0.78 0.78 0.78 49 accuracy 0.78 100 macro avg 0.78 0.78 0.78 100 weighted avg 0.78 0.78 0.78 100 We get an F1-score of 0.78 without any hyperparameter-optimisation; this task may be feasible to solve with AI. Next: Part 2","title":"Explore data and feasibility of approach"},{"location":"examples/scibert/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); 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background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Create an inference function \u00b6 Everything is ready to wrap the previously trained model and deploy it. First, we need to configure the LargeFileBackend, the TracingDatabase and GreatAI. In [1]: Copied! from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure , MongoDbDriver configuration = ConfigFile ( \"config.ini\" ) LargeFileS3 . configure_credentials_from_file ( configuration ) MongoDbDriver . configure_credentials_from_file ( configuration ) configure ( dashboard_table_size = 100 , # traces are small, we can show many prediction_cache_size = 4096 , # predictions are expensive, cache them ) from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure, MongoDbDriver configuration = ConfigFile(\"config.ini\") LargeFileS3.configure_credentials_from_file(configuration) MongoDbDriver.configure_credentials_from_file(configuration) configure( dashboard_table_size=100, # traces are small, we can show many prediction_cache_size=4096, # predictions are expensive, cache them ) The value of `ENVIRONMENT` contains the \"ENV` prefix but `ENVIRONMENT` is not defined as an environment variable, using the default value defined above (`DEVELOPMENT`) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f MongoDbDriver has been already configured: skipping initialisation LargeFileS3 has been already configured: skipping initialisation GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: MongoDbDriver \ud83d\udd29 large_file_implementation: LargeFileS3 \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 4096 \ud83d\udd29 dashboard_table_size: 100 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices For a pleasant developer experience, we create some typed models that will show up in the automatically generated OpenAPI schema specification and will also provide runtime type validation. In [2]: Copied! from typing import List from pydantic import BaseModel class Attention ( BaseModel ): weight : float token : str class EvaluatedSentence ( BaseModel ): score : float text : str explanation : List [ Attention ] from typing import List from pydantic import BaseModel class Attention(BaseModel): weight: float token: str class EvaluatedSentence(BaseModel): score: float text: str explanation: List[Attention] Even though @use_model caches the remote files locally and it also handles deserialising objects, we only use it to store a directory. In this case, it gives back a path, the path to that directory. So, we need to load the files from that folder ourselves. In order to only load it once per process, we create a small model loader helper function. This is usually not needed, however, when we can outsmart dill so for optimisation purposes, we do it. In [3]: Copied! from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel , PreTrainedTokenizer , ) from transformers import ( AutoConfig , AutoModelForSequenceClassification , AutoTokenizer , ) _tokenizer : PreTrainedTokenizer = None _loaded_model : PreTrainedModel = None @use_model ( \"scibert-highlights\" , version = \"latest\" , model_kwarg_name = \"model_path\" ) def get_tokenizer_and_model ( model_path : Path , original_model : str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple [ PreTrainedTokenizer , PreTrainedModel ]: global _tokenizer , _loaded_model if _tokenizer is None : _tokenizer = AutoTokenizer . from_pretrained ( original_model ) if _loaded_model is None : config = AutoConfig . from_pretrained ( model_path , output_hidden_states = True , output_attentions = True ) _loaded_model = AutoModelForSequenceClassification . from_pretrained ( model_path , config = config ) return _tokenizer , _loaded_model from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel, PreTrainedTokenizer, ) from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, ) _tokenizer: PreTrainedTokenizer = None _loaded_model: PreTrainedModel = None @use_model(\"scibert-highlights\", version=\"latest\", model_kwarg_name=\"model_path\") def get_tokenizer_and_model( model_path: Path, original_model: str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple[PreTrainedTokenizer, PreTrainedModel]: global _tokenizer, _loaded_model if _tokenizer is None: _tokenizer = AutoTokenizer.from_pretrained(original_model) if _loaded_model is None: config = AutoConfig.from_pretrained( model_path, output_hidden_states=True, output_attentions=True ) _loaded_model = AutoModelForSequenceClassification.from_pretrained( model_path, config=config ) return _tokenizer, _loaded_model Latest version of scibert-highlights is 0 (from versions: 0) File scibert-highlights-0 found in cache Finally, implement the inference function. In [4]: Copied! from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI . create def find_highlights ( sentence : str ) -> EvaluatedSentence : \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer , loaded_model = get_tokenizer_and_model () sentence = clean ( sentence , convert_to_ascii = True , remove_brackets = True ) tensors = tokenizer ( sentence , return_tensors = \"pt\" , truncation = True , max_length = 512 ) with torch . inference_mode (): result : SequenceClassifierOutput = loaded_model ( ** tensors ) positive_likelihood = torch . nn . Softmax ( dim = 1 )( result . logits )[ 0 ][ 1 ] tokens = tensors [ \"input_ids\" ][ 0 ] attentions = np . sum ( result . attentions [ - 1 ] . numpy ()[ 0 ], axis = 0 )[ 0 ][ 1 : - 1 ] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list ( zip ( attentions , tokens [ 1 : - 1 ])) for token in re . split ( r \"([ .,])\" , sentence ): token = token . strip () if not token : continue bert_tokens = tokenizer ( token , return_tensors = \"pt\" , truncation = True , max_length = 512 )[ \"input_ids\" ][ 0 ][ 1 : - 1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens : if not token_attentions : break a , t2 = token_attentions . pop ( 0 ) assert t1 == t2 , sentence weight += a explanation . append ( Attention ( token = token if token in \".,\" else \" \" + token , weight = round ( weight , 4 ) ) ) if not token_attentions : break return EvaluatedSentence ( score = positive_likelihood , text = sentence , explanation = explanation ) from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI.create def find_highlights(sentence: str) -> EvaluatedSentence: \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer, loaded_model = get_tokenizer_and_model() sentence = clean(sentence, convert_to_ascii=True, remove_brackets=True) tensors = tokenizer(sentence, return_tensors=\"pt\", truncation=True, max_length=512) with torch.inference_mode(): result: SequenceClassifierOutput = loaded_model(**tensors) positive_likelihood = torch.nn.Softmax(dim=1)(result.logits)[0][1] tokens = tensors[\"input_ids\"][0] attentions = np.sum(result.attentions[-1].numpy()[0], axis=0)[0][1:-1] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list(zip(attentions, tokens[1:-1])) for token in re.split(r\"([ .,])\", sentence): token = token.strip() if not token: continue bert_tokens = tokenizer( token, return_tensors=\"pt\", truncation=True, max_length=512 )[\"input_ids\"][0][ 1:-1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens: if not token_attentions: break a, t2 = token_attentions.pop(0) assert t1 == t2, sentence weight += a explanation.append( Attention( token=token if token in \".,\" else \" \" + token, weight=round(weight, 4) ) ) if not token_attentions: break return EvaluatedSentence( score=positive_likelihood, text=sentence, explanation=explanation ) A simple test to see everything works. Note that the models list is filled by the @use_model call even though it's not on the main inference function. In [5]: Copied! if __name__ == \"__main__\" : find_highlights ( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights ( \"Their solution did not perform well.\" ) if __name__ == \"__main__\": find_highlights( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights(\"Their solution did not perform well.\") Out[5]: (Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:29.581701', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 51, 'arg:sentence:value': 'Our solution has outperformed the ' 'state-of-the-art.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 7127.2063, 'output': { 'explanation': [ {'token': ' Our', 'weight': 0.3993}, {'token': ' solution', 'weight': 0.3481}, {'token': ' has', 'weight': 0.2945}, {'token': ' outperformed', 'weight': 0.4011}, {'token': ' the', 'weight': 0.1484}, {'token': ' state-of-the-art', 'weight': 0.5727}, {'token': '.', 'weight': 7.775}], 'score': 0.9991180300712585, 'text': 'Our solution has outperformed the state-of-the-art.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '56e20e94-79df-4793-ae61-d20820ebe2d3'}), Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:37.020275', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 36, 'arg:sentence:value': 'Their solution did not perform ' 'well.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 170.7057, 'output': { 'explanation': [ {'token': ' Their', 'weight': 1.1475}, {'token': ' solution', 'weight': 0.8205}, {'token': ' did', 'weight': 0.3254}, {'token': ' not', 'weight': 0.2921}, {'token': ' perform', 'weight': 0.4293}, {'token': ' well', 'weight': 0.2772}, {'token': '.', 'weight': 4.4723}], 'score': 0.12305451184511185, 'text': 'Their solution did not perform well.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '7fcf8271-1738-4025-8305-d5a1e5100aea'})) In this case, the service is built as a docker image, pushed to our image registry and subsequent rolling update is performed in the production cluster. To check out the Dockerimage, go to the additional files page .","title":"Create an inference function"},{"location":"examples/scibert/deploy/#create-an-inference-function","text":"Everything is ready to wrap the previously trained model and deploy it. First, we need to configure the LargeFileBackend, the TracingDatabase and GreatAI. In [1]: Copied! from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure , MongoDbDriver configuration = ConfigFile ( \"config.ini\" ) LargeFileS3 . configure_credentials_from_file ( configuration ) MongoDbDriver . configure_credentials_from_file ( configuration ) configure ( dashboard_table_size = 100 , # traces are small, we can show many prediction_cache_size = 4096 , # predictions are expensive, cache them ) from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure, MongoDbDriver configuration = ConfigFile(\"config.ini\") LargeFileS3.configure_credentials_from_file(configuration) MongoDbDriver.configure_credentials_from_file(configuration) configure( dashboard_table_size=100, # traces are small, we can show many prediction_cache_size=4096, # predictions are expensive, cache them ) The value of `ENVIRONMENT` contains the \"ENV` prefix but `ENVIRONMENT` is not defined as an environment variable, using the default value defined above (`DEVELOPMENT`) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f MongoDbDriver has been already configured: skipping initialisation LargeFileS3 has been already configured: skipping initialisation GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: MongoDbDriver \ud83d\udd29 large_file_implementation: LargeFileS3 \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 4096 \ud83d\udd29 dashboard_table_size: 100 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices For a pleasant developer experience, we create some typed models that will show up in the automatically generated OpenAPI schema specification and will also provide runtime type validation. In [2]: Copied! from typing import List from pydantic import BaseModel class Attention ( BaseModel ): weight : float token : str class EvaluatedSentence ( BaseModel ): score : float text : str explanation : List [ Attention ] from typing import List from pydantic import BaseModel class Attention(BaseModel): weight: float token: str class EvaluatedSentence(BaseModel): score: float text: str explanation: List[Attention] Even though @use_model caches the remote files locally and it also handles deserialising objects, we only use it to store a directory. In this case, it gives back a path, the path to that directory. So, we need to load the files from that folder ourselves. In order to only load it once per process, we create a small model loader helper function. This is usually not needed, however, when we can outsmart dill so for optimisation purposes, we do it. In [3]: Copied! from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel , PreTrainedTokenizer , ) from transformers import ( AutoConfig , AutoModelForSequenceClassification , AutoTokenizer , ) _tokenizer : PreTrainedTokenizer = None _loaded_model : PreTrainedModel = None @use_model ( \"scibert-highlights\" , version = \"latest\" , model_kwarg_name = \"model_path\" ) def get_tokenizer_and_model ( model_path : Path , original_model : str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple [ PreTrainedTokenizer , PreTrainedModel ]: global _tokenizer , _loaded_model if _tokenizer is None : _tokenizer = AutoTokenizer . from_pretrained ( original_model ) if _loaded_model is None : config = AutoConfig . from_pretrained ( model_path , output_hidden_states = True , output_attentions = True ) _loaded_model = AutoModelForSequenceClassification . from_pretrained ( model_path , config = config ) return _tokenizer , _loaded_model from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel, PreTrainedTokenizer, ) from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, ) _tokenizer: PreTrainedTokenizer = None _loaded_model: PreTrainedModel = None @use_model(\"scibert-highlights\", version=\"latest\", model_kwarg_name=\"model_path\") def get_tokenizer_and_model( model_path: Path, original_model: str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple[PreTrainedTokenizer, PreTrainedModel]: global _tokenizer, _loaded_model if _tokenizer is None: _tokenizer = AutoTokenizer.from_pretrained(original_model) if _loaded_model is None: config = AutoConfig.from_pretrained( model_path, output_hidden_states=True, output_attentions=True ) _loaded_model = AutoModelForSequenceClassification.from_pretrained( model_path, config=config ) return _tokenizer, _loaded_model Latest version of scibert-highlights is 0 (from versions: 0) File scibert-highlights-0 found in cache Finally, implement the inference function. In [4]: Copied! from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI . create def find_highlights ( sentence : str ) -> EvaluatedSentence : \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer , loaded_model = get_tokenizer_and_model () sentence = clean ( sentence , convert_to_ascii = True , remove_brackets = True ) tensors = tokenizer ( sentence , return_tensors = \"pt\" , truncation = True , max_length = 512 ) with torch . inference_mode (): result : SequenceClassifierOutput = loaded_model ( ** tensors ) positive_likelihood = torch . nn . Softmax ( dim = 1 )( result . logits )[ 0 ][ 1 ] tokens = tensors [ \"input_ids\" ][ 0 ] attentions = np . sum ( result . attentions [ - 1 ] . numpy ()[ 0 ], axis = 0 )[ 0 ][ 1 : - 1 ] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list ( zip ( attentions , tokens [ 1 : - 1 ])) for token in re . split ( r \"([ .,])\" , sentence ): token = token . strip () if not token : continue bert_tokens = tokenizer ( token , return_tensors = \"pt\" , truncation = True , max_length = 512 )[ \"input_ids\" ][ 0 ][ 1 : - 1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens : if not token_attentions : break a , t2 = token_attentions . pop ( 0 ) assert t1 == t2 , sentence weight += a explanation . append ( Attention ( token = token if token in \".,\" else \" \" + token , weight = round ( weight , 4 ) ) ) if not token_attentions : break return EvaluatedSentence ( score = positive_likelihood , text = sentence , explanation = explanation ) from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI.create def find_highlights(sentence: str) -> EvaluatedSentence: \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer, loaded_model = get_tokenizer_and_model() sentence = clean(sentence, convert_to_ascii=True, remove_brackets=True) tensors = tokenizer(sentence, return_tensors=\"pt\", truncation=True, max_length=512) with torch.inference_mode(): result: SequenceClassifierOutput = loaded_model(**tensors) positive_likelihood = torch.nn.Softmax(dim=1)(result.logits)[0][1] tokens = tensors[\"input_ids\"][0] attentions = np.sum(result.attentions[-1].numpy()[0], axis=0)[0][1:-1] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list(zip(attentions, tokens[1:-1])) for token in re.split(r\"([ .,])\", sentence): token = token.strip() if not token: continue bert_tokens = tokenizer( token, return_tensors=\"pt\", truncation=True, max_length=512 )[\"input_ids\"][0][ 1:-1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens: if not token_attentions: break a, t2 = token_attentions.pop(0) assert t1 == t2, sentence weight += a explanation.append( Attention( token=token if token in \".,\" else \" \" + token, weight=round(weight, 4) ) ) if not token_attentions: break return EvaluatedSentence( score=positive_likelihood, text=sentence, explanation=explanation ) A simple test to see everything works. Note that the models list is filled by the @use_model call even though it's not on the main inference function. In [5]: Copied! if __name__ == \"__main__\" : find_highlights ( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights ( \"Their solution did not perform well.\" ) if __name__ == \"__main__\": find_highlights( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights(\"Their solution did not perform well.\") Out[5]: (Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:29.581701', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 51, 'arg:sentence:value': 'Our solution has outperformed the ' 'state-of-the-art.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 7127.2063, 'output': { 'explanation': [ {'token': ' Our', 'weight': 0.3993}, {'token': ' solution', 'weight': 0.3481}, {'token': ' has', 'weight': 0.2945}, {'token': ' outperformed', 'weight': 0.4011}, {'token': ' the', 'weight': 0.1484}, {'token': ' state-of-the-art', 'weight': 0.5727}, {'token': '.', 'weight': 7.775}], 'score': 0.9991180300712585, 'text': 'Our solution has outperformed the state-of-the-art.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '56e20e94-79df-4793-ae61-d20820ebe2d3'}), Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:37.020275', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 36, 'arg:sentence:value': 'Their solution did not perform ' 'well.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 170.7057, 'output': { 'explanation': [ {'token': ' Their', 'weight': 1.1475}, {'token': ' solution', 'weight': 0.8205}, {'token': ' did', 'weight': 0.3254}, {'token': ' not', 'weight': 0.2921}, {'token': ' perform', 'weight': 0.4293}, {'token': ' well', 'weight': 0.2772}, {'token': '.', 'weight': 4.4723}], 'score': 0.12305451184511185, 'text': 'Their solution did not perform well.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '7fcf8271-1738-4025-8305-d5a1e5100aea'})) In this case, the service is built as a docker image, pushed to our image registry and subsequent rolling update is performed in the production cluster. To check out the Dockerimage, go to the additional files page .","title":"Create an inference function"},{"location":"examples/scibert/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); 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font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Fine-tune SciBERT \u00b6 We are planning to do a simple classification task on scientific text. For that, SciBERT is an ideal model to fine-tune since it has been pretrained of academic publications. This notebook was updated so that it can run in Google Colab . First, we need to install the dependencies. In [1]: Copied! ! pip install transformers datasets great - ai > / dev / null !pip install transformers datasets great-ai > /dev/null Load the training data from S3. (We have uploaded this to S3 in the data notebook.) In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , encoding = \"utf-8\" ) as f : # splitting training and test data is done later by `datasets` X , y = json . load ( f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", encoding=\"utf-8\") as f: # splitting training and test data is done later by `datasets` X, y = json.load(f) Latest version of summary-train-dataset-small is 0 (from versions: 0) File summary-train-dataset-small-0 found in cache Finetune SciBERT, for more info about this step, check out HuggingFace . If you're only here for great-ai , feel free to skip the next cell. In [22]: Copied! from transformers import ( AutoModelForSequenceClassification , AutoTokenizer , DataCollatorWithPadding , Trainer , TrainingArguments , EarlyStoppingCallback , ) from pathlib import Path import numpy as np from datasets import Dataset , load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer . from_pretrained ( MODEL ) model = AutoModelForSequenceClassification . from_pretrained ( MODEL , num_labels = 2 ) data_collator = DataCollatorWithPadding ( tokenizer = tokenizer ) def tokenize_function ( v ): return tokenizer ( v [ \"text\" ]) dataset = ( Dataset . from_dict ({ \"text\" : X , \"label\" : y }) . map ( lambda v : tokenizer ( v [ \"text\" ], truncation = True ), batched = True ) . remove_columns ( \"text\" ) . train_test_split ( test_size = 0.2 , shuffle = True ) # test is actually validation ) f1_score = load_metric ( \"f1\" ) def compute_metrics ( p ): pred , labels = p pred = np . argmax ( pred , axis = 1 ) return f1_score . compute ( predictions = pred , references = labels ) training_args = TrainingArguments ( output_dir = Path ( \"models\" ), per_device_train_batch_size = BATCH_SIZE , per_device_eval_batch_size = BATCH_SIZE , save_total_limit = 5 , num_train_epochs = 50 , save_strategy = \"epoch\" , evaluation_strategy = \"epoch\" , logging_strategy = \"epoch\" , weight_decay = 0.01 , metric_for_best_model = \"f1\" , load_best_model_at_end = True , ) result = Trainer ( model = model , args = training_args , train_dataset = dataset [ \"train\" ], eval_dataset = dataset [ \"test\" ], data_collator = data_collator , compute_metrics = compute_metrics , callbacks = [ EarlyStoppingCallback ( early_stopping_patience = 5 )], ) . train () from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, Trainer, TrainingArguments, EarlyStoppingCallback, ) from pathlib import Path import numpy as np from datasets import Dataset, load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer.from_pretrained(MODEL) model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=2) data_collator = DataCollatorWithPadding(tokenizer=tokenizer) def tokenize_function(v): return tokenizer(v[\"text\"]) dataset = ( Dataset.from_dict({\"text\": X, \"label\": y}) .map(lambda v: tokenizer(v[\"text\"], truncation=True), batched=True) .remove_columns(\"text\") .train_test_split(test_size=0.2, shuffle=True) # test is actually validation ) f1_score = load_metric(\"f1\") def compute_metrics(p): pred, labels = p pred = np.argmax(pred, axis=1) return f1_score.compute(predictions=pred, references=labels) training_args = TrainingArguments( output_dir=Path(\"models\"), per_device_train_batch_size=BATCH_SIZE, per_device_eval_batch_size=BATCH_SIZE, save_total_limit=5, num_train_epochs=50, save_strategy=\"epoch\", evaluation_strategy=\"epoch\", logging_strategy=\"epoch\", weight_decay=0.01, metric_for_best_model=\"f1\", load_best_model_at_end=True, ) result = Trainer( model=model, args=training_args, train_dataset=dataset[\"train\"], eval_dataset=dataset[\"test\"], data_collator=data_collator, compute_metrics=compute_metrics, callbacks=[EarlyStoppingCallback(early_stopping_patience=5)], ).train() 0%| | 0/1 [00:00 / dev / null !pip install transformers datasets great-ai > /dev/null Load the training data from S3. (We have uploaded this to S3 in the data notebook.) In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , encoding = \"utf-8\" ) as f : # splitting training and test data is done later by `datasets` X , y = json . load ( f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", encoding=\"utf-8\") as f: # splitting training and test data is done later by `datasets` X, y = json.load(f) Latest version of summary-train-dataset-small is 0 (from versions: 0) File summary-train-dataset-small-0 found in cache Finetune SciBERT, for more info about this step, check out HuggingFace . If you're only here for great-ai , feel free to skip the next cell. In [22]: Copied! from transformers import ( AutoModelForSequenceClassification , AutoTokenizer , DataCollatorWithPadding , Trainer , TrainingArguments , EarlyStoppingCallback , ) from pathlib import Path import numpy as np from datasets import Dataset , load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer . from_pretrained ( MODEL ) model = AutoModelForSequenceClassification . from_pretrained ( MODEL , num_labels = 2 ) data_collator = DataCollatorWithPadding ( tokenizer = tokenizer ) def tokenize_function ( v ): return tokenizer ( v [ \"text\" ]) dataset = ( Dataset . from_dict ({ \"text\" : X , \"label\" : y }) . map ( lambda v : tokenizer ( v [ \"text\" ], truncation = True ), batched = True ) . remove_columns ( \"text\" ) . train_test_split ( test_size = 0.2 , shuffle = True ) # test is actually validation ) f1_score = load_metric ( \"f1\" ) def compute_metrics ( p ): pred , labels = p pred = np . argmax ( pred , axis = 1 ) return f1_score . compute ( predictions = pred , references = labels ) training_args = TrainingArguments ( output_dir = Path ( \"models\" ), per_device_train_batch_size = BATCH_SIZE , per_device_eval_batch_size = BATCH_SIZE , save_total_limit = 5 , num_train_epochs = 50 , save_strategy = \"epoch\" , evaluation_strategy = \"epoch\" , logging_strategy = \"epoch\" , weight_decay = 0.01 , metric_for_best_model = \"f1\" , load_best_model_at_end = True , ) result = Trainer ( model = model , args = training_args , train_dataset = dataset [ \"train\" ], eval_dataset = dataset [ \"test\" ], data_collator = data_collator , compute_metrics = compute_metrics , callbacks = [ EarlyStoppingCallback ( early_stopping_patience = 5 )], ) . train () from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, Trainer, TrainingArguments, EarlyStoppingCallback, ) from pathlib import Path import numpy as np from datasets import Dataset, load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer.from_pretrained(MODEL) model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=2) data_collator = DataCollatorWithPadding(tokenizer=tokenizer) def tokenize_function(v): return tokenizer(v[\"text\"]) dataset = ( Dataset.from_dict({\"text\": X, \"label\": y}) .map(lambda v: tokenizer(v[\"text\"], truncation=True), batched=True) .remove_columns(\"text\") .train_test_split(test_size=0.2, shuffle=True) # test is actually validation ) f1_score = load_metric(\"f1\") def compute_metrics(p): pred, labels = p pred = np.argmax(pred, axis=1) return f1_score.compute(predictions=pred, references=labels) training_args = TrainingArguments( output_dir=Path(\"models\"), per_device_train_batch_size=BATCH_SIZE, per_device_eval_batch_size=BATCH_SIZE, save_total_limit=5, num_train_epochs=50, save_strategy=\"epoch\", evaluation_strategy=\"epoch\", logging_strategy=\"epoch\", weight_decay=0.01, metric_for_best_model=\"f1\", load_best_model_at_end=True, ) result = Trainer( model=model, args=training_args, train_dataset=dataset[\"train\"], eval_dataset=dataset[\"test\"], data_collator=data_collator, compute_metrics=compute_metrics, callbacks=[EarlyStoppingCallback(early_stopping_patience=5)], ).train() 0%| | 0/1 [00:00.p-SplitPanel-handle,.jupyter-wrapper .lm-SplitPanel[data-orientation=horizontal]>.lm-SplitPanel-handle{cursor:ew-resize}.jupyter-wrapper .p-SplitPanel[data-orientation=vertical]>.p-SplitPanel-handle,.jupyter-wrapper .lm-SplitPanel[data-orientation=vertical]>.lm-SplitPanel-handle{cursor:ns-resize}.jupyter-wrapper .p-SplitPanel[data-orientation=horizontal]>.p-SplitPanel-handle:after,.jupyter-wrapper .lm-SplitPanel[data-orientation=horizontal]>.lm-SplitPanel-handle:after{left:50%;min-width:8px;transform:translateX(-50%)}.jupyter-wrapper .p-SplitPanel[data-orientation=vertical]>.p-SplitPanel-handle:after,.jupyter-wrapper 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Simple example: data engineering \u00b6 Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best practices. In short, we train a domain classifier on the semantic scholar dataset by taking full advantage of great-ai . Subsequently, we create a production-ready deployment. The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook. Since the true scope of great-ai is the phase between proof-of-concept code and production-ready service, it is predominantly used in the deployment notebook . Feel free to skip there, or continue reading if you'd like to see the full picture. Extract \u00b6 This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this example, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 4 MAX_CHUNK_COUNT = 4 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"\"\"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\"\"\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"\"\"Processing {len(chunks)} out of the { len(manifest.split()) } available chunks\"\"\" Out[2]: 'Processing 4 out of the 6002 available chunks' Transform \u00b6 Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.simple_parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map , clean , is_english , predict_language , unchunk , ) def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f \"open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } ' f ' { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if ( c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ]))) ] preprocessed_data = unchunk ( simple_parallel_map ( preprocess_chunk , chunks , concurrency = 4 ) ) from typing import List, Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map, clean, is_english, predict_language, unchunk, ) def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f\"open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} ' f'{c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if (c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))) ] preprocessed_data = unchunk( simple_parallel_map(preprocess_chunk, chunks, concurrency=4) ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [04:22<00:00, 65.51s/it] In [4]: Copied! X , y = zip ( * preprocessed_data ) # X is the input, y is the expected output X, y = zip(*preprocessed_data) # X is the input, y is the expected output Load \u00b6 Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each data-point with a split label according to the ratios we set. Additional tags can be also given. Production-ready backend \u00b6 The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service Otherwise, TinyDB is used which is just a local JSON file. In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Next: Part 2 \u00b6","title":"Simple example: data engineering"},{"location":"examples/simple/data/#simple-example-data-engineering","text":"Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best practices. In short, we train a domain classifier on the semantic scholar dataset by taking full advantage of great-ai . Subsequently, we create a production-ready deployment. The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook. Since the true scope of great-ai is the phase between proof-of-concept code and production-ready service, it is predominantly used in the deployment notebook . Feel free to skip there, or continue reading if you'd like to see the full picture.","title":"Simple example: data engineering"},{"location":"examples/simple/data/#extract","text":"This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this example, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 4 MAX_CHUNK_COUNT = 4 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"\"\"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\"\"\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"\"\"Processing {len(chunks)} out of the { len(manifest.split()) } available chunks\"\"\" Out[2]: 'Processing 4 out of the 6002 available chunks'","title":"Extract"},{"location":"examples/simple/data/#transform","text":"Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.simple_parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map , clean , is_english , predict_language , unchunk , ) def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f \"open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } ' f ' { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if ( c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ]))) ] preprocessed_data = unchunk ( simple_parallel_map ( preprocess_chunk , chunks , concurrency = 4 ) ) from typing import List, Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map, clean, is_english, predict_language, unchunk, ) def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f\"open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} ' f'{c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if (c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))) ] preprocessed_data = unchunk( simple_parallel_map(preprocess_chunk, chunks, concurrency=4) ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [04:22<00:00, 65.51s/it] In [4]: Copied! X , y = zip ( * preprocessed_data ) # X is the input, y is the expected output X, y = zip(*preprocessed_data) # X is the input, y is the expected output","title":"Transform"},{"location":"examples/simple/data/#load","text":"Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each data-point with a split label according to the ratios we set. Additional tags can be also given.","title":"Load"},{"location":"examples/simple/data/#production-ready-backend","text":"The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service Otherwise, TinyDB is used which is just a local JSON file. In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices","title":"Production-ready backend"},{"location":"examples/simple/data/#next-part-2","text":"","title":"Next: Part 2"},{"location":"examples/simple/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Hardening and deployment \u00b6 Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment. Create the inference function \u00b6 Next to the prediction, we also return the top-n most influential words based on their weights. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validate = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validate=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of small-domain-prediction Latest version of small-domain-prediction is 2 (from versions: 0, 1, 2) File small-domain-prediction-2 found in cache Check accuracy on the test split \u00b6 Anything under if __name__ == \"__main__\": will not be run when the script is executed by the great-ai CLI app. This, combined with query_ground_truth and the /traces/{trace_id}/feedback endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment. In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 19 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 16 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"ss-confusion.png\" , dpi = 600 ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (19, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 16 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"ss-confusion.png\", dpi=600) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12272/12272 [01:10<00:00, 174.77it/s] precision recall f1-score support Art 0.54 0.38 0.45 126 Biology 0.77 0.84 0.80 1215 Business 0.47 0.73 0.57 311 Chemistry 0.82 0.67 0.74 1205 Computer Science 0.77 0.76 0.76 1277 Economics 0.69 0.55 0.61 270 Engineering 0.55 0.52 0.53 754 Environmental Science 0.56 0.55 0.55 227 Geography 0.54 0.39 0.45 276 Geology 0.74 0.67 0.70 265 History 0.35 0.18 0.24 140 Materials Science 0.72 0.81 0.76 1011 Mathematics 0.77 0.70 0.74 498 Medicine 0.96 0.77 0.86 2835 Philosophy 0.57 0.06 0.10 71 Physics 0.66 0.75 0.70 611 Political Science 0.44 0.61 0.51 291 Psychology 0.52 0.84 0.64 574 Sociology 0.33 0.59 0.42 315 accuracy 0.71 12272 macro avg 0.62 0.60 0.59 12272 weighted avg 0.74 0.71 0.71 12272","title":"Hardening and deployment"},{"location":"examples/simple/deploy/#hardening-and-deployment","text":"Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment.","title":"Hardening and deployment"},{"location":"examples/simple/deploy/#create-the-inference-function","text":"Next to the prediction, we also return the top-n most influential words based on their weights. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validate = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validate=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of small-domain-prediction Latest version of small-domain-prediction is 2 (from versions: 0, 1, 2) File small-domain-prediction-2 found in cache","title":"Create the inference function"},{"location":"examples/simple/deploy/#check-accuracy-on-the-test-split","text":"Anything under if __name__ == \"__main__\": will not be run when the script is executed by the great-ai CLI app. This, combined with query_ground_truth and the /traces/{trace_id}/feedback endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment. In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 19 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 16 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"ss-confusion.png\" , dpi = 600 ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (19, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 16 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"ss-confusion.png\", dpi=600) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12272/12272 [01:10<00:00, 174.77it/s] precision recall f1-score support Art 0.54 0.38 0.45 126 Biology 0.77 0.84 0.80 1215 Business 0.47 0.73 0.57 311 Chemistry 0.82 0.67 0.74 1205 Computer Science 0.77 0.76 0.76 1277 Economics 0.69 0.55 0.61 270 Engineering 0.55 0.52 0.53 754 Environmental Science 0.56 0.55 0.55 227 Geography 0.54 0.39 0.45 276 Geology 0.74 0.67 0.70 265 History 0.35 0.18 0.24 140 Materials Science 0.72 0.81 0.76 1011 Mathematics 0.77 0.70 0.74 498 Medicine 0.96 0.77 0.86 2835 Philosophy 0.57 0.06 0.10 71 Physics 0.66 0.75 0.70 611 Political Science 0.44 0.61 0.51 291 Psychology 0.52 0.84 0.64 574 Sociology 0.33 0.59 0.42 315 accuracy 0.71 12272 macro avg 0.62 0.60 0.59 12272 weighted avg 0.74 0.71 0.71 12272","title":"Check accuracy on the test split"},{"location":"examples/simple/stacked-bars/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) from great_ai import query_ground_truth data = query_ground_truth(\"train\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [9]: Copied! from collections import Counter import matplotlib.pyplot as plt from great_ai.utilities import unique import numpy as np import matplotlib.ticker as mtick first = Counter ( d . feedback [ 0 ] for d in data if len ( d . feedback ) > 0 ) second = Counter ( d . feedback [ 1 ] for d in data if len ( d . feedback ) > 1 ) third = Counter ( d . feedback [ 2 ] for d in data if len ( d . feedback ) > 2 ) domains = sorted ( unique ( domain for d in data for domain in d . feedback )) first = [ first [ d ] / len ( data ) for d in domains ] second = [ second [ d ] / len ( data ) for d in domains ] third = [ third [ d ] / len ( data ) for d in domains ] # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 20 plt . rcParams [ \"figure.figsize\" ] = ( 14 , 10 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , first , label = \"Primary domain\" ) ax . bar ( domains , second , label = \"Secondary domain\" , bottom = first ) ax . bar ( domains , third , label = \"Tertiary domain\" , bottom = np . add ( first , second )) ax . yaxis . set_major_formatter ( mtick . PercentFormatter ( 1 )) ax . legend () ax . set_ylabel ( \"Ratio of all publications\" ) fig . tight_layout () fig . savefig ( \"ss-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt from great_ai.utilities import unique import numpy as np import matplotlib.ticker as mtick first = Counter(d.feedback[0] for d in data if len(d.feedback) > 0) second = Counter(d.feedback[1] for d in data if len(d.feedback) > 1) third = Counter(d.feedback[2] for d in data if len(d.feedback) > 2) domains = sorted(unique(domain for d in data for domain in d.feedback)) first = [first[d] / len(data) for d in domains] second = [second[d] / len(data) for d in domains] third = [third[d] / len(data) for d in domains] # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 20 plt.rcParams[\"figure.figsize\"] = (14, 10) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, first, label=\"Primary domain\") ax.bar(domains, second, label=\"Secondary domain\", bottom=first) ax.bar(domains, third, label=\"Tertiary domain\", bottom=np.add(first, second)) ax.yaxis.set_major_formatter(mtick.PercentFormatter(1)) ax.legend() ax.set_ylabel(\"Ratio of all publications\") fig.tight_layout() fig.savefig(\"ss-distribution.png\", dpi=500) None","title":"Stacked bars"},{"location":"examples/simple/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Optimise and train a model \u00b6 The blue boxes show the steps implemented in this notebook. In the first part, we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using great_ai.save_model . Load data that has been extracted in part 1 \u00b6 In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 5 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . xticks ( rotation = 90 ) plt . bar ( domains , counts ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 5) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.xticks(rotation=90) plt.bar(domains, counts) None Optimise and train Multinomial Naive Bayes classifier \u00b6 In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 14.549476 0.361685 8.476837 0.222398 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.518061 0.514842 0.511599 0.514834 0.002638 1 10 11.235289 0.130426 4.092868 0.082518 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513897 0.515661 0.507867 0.512475 0.003337 2 19 7.383645 0.138110 4.130709 0.250048 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.496825 0.501045 0.496854 0.498241 0.001983 3 11 10.435154 0.305144 3.882101 0.128886 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.493247 0.497814 0.502245 0.497769 0.003674 4 8 13.643193 0.310696 4.173707 0.142980 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.489609 0.495207 0.498154 0.494323 0.003544 5 22 7.048340 0.050070 3.172948 0.152418 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.487456 0.493865 0.491157 0.490826 0.002627 6 23 7.564685 0.146092 2.374111 0.285026 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.485160 0.494039 0.490127 0.489776 0.003633 7 20 7.172353 0.212599 3.747219 0.130217 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.481303 0.490002 0.488269 0.486524 0.003759 8 6 14.276345 0.456576 8.318859 0.268701 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482429 0.487735 0.484888 0.485017 0.002168 9 2 14.902358 0.737693 5.975091 0.171150 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.469598 0.474490 0.473637 0.472575 0.002134 10 9 12.677349 0.145143 4.374204 0.175674 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.468872 0.476451 0.470921 0.472082 0.003201 11 5 13.423686 0.482872 8.008324 0.442975 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.465726 0.474548 0.471879 0.470718 0.003694 12 1 13.819117 0.838347 6.161175 0.336590 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.463395 0.473982 0.471262 0.469546 0.004489 13 4 13.281476 0.588822 8.335852 0.254627 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458734 0.468053 0.464418 0.463735 0.003835 14 14 7.282247 0.444940 3.567094 0.044519 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.438189 0.450160 0.446180 0.444843 0.004978 15 17 7.098797 0.196241 3.838628 0.091128 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.436488 0.444503 0.445900 0.442297 0.004147 16 18 7.492791 0.288889 3.843224 0.073438 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428196 0.431945 0.430160 0.430100 0.001531 17 21 7.229826 0.099823 3.656332 0.073780 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.403130 0.410170 0.409801 0.407700 0.003235 18 13 7.158370 0.169818 3.765632 0.082924 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.399237 0.412872 0.407982 0.406697 0.005640 19 16 7.064643 0.119529 3.810983 0.125897 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.388060 0.399247 0.396325 0.394544 0.004737 20 0 13.749660 0.465174 6.407841 0.549166 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.384852 0.386487 0.386796 0.386045 0.000853 21 3 15.954147 0.318013 6.337361 0.261697 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.369785 0.375645 0.375858 0.373763 0.002814 22 12 7.120198 0.050452 3.833905 0.069540 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.277741 0.280564 0.285337 0.281214 0.003135 23 15 7.497707 0.183054 3.870714 0.062888 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.255578 0.263381 0.266184 0.261714 0.004487 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False) Export the model using GreatAI \u00b6 In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) Fetching cached versions of small-domain-prediction Copying file for small-domain-prediction-2 Compressing small-domain-prediction-2 Model small-domain-prediction uploaded with version 2 Out[6]: 'small-domain-prediction:2' Next: Part 3 \u00b6","title":"Optimise and train a model"},{"location":"examples/simple/train/#optimise-and-train-a-model","text":"The blue boxes show the steps implemented in this notebook. In the first part, we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using great_ai.save_model .","title":"Optimise and train a model"},{"location":"examples/simple/train/#load-data-that-has-been-extracted-in-part-1","text":"In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 5 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . xticks ( rotation = 90 ) plt . bar ( domains , counts ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 5) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.xticks(rotation=90) plt.bar(domains, counts) None","title":"Load data that has been extracted in part 1"},{"location":"examples/simple/train/#optimise-and-train-multinomial-naive-bayes-classifier","text":"In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 14.549476 0.361685 8.476837 0.222398 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.518061 0.514842 0.511599 0.514834 0.002638 1 10 11.235289 0.130426 4.092868 0.082518 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513897 0.515661 0.507867 0.512475 0.003337 2 19 7.383645 0.138110 4.130709 0.250048 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.496825 0.501045 0.496854 0.498241 0.001983 3 11 10.435154 0.305144 3.882101 0.128886 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.493247 0.497814 0.502245 0.497769 0.003674 4 8 13.643193 0.310696 4.173707 0.142980 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.489609 0.495207 0.498154 0.494323 0.003544 5 22 7.048340 0.050070 3.172948 0.152418 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.487456 0.493865 0.491157 0.490826 0.002627 6 23 7.564685 0.146092 2.374111 0.285026 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.485160 0.494039 0.490127 0.489776 0.003633 7 20 7.172353 0.212599 3.747219 0.130217 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.481303 0.490002 0.488269 0.486524 0.003759 8 6 14.276345 0.456576 8.318859 0.268701 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482429 0.487735 0.484888 0.485017 0.002168 9 2 14.902358 0.737693 5.975091 0.171150 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.469598 0.474490 0.473637 0.472575 0.002134 10 9 12.677349 0.145143 4.374204 0.175674 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.468872 0.476451 0.470921 0.472082 0.003201 11 5 13.423686 0.482872 8.008324 0.442975 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.465726 0.474548 0.471879 0.470718 0.003694 12 1 13.819117 0.838347 6.161175 0.336590 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.463395 0.473982 0.471262 0.469546 0.004489 13 4 13.281476 0.588822 8.335852 0.254627 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458734 0.468053 0.464418 0.463735 0.003835 14 14 7.282247 0.444940 3.567094 0.044519 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.438189 0.450160 0.446180 0.444843 0.004978 15 17 7.098797 0.196241 3.838628 0.091128 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.436488 0.444503 0.445900 0.442297 0.004147 16 18 7.492791 0.288889 3.843224 0.073438 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428196 0.431945 0.430160 0.430100 0.001531 17 21 7.229826 0.099823 3.656332 0.073780 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.403130 0.410170 0.409801 0.407700 0.003235 18 13 7.158370 0.169818 3.765632 0.082924 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.399237 0.412872 0.407982 0.406697 0.005640 19 16 7.064643 0.119529 3.810983 0.125897 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.388060 0.399247 0.396325 0.394544 0.004737 20 0 13.749660 0.465174 6.407841 0.549166 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.384852 0.386487 0.386796 0.386045 0.000853 21 3 15.954147 0.318013 6.337361 0.261697 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.369785 0.375645 0.375858 0.373763 0.002814 22 12 7.120198 0.050452 3.833905 0.069540 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.277741 0.280564 0.285337 0.281214 0.003135 23 15 7.497707 0.183054 3.870714 0.062888 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.255578 0.263381 0.266184 0.261714 0.004487 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False)","title":"Optimise and train Multinomial Naive Bayes classifier"},{"location":"examples/simple/train/#export-the-model-using-greatai","text":"In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) Fetching cached versions of small-domain-prediction Copying file for small-domain-prediction-2 Compressing small-domain-prediction-2 Model small-domain-prediction uploaded with version 2 Out[6]: 'small-domain-prediction:2'","title":"Export the model using GreatAI"},{"location":"examples/simple/train/#next-part-3","text":"","title":"Next: Part 3"},{"location":"examples/simple-mag/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a field of study (domain) classification of sentences on the MAG dataset \u00b6 SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , simple_parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"../../tutorial/data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"../../tutorial/data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, simple_parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"../../tutorial/data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"../../tutorial/data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:16<00:00, 4964.08it/s] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:05<00:00, 3847.04it/s] In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y_train ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 18 plt . rcParams [ \"figure.figsize\" ] = ( 8 , 5 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , counts ) ax . set_ylabel ( \"Count\" ) fig . tight_layout () fig . savefig ( \"mag-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y_train).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 18 plt.rcParams[\"figure.figsize\"] = (8, 5) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, counts) ax.set_ylabel(\"Count\") fig.tight_layout() fig.savefig(\"mag-distribution.png\", dpi=500) None In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 2.352721 0.257936 1.357399 0.094580 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 2.544958 0.051976 1.245658 0.137108 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 2.472888 0.053978 1.447181 0.051170 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 2.467637 0.005358 1.216254 0.048319 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 2.477612 0.134880 1.428521 0.017417 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 2.484810 0.067836 1.249979 0.209959 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 2.452453 0.049376 1.279694 0.089200 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 2.748628 0.098077 1.255213 0.101334 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 2.371907 0.204820 1.375143 0.193984 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 2.557486 0.184332 1.272137 0.133980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 2.362093 0.109796 1.344960 0.083562 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 2.668012 0.131751 1.383441 0.049157 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 2.517576 0.154192 1.374269 0.026773 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 2.386240 0.069718 0.967638 0.076766 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 2.416211 0.138001 1.316785 0.036402 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 2.397768 0.244951 1.330007 0.008983 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 2.590127 0.054396 1.155976 0.187602 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 2.497556 0.085952 1.169808 0.061336 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 2.467611 0.168399 1.313936 0.109695 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 2.339322 0.092595 1.209515 0.132825 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 2.644488 0.119967 1.238554 0.048871 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 2.456030 0.072136 1.240463 0.092383 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 2.335796 0.082338 0.786187 0.073499 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 2.530331 0.107199 1.247842 0.078229 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [12]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 22 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"mag-confusion.png\" , dpi = 600 ) from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 22 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"mag-confusion.png\", dpi=600) precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"examples/simple-mag/train/#train-a-field-of-study-domain-classification-of-sentences-on-the-mag-dataset","text":"SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , simple_parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"../../tutorial/data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"../../tutorial/data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, simple_parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"../../tutorial/data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"../../tutorial/data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:16<00:00, 4964.08it/s] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:05<00:00, 3847.04it/s] In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y_train ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 18 plt . rcParams [ \"figure.figsize\" ] = ( 8 , 5 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , counts ) ax . set_ylabel ( \"Count\" ) fig . tight_layout () fig . savefig ( \"mag-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y_train).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 18 plt.rcParams[\"figure.figsize\"] = (8, 5) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, counts) ax.set_ylabel(\"Count\") fig.tight_layout() fig.savefig(\"mag-distribution.png\", dpi=500) None In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 2.352721 0.257936 1.357399 0.094580 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 2.544958 0.051976 1.245658 0.137108 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 2.472888 0.053978 1.447181 0.051170 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 2.467637 0.005358 1.216254 0.048319 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 2.477612 0.134880 1.428521 0.017417 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 2.484810 0.067836 1.249979 0.209959 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 2.452453 0.049376 1.279694 0.089200 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 2.748628 0.098077 1.255213 0.101334 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 2.371907 0.204820 1.375143 0.193984 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 2.557486 0.184332 1.272137 0.133980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 2.362093 0.109796 1.344960 0.083562 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 2.668012 0.131751 1.383441 0.049157 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 2.517576 0.154192 1.374269 0.026773 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 2.386240 0.069718 0.967638 0.076766 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 2.416211 0.138001 1.316785 0.036402 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 2.397768 0.244951 1.330007 0.008983 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 2.590127 0.054396 1.155976 0.187602 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 2.497556 0.085952 1.169808 0.061336 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 2.467611 0.168399 1.313936 0.109695 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 2.339322 0.092595 1.209515 0.132825 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 2.644488 0.119967 1.238554 0.048871 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 2.456030 0.072136 1.240463 0.092383 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 2.335796 0.082338 0.786187 0.073499 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 2.530331 0.107199 1.247842 0.078229 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [12]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 22 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"mag-confusion.png\" , dpi = 600 ) from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 22 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"mag-confusion.png\", dpi=600) precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"how-to-guides/call-remote/","text":"How to call remote GreatAI instances # Microservices architecture (or SOA ) work well with ML applications. This is because their interfaces are usually very narrow, while the functionality provided quite comprehensive. Hence, drawing the boundaries of responsibilities is more straightforward in the case of ML services than in the case of more traditional business applications. For this reason, it is common to have a tree of models (preferably wrapped in GreatAI instances) communicating with each other. Although regular HTTP POST requests could be sent to each service's /predict endpoint, great-ai comes with two convenience functions: call_remote_great_ai and call_remote_great_ai_async to wrap this request. These provide you with some level of robustness and deserialisation. Inside notebooks The async variant, call_remote_great_ai_async , requires a running event loop while the synchronous variant disallows other running event-loops. Therefore, when running inside a Jupyter Notebook, always call call_remote_great_ai_async . Simple example # Let's create two processes: a server and a client. Server # server.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def slow_greeter ( your_name ): await sleep ( 2 ) return f 'Hi { your_name } !' Run this in development mode by executing great-ai server.py or python3 -m great_ai server.py if you're on Windows and great-ai is not in your PATH . Client # client.py from great_ai import call_remote_great_ai names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] results = [ call_remote_great_ai ( 'http://localhost:6060' , { 'your_name' : name } ) . output #(1) for name in names ] print ( results ) Only return the outputs so we don't clutter up the terminal. Run this script as a regular Python script by executing python3 client.py . As you can see, everything worked as expected. There is one way to improve it though. An async example # Let's send multiple requests at the same time to speed up the overall execution time. To do this, we will use the call_remote_great_ai_async function. Why is this possible? Note, that in server.py , the inference function is declared async . This means that multiple \"copies\" of it can run at the same time in the same thread. Since, there is no CPU bottleneck, the server has a quite large throughput (requests responded to per second), but its latency will stay around 2 seconds due to the async sleep command. If your great-ai server is not async , higher throughput can be achieved by running multiple instances of it, either manually, or by running it with multiple uvicorn workers like this: ENVIRONMENT=production great-ai server.py --worker_count 4 Async client # async-client.py from great_ai import call_remote_great_ai_async import asyncio names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] async def main (): futures = [ call_remote_great_ai_async ( 'http://localhost:6060' , { 'your_name' : name } ) for name in names ] results = await asyncio . gather ( * futures ) print ([ r . output for r in results ]) asyncio . run ( main ()) Replace client.py with this async client. Note that even though async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart. This also works, and in some use-cases might be considerably quicker.","title":"How to call remote GreatAI instances"},{"location":"how-to-guides/call-remote/#how-to-call-remote-greatai-instances","text":"Microservices architecture (or SOA ) work well with ML applications. This is because their interfaces are usually very narrow, while the functionality provided quite comprehensive. Hence, drawing the boundaries of responsibilities is more straightforward in the case of ML services than in the case of more traditional business applications. For this reason, it is common to have a tree of models (preferably wrapped in GreatAI instances) communicating with each other. Although regular HTTP POST requests could be sent to each service's /predict endpoint, great-ai comes with two convenience functions: call_remote_great_ai and call_remote_great_ai_async to wrap this request. These provide you with some level of robustness and deserialisation. Inside notebooks The async variant, call_remote_great_ai_async , requires a running event loop while the synchronous variant disallows other running event-loops. Therefore, when running inside a Jupyter Notebook, always call call_remote_great_ai_async .","title":"How to call remote GreatAI instances"},{"location":"how-to-guides/call-remote/#simple-example","text":"Let's create two processes: a server and a client.","title":"Simple example"},{"location":"how-to-guides/call-remote/#server","text":"server.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def slow_greeter ( your_name ): await sleep ( 2 ) return f 'Hi { your_name } !' Run this in development mode by executing great-ai server.py or python3 -m great_ai server.py if you're on Windows and great-ai is not in your PATH .","title":"Server"},{"location":"how-to-guides/call-remote/#client","text":"client.py from great_ai import call_remote_great_ai names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] results = [ call_remote_great_ai ( 'http://localhost:6060' , { 'your_name' : name } ) . output #(1) for name in names ] print ( results ) Only return the outputs so we don't clutter up the terminal. Run this script as a regular Python script by executing python3 client.py . As you can see, everything worked as expected. There is one way to improve it though.","title":"Client"},{"location":"how-to-guides/call-remote/#an-async-example","text":"Let's send multiple requests at the same time to speed up the overall execution time. To do this, we will use the call_remote_great_ai_async function. Why is this possible? Note, that in server.py , the inference function is declared async . This means that multiple \"copies\" of it can run at the same time in the same thread. Since, there is no CPU bottleneck, the server has a quite large throughput (requests responded to per second), but its latency will stay around 2 seconds due to the async sleep command. If your great-ai server is not async , higher throughput can be achieved by running multiple instances of it, either manually, or by running it with multiple uvicorn workers like this: ENVIRONMENT=production great-ai server.py --worker_count 4","title":"An async example"},{"location":"how-to-guides/call-remote/#async-client","text":"async-client.py from great_ai import call_remote_great_ai_async import asyncio names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] async def main (): futures = [ call_remote_great_ai_async ( 'http://localhost:6060' , { 'your_name' : name } ) for name in names ] results = await asyncio . gather ( * futures ) print ([ r . output for r in results ]) asyncio . run ( main ()) Replace client.py with this async client. Note that even though async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart. This also works, and in some use-cases might be considerably quicker.","title":"Async client"},{"location":"how-to-guides/configure-service/","text":"How to configure GreatAI # GreatAI aims to provide reasonable defaults wherever possible. The current configuration is always prominently displayed (and updated) on the dashboard and in the command-line start-up banner. Using great_ai.configure # You can override any of the default settings by calling great_ai.configure . If you don't call configure , the default settings are applied on the first call to most great-ai functions. Warning You must call great_ai.configure before calling (or decorating with) any other great-ai function. However, importing other functions before calling great_ai.configure is permitted. configure-demo.py from great_ai import configure , RouteConfig import logging configure ( version = '1.0.0' , log_level = logging . INFO , seed = 2 , should_log_exception_stack = False , prediction_cache_size = 0 , #(1) disable_se4ml_banner = True , dashboard_table_size = 200 , route_config = RouteConfig ( #(2) feedback_endpoints_enabled = False , dashboard_enabled = False ) ) Completely disable caching. The unspecified routes are enabled by default. Using remote storage # The only aspect that cannot be automated is choosing the backing storage for the database and file storage. Right now, you have 3 options for storing the models and large datasets: LargeFileLocal , LargeFileMongo , and LargeFileS3 . Without explicit configuration, LargeFileLocal is selected by default. This one still version-controls your files but it only stores them in a local path (which of course can be a remote volume attached by NFS , HDFS , etc.). Important If your working directory contains a mongo.ini or s3.ini file, an attempt is made to auto-configure LargeFileMongo or LargeFileS3 respectively. To use LargeFileMongo or LargeFileS3 explicitly, configure them before calling any other great-ai function. S3-compatible # s3.ini aws_region_name = eu-west-2 aws_access_key_id = MY_AWS_ACCESS_KEY # ENV:MY_AWS_ACCESS_KEY would also work aws_secret_access_key = MY_AWS_SECRET_KEY large_files_bucket_name = bucket-for-models use-s3.py from great_ai.large_file import LargeFileS3 from great_ai import save_model LargeFileS3 . configure_credentials_from_file ( 's3.ini' ) #(1) model = [ 4 , 3 ] save_model ( model , 'my-model' ) This line isn't strictly necessary because if s3.ini (or mongo.ini ) is available in the current working directory, they are automatically used to configure their respective LargeFile implementations/databases. Departing from AWS With the aws_endpoint_url argument, it is possible to use any other S3-compatible service such as Backblaze . In that case, it would be aws_endpoint_url=https://s3.us-west-002.backblazeb2.com . GridFS # GridFS specifies how to store files in MongoDB. The official MongoDB server and many compatible implementations support it. mongo.ini MONGO_CONNECTION_STRING = mongodb : // localhost : 27017 # this is the default value # if `MONGO_CONNECTION_STRING` is specified, this default is overridden MONGO_CONNECTION_STRING = ENV : MONGO_CONNECTION_STRING MONGO_DATABASE = my-database # it is automatically created if doesn't exist use-mongo.py from great_ai.large_file import LargeFileMongo from great_ai import save_model LargeFileMongo . configure_credentials_from_file ( 'mongo.ini' ) model = [ 4 , 3 ] save_model ( model , 'my-model' ) Simplifying config files You can combine mongo.ini or s3.ini with your application's config file because the unneeded keys are ignored by the configure_credentials_from_file method. Using a database # By default, a thread-safe version of TinyDB is utilised for saving the prediction traces into a local file. Unfortunately, for most production needs, this method is not suitable. MongoDB # At the moment, only MongoDB is supported as a production-ready TracingDatabase . In order to use it, you have to either place a file named mongo.ini in your working directory, or explicitly call either MongoDbDriver.configure_credentials_from_file or MongoDbDriver.configure_credentials .","title":"How to configure GreatAI"},{"location":"how-to-guides/configure-service/#how-to-configure-greatai","text":"GreatAI aims to provide reasonable defaults wherever possible. The current configuration is always prominently displayed (and updated) on the dashboard and in the command-line start-up banner.","title":"How to configure GreatAI"},{"location":"how-to-guides/configure-service/#using-great_aiconfigure","text":"You can override any of the default settings by calling great_ai.configure . If you don't call configure , the default settings are applied on the first call to most great-ai functions. Warning You must call great_ai.configure before calling (or decorating with) any other great-ai function. However, importing other functions before calling great_ai.configure is permitted. configure-demo.py from great_ai import configure , RouteConfig import logging configure ( version = '1.0.0' , log_level = logging . INFO , seed = 2 , should_log_exception_stack = False , prediction_cache_size = 0 , #(1) disable_se4ml_banner = True , dashboard_table_size = 200 , route_config = RouteConfig ( #(2) feedback_endpoints_enabled = False , dashboard_enabled = False ) ) Completely disable caching. The unspecified routes are enabled by default.","title":"Using great_ai.configure"},{"location":"how-to-guides/configure-service/#using-remote-storage","text":"The only aspect that cannot be automated is choosing the backing storage for the database and file storage. Right now, you have 3 options for storing the models and large datasets: LargeFileLocal , LargeFileMongo , and LargeFileS3 . Without explicit configuration, LargeFileLocal is selected by default. This one still version-controls your files but it only stores them in a local path (which of course can be a remote volume attached by NFS , HDFS , etc.). Important If your working directory contains a mongo.ini or s3.ini file, an attempt is made to auto-configure LargeFileMongo or LargeFileS3 respectively. To use LargeFileMongo or LargeFileS3 explicitly, configure them before calling any other great-ai function.","title":"Using remote storage"},{"location":"how-to-guides/configure-service/#s3-compatible","text":"s3.ini aws_region_name = eu-west-2 aws_access_key_id = MY_AWS_ACCESS_KEY # ENV:MY_AWS_ACCESS_KEY would also work aws_secret_access_key = MY_AWS_SECRET_KEY large_files_bucket_name = bucket-for-models use-s3.py from great_ai.large_file import LargeFileS3 from great_ai import save_model LargeFileS3 . configure_credentials_from_file ( 's3.ini' ) #(1) model = [ 4 , 3 ] save_model ( model , 'my-model' ) This line isn't strictly necessary because if s3.ini (or mongo.ini ) is available in the current working directory, they are automatically used to configure their respective LargeFile implementations/databases. Departing from AWS With the aws_endpoint_url argument, it is possible to use any other S3-compatible service such as Backblaze . In that case, it would be aws_endpoint_url=https://s3.us-west-002.backblazeb2.com .","title":"S3-compatible"},{"location":"how-to-guides/configure-service/#gridfs","text":"GridFS specifies how to store files in MongoDB. The official MongoDB server and many compatible implementations support it. mongo.ini MONGO_CONNECTION_STRING = mongodb : // localhost : 27017 # this is the default value # if `MONGO_CONNECTION_STRING` is specified, this default is overridden MONGO_CONNECTION_STRING = ENV : MONGO_CONNECTION_STRING MONGO_DATABASE = my-database # it is automatically created if doesn't exist use-mongo.py from great_ai.large_file import LargeFileMongo from great_ai import save_model LargeFileMongo . configure_credentials_from_file ( 'mongo.ini' ) model = [ 4 , 3 ] save_model ( model , 'my-model' ) Simplifying config files You can combine mongo.ini or s3.ini with your application's config file because the unneeded keys are ignored by the configure_credentials_from_file method.","title":"GridFS"},{"location":"how-to-guides/configure-service/#using-a-database","text":"By default, a thread-safe version of TinyDB is utilised for saving the prediction traces into a local file. Unfortunately, for most production needs, this method is not suitable.","title":"Using a database"},{"location":"how-to-guides/configure-service/#mongodb","text":"At the moment, only MongoDB is supported as a production-ready TracingDatabase . In order to use it, you have to either place a file named mongo.ini in your working directory, or explicitly call either MongoDbDriver.configure_credentials_from_file or MongoDbDriver.configure_credentials .","title":"MongoDB"},{"location":"how-to-guides/create-service/","text":"How to create a GreatAI service # The core value of great-ai lies in its GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function using the @GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of @GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead. With types # Even though it's not required by GreatAI, type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard . With async # Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-bound operation return f 'Hi { your_name } !' With decorators # GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create must come last. There are two built-in decorators that you can use to customise your function but you can you use any third-party decorator as well. Using @use_model # If you have previously saved a model with save_model , you can inject it into your function by calling @use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @use_model in other places of the codebase, it works equally well outside of GreatAI services. Using @parameter # If you wish to turn off logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model are automatically decorated with @parameter when @GreatAI.create is called. greeter_with_validation.py from great_ai import GreatAI , use_model @GreatAI . create @parameter ( 'your_name' , disable_logging = True ) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' Important You must call @parameter before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @parameter in other places of the codebase, it works equally well outside of GreatAI services. Complex example # Refer to the following example summarising the options you have when instantiating a GreatAI service. complex.py from great_ai import save_model , GreatAI , parameter , use_model , log_metric save_model ( 4 , 'secret-number' ) #(1) @GreatAI . create @parameter ( 'positive_number' , validate = lambda n : n > 0 , disable_logging = True ) @use_model ( 'secret-number' , version = 'latest' , model_kwarg_name = 'secret' ) def add_number ( positive_number : int , secret : int ) -> int : log_metric ( 'log directly into the returned Trace' , positive_number * 2 ) return positive_number + secret assert add_number ( 1 ) . output == 5 Refer to the configuration page for specifying where to store your models.","title":"How to create a GreatAI service"},{"location":"how-to-guides/create-service/#how-to-create-a-greatai-service","text":"The core value of great-ai lies in its GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function using the @GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of @GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead.","title":"How to create a GreatAI service"},{"location":"how-to-guides/create-service/#with-types","text":"Even though it's not required by GreatAI, type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard .","title":"With types"},{"location":"how-to-guides/create-service/#with-async","text":"Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-bound operation return f 'Hi { your_name } !'","title":"With async"},{"location":"how-to-guides/create-service/#with-decorators","text":"GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create must come last. There are two built-in decorators that you can use to customise your function but you can you use any third-party decorator as well.","title":"With decorators"},{"location":"how-to-guides/create-service/#using-use_model","text":"If you have previously saved a model with save_model , you can inject it into your function by calling @use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @use_model in other places of the codebase, it works equally well outside of GreatAI services.","title":"Using @use_model"},{"location":"how-to-guides/create-service/#using-parameter","text":"If you wish to turn off logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model are automatically decorated with @parameter when @GreatAI.create is called. greeter_with_validation.py from great_ai import GreatAI , use_model @GreatAI . create @parameter ( 'your_name' , disable_logging = True ) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' Important You must call @parameter before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @parameter in other places of the codebase, it works equally well outside of GreatAI services.","title":"Using @parameter"},{"location":"how-to-guides/create-service/#complex-example","text":"Refer to the following example summarising the options you have when instantiating a GreatAI service. complex.py from great_ai import save_model , GreatAI , parameter , use_model , log_metric save_model ( 4 , 'secret-number' ) #(1) @GreatAI . create @parameter ( 'positive_number' , validate = lambda n : n > 0 , disable_logging = True ) @use_model ( 'secret-number' , version = 'latest' , model_kwarg_name = 'secret' ) def add_number ( positive_number : int , secret : int ) -> int : log_metric ( 'log directly into the returned Trace' , positive_number * 2 ) return positive_number + secret assert add_number ( 1 ) . output == 5 Refer to the configuration page for specifying where to store your models.","title":"Complex example"},{"location":"how-to-guides/handle-training-data/","text":"How to manage training data # In order to simplify your training data management, great-ai provide two complementing approaches for inputting new data-points. Upload data # At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call great_ai.add_ground_truth . This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, great_ai.query_ground_truth can be called to get a filtered view of the training data. Train-test-validation splits It is a best practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it. With great_ai.add_ground_truth , there is an option to tag the samples with train , test , and validation randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags. The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces. from great_ai import add_ground_truth add_ground_truth ( [ 1 , 2 ], [ 'odd' , 'even' ], tags = 'my_tag' , train_split_ratio = 1 , #(1) test_split_ratio = 1 ) Note that the ratios don't have to add up to 1. They are just weights. There is also a validation_split_ratio which is 0 by default. >>> from great_ai import query_ground_truth >>> query_ground_truth ( 'my_tag' ) [ Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'odd' , #(1) 'logged_values' : { 'input' : 1 }, #(2) 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'odd' , 'tags' : [ 'ground_truth' , 'test' , 'my_tag' ], #(3) 'trace_id' : '4fcf2ce6-a172-469d-94b2-874577655814' }), Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'even' , 'logged_values' : { 'input' : 2 }, 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'even' , 'tags' : [ 'ground_truth' , 'train' , 'my_tag' ], 'trace_id' : 'abee0671-beb9-4284-8c3b-c65e5836ce38' })] Expected output. This can be also accessed through the .output property. The input value is stored here. Notice how ground_truth is always included as a tag when using great_ai.add_ground_truth . Get feedback # After the initial data gathering, end-to-end feedback can be also integrated into the dataset. The scaffolded REST API contains endpoints for managing traces and their feedbacks. When great_ai.query_ground_truth is executed, it implicitly filters for traces that have feedback. Therefore, both the ground_truth and the online traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API. Remove clutter # Traces can be deleted either through the REST API or by calling great_ai.delete_ground_truth . The latter provides the same interface as great_ai.query_ground_truth except it deletes the matched points.","title":"How to manage training data"},{"location":"how-to-guides/handle-training-data/#how-to-manage-training-data","text":"In order to simplify your training data management, great-ai provide two complementing approaches for inputting new data-points.","title":"How to manage training data"},{"location":"how-to-guides/handle-training-data/#upload-data","text":"At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call great_ai.add_ground_truth . This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, great_ai.query_ground_truth can be called to get a filtered view of the training data. Train-test-validation splits It is a best practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it. With great_ai.add_ground_truth , there is an option to tag the samples with train , test , and validation randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags. The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces. from great_ai import add_ground_truth add_ground_truth ( [ 1 , 2 ], [ 'odd' , 'even' ], tags = 'my_tag' , train_split_ratio = 1 , #(1) test_split_ratio = 1 ) Note that the ratios don't have to add up to 1. They are just weights. There is also a validation_split_ratio which is 0 by default. >>> from great_ai import query_ground_truth >>> query_ground_truth ( 'my_tag' ) [ Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'odd' , #(1) 'logged_values' : { 'input' : 1 }, #(2) 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'odd' , 'tags' : [ 'ground_truth' , 'test' , 'my_tag' ], #(3) 'trace_id' : '4fcf2ce6-a172-469d-94b2-874577655814' }), Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'even' , 'logged_values' : { 'input' : 2 }, 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'even' , 'tags' : [ 'ground_truth' , 'train' , 'my_tag' ], 'trace_id' : 'abee0671-beb9-4284-8c3b-c65e5836ce38' })] Expected output. This can be also accessed through the .output property. The input value is stored here. Notice how ground_truth is always included as a tag when using great_ai.add_ground_truth .","title":"Upload data"},{"location":"how-to-guides/handle-training-data/#get-feedback","text":"After the initial data gathering, end-to-end feedback can be also integrated into the dataset. The scaffolded REST API contains endpoints for managing traces and their feedbacks. When great_ai.query_ground_truth is executed, it implicitly filters for traces that have feedback. Therefore, both the ground_truth and the online traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API.","title":"Get feedback"},{"location":"how-to-guides/handle-training-data/#remove-clutter","text":"Traces can be deleted either through the REST API or by calling great_ai.delete_ground_truth . The latter provides the same interface as great_ai.query_ground_truth except it deletes the matched points.","title":"Remove clutter"},{"location":"how-to-guides/install/","text":"Installation guide # Provided you already have Python3 (and pip) installed, simply execute: pip install great-ai Python 3.7 or later is required. This will work on all major operating systems. Google Colab # In order to use GreatAI in Google Colab , you need to downgrade pyyaml to a Colab compatible version. See related StackOverflow question . pip install great-ai pyyaml == 5 .4.1 This will make GreatAI work in Colab. Command-line tools # After installation, great-ai and large-file are available as commands. The former is required for deploying your application while the latter lets you manage models and datasets from your terminal. Snakes & kebabs The library is called great-ai , therefore, its command-line entrypoint is also called great-ai . However, Python module names cannot contain hyphens, that's why you have to import great_ai with an underscore. The great-ai CLI tool is also available as python3 -m great_ai . To help with the confusion, a CLI executable called great_ai (and large_file ) are also installed, thus, if you prefer, you can always refer to GreatAI using its underscored name variant ( great_ai ). Windows On Windows, you might encounter a similar warning from pip : WARNING: The scripts great-ai.exe, great_ai.exe, large-file.exe and large_file.exe are installed in 'C:\\Users\\...\\Scripts' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. This means that great-ai.exe and large-file.exe are not in your PATH . Either add their containing directory ('C:\\Users...\\Scripts' in this case) to your PATH or use python3 -m great_ai and python3 -m great_ai.large_file instead of the exe-s. Update # If you wish to update to the latest version execute: pip install --upgrade great-ai Bleeding edge # You can also install the latest (usually unreleased) version from GitHub. pip install --upgrade git+https://github.com/schmelczer/great-ai.git Python 3.7 or later is required.","title":"Installation guide"},{"location":"how-to-guides/install/#installation-guide","text":"Provided you already have Python3 (and pip) installed, simply execute: pip install great-ai Python 3.7 or later is required. This will work on all major operating systems.","title":"Installation guide"},{"location":"how-to-guides/install/#google-colab","text":"In order to use GreatAI in Google Colab , you need to downgrade pyyaml to a Colab compatible version. See related StackOverflow question . pip install great-ai pyyaml == 5 .4.1 This will make GreatAI work in Colab.","title":"Google Colab"},{"location":"how-to-guides/install/#command-line-tools","text":"After installation, great-ai and large-file are available as commands. The former is required for deploying your application while the latter lets you manage models and datasets from your terminal. Snakes & kebabs The library is called great-ai , therefore, its command-line entrypoint is also called great-ai . However, Python module names cannot contain hyphens, that's why you have to import great_ai with an underscore. The great-ai CLI tool is also available as python3 -m great_ai . To help with the confusion, a CLI executable called great_ai (and large_file ) are also installed, thus, if you prefer, you can always refer to GreatAI using its underscored name variant ( great_ai ). Windows On Windows, you might encounter a similar warning from pip : WARNING: The scripts great-ai.exe, great_ai.exe, large-file.exe and large_file.exe are installed in 'C:\\Users\\...\\Scripts' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. This means that great-ai.exe and large-file.exe are not in your PATH . Either add their containing directory ('C:\\Users...\\Scripts' in this case) to your PATH or use python3 -m great_ai and python3 -m great_ai.large_file instead of the exe-s.","title":"Command-line tools"},{"location":"how-to-guides/install/#update","text":"If you wish to update to the latest version execute: pip install --upgrade great-ai","title":"Update"},{"location":"how-to-guides/install/#bleeding-edge","text":"You can also install the latest (usually unreleased) version from GitHub. pip install --upgrade git+https://github.com/schmelczer/great-ai.git Python 3.7 or later is required.","title":"Bleeding edge"},{"location":"how-to-guides/large-file/","text":"How to use LargeFiles # The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile , users have few reasons to use LargeFiles directly. Motivation # Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support deleting large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. DVC is a viable alternative, however, it requires users to learn to use one more CLI tool. Using LargeFile-s directly (usually not needed) LargeFile doesn't require users to learn too much new. It is a nearly exact copy of the built-in open() function of Python with which users are certainly already familiar. Simple example # from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version # leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # The latest version is returned by default # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : #(1) print ( f . readlines ()[ 0 ]) In this case, the latest version is already in the local cache, no download is required. More details # LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reads and writes are supported along with the different keywords open() accepts . The local cache can be configured with these properties: LargeFileS3 . cache_path = Path ( '.cache' ) LargeFileS3 . max_cache_size = \"30 GB\" I only need a path # In case you only need a path to the (proxy of the) remote file, this pattern can be applied: path_to_model = LargeFileS3 ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFileS3 ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFileS3(\"my-file\").delete() . It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned. From the command-line # The main reason for using the large-file or python3 -m great_ai.large_file commands is to upload or download models from the terminal. For example, when building a docker image, it is best practice to cache the referred models. Setup # Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files Upload some files # large-file --backend s3 --secrets secrets.ini \\ --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Using MongoDB The possible values for --backend are s3 , mongo , and local . The latter doesn't need credentials, it only versions and stores your files in a local folder. MongoDB on the other hand requires a mongo_connection_string and a mongo_database to be specified. For storing large files, it uses the GridFS specification. Download some files to the local cache # This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. large-file --backend s3 --secrets ~/.aws/credentials \\ --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s. Delete remote files # large-file --backend s3 --secrets ~/.aws/credentials \\ --delete my_first_file.json","title":"How to use LargeFiles"},{"location":"how-to-guides/large-file/#how-to-use-largefiles","text":"The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile , users have few reasons to use LargeFiles directly.","title":"How to use LargeFiles"},{"location":"how-to-guides/large-file/#motivation","text":"Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support deleting large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. DVC is a viable alternative, however, it requires users to learn to use one more CLI tool. Using LargeFile-s directly (usually not needed) LargeFile doesn't require users to learn too much new. It is a nearly exact copy of the built-in open() function of Python with which users are certainly already familiar.","title":"Motivation"},{"location":"how-to-guides/large-file/#simple-example","text":"from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version # leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # The latest version is returned by default # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : #(1) print ( f . readlines ()[ 0 ]) In this case, the latest version is already in the local cache, no download is required.","title":"Simple example"},{"location":"how-to-guides/large-file/#more-details","text":"LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reads and writes are supported along with the different keywords open() accepts . The local cache can be configured with these properties: LargeFileS3 . cache_path = Path ( '.cache' ) LargeFileS3 . max_cache_size = \"30 GB\"","title":"More details"},{"location":"how-to-guides/large-file/#i-only-need-a-path","text":"In case you only need a path to the (proxy of the) remote file, this pattern can be applied: path_to_model = LargeFileS3 ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFileS3 ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFileS3(\"my-file\").delete() . It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned.","title":"I only need a path"},{"location":"how-to-guides/large-file/#from-the-command-line","text":"The main reason for using the large-file or python3 -m great_ai.large_file commands is to upload or download models from the terminal. For example, when building a docker image, it is best practice to cache the referred models.","title":"From the command-line"},{"location":"how-to-guides/large-file/#setup","text":"Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files","title":"Setup"},{"location":"how-to-guides/large-file/#upload-some-files","text":"large-file --backend s3 --secrets secrets.ini \\ --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Using MongoDB The possible values for --backend are s3 , mongo , and local . The latter doesn't need credentials, it only versions and stores your files in a local folder. MongoDB on the other hand requires a mongo_connection_string and a mongo_database to be specified. For storing large files, it uses the GridFS specification.","title":"Upload some files"},{"location":"how-to-guides/large-file/#download-some-files-to-the-local-cache","text":"This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. large-file --backend s3 --secrets ~/.aws/credentials \\ --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s.","title":"Download some files to the local cache"},{"location":"how-to-guides/large-file/#delete-remote-files","text":"large-file --backend s3 --secrets ~/.aws/credentials \\ --delete my_first_file.json","title":"Delete remote files"},{"location":"how-to-guides/use-service/","text":"How to perform prediction with GreatAI # After creating a GreatAI service by wrapping your prediction function, and optionally configuring it , it's time to do some prediction. Let's take the following example: greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' One-off prediction # Even though greeter is now an instance of GreatAI , you can continue using it as a regular function. >>> greeter ( 'Bob' ) Trace [ str ]({ 'created' : '2022-07-11T14:31:46.183764' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0381 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '7c284fd7-7f0d-4464-b5f8-3ef126df34af' }) As you can see, the original return value is wrapped in a Trace object (which is also persisted in your database of choice). You can access the original value under the output property. Online prediction # Likely, the main way you would like to expose your model is through an HTTP API. @GreatAI.create scaffolds many REST API endpoints for your model and creates a FastAPI app available under GreatAI.app . This can be served using uvicorn or any other ASGI server . Since most ML code lives in Jupyter notebooks, therefore, deploying a notebook containing the inference function is supported. To achieve this, uvicorn is wrapped by the great-ai command-line utility which \u2014 among others \u2014 takes care of feeding a notebook into uvicorn . It also supports auto-reloading. In development # great-ai greeter.py Success Your model is accessible at localhost:6060 . Some configuration options are also supported. great-ai greeter.py --port 8000 --host 127 .0.0.1 --timeout_keep_alive 10 More options For more options (but no Notebook support), simply use uvicorn for starting your app (available at greeter.app ). In production # There are three main approaches for deploying a GreatAI service. Manual deployment # The app is run in production-mode if the value of the ENVIRONMENT environment variable is set to production . ENVIRONMENT = production great-ai greeter.py Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach; however, it might be fitting for some contexts. Containerised deployment # Run the notebook directly in a container or create a service for it using your favourite container orchestrator. docker run -p 6060 :6060 --volume ` pwd ` :/app --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder. Use a Platform-as-a-Service # Similar to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as AWS ECS , DO App platform , MLEM , Streamlit ) that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as PyTorch or TensorFlow . FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to cache them in the image so that you don't # have to download it each time a container starts RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor # Add you application code to the image COPY . . # The default ENTRYPOINT is great-ai, specify its argument using CMD CMD [ \"deploy.ipynb\" ] Batch prediction # Processing larger amounts of data on a single machine is made easy by the GreatAI 's process_batch method. This relies on multiprocessing ( parallel_map ) to take full advantage of all available CPU-cores. >>> greeter . process_batch ([ 'Alice' , 'Bob' ]) [ Trace [ str ]({ 'created' : '2022-07-11T14:36:37.119183' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 5 , 'arg:your_name:value' : 'Alice' }, 'models' : [], 'original_execution_time_ms' : 0.1251 , 'output' : 'Hi Alice!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '90ffa15f-e839-41c4-8e7a-3211168bc138' }), Trace [ str ]({ 'created' : '2022-07-11T14:36:37.166659' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0571 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : 'f48e94c7-0815-48b3-a864-41349d3dae84' })]","title":"How to perform prediction with GreatAI"},{"location":"how-to-guides/use-service/#how-to-perform-prediction-with-greatai","text":"After creating a GreatAI service by wrapping your prediction function, and optionally configuring it , it's time to do some prediction. Let's take the following example: greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f 'Hi { your_name } !'","title":"How to perform prediction with GreatAI"},{"location":"how-to-guides/use-service/#one-off-prediction","text":"Even though greeter is now an instance of GreatAI , you can continue using it as a regular function. >>> greeter ( 'Bob' ) Trace [ str ]({ 'created' : '2022-07-11T14:31:46.183764' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0381 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '7c284fd7-7f0d-4464-b5f8-3ef126df34af' }) As you can see, the original return value is wrapped in a Trace object (which is also persisted in your database of choice). You can access the original value under the output property.","title":"One-off prediction"},{"location":"how-to-guides/use-service/#online-prediction","text":"Likely, the main way you would like to expose your model is through an HTTP API. @GreatAI.create scaffolds many REST API endpoints for your model and creates a FastAPI app available under GreatAI.app . This can be served using uvicorn or any other ASGI server . Since most ML code lives in Jupyter notebooks, therefore, deploying a notebook containing the inference function is supported. To achieve this, uvicorn is wrapped by the great-ai command-line utility which \u2014 among others \u2014 takes care of feeding a notebook into uvicorn . It also supports auto-reloading.","title":"Online prediction"},{"location":"how-to-guides/use-service/#in-development","text":"great-ai greeter.py Success Your model is accessible at localhost:6060 . Some configuration options are also supported. great-ai greeter.py --port 8000 --host 127 .0.0.1 --timeout_keep_alive 10 More options For more options (but no Notebook support), simply use uvicorn for starting your app (available at greeter.app ).","title":"In development"},{"location":"how-to-guides/use-service/#in-production","text":"There are three main approaches for deploying a GreatAI service.","title":"In production"},{"location":"how-to-guides/use-service/#manual-deployment","text":"The app is run in production-mode if the value of the ENVIRONMENT environment variable is set to production . ENVIRONMENT = production great-ai greeter.py Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach; however, it might be fitting for some contexts.","title":"Manual deployment"},{"location":"how-to-guides/use-service/#containerised-deployment","text":"Run the notebook directly in a container or create a service for it using your favourite container orchestrator. docker run -p 6060 :6060 --volume ` pwd ` :/app --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder.","title":"Containerised deployment"},{"location":"how-to-guides/use-service/#use-a-platform-as-a-service","text":"Similar to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as AWS ECS , DO App platform , MLEM , Streamlit ) that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as PyTorch or TensorFlow . FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to cache them in the image so that you don't # have to download it each time a container starts RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor # Add you application code to the image COPY . . # The default ENTRYPOINT is great-ai, specify its argument using CMD CMD [ \"deploy.ipynb\" ]","title":"Use a Platform-as-a-Service"},{"location":"how-to-guides/use-service/#batch-prediction","text":"Processing larger amounts of data on a single machine is made easy by the GreatAI 's process_batch method. This relies on multiprocessing ( parallel_map ) to take full advantage of all available CPU-cores. >>> greeter . process_batch ([ 'Alice' , 'Bob' ]) [ Trace [ str ]({ 'created' : '2022-07-11T14:36:37.119183' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 5 , 'arg:your_name:value' : 'Alice' }, 'models' : [], 'original_execution_time_ms' : 0.1251 , 'output' : 'Hi Alice!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '90ffa15f-e839-41c4-8e7a-3211168bc138' }), Trace [ str ]({ 'created' : '2022-07-11T14:36:37.166659' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0571 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : 'f48e94c7-0815-48b3-a864-41349d3dae84' })]","title":"Batch prediction"},{"location":"reference/","text":"GreatAI reference # from great_ai import * Core # GreatAI # Bases: Generic [ T , V ] Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using configure(prediction_cache_size=0) . Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: Name Type Description app FastAPI instance wrapping the scaffolded endpoints and the Dash app. version SemVer derived from the app's version and the model names and versions registered through use_model. Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 class GreatAI ( Generic [ T , V ]): \"\"\"Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using `configure(prediction_cache_size=0)`. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: app: FastAPI instance wrapping the scaffolded endpoints and the Dash app. version: SemVer derived from the app's version and the model names and versions registered through use_model. \"\"\" __name__ : str # help for MyPy __doc__ : str # help for MyPy def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), ) create ( func ) staticmethod # Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure . Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) process_batch ( batch , * , concurrency = None , unpack_arguments = False , do_not_persist_traces = False ) # Map the wrapped function over a list of input_values ( batch ). A wrapper over parallel_map providing type-safety and a progressbar through tqdm. Parameters: Name Type Description Default batch Sequence A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set unpack_arguments=True . required concurrency Optional [ int ] Number of processes to start. Don't set it too much higher than the number of available CPU cores. None unpack_arguments bool Expect a list of tuples and unpack the tuples before giving them to the wrapped function. False do_not_persist_traces bool Don't save the traces in the database. Useful for evaluations run part of the CI. False Source code in great_ai/deploy/great_ai.py 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) configure ( * , version = '0.0.1' , log_level = DEBUG , seed = 42 , tracing_database_factory = None , large_file_implementation = None , should_log_exception_stack = None , prediction_cache_size = 512 , disable_se4ml_banner = False , dashboard_table_size = 50 , route_config = RouteConfig ()) # Set the global configuration used by the great-ai library. You must call configure before calling (or decorating with) any other great-ai function. If tracing_database_factory or large_file_implementation is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure ( prediction_cache_size = 0 ) Parameters: Name Type Description Default version Union [ int , str ] The version of your application (using SemVer is recommended). '0.0.1' log_level int Set the default logging level of logging . DEBUG seed int Set seed of random (and numpy if installed) for reproducibility. 42 tracing_database_factory Optional [ Type [ TracingDatabaseDriver ]] Specify a different TracingDatabaseDriver than the one already configured. None large_file_implementation Optional [ Type [ LargeFileBase ]] Specify a different LargeFile than the one already configured. None should_log_exception_stack Optional [ bool ] Log the traces of unhandled exceptions. None prediction_cache_size int Size of the LRU cache applied over the prediction functions. 512 disable_se4ml_banner bool Turn off the warning about the importance of SE4ML best- practices. False dashboard_table_size int Number of rows to display in the dashboard's table. 50 route_config RouteConfig Enable or disable specific HTTP API endpoints. RouteConfig() Source code in great_ai/context.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 50 , route_config : RouteConfig = RouteConfig (), ) -> None : \"\"\"Set the global configuration used by the great-ai library. You must call `configure` before calling (or decorating with) any other great-ai function. If `tracing_database_factory` or `large_file_implementation` is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure(prediction_cache_size=0) Arguments: version: The version of your application (using SemVer is recommended). log_level: Set the default logging level of `logging`. seed: Set seed of `random` (and `numpy` if installed) for reproducibility. tracing_database_factory: Specify a different TracingDatabaseDriver than the one already configured. large_file_implementation: Specify a different LargeFile than the one already configured. should_log_exception_stack: Log the traces of unhandled exceptions. prediction_cache_size: Size of the LRU cache applied over the prediction functions. disable_se4ml_banner: Turn off the warning about the importance of SE4ML best- practices. dashboard_table_size: Number of rows to display in the dashboard's table. route_config: Enable or disable specific HTTP API endpoints. \"\"\" global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"\"\"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\"\"\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before \" \"trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" ) save_model ( model , key , * , keep_last_n = None ) # Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \" use_model ( key , * , version = 'latest' , model_kwarg_name = 'model' ) # Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default key str The model's name as stored by the LargeFile implementation. required version Union [ int , Literal [ latest ]] The model's version as stored by the LargeFile implementation. 'latest' model_kwarg_name str the parameter to use for injecting the loaded model 'model' Returns: Type Description Callable [[ F ], F ] A decorator for model injection. Source code in great_ai/models/use_model.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator parameter ( parameter_name , * , validate = lambda _ : True , disable_logging = False ) # Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validate = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider. required validate Callable [[ Any ], bool ] Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def parameter ( parameter_name : str , * , validate : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validate=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider. validate: Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validate ( argument ): raise ArgumentValidationError ( f \"\"\"Argument { parameter_name } in { func . __name__ } did not pass validation\"\"\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator log_metric ( argument_name , value , disable_logging = False ) # Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Parameters: Name Type Description Default argument_name str The key for storing the value. required value Any Value to log. Must be JSON-serialisable. required disable_logging bool If True, only persist in trace but don't show in console False Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 def log_metric ( argument_name : str , value : Any , disable_logging : bool = False ) -> None : \"\"\"Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Args: argument_name: The key for storing the value. value: Value to log. Must be JSON-serialisable. disable_logging: If True, only persist in trace but don't show in console \"\"\" tracing_context = TracingContext . get_current_tracing_context () try : caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" except : # inspect might not work in notebooks actual_name = f \"metric: { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) if not disable_logging : get_context () . logger . info ( f \" { actual_name } = { value } \" ) Remote calls # call_remote_great_ai ( base_uri , data , retry_count = 4 , timeout_in_seconds = 300 , model_class = None ) # Communicate with a GreatAI object through an HTTP request. Wrapper over call_remote_great_ai_async making it synchronous. For more info, see call_remote_great_ai_async . Source code in great_ai/remote/call_remote_great_ai.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def call_remote_great_ai ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Wrapper over `call_remote_great_ai_async` making it synchronous. For more info, see `call_remote_great_ai_async`. \"\"\" try : asyncio . get_running_loop () raise Exception ( f \"Already running in an event loop, you have to call ` { call_remote_great_ai_async . __name__ } `\" ) except RuntimeError : pass future = call_remote_great_ai_async ( base_uri = base_uri , data = data , retry_count = retry_count , timeout_in_seconds = timeout_in_seconds , model_class = model_class , ) return asyncio . run ( future ) call_remote_great_ai_async ( base_uri , data , retry_count = 4 , timeout_in_seconds = 300 , model_class = None ) async # Communicate with a GreatAI object through an HTTP request. Send a POST request using httpx to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If model_class is specified, the original output is deserialised. Parameters: Name Type Description Default base_uri str Address of the remote instance, example: 'http://localhost:6060' required data Mapping [ str , Any ] The input sent as a json to the remote instance. required retry_count int Retry on any HTTP communication failure. 4 timeout_in_seconds Optional [ int ] Overall permissible max length of the request. None means no timeout. 300 model_class Optional [ Type [ T ]] A subtype of BaseModel to be used for deserialising the .output of the trace. None Source code in great_ai/remote/call_remote_great_ai_async.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 async def call_remote_great_ai_async ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Send a POST request using [httpx](https://www.python-httpx.org/) to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If `model_class` is specified, the original output is deserialised. Args: base_uri: Address of the remote instance, example: 'http://localhost:6060' data: The input sent as a json to the remote instance. retry_count: Retry on any HTTP communication failure. timeout_in_seconds: Overall permissible max length of the request. `None` means no timeout. model_class: A subtype of BaseModel to be used for deserialising the `.output` of the trace. \"\"\" if base_uri . endswith ( \"/\" ): base_uri = base_uri [: - 1 ] if not base_uri . endswith ( \"/predict\" ): base_uri = f \" { base_uri } /predict\" transport = httpx . AsyncHTTPTransport ( retries = retry_count ) try : async with httpx . AsyncClient ( transport = transport , timeout = timeout_in_seconds ) as client : response = await client . post ( base_uri , json = data ) try : response . raise_for_status () except Exception : raise RemoteCallError ( f \"Unexpected status code, reason: { response . text } \" ) except Exception as e : raise RemoteCallError from e try : trace = response . json () except Exception : raise RemoteCallError ( f \"JSON parsing failed { response . text } \" , ) try : if model_class is not None : trace [ \"output\" ] = model_class . parse_obj ( trace [ \"output\" ]) return Trace . parse_obj ( trace ) except Exception : raise RemoteCallError ( \"Could not parse Trace\" ) Ground-truth # add_ground_truth ( inputs , expected_outputs , * , tags = [], train_split_ratio = 1 , test_split_ratio = 0 , validation_split_ratio = 0 ) # Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The inputs are available via the .input property, while expected_outputs under both the .output and .feedback properties. All generated traces are tagged with ground_truth by default. Additional tags can be also provided. Using the split_ratio arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth ( ... [ 1 , 2 , 3 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) >>> add_ground_truth ( ... [ 1 , 2 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) Traceback (most recent call last): ... AssertionError : The length of the inputs and expected_outputs must be equal Parameters: Name Type Description Default inputs Iterable [ Any ] The inputs. (X in scikit-learn) required expected_outputs Iterable [ T ] The ground-truth values corresponding to the inputs. (y in scikit-learn) required tags Union [ List [ str ], str ] A single tag or a list of tags to append to each generated trace's tags. [] train_split_ratio float The probability-weight of giving each trace the train tag. 1 test_split_ratio float The probability-weight of giving each trace the test tag. 0 validation_split_ratio float The probability-weight of giving each trace the validation tag. 0 Source code in great_ai/tracing/add_ground_truth.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def add_ground_truth ( inputs : Iterable [ Any ], expected_outputs : Iterable [ T ], * , tags : Union [ List [ str ], str ] = [], train_split_ratio : float = 1 , test_split_ratio : float = 0 , validation_split_ratio : float = 0 ) -> None : \"\"\"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The `inputs` are available via the `.input` property, while `expected_outputs` under both the `.output` and `.feedback` properties. All generated traces are tagged with `ground_truth` by default. Additional tags can be also provided. Using the `split_ratio` arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth( ... [1, 2, 3], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) >>> add_ground_truth( ... [1, 2], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) Traceback (most recent call last): ... AssertionError: The length of the inputs and expected_outputs must be equal Args: inputs: The inputs. (X in scikit-learn) expected_outputs: The ground-truth values corresponding to the inputs. (y in scikit-learn) tags: A single tag or a list of tags to append to each generated trace's tags. train_split_ratio: The probability-weight of giving each trace the `train` tag. test_split_ratio: The probability-weight of giving each trace the `test` tag. validation_split_ratio: The probability-weight of giving each trace the `validation` tag. \"\"\" inputs = list ( inputs ) expected_outputs = list ( expected_outputs ) assert len ( inputs ) == len ( expected_outputs ), \"The length of the inputs and expected_outputs must be equal\" tags = tags if isinstance ( tags , list ) else [ tags ] sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio assert sum_ratio > 0 , \"The sum of the split ratios must be a positive number\" train_split_ratio /= sum_ratio test_split_ratio /= sum_ratio validation_split_ratio /= sum_ratio values = list ( zip ( inputs , expected_outputs )) shuffle ( values ) split_tags = ( [ TRAIN_SPLIT_TAG_NAME ] * ceil ( train_split_ratio * len ( inputs )) + [ TEST_SPLIT_TAG_NAME ] * ceil ( test_split_ratio * len ( inputs )) + [ VALIDATION_SPLIT_TAG_NAME ] * ceil ( validation_split_ratio * len ( inputs )) ) shuffle ( split_tags ) created = datetime . utcnow () . isoformat () traces = [ cast ( Trace [ T ], Trace ( # avoid ValueError: \"Trace\" object has no field \"__orig_class__\" trace_id = str ( uuid4 ()), created = created , original_execution_time_ms = 0 , logged_values = X if isinstance ( X , dict ) else { \"input\" : X }, models = [], output = y , feedback = y , exception = None , tags = [ GROUND_TRUTH_TAG_NAME , split_tag , * tags ], ), ) for (( X , y ), split_tag ) in zip ( values , split_tags ) ] get_context () . tracing_database . save_batch ( traces ) query_ground_truth ( conjunctive_tags = [], * , since = None , until = None , return_max_count = None ) # Return training samples. Combines, filters, and returns data-points that have been either added by add_ground_truth or were the result of a prediction after which their trace got feedback through the RESP API-s /traces/{trace_id}/feedback endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth () [...] Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only return traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only return traces created before the given timestamp. None means no filtering. None return_max_count Optional [ int ] Return at-most this many traces. (take, limit) None Source code in great_ai/tracing/query_ground_truth.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def query_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , return_max_count : Optional [ int ] = None ) -> List [ Trace ]: \"\"\"Return training samples. Combines, filters, and returns data-points that have been either added by `add_ground_truth` or were the result of a prediction after which their trace got feedback through the RESP API-s `/traces/{trace_id}/feedback` endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth() [...] Args: conjunctive_tags: Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). since: Only return traces created after the given timestamp. `None` means no filtering. until: Only return traces created before the given timestamp. `None` means no filtering. return_max_count: Return at-most this many traces. (take, limit) \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , since = since , until = until , take = return_max_count , has_feedback = True , ) return items delete_ground_truth ( conjunctive_tags = [], * , since = None , until = None ) # Delete traces matching the given criteria. Takes the same arguments as query_ground_truth but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth ([ 'train' , 'test' , 'validation' ]) Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only delete traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only delete traces created before the given timestamp. None means no filtering. None Source code in great_ai/tracing/delete_ground_truth.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 def delete_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , ) -> None : \"\"\"Delete traces matching the given criteria. Takes the same arguments as `query_ground_truth` but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth(['train', 'test', 'validation']) Args: conjunctive_tags: Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). since: Only delete traces created after the given timestamp. `None` means no filtering. until: Only delete traces created before the given timestamp. `None` means no filtering. \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , until = until , since = since , has_feedback = True ) db . delete_batch ([ i . trace_id for i in items ]) Tracing databases # TracingDatabaseDriver # Bases: ABC Interface expected from a database to be used for storing traces. Source code in great_ai/persistence/tracing_database_driver.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 class TracingDatabaseDriver ( ABC ): \"\"\"Interface expected from a database to be used for storing traces.\"\"\" is_production_ready : bool initialized : bool = False @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ) -> None : cls . initialized = True @abstractmethod def save ( self , document : Trace ) -> str : pass @abstractmethod def save_batch ( self , documents : List [ Trace ], ) -> List [ str ]: pass @abstractmethod def get ( self , id : str ) -> Optional [ Trace ]: pass @abstractmethod def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], until : Optional [ datetime ] = None , since : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: pass @abstractmethod def update ( self , id : str , new_version : Trace ) -> None : pass @abstractmethod def delete ( self , id : str ) -> None : pass @abstractmethod def delete_batch ( self , ids : List [ str ], ) -> None : pass MongoDbDriver # Bases: TracingDatabaseDriver TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout MongoDB Atlas for a hosted MongoDB solution. Source code in great_ai/persistence/mongodb_driver.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 class MongoDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register) for a hosted MongoDB solution. \"\"\" is_production_ready = True mongo_connection_string : str mongo_database : str def __init__ ( self ) -> None : super () . __init__ () if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling \" \"MongoDbDriver.configure_credentials\" ) with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . create_index ( [( \"tags\" , ASCENDING ), ( \"created\" , DESCENDING )], background = True ) @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () def save ( self , trace : Trace ) -> str : serialized = trace . to_flat_dict () serialized [ \"_id\" ] = trace . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : return client [ self . mongo_database ] . traces . insert_one ( serialized ) . inserted_id def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: serialized = [ d . to_flat_dict () for d in documents ] for s in serialized : s [ \"_id\" ] = s [ \"trace_id\" ] with MongoClient [ Any ]( self . mongo_connection_string ) as client : return ( client [ self . mongo_database ] . traces . insert_many ( serialized , ordered = False ) . inserted_ids ) def get ( self , id : str ) -> Optional [ Trace ]: with MongoClient [ Any ]( self . mongo_connection_string ) as client : value = client [ self . mongo_database ] . traces . find_one ( id ) if value : value = Trace . parse_obj ( value ) return value def _get_operator ( self , filter : Filter ) -> str : if filter . operator == \"contains\" and not isinstance ( filter . value , str ): return operator_mapping [ \"=\" ] return operator_mapping [ filter . operator ] def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [], ) -> Tuple [ List [ Trace ], int ]: query : Dict [ str , Any ] = { \"filter\" : {}, } and_query : List [ Dict [ str , Any ]] = [] and_query . extend ({ \"tags\" : tag } for tag in conjunctive_tags ) and_query . extend ( { f . property : { self . _get_operator ( f ): f . value }} for f in conjunctive_filters ) if not and_query : and_query . append ({}) if since : and_query . append ({ \"created\" : { \"$gte\" : since }}) if until : and_query . append ({ \"created\" : { \"$lte\" : until }}) if has_feedback is not None : and_query . append ( { \"feedback\" : { \"$ne\" : None }} if has_feedback else { \"feedback\" : None } ) query [ \"filter\" ][ \"$and\" ] = and_query with MongoClient [ Any ]( self . mongo_connection_string ) as client : count = client [ self . mongo_database ] . traces . count_documents ( ** query ) if skip : query [ \"skip\" ] = skip if take : query [ \"limit\" ] = take query [ \"sort\" ] = [ ( col . column_id , 1 if col . direction == \"asc\" else - 1 ) for col in sort_by ] with client [ self . mongo_database ] . traces . find ( ** query ) as cursor : documents = [ Trace [ Any ] . parse_obj ( t ) for t in cursor ] return documents , count def update ( self , id : str , new_version : Trace ) -> None : serialized = new_version . to_flat_dict () serialized [ \"_id\" ] = new_version . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . update_one ({ \"_id\" : id }, serialized ) def delete ( self , id : str ) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . delete_one ({ \"_id\" : id }) def delete_batch ( self , ids : List [ str ]) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : for c in chunk ( ids , chunk_size = 10000 ): # avoid: 'delete' command document too large delete_filter = { \"_id\" : { \"$in\" : c }} client [ self . mongo_database ] . traces . delete_many ( delete_filter ) configure_credentials ( * , mongo_connection_string , mongo_database , ** _ ) classmethod # Configure the connection details for MongoDB. Parameters: Name Type Description Default mongo_connection_string str For example: 'mongodb://my_user:my_pass@localhost:27017' required mongo_database str Name of the database to use. If doesn't exist, it is created and initialised. required Source code in great_ai/persistence/mongodb_driver.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () ParallelTinyDbDriver # Bases: TracingDatabaseDriver TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. Source code in great_ai/persistence/parallel_tinydb_driver.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 class ParallelTinyDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. \"\"\" is_production_ready = False path_to_db = Path ( DEFAULT_TRACING_DB_FILENAME ) def save ( self , trace : Trace ) -> str : return self . _safe_execute ( lambda db : db . insert ( trace . dict ())) def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: traces = [ d . dict () for d in documents ] return self . _safe_execute ( lambda db : db . insert_multiple ( traces )) def get ( self , id : str ) -> Optional [ Trace ]: value = self . _safe_execute ( lambda db : db . get ( lambda d : d [ \"trace_id\" ] == id )) if value : value = Trace . parse_obj ( value ) return value def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: def does_match ( d : Dict [ str , Any ]) -> bool : return ( not set ( conjunctive_tags ) - set ( d [ \"tags\" ]) and ( since is None or datetime . fromisoformat ( d [ \"created\" ]) >= since ) and ( until is None or datetime . fromisoformat ( d [ \"created\" ]) <= until ) and ( has_feedback is None or has_feedback == ( d [ \"feedback\" ] is not None ) ) ) documents = self . _safe_execute ( lambda db : db . search ( does_match )) if not documents : return [], 0 df = pd . DataFrame ([ Trace . parse_obj ( d ) . to_flat_dict () for d in documents ]) for f in conjunctive_filters : operator = f . operator . lower () if operator in operator_mapping : df = df . loc [ getattr ( df [ f . property ], operator_mapping [ f . operator ])( f . value ) ] elif operator == \"contains\" : df = df . loc [ df [ f . property ] . str . contains ( str ( int ( f . value )) if isinstance ( f . value , float ) else f . value , case = False , ) ] if sort_by : df . sort_values ( [ col . column_id for col in sort_by ], ascending = [ col . direction == \"asc\" for col in sort_by ], inplace = True , ) count = len ( df ) result = df . iloc [ skip :] if take is None else df . iloc [ skip : skip + take ] return [ Trace . parse_obj ( trace ) for _ , trace in result . iterrows ()], count def update ( self , id : str , new_version : Trace ) -> None : self . _safe_execute ( lambda db : db . update ( new_version . dict (), lambda d : d [ \"trace_id\" ] == id ) ) def delete ( self , id : str ) -> None : self . _safe_execute ( lambda db : db . remove ( lambda d : d [ \"trace_id\" ] == id )) def delete_batch ( self , ids : List [ str ]) -> None : with lock : with TinyDB ( self . path_to_db ) as db : for id in ids : db . remove ( lambda d : d [ \"trace_id\" ] == id ) def _safe_execute ( self , func : Callable [[ TinyDB ], Any ]) -> Any : with lock : with TinyDB ( self . path_to_db ) as db : return func ( db )","title":"GreatAI reference"},{"location":"reference/#greatai-reference","text":"from great_ai import *","title":"GreatAI reference"},{"location":"reference/#core","text":"","title":"Core"},{"location":"reference/#great_ai.GreatAI","text":"Bases: Generic [ T , V ] Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using configure(prediction_cache_size=0) . Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: Name Type Description app FastAPI instance wrapping the scaffolded endpoints and the Dash app. version SemVer derived from the app's version and the model names and versions registered through use_model. Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 class GreatAI ( Generic [ T , V ]): \"\"\"Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using `configure(prediction_cache_size=0)`. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: app: FastAPI instance wrapping the scaffolded endpoints and the Dash app. version: SemVer derived from the app's version and the model names and versions registered through use_model. \"\"\" __name__ : str # help for MyPy __doc__ : str # help for MyPy def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), )","title":"GreatAI"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.create","text":"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure . Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , )","title":"create()"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.process_batch","text":"Map the wrapped function over a list of input_values ( batch ). A wrapper over parallel_map providing type-safety and a progressbar through tqdm. Parameters: Name Type Description Default batch Sequence A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set unpack_arguments=True . required concurrency Optional [ int ] Number of processes to start. Don't set it too much higher than the number of available CPU cores. None unpack_arguments bool Expect a list of tuples and unpack the tuples before giving them to the wrapped function. False do_not_persist_traces bool Don't save the traces in the database. Useful for evaluations run part of the CI. False Source code in great_ai/deploy/great_ai.py 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) )","title":"process_batch()"},{"location":"reference/#great_ai.configure","text":"Set the global configuration used by the great-ai library. You must call configure before calling (or decorating with) any other great-ai function. If tracing_database_factory or large_file_implementation is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure ( prediction_cache_size = 0 ) Parameters: Name Type Description Default version Union [ int , str ] The version of your application (using SemVer is recommended). '0.0.1' log_level int Set the default logging level of logging . DEBUG seed int Set seed of random (and numpy if installed) for reproducibility. 42 tracing_database_factory Optional [ Type [ TracingDatabaseDriver ]] Specify a different TracingDatabaseDriver than the one already configured. None large_file_implementation Optional [ Type [ LargeFileBase ]] Specify a different LargeFile than the one already configured. None should_log_exception_stack Optional [ bool ] Log the traces of unhandled exceptions. None prediction_cache_size int Size of the LRU cache applied over the prediction functions. 512 disable_se4ml_banner bool Turn off the warning about the importance of SE4ML best- practices. False dashboard_table_size int Number of rows to display in the dashboard's table. 50 route_config RouteConfig Enable or disable specific HTTP API endpoints. RouteConfig() Source code in great_ai/context.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 50 , route_config : RouteConfig = RouteConfig (), ) -> None : \"\"\"Set the global configuration used by the great-ai library. You must call `configure` before calling (or decorating with) any other great-ai function. If `tracing_database_factory` or `large_file_implementation` is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure(prediction_cache_size=0) Arguments: version: The version of your application (using SemVer is recommended). log_level: Set the default logging level of `logging`. seed: Set seed of `random` (and `numpy` if installed) for reproducibility. tracing_database_factory: Specify a different TracingDatabaseDriver than the one already configured. large_file_implementation: Specify a different LargeFile than the one already configured. should_log_exception_stack: Log the traces of unhandled exceptions. prediction_cache_size: Size of the LRU cache applied over the prediction functions. disable_se4ml_banner: Turn off the warning about the importance of SE4ML best- practices. dashboard_table_size: Number of rows to display in the dashboard's table. route_config: Enable or disable specific HTTP API endpoints. \"\"\" global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"\"\"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\"\"\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before \" \"trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" )","title":"configure()"},{"location":"reference/#great_ai.save_model","text":"Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \"","title":"save_model()"},{"location":"reference/#great_ai.use_model","text":"Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default key str The model's name as stored by the LargeFile implementation. required version Union [ int , Literal [ latest ]] The model's version as stored by the LargeFile implementation. 'latest' model_kwarg_name str the parameter to use for injecting the loaded model 'model' Returns: Type Description Callable [[ F ], F ] A decorator for model injection. Source code in great_ai/models/use_model.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator","title":"use_model()"},{"location":"reference/#great_ai.parameter","text":"Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validate = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider. required validate Callable [[ Any ], bool ] Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def parameter ( parameter_name : str , * , validate : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validate=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider. validate: Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validate ( argument ): raise ArgumentValidationError ( f \"\"\"Argument { parameter_name } in { func . __name__ } did not pass validation\"\"\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator","title":"parameter()"},{"location":"reference/#great_ai.log_metric","text":"Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Parameters: Name Type Description Default argument_name str The key for storing the value. required value Any Value to log. Must be JSON-serialisable. required disable_logging bool If True, only persist in trace but don't show in console False Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 def log_metric ( argument_name : str , value : Any , disable_logging : bool = False ) -> None : \"\"\"Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Args: argument_name: The key for storing the value. value: Value to log. Must be JSON-serialisable. disable_logging: If True, only persist in trace but don't show in console \"\"\" tracing_context = TracingContext . get_current_tracing_context () try : caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" except : # inspect might not work in notebooks actual_name = f \"metric: { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) if not disable_logging : get_context () . logger . info ( f \" { actual_name } = { value } \" )","title":"log_metric()"},{"location":"reference/#remote-calls","text":"","title":"Remote calls"},{"location":"reference/#great_ai.call_remote_great_ai","text":"Communicate with a GreatAI object through an HTTP request. Wrapper over call_remote_great_ai_async making it synchronous. For more info, see call_remote_great_ai_async . Source code in great_ai/remote/call_remote_great_ai.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def call_remote_great_ai ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Wrapper over `call_remote_great_ai_async` making it synchronous. For more info, see `call_remote_great_ai_async`. \"\"\" try : asyncio . get_running_loop () raise Exception ( f \"Already running in an event loop, you have to call ` { call_remote_great_ai_async . __name__ } `\" ) except RuntimeError : pass future = call_remote_great_ai_async ( base_uri = base_uri , data = data , retry_count = retry_count , timeout_in_seconds = timeout_in_seconds , model_class = model_class , ) return asyncio . run ( future )","title":"call_remote_great_ai()"},{"location":"reference/#great_ai.call_remote_great_ai_async","text":"Communicate with a GreatAI object through an HTTP request. Send a POST request using httpx to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If model_class is specified, the original output is deserialised. Parameters: Name Type Description Default base_uri str Address of the remote instance, example: 'http://localhost:6060' required data Mapping [ str , Any ] The input sent as a json to the remote instance. required retry_count int Retry on any HTTP communication failure. 4 timeout_in_seconds Optional [ int ] Overall permissible max length of the request. None means no timeout. 300 model_class Optional [ Type [ T ]] A subtype of BaseModel to be used for deserialising the .output of the trace. None Source code in great_ai/remote/call_remote_great_ai_async.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 async def call_remote_great_ai_async ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Send a POST request using [httpx](https://www.python-httpx.org/) to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If `model_class` is specified, the original output is deserialised. Args: base_uri: Address of the remote instance, example: 'http://localhost:6060' data: The input sent as a json to the remote instance. retry_count: Retry on any HTTP communication failure. timeout_in_seconds: Overall permissible max length of the request. `None` means no timeout. model_class: A subtype of BaseModel to be used for deserialising the `.output` of the trace. \"\"\" if base_uri . endswith ( \"/\" ): base_uri = base_uri [: - 1 ] if not base_uri . endswith ( \"/predict\" ): base_uri = f \" { base_uri } /predict\" transport = httpx . AsyncHTTPTransport ( retries = retry_count ) try : async with httpx . AsyncClient ( transport = transport , timeout = timeout_in_seconds ) as client : response = await client . post ( base_uri , json = data ) try : response . raise_for_status () except Exception : raise RemoteCallError ( f \"Unexpected status code, reason: { response . text } \" ) except Exception as e : raise RemoteCallError from e try : trace = response . json () except Exception : raise RemoteCallError ( f \"JSON parsing failed { response . text } \" , ) try : if model_class is not None : trace [ \"output\" ] = model_class . parse_obj ( trace [ \"output\" ]) return Trace . parse_obj ( trace ) except Exception : raise RemoteCallError ( \"Could not parse Trace\" )","title":"call_remote_great_ai_async()"},{"location":"reference/#ground-truth","text":"","title":"Ground-truth"},{"location":"reference/#great_ai.add_ground_truth","text":"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The inputs are available via the .input property, while expected_outputs under both the .output and .feedback properties. All generated traces are tagged with ground_truth by default. Additional tags can be also provided. Using the split_ratio arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth ( ... [ 1 , 2 , 3 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) >>> add_ground_truth ( ... [ 1 , 2 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) Traceback (most recent call last): ... AssertionError : The length of the inputs and expected_outputs must be equal Parameters: Name Type Description Default inputs Iterable [ Any ] The inputs. (X in scikit-learn) required expected_outputs Iterable [ T ] The ground-truth values corresponding to the inputs. (y in scikit-learn) required tags Union [ List [ str ], str ] A single tag or a list of tags to append to each generated trace's tags. [] train_split_ratio float The probability-weight of giving each trace the train tag. 1 test_split_ratio float The probability-weight of giving each trace the test tag. 0 validation_split_ratio float The probability-weight of giving each trace the validation tag. 0 Source code in great_ai/tracing/add_ground_truth.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def add_ground_truth ( inputs : Iterable [ Any ], expected_outputs : Iterable [ T ], * , tags : Union [ List [ str ], str ] = [], train_split_ratio : float = 1 , test_split_ratio : float = 0 , validation_split_ratio : float = 0 ) -> None : \"\"\"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The `inputs` are available via the `.input` property, while `expected_outputs` under both the `.output` and `.feedback` properties. All generated traces are tagged with `ground_truth` by default. Additional tags can be also provided. Using the `split_ratio` arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth( ... [1, 2, 3], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) >>> add_ground_truth( ... [1, 2], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) Traceback (most recent call last): ... AssertionError: The length of the inputs and expected_outputs must be equal Args: inputs: The inputs. (X in scikit-learn) expected_outputs: The ground-truth values corresponding to the inputs. (y in scikit-learn) tags: A single tag or a list of tags to append to each generated trace's tags. train_split_ratio: The probability-weight of giving each trace the `train` tag. test_split_ratio: The probability-weight of giving each trace the `test` tag. validation_split_ratio: The probability-weight of giving each trace the `validation` tag. \"\"\" inputs = list ( inputs ) expected_outputs = list ( expected_outputs ) assert len ( inputs ) == len ( expected_outputs ), \"The length of the inputs and expected_outputs must be equal\" tags = tags if isinstance ( tags , list ) else [ tags ] sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio assert sum_ratio > 0 , \"The sum of the split ratios must be a positive number\" train_split_ratio /= sum_ratio test_split_ratio /= sum_ratio validation_split_ratio /= sum_ratio values = list ( zip ( inputs , expected_outputs )) shuffle ( values ) split_tags = ( [ TRAIN_SPLIT_TAG_NAME ] * ceil ( train_split_ratio * len ( inputs )) + [ TEST_SPLIT_TAG_NAME ] * ceil ( test_split_ratio * len ( inputs )) + [ VALIDATION_SPLIT_TAG_NAME ] * ceil ( validation_split_ratio * len ( inputs )) ) shuffle ( split_tags ) created = datetime . utcnow () . isoformat () traces = [ cast ( Trace [ T ], Trace ( # avoid ValueError: \"Trace\" object has no field \"__orig_class__\" trace_id = str ( uuid4 ()), created = created , original_execution_time_ms = 0 , logged_values = X if isinstance ( X , dict ) else { \"input\" : X }, models = [], output = y , feedback = y , exception = None , tags = [ GROUND_TRUTH_TAG_NAME , split_tag , * tags ], ), ) for (( X , y ), split_tag ) in zip ( values , split_tags ) ] get_context () . tracing_database . save_batch ( traces )","title":"add_ground_truth()"},{"location":"reference/#great_ai.query_ground_truth","text":"Return training samples. Combines, filters, and returns data-points that have been either added by add_ground_truth or were the result of a prediction after which their trace got feedback through the RESP API-s /traces/{trace_id}/feedback endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth () [...] Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only return traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only return traces created before the given timestamp. None means no filtering. None return_max_count Optional [ int ] Return at-most this many traces. (take, limit) None Source code in great_ai/tracing/query_ground_truth.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def query_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , return_max_count : Optional [ int ] = None ) -> List [ Trace ]: \"\"\"Return training samples. Combines, filters, and returns data-points that have been either added by `add_ground_truth` or were the result of a prediction after which their trace got feedback through the RESP API-s `/traces/{trace_id}/feedback` endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth() [...] Args: conjunctive_tags: Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). since: Only return traces created after the given timestamp. `None` means no filtering. until: Only return traces created before the given timestamp. `None` means no filtering. return_max_count: Return at-most this many traces. (take, limit) \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , since = since , until = until , take = return_max_count , has_feedback = True , ) return items","title":"query_ground_truth()"},{"location":"reference/#great_ai.delete_ground_truth","text":"Delete traces matching the given criteria. Takes the same arguments as query_ground_truth but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth ([ 'train' , 'test' , 'validation' ]) Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only delete traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only delete traces created before the given timestamp. None means no filtering. None Source code in great_ai/tracing/delete_ground_truth.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 def delete_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , ) -> None : \"\"\"Delete traces matching the given criteria. Takes the same arguments as `query_ground_truth` but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth(['train', 'test', 'validation']) Args: conjunctive_tags: Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). since: Only delete traces created after the given timestamp. `None` means no filtering. until: Only delete traces created before the given timestamp. `None` means no filtering. \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , until = until , since = since , has_feedback = True ) db . delete_batch ([ i . trace_id for i in items ])","title":"delete_ground_truth()"},{"location":"reference/#tracing-databases","text":"","title":"Tracing databases"},{"location":"reference/#great_ai.TracingDatabaseDriver","text":"Bases: ABC Interface expected from a database to be used for storing traces. Source code in great_ai/persistence/tracing_database_driver.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 class TracingDatabaseDriver ( ABC ): \"\"\"Interface expected from a database to be used for storing traces.\"\"\" is_production_ready : bool initialized : bool = False @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ) -> None : cls . initialized = True @abstractmethod def save ( self , document : Trace ) -> str : pass @abstractmethod def save_batch ( self , documents : List [ Trace ], ) -> List [ str ]: pass @abstractmethod def get ( self , id : str ) -> Optional [ Trace ]: pass @abstractmethod def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], until : Optional [ datetime ] = None , since : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: pass @abstractmethod def update ( self , id : str , new_version : Trace ) -> None : pass @abstractmethod def delete ( self , id : str ) -> None : pass @abstractmethod def delete_batch ( self , ids : List [ str ], ) -> None : pass","title":"TracingDatabaseDriver"},{"location":"reference/#great_ai.MongoDbDriver","text":"Bases: TracingDatabaseDriver TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout MongoDB Atlas for a hosted MongoDB solution. Source code in great_ai/persistence/mongodb_driver.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 class MongoDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register) for a hosted MongoDB solution. \"\"\" is_production_ready = True mongo_connection_string : str mongo_database : str def __init__ ( self ) -> None : super () . __init__ () if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling \" \"MongoDbDriver.configure_credentials\" ) with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . create_index ( [( \"tags\" , ASCENDING ), ( \"created\" , DESCENDING )], background = True ) @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () def save ( self , trace : Trace ) -> str : serialized = trace . to_flat_dict () serialized [ \"_id\" ] = trace . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : return client [ self . mongo_database ] . traces . insert_one ( serialized ) . inserted_id def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: serialized = [ d . to_flat_dict () for d in documents ] for s in serialized : s [ \"_id\" ] = s [ \"trace_id\" ] with MongoClient [ Any ]( self . mongo_connection_string ) as client : return ( client [ self . mongo_database ] . traces . insert_many ( serialized , ordered = False ) . inserted_ids ) def get ( self , id : str ) -> Optional [ Trace ]: with MongoClient [ Any ]( self . mongo_connection_string ) as client : value = client [ self . mongo_database ] . traces . find_one ( id ) if value : value = Trace . parse_obj ( value ) return value def _get_operator ( self , filter : Filter ) -> str : if filter . operator == \"contains\" and not isinstance ( filter . value , str ): return operator_mapping [ \"=\" ] return operator_mapping [ filter . operator ] def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [], ) -> Tuple [ List [ Trace ], int ]: query : Dict [ str , Any ] = { \"filter\" : {}, } and_query : List [ Dict [ str , Any ]] = [] and_query . extend ({ \"tags\" : tag } for tag in conjunctive_tags ) and_query . extend ( { f . property : { self . _get_operator ( f ): f . value }} for f in conjunctive_filters ) if not and_query : and_query . append ({}) if since : and_query . append ({ \"created\" : { \"$gte\" : since }}) if until : and_query . append ({ \"created\" : { \"$lte\" : until }}) if has_feedback is not None : and_query . append ( { \"feedback\" : { \"$ne\" : None }} if has_feedback else { \"feedback\" : None } ) query [ \"filter\" ][ \"$and\" ] = and_query with MongoClient [ Any ]( self . mongo_connection_string ) as client : count = client [ self . mongo_database ] . traces . count_documents ( ** query ) if skip : query [ \"skip\" ] = skip if take : query [ \"limit\" ] = take query [ \"sort\" ] = [ ( col . column_id , 1 if col . direction == \"asc\" else - 1 ) for col in sort_by ] with client [ self . mongo_database ] . traces . find ( ** query ) as cursor : documents = [ Trace [ Any ] . parse_obj ( t ) for t in cursor ] return documents , count def update ( self , id : str , new_version : Trace ) -> None : serialized = new_version . to_flat_dict () serialized [ \"_id\" ] = new_version . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . update_one ({ \"_id\" : id }, serialized ) def delete ( self , id : str ) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . delete_one ({ \"_id\" : id }) def delete_batch ( self , ids : List [ str ]) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : for c in chunk ( ids , chunk_size = 10000 ): # avoid: 'delete' command document too large delete_filter = { \"_id\" : { \"$in\" : c }} client [ self . mongo_database ] . traces . delete_many ( delete_filter )","title":"MongoDbDriver"},{"location":"reference/#great_ai.persistence.mongodb_driver.MongoDbDriver.configure_credentials","text":"Configure the connection details for MongoDB. Parameters: Name Type Description Default mongo_connection_string str For example: 'mongodb://my_user:my_pass@localhost:27017' required mongo_database str Name of the database to use. If doesn't exist, it is created and initialised. required Source code in great_ai/persistence/mongodb_driver.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials ()","title":"configure_credentials()"},{"location":"reference/#great_ai.ParallelTinyDbDriver","text":"Bases: TracingDatabaseDriver TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. Source code in great_ai/persistence/parallel_tinydb_driver.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 class ParallelTinyDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. \"\"\" is_production_ready = False path_to_db = Path ( DEFAULT_TRACING_DB_FILENAME ) def save ( self , trace : Trace ) -> str : return self . _safe_execute ( lambda db : db . insert ( trace . dict ())) def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: traces = [ d . dict () for d in documents ] return self . _safe_execute ( lambda db : db . insert_multiple ( traces )) def get ( self , id : str ) -> Optional [ Trace ]: value = self . _safe_execute ( lambda db : db . get ( lambda d : d [ \"trace_id\" ] == id )) if value : value = Trace . parse_obj ( value ) return value def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: def does_match ( d : Dict [ str , Any ]) -> bool : return ( not set ( conjunctive_tags ) - set ( d [ \"tags\" ]) and ( since is None or datetime . fromisoformat ( d [ \"created\" ]) >= since ) and ( until is None or datetime . fromisoformat ( d [ \"created\" ]) <= until ) and ( has_feedback is None or has_feedback == ( d [ \"feedback\" ] is not None ) ) ) documents = self . _safe_execute ( lambda db : db . search ( does_match )) if not documents : return [], 0 df = pd . DataFrame ([ Trace . parse_obj ( d ) . to_flat_dict () for d in documents ]) for f in conjunctive_filters : operator = f . operator . lower () if operator in operator_mapping : df = df . loc [ getattr ( df [ f . property ], operator_mapping [ f . operator ])( f . value ) ] elif operator == \"contains\" : df = df . loc [ df [ f . property ] . str . contains ( str ( int ( f . value )) if isinstance ( f . value , float ) else f . value , case = False , ) ] if sort_by : df . sort_values ( [ col . column_id for col in sort_by ], ascending = [ col . direction == \"asc\" for col in sort_by ], inplace = True , ) count = len ( df ) result = df . iloc [ skip :] if take is None else df . iloc [ skip : skip + take ] return [ Trace . parse_obj ( trace ) for _ , trace in result . iterrows ()], count def update ( self , id : str , new_version : Trace ) -> None : self . _safe_execute ( lambda db : db . update ( new_version . dict (), lambda d : d [ \"trace_id\" ] == id ) ) def delete ( self , id : str ) -> None : self . _safe_execute ( lambda db : db . remove ( lambda d : d [ \"trace_id\" ] == id )) def delete_batch ( self , ids : List [ str ]) -> None : with lock : with TinyDB ( self . path_to_db ) as db : for id in ids : db . remove ( lambda d : d [ \"trace_id\" ] == id ) def _safe_execute ( self , func : Callable [[ TinyDB ], Any ]) -> Any : with lock : with TinyDB ( self . path_to_db ) as db : return func ( db )","title":"ParallelTinyDbDriver"},{"location":"reference/large-file/","text":"LargeFile # from great_ai.large_file import * For more details about using LargeFiles, check out the how to guide . LargeFileLocal # Bases: LargeFileBase LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal . cache_path = Path ( \".cache\" ) >>> LargeFileLocal . max_cache_size = \"30GB\" >>> with LargeFileLocal ( \"my_test.txt\" , \"w\" , keep_last_n = 2 ) as f : ... f . write ( 'test' ) 4 >>> with LargeFileLocal ( \"my_test.txt\" ) as f : ... print ( f . read ()) test Source code in great_ai/large_file/large_file/large_file_local.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 class LargeFileLocal ( LargeFileBase ): \"\"\"LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal.cache_path = Path(\".cache\") >>> LargeFileLocal.max_cache_size = \"30GB\" >>> with LargeFileLocal(\"my_test.txt\", \"w\", keep_last_n=2) as f: ... f.write('test') 4 >>> with LargeFileLocal(\"my_test.txt\") as f: ... print(f.read()) test \"\"\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , ): super () . __init__ ( name , mode , buffering = buffering , encoding = encoding , errors = errors , newline = newline , version = version , keep_last_n = keep_last_n , cache_only_mode = True , ) super () . configure_credentials () def _find_remote_instances ( self ) -> List [ DataInstance ]: return [] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : # This will never be called because the file must be in the cache raise NotImplementedError () def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass # the \"upload\" is already done py the parent's caching mechanism def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) i . remote_path . unlink () LargeFileS3 # Bases: LargeFileBase LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_s3.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 class LargeFileS3 ( LargeFileBase ): \"\"\"LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" region_name = None access_key_id = None secret_access_key = None bucket_name = None endpoint_url = None @classmethod def configure_credentials ( # type: ignore cls , * , aws_region_name : str , aws_access_key_id : str , aws_secret_access_key : str , large_files_bucket_name : str , aws_endpoint_url : Optional [ str ] = None , ** _ : Any , ) -> None : cls . region_name = aws_region_name cls . access_key_id = aws_access_key_id cls . secret_access_key = aws_secret_access_key cls . bucket_name = large_files_bucket_name cls . endpoint_url = aws_endpoint_url super () . configure_credentials () @cached_property def _client ( self ) -> boto3 . client : if ( self . region_name is None or self . access_key_id is None or self . secret_access_key is None or self . bucket_name is None ): raise ValueError ( \"Please configure the S3 access options by calling LargeFileS3.configure_credentials or set offline_mode=True in the constructor.\" ) return boto3 . client ( \"s3\" , aws_access_key_id = self . access_key_id , aws_secret_access_key = self . secret_access_key , region_name = self . region_name , endpoint_url = self . endpoint_url , ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching S3 versions of { self . _name } \" ) found_objects = self . _client . list_objects_v2 ( Bucket = self . bucket_name , Prefix = self . _name ) return ( [ DataInstance ( name = o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ], version = int ( o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = o [ \"Key\" ], ) for o in found_objects [ \"Contents\" ] if o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ] == self . _name ] if \"Contents\" in found_objects else [] ) def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path } from S3\" ) size = self . _client . head_object ( Bucket = self . bucket_name , Key = remote_path )[ \"ContentLength\" ] self . _client . download_file ( Bucket = self . bucket_name , Key = remote_path , Filename = str ( local_path ), Callback = None if hide_progress else DownloadProgressBar ( name = str ( remote_path ), size = size , logger = logger ), ) def _upload ( self , local_path : Path , hide_progress : bool ) -> None : key = f \" { self . _name } / { self . version } \" logger . info ( f \"Uploading { self . _local_name } to S3 as { key } \" ) self . _client . upload_file ( Filename = str ( local_path ), Bucket = self . bucket_name , Key = key , Callback = None if hide_progress else UploadProgressBar ( path = local_path , logger = logger ), ) def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from S3 (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) self . _client . delete_object ( Bucket = self . bucket_name , Key = i . remote_path ) LargeFileMongo # Bases: LargeFileBase LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_mongo.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 class LargeFileMongo ( LargeFileBase ): \"\"\"LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" mongo_connection_string = None mongo_database = None @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () @cached_property def _client ( self ) -> GridFSBucket : if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling LargeFileMongo.configure_credentials or set offline_mode=True in the constructor.\" ) db : Database = MongoClient ( self . mongo_connection_string )[ self . mongo_database ] return GridFSBucket ( db ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching Mongo (GridFS) versions of { self . _name } \" ) return [ DataInstance ( name = MONGO_NAME_VERSION_SEPARATOR . join ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = ( f . _id , f . length ), origin = \"mongodb\" , ) for f in self . _client . find ( { \"filename\" : re . compile ( re . escape ( self . _name + MONGO_NAME_VERSION_SEPARATOR ) + \".*\" ) } ) ] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path [ 0 ] } from Mongo (GridFS)\" ) progress = ( DownloadProgressBar ( name = str ( remote_path [ 0 ]), size = remote_path [ 1 ], logger = logger ) if not hide_progress else None ) with self . _client . open_download_stream ( remote_path [ 0 ]) as stream : with open ( local_path , \"wb\" ) as f : while True : content = stream . read ( DEFAULT_CHUNK_SIZE ) f . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _upload ( self , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Uploading { local_path } to Mongo (GridFS)\" ) progress = ( UploadProgressBar ( path = local_path , logger = logger ) if not hide_progress else None ) with self . _client . open_upload_stream ( f \" { self . _name }{ MONGO_NAME_VERSION_SEPARATOR }{ self . version } \" ) as stream : with open ( local_path , \"rb\" ) as f : while True : content = f . read ( DEFAULT_CHUNK_SIZE ) stream . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from MongoDB (GridFS) (keep_last_n= { self . _keep_last_n } ): { i . name }{ MONGO_NAME_VERSION_SEPARATOR }{ i . version } \" ) self . _client . delete ( i . remote_path [ 0 ]) LargeFileBase # Bases: ABC Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase . cache_path = Path ( \".cache\" ) >>> LargeFileBase . max_cache_size = \"30GB\" Attributes: Name Type Description initialized Tell whether configure_credentials or configure_credentials_from_file has been already called. cache_path Storage location for cached files. max_cache_size Optional [ str ] Delete files until the folder at cache_path is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to None for no automatic cache-pruning. Source code in great_ai/large_file/large_file/large_file_base.py 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 class LargeFileBase ( ABC ): \"\"\"Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase.cache_path = Path(\".cache\") >>> LargeFileBase.max_cache_size = \"30GB\" Attributes: initialized: Tell whether `configure_credentials` or `configure_credentials_from_file` has been already called. cache_path: Storage location for cached files. max_cache_size: Delete files until the folder at `cache_path` is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to `None` for no automatic cache-pruning. \"\"\" initialized = False cache_path = Path ( \".cache\" ) max_cache_size : Optional [ str ] = \"30GB\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , cache_only_mode : bool = False , ): self . _name = name self . _version = version self . _mode = mode self . _keep_last_n = keep_last_n self . _cache_only_mode = cache_only_mode self . _buffering = buffering self . _encoding = encoding if errors is not None and sys . version_info [ 1 ] < 8 : raise RuntimeError ( \"The `errors` kwarg is only supported in 3.8 <= Python versions.\" ) self . _errors = errors self . _newline = newline LargeFileBase . cache_path . mkdir ( parents = True , exist_ok = True ) self . _find_instances () self . _check_mode_and_set_version () @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True def __enter__ ( self ) -> IO : params = dict ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , delete = False , prefix = \"large_file-\" , ) if sys . version_info [ 1 ] >= 8 : params [ \"errors\" ] = self . _errors self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( ** params ) # type: ignore if \"w\" in self . _mode else open ( self . get (), mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , ) ) return self . _file def __exit__ ( self , type : Optional [ Type [ BaseException ]], exc : Optional [ BaseException ], traceback : Optional [ TracebackType ], ) -> Literal [ False ]: self . _file . close () if type is None : if \"w\" in self . _mode : self . push ( Path ( self . _file . name )) os . unlink ( self . _file . name ) else : logger . exception ( \"Could not finish operation.\" ) return False @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions () @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache () @property def _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" def _find_instances ( self ) -> None : if self . _cache_only_mode : self . _instances = self . _find_instances_from_cache () else : self . _instances = self . _find_remote_instances () self . _instances = sorted ( self . _instances , key = lambda i : i . version ) def _find_instances_from_cache ( self ) -> List [ DataInstance ]: logger . info ( f \"Fetching cached versions of { self . _name } \" ) candidates = [ DataInstance ( name = CACHE_NAME_VERSION_SEPARATOR . join ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = f , ) for f in self . cache_path . glob ( f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR } *\" ) ] return [ c for c in candidates if c . name == self . _name ] def _check_mode_and_set_version ( self ) -> None : if \"+\" in self . _mode : raise ValueError ( f \"File mode ` { self . _mode } ` is not allowed3, remove the `+`.\" ) if \"w\" in self . _mode : if self . _version is not None : raise ValueError ( \"Providing a version is not allowed in write mode.\" ) self . _version = self . _instances [ - 1 ] . version + 1 if self . _instances else 0 elif \"r\" in self . _mode : if not self . _instances : raise FileNotFoundError ( f \"File { self . _name } not found. No versions are available.\" ) if self . _version is None : self . _version = self . _instances [ - 1 ] . version logger . info ( f \"Latest version of { self . _name } is { self . _version } \" + f \"(from versions: { self . versions_pretty } )\" ) elif self . _version not in [ i . version for i in self . _instances ]: raise FileNotFoundError ( f \"File { self . _name } not found with version { self . _version } . \" + f \"(from versions: { self . versions_pretty } )\" ) else : raise ValueError ( \"Unsupported file mode.\" ) def _prune_cache ( self ) -> None : self . cache_path . mkdir ( parents = True , exist_ok = True ) if self . max_cache_size is None : return allowed_size = human_readable_to_byte ( self . max_cache_size ) assert allowed_size >= 0 least_recently_read = sorted ( [ f for f in self . cache_path . glob ( \"*\" )], key = lambda f : f . stat () . st_atime ) while sum ( os . path . getsize ( f ) for f in least_recently_read ) > allowed_size : file = least_recently_read . pop ( 0 ) logger . info ( f \"Deleting file from cache to meet quota (max_cache_size= { self . max_cache_size } ): { file } \" ) os . unlink ( file ) @abstractmethod def _find_remote_instances ( self ) -> List [ DataInstance ]: pass @abstractmethod def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _delete_old_remote_versions ( self ) -> None : pass clean_up () # Delete local and remote versions according to currently set cache and retention policy. Source code in great_ai/large_file/large_file/large_file_base.py 250 251 252 253 254 def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache () configure_credentials ( ** kwargs ) classmethod # Configure required credentials for the LargeFile backend. Source code in great_ai/large_file/large_file/large_file_base.py 102 103 104 105 106 @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True configure_credentials_from_file ( secrets ) classmethod # Load file and feed its content to configure_credentials . Extra keys are ignored. Source code in great_ai/large_file/large_file/large_file_base.py 88 89 90 91 92 93 94 95 96 97 98 99 100 @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) delete () # Delete all versions of the files under this key . Source code in great_ai/large_file/large_file/large_file_base.py 239 240 241 242 243 def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions () get ( hide_progress = False ) cached # Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination push ( path , hide_progress = False ) # Upload a file (or directory) as a new version of key . The file/directory is compressed before upload. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () version () property # Numeric version of the file proxied by this LargeFile instance. Source code in great_ai/large_file/large_file/large_file_base.py 153 154 155 156 157 @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version ) versions_pretty () property # Formatted string of all available versions. Source code in great_ai/large_file/large_file/large_file_base.py 245 246 247 248 @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances ))","title":"LargeFile"},{"location":"reference/large-file/#largefile","text":"from great_ai.large_file import * For more details about using LargeFiles, check out the how to guide .","title":"LargeFile"},{"location":"reference/large-file/#great_ai.large_file.LargeFileLocal","text":"Bases: LargeFileBase LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal . cache_path = Path ( \".cache\" ) >>> LargeFileLocal . max_cache_size = \"30GB\" >>> with LargeFileLocal ( \"my_test.txt\" , \"w\" , keep_last_n = 2 ) as f : ... f . write ( 'test' ) 4 >>> with LargeFileLocal ( \"my_test.txt\" ) as f : ... print ( f . read ()) test Source code in great_ai/large_file/large_file/large_file_local.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 class LargeFileLocal ( LargeFileBase ): \"\"\"LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal.cache_path = Path(\".cache\") >>> LargeFileLocal.max_cache_size = \"30GB\" >>> with LargeFileLocal(\"my_test.txt\", \"w\", keep_last_n=2) as f: ... f.write('test') 4 >>> with LargeFileLocal(\"my_test.txt\") as f: ... print(f.read()) test \"\"\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , ): super () . __init__ ( name , mode , buffering = buffering , encoding = encoding , errors = errors , newline = newline , version = version , keep_last_n = keep_last_n , cache_only_mode = True , ) super () . configure_credentials () def _find_remote_instances ( self ) -> List [ DataInstance ]: return [] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : # This will never be called because the file must be in the cache raise NotImplementedError () def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass # the \"upload\" is already done py the parent's caching mechanism def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) i . remote_path . unlink ()","title":"LargeFileLocal"},{"location":"reference/large-file/#great_ai.large_file.LargeFileS3","text":"Bases: LargeFileBase LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_s3.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 class LargeFileS3 ( LargeFileBase ): \"\"\"LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" region_name = None access_key_id = None secret_access_key = None bucket_name = None endpoint_url = None @classmethod def configure_credentials ( # type: ignore cls , * , aws_region_name : str , aws_access_key_id : str , aws_secret_access_key : str , large_files_bucket_name : str , aws_endpoint_url : Optional [ str ] = None , ** _ : Any , ) -> None : cls . region_name = aws_region_name cls . access_key_id = aws_access_key_id cls . secret_access_key = aws_secret_access_key cls . bucket_name = large_files_bucket_name cls . endpoint_url = aws_endpoint_url super () . configure_credentials () @cached_property def _client ( self ) -> boto3 . client : if ( self . region_name is None or self . access_key_id is None or self . secret_access_key is None or self . bucket_name is None ): raise ValueError ( \"Please configure the S3 access options by calling LargeFileS3.configure_credentials or set offline_mode=True in the constructor.\" ) return boto3 . client ( \"s3\" , aws_access_key_id = self . access_key_id , aws_secret_access_key = self . secret_access_key , region_name = self . region_name , endpoint_url = self . endpoint_url , ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching S3 versions of { self . _name } \" ) found_objects = self . _client . list_objects_v2 ( Bucket = self . bucket_name , Prefix = self . _name ) return ( [ DataInstance ( name = o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ], version = int ( o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = o [ \"Key\" ], ) for o in found_objects [ \"Contents\" ] if o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ] == self . _name ] if \"Contents\" in found_objects else [] ) def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path } from S3\" ) size = self . _client . head_object ( Bucket = self . bucket_name , Key = remote_path )[ \"ContentLength\" ] self . _client . download_file ( Bucket = self . bucket_name , Key = remote_path , Filename = str ( local_path ), Callback = None if hide_progress else DownloadProgressBar ( name = str ( remote_path ), size = size , logger = logger ), ) def _upload ( self , local_path : Path , hide_progress : bool ) -> None : key = f \" { self . _name } / { self . version } \" logger . info ( f \"Uploading { self . _local_name } to S3 as { key } \" ) self . _client . upload_file ( Filename = str ( local_path ), Bucket = self . bucket_name , Key = key , Callback = None if hide_progress else UploadProgressBar ( path = local_path , logger = logger ), ) def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from S3 (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) self . _client . delete_object ( Bucket = self . bucket_name , Key = i . remote_path )","title":"LargeFileS3"},{"location":"reference/large-file/#great_ai.large_file.LargeFileMongo","text":"Bases: LargeFileBase LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_mongo.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 class LargeFileMongo ( LargeFileBase ): \"\"\"LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" mongo_connection_string = None mongo_database = None @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () @cached_property def _client ( self ) -> GridFSBucket : if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling LargeFileMongo.configure_credentials or set offline_mode=True in the constructor.\" ) db : Database = MongoClient ( self . mongo_connection_string )[ self . mongo_database ] return GridFSBucket ( db ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching Mongo (GridFS) versions of { self . _name } \" ) return [ DataInstance ( name = MONGO_NAME_VERSION_SEPARATOR . join ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = ( f . _id , f . length ), origin = \"mongodb\" , ) for f in self . _client . find ( { \"filename\" : re . compile ( re . escape ( self . _name + MONGO_NAME_VERSION_SEPARATOR ) + \".*\" ) } ) ] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path [ 0 ] } from Mongo (GridFS)\" ) progress = ( DownloadProgressBar ( name = str ( remote_path [ 0 ]), size = remote_path [ 1 ], logger = logger ) if not hide_progress else None ) with self . _client . open_download_stream ( remote_path [ 0 ]) as stream : with open ( local_path , \"wb\" ) as f : while True : content = stream . read ( DEFAULT_CHUNK_SIZE ) f . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _upload ( self , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Uploading { local_path } to Mongo (GridFS)\" ) progress = ( UploadProgressBar ( path = local_path , logger = logger ) if not hide_progress else None ) with self . _client . open_upload_stream ( f \" { self . _name }{ MONGO_NAME_VERSION_SEPARATOR }{ self . version } \" ) as stream : with open ( local_path , \"rb\" ) as f : while True : content = f . read ( DEFAULT_CHUNK_SIZE ) stream . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from MongoDB (GridFS) (keep_last_n= { self . _keep_last_n } ): { i . name }{ MONGO_NAME_VERSION_SEPARATOR }{ i . version } \" ) self . _client . delete ( i . remote_path [ 0 ])","title":"LargeFileMongo"},{"location":"reference/large-file/#great_ai.large_file.LargeFileBase","text":"Bases: ABC Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase . cache_path = Path ( \".cache\" ) >>> LargeFileBase . max_cache_size = \"30GB\" Attributes: Name Type Description initialized Tell whether configure_credentials or configure_credentials_from_file has been already called. cache_path Storage location for cached files. max_cache_size Optional [ str ] Delete files until the folder at cache_path is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to None for no automatic cache-pruning. Source code in great_ai/large_file/large_file/large_file_base.py 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 class LargeFileBase ( ABC ): \"\"\"Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase.cache_path = Path(\".cache\") >>> LargeFileBase.max_cache_size = \"30GB\" Attributes: initialized: Tell whether `configure_credentials` or `configure_credentials_from_file` has been already called. cache_path: Storage location for cached files. max_cache_size: Delete files until the folder at `cache_path` is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to `None` for no automatic cache-pruning. \"\"\" initialized = False cache_path = Path ( \".cache\" ) max_cache_size : Optional [ str ] = \"30GB\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , cache_only_mode : bool = False , ): self . _name = name self . _version = version self . _mode = mode self . _keep_last_n = keep_last_n self . _cache_only_mode = cache_only_mode self . _buffering = buffering self . _encoding = encoding if errors is not None and sys . version_info [ 1 ] < 8 : raise RuntimeError ( \"The `errors` kwarg is only supported in 3.8 <= Python versions.\" ) self . _errors = errors self . _newline = newline LargeFileBase . cache_path . mkdir ( parents = True , exist_ok = True ) self . _find_instances () self . _check_mode_and_set_version () @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True def __enter__ ( self ) -> IO : params = dict ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , delete = False , prefix = \"large_file-\" , ) if sys . version_info [ 1 ] >= 8 : params [ \"errors\" ] = self . _errors self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( ** params ) # type: ignore if \"w\" in self . _mode else open ( self . get (), mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , ) ) return self . _file def __exit__ ( self , type : Optional [ Type [ BaseException ]], exc : Optional [ BaseException ], traceback : Optional [ TracebackType ], ) -> Literal [ False ]: self . _file . close () if type is None : if \"w\" in self . _mode : self . push ( Path ( self . _file . name )) os . unlink ( self . _file . name ) else : logger . exception ( \"Could not finish operation.\" ) return False @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions () @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache () @property def _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" def _find_instances ( self ) -> None : if self . _cache_only_mode : self . _instances = self . _find_instances_from_cache () else : self . _instances = self . _find_remote_instances () self . _instances = sorted ( self . _instances , key = lambda i : i . version ) def _find_instances_from_cache ( self ) -> List [ DataInstance ]: logger . info ( f \"Fetching cached versions of { self . _name } \" ) candidates = [ DataInstance ( name = CACHE_NAME_VERSION_SEPARATOR . join ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = f , ) for f in self . cache_path . glob ( f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR } *\" ) ] return [ c for c in candidates if c . name == self . _name ] def _check_mode_and_set_version ( self ) -> None : if \"+\" in self . _mode : raise ValueError ( f \"File mode ` { self . _mode } ` is not allowed3, remove the `+`.\" ) if \"w\" in self . _mode : if self . _version is not None : raise ValueError ( \"Providing a version is not allowed in write mode.\" ) self . _version = self . _instances [ - 1 ] . version + 1 if self . _instances else 0 elif \"r\" in self . _mode : if not self . _instances : raise FileNotFoundError ( f \"File { self . _name } not found. No versions are available.\" ) if self . _version is None : self . _version = self . _instances [ - 1 ] . version logger . info ( f \"Latest version of { self . _name } is { self . _version } \" + f \"(from versions: { self . versions_pretty } )\" ) elif self . _version not in [ i . version for i in self . _instances ]: raise FileNotFoundError ( f \"File { self . _name } not found with version { self . _version } . \" + f \"(from versions: { self . versions_pretty } )\" ) else : raise ValueError ( \"Unsupported file mode.\" ) def _prune_cache ( self ) -> None : self . cache_path . mkdir ( parents = True , exist_ok = True ) if self . max_cache_size is None : return allowed_size = human_readable_to_byte ( self . max_cache_size ) assert allowed_size >= 0 least_recently_read = sorted ( [ f for f in self . cache_path . glob ( \"*\" )], key = lambda f : f . stat () . st_atime ) while sum ( os . path . getsize ( f ) for f in least_recently_read ) > allowed_size : file = least_recently_read . pop ( 0 ) logger . info ( f \"Deleting file from cache to meet quota (max_cache_size= { self . max_cache_size } ): { file } \" ) os . unlink ( file ) @abstractmethod def _find_remote_instances ( self ) -> List [ DataInstance ]: pass @abstractmethod def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _delete_old_remote_versions ( self ) -> None : pass","title":"LargeFileBase"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.clean_up","text":"Delete local and remote versions according to currently set cache and retention policy. Source code in great_ai/large_file/large_file/large_file_base.py 250 251 252 253 254 def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache ()","title":"clean_up()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.configure_credentials","text":"Configure required credentials for the LargeFile backend. Source code in great_ai/large_file/large_file/large_file_base.py 102 103 104 105 106 @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True","title":"configure_credentials()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.configure_credentials_from_file","text":"Load file and feed its content to configure_credentials . Extra keys are ignored. Source code in great_ai/large_file/large_file/large_file_base.py 88 89 90 91 92 93 94 95 96 97 98 99 100 @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()})","title":"configure_credentials_from_file()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.delete","text":"Delete all versions of the files under this key . Source code in great_ai/large_file/large_file/large_file_base.py 239 240 241 242 243 def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions ()","title":"delete()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.get","text":"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination","title":"get()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.push","text":"Upload a file (or directory) as a new version of key . The file/directory is compressed before upload. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up ()","title":"push()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.version","text":"Numeric version of the file proxied by this LargeFile instance. Source code in great_ai/large_file/large_file/large_file_base.py 153 154 155 156 157 @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version )","title":"version()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.versions_pretty","text":"Formatted string of all available versions. Source code in great_ai/large_file/large_file/large_file_base.py 245 246 247 248 @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances ))","title":"versions_pretty()"},{"location":"reference/utilities/","text":"Utilities # from great_ai.utilities import * NLP tools # Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well. clean ( text , ignore_xml = False , ignore_latex = False , remove_brackets = False , convert_to_ascii = False ) # Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... ignore_xml = True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... ignore_xml=True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text get_sentences ( text , ignore_partial = False , true_case = False , remove_punctuation = False ) # Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a punctuation. False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences predict_language ( text ) # Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag () english_name_of_language ( language_code ) # Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.language.predict_language.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name () is_english ( language_code ) # Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.language.predict_language.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15 evaluate_ranking ( expected , actual_scores , target_recall , title = '' , disable_interpolation = False , axes = None , output_svg = None , reverse_order = False , plot = True ) # Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for plotting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for plotting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 10 , 10 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] sorted_expected_actual = sorted ( zip ( binarized_expected , actual_scores ), key = lambda v : v [ 1 ], reverse = True ) precision = [] recall = [] correct = 0 for all , ( e , score ) in enumerate ( sorted_expected_actual , start = 1 ): correct += int ( e ) precision . append ( correct / all ) recall . append ( all / len ( sorted_expected_actual )) if not disable_interpolation : for j in range ( len ( precision ) - 2 , - 1 , - 1 ): precision [ j ] = max ( precision [ j ], precision [ j + 1 ]) closest_recall_index = np . argmin ( np . abs ( np . array ( recall ) - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results Parallel processing # Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess . simple_parallel_map ( func , input_values , * , chunk_size = None , concurrency = None ) # Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence generator = parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) return list ( tqdm ( generator , total = len ( input_values ), ) ) parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown () threaded_parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 ) Composable parallel processing # Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions. chunk ( values , chunk_size ) # Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result unchunk ( chunks ) # Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Similar to itertools.chain but ignores None chunks. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Similar to itertools.chain but ignores `None` chunks. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk Operations # ConfigFile # Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"\"\"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\"\"\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path . as_posix () } ) { self . _key_values } \" __init__ ( path , * , ignore_missing = False ) # Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () path () property # Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path get_logger ( name , level = logging . INFO , disable_colors = False ) # Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. \"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) try : get_ipython () # type: ignore log_format = \" %(message)s \" except NameError : # will fail outside of a notebook https://ipython.org/ log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"Utilities"},{"location":"reference/utilities/#utilities","text":"from great_ai.utilities import *","title":"Utilities"},{"location":"reference/utilities/#nlp-tools","text":"Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well.","title":"NLP tools"},{"location":"reference/utilities/#great_ai.utilities.clean.clean","text":"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... ignore_xml = True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... ignore_xml=True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text","title":"clean()"},{"location":"reference/utilities/#great_ai.utilities.get_sentences.get_sentences","text":"Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a punctuation. False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences","title":"get_sentences()"},{"location":"reference/utilities/#great_ai.utilities.language.predict_language.predict_language","text":"Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag ()","title":"predict_language()"},{"location":"reference/utilities/#great_ai.utilities.language.english_name_of_language.english_name_of_language","text":"Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.language.predict_language.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name ()","title":"english_name_of_language()"},{"location":"reference/utilities/#great_ai.utilities.language.is_english.is_english","text":"Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.language.predict_language.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15","title":"is_english()"},{"location":"reference/utilities/#great_ai.utilities.evaluate_ranking.evaluate_ranking.evaluate_ranking","text":"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for plotting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for plotting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 10 , 10 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] sorted_expected_actual = sorted ( zip ( binarized_expected , actual_scores ), key = lambda v : v [ 1 ], reverse = True ) precision = [] recall = [] correct = 0 for all , ( e , score ) in enumerate ( sorted_expected_actual , start = 1 ): correct += int ( e ) precision . append ( correct / all ) recall . append ( all / len ( sorted_expected_actual )) if not disable_interpolation : for j in range ( len ( precision ) - 2 , - 1 , - 1 ): precision [ j ] = max ( precision [ j ], precision [ j + 1 ]) closest_recall_index = np . argmin ( np . abs ( np . array ( recall ) - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results","title":"evaluate_ranking()"},{"location":"reference/utilities/#parallel-processing","text":"Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess .","title":"Parallel processing"},{"location":"reference/utilities/#great_ai.utilities.simple_parallel_map","text":"Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence generator = parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) return list ( tqdm ( generator , total = len ( input_values ), ) )","title":"simple_parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.parallel_map.parallel_map.parallel_map","text":"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown ()","title":"parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.threaded_parallel_map","text":"Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 )","title":"threaded_parallel_map()"},{"location":"reference/utilities/#composable-parallel-processing","text":"Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions.","title":"Composable parallel processing"},{"location":"reference/utilities/#great_ai.utilities.chunk.chunk","text":"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result","title":"chunk()"},{"location":"reference/utilities/#great_ai.utilities.unchunk.unchunk","text":"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Similar to itertools.chain but ignores None chunks. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Similar to itertools.chain but ignores `None` chunks. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk","title":"unchunk()"},{"location":"reference/utilities/#operations","text":"","title":"Operations"},{"location":"reference/utilities/#great_ai.utilities.ConfigFile","text":"Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"\"\"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\"\"\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path . as_posix () } ) { self . _key_values } \"","title":"ConfigFile"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.__init__","text":"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse ()","title":"__init__()"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.path","text":"Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path","title":"path()"},{"location":"reference/utilities/#great_ai.utilities.get_logger","text":"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. \"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) try : get_ipython () # type: ignore log_format = \" %(message)s \" except NameError : # will fail outside of a notebook https://ipython.org/ log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"get_logger()"},{"location":"reference/views/","text":"View models # Trace # Bases: Generic [ T ] , HashableBaseModel Universal structure for storing prediction traces and training data. Attributes: Name Type Description trace_id str UUID4 identifier for uniquely referring to a trace. created str Timestamp of its (original) construction. original_execution_time_ms float Wall-time elapsed while its generating TracingContext was alive. logged_values Dict [ str , Any ] Values persisted through using @parameter or log_metric() . models List [ Model ] Marks left by each encountered @use_model decorated function. exception Optional [ str ] Exception description if any was encountered. output Optional [ T ] Return value of the function wrapped by GreatAI. feedback Any Feedback obtained using the REST API of add_ground_truth . tags List [ str ] Tags used for filtering traces. Contains the name of the original function, value of ENVIRONMENT , its split if has any, and either ground_truth or online depending on the origin of the Trace. Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 class Trace ( Generic [ T ], HashableBaseModel ): \"\"\"Universal structure for storing prediction traces and training data. Attributes: trace_id: UUID4 identifier for uniquely referring to a trace. created: Timestamp of its (original) construction. original_execution_time_ms: Wall-time elapsed while its generating TracingContext was alive. logged_values: Values persisted through using `@parameter` or `log_metric()`. models: Marks left by each encountered `@use_model` decorated function. exception: Exception description if any was encountered. output: Return value of the function wrapped by GreatAI. feedback: Feedback obtained using the REST API of `add_ground_truth`. tags: Tags used for filtering traces. Contains the name of the original function, value of `ENVIRONMENT`, its split if has any, and either `ground_truth` or `online` depending on the origin of the Trace. \"\"\" trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : v if ( isinstance ( v , float ) or isinstance ( v , int )) else pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"\"\"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) . replace ( '{ ' , '{' , 1 ) } )\"\"\" RouteConfig # Bases: BaseModel Source code in great_ai/views/route_config.py 4 5 6 7 8 9 10 class RouteConfig ( BaseModel ): prediction_endpoint_enabled : bool = True docs_endpoints_enabled : bool = True dashboard_enabled : bool = True feedback_endpoints_enabled : bool = True trace_endpoints_enabled : bool = True meta_endpoints_enabled : bool = True ClassificationOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/classification_output.py 6 7 8 9 class ClassificationOutput ( HashableBaseModel ): label : Union [ str , int ] confidence : float explanation : Optional [ Any ] MultiLabelClassificationOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/multi_label_classification_output.py 7 8 class MultiLabelClassificationOutput ( HashableBaseModel ): labels : List [ ClassificationOutput ] = [] RegressionOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/regression_output.py 6 7 8 class RegressionOutput ( HashableBaseModel ): value : Union [ int , float ] explanation : Optional [ Any ] SequenceLabelingOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/sequence_labeling_output.py 15 16 17 class SequenceLabelingOutput ( HashableBaseModel ): labeled_tokens : List [ LabeledToken ] explanation : Optional [ Any ]","title":"View models"},{"location":"reference/views/#view-models","text":"","title":"View models"},{"location":"reference/views/#great_ai.Trace","text":"Bases: Generic [ T ] , HashableBaseModel Universal structure for storing prediction traces and training data. Attributes: Name Type Description trace_id str UUID4 identifier for uniquely referring to a trace. created str Timestamp of its (original) construction. original_execution_time_ms float Wall-time elapsed while its generating TracingContext was alive. logged_values Dict [ str , Any ] Values persisted through using @parameter or log_metric() . models List [ Model ] Marks left by each encountered @use_model decorated function. exception Optional [ str ] Exception description if any was encountered. output Optional [ T ] Return value of the function wrapped by GreatAI. feedback Any Feedback obtained using the REST API of add_ground_truth . tags List [ str ] Tags used for filtering traces. Contains the name of the original function, value of ENVIRONMENT , its split if has any, and either ground_truth or online depending on the origin of the Trace. Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 class Trace ( Generic [ T ], HashableBaseModel ): \"\"\"Universal structure for storing prediction traces and training data. Attributes: trace_id: UUID4 identifier for uniquely referring to a trace. created: Timestamp of its (original) construction. original_execution_time_ms: Wall-time elapsed while its generating TracingContext was alive. logged_values: Values persisted through using `@parameter` or `log_metric()`. models: Marks left by each encountered `@use_model` decorated function. exception: Exception description if any was encountered. output: Return value of the function wrapped by GreatAI. feedback: Feedback obtained using the REST API of `add_ground_truth`. tags: Tags used for filtering traces. Contains the name of the original function, value of `ENVIRONMENT`, its split if has any, and either `ground_truth` or `online` depending on the origin of the Trace. \"\"\" trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : v if ( isinstance ( v , float ) or isinstance ( v , int )) else pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"\"\"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) . replace ( '{ ' , '{' , 1 ) } )\"\"\"","title":"Trace"},{"location":"reference/views/#great_ai.RouteConfig","text":"Bases: BaseModel Source code in great_ai/views/route_config.py 4 5 6 7 8 9 10 class RouteConfig ( BaseModel ): prediction_endpoint_enabled : bool = True docs_endpoints_enabled : bool = True dashboard_enabled : bool = True feedback_endpoints_enabled : bool = True trace_endpoints_enabled : bool = True meta_endpoints_enabled : bool = True","title":"RouteConfig"},{"location":"reference/views/#great_ai.ClassificationOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/classification_output.py 6 7 8 9 class ClassificationOutput ( HashableBaseModel ): label : Union [ str , int ] confidence : float explanation : Optional [ Any ]","title":"ClassificationOutput"},{"location":"reference/views/#great_ai.MultiLabelClassificationOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/multi_label_classification_output.py 7 8 class MultiLabelClassificationOutput ( HashableBaseModel ): labels : List [ ClassificationOutput ] = []","title":"MultiLabelClassificationOutput"},{"location":"reference/views/#great_ai.RegressionOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/regression_output.py 6 7 8 class RegressionOutput ( HashableBaseModel ): value : Union [ int , float ] explanation : Optional [ Any ]","title":"RegressionOutput"},{"location":"reference/views/#great_ai.SequenceLabelingOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/sequence_labeling_output.py 15 16 17 class SequenceLabelingOutput ( HashableBaseModel ): labeled_tokens : List [ LabeledToken ] explanation : Optional [ Any ]","title":"SequenceLabelingOutput"},{"location":"tutorial/","text":"Train and deploy a SOTA model # Let's see great-ai in action by going over the life-cycle of a simple service. Objectives # You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment. Overview # You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it Summary # Training notebook # We load and preprocess the dataset while relying on great_ai.utilities.clean for doing the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) For more info, checkout the configuration how-to page . Deployment notebook # We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that the function is always provided with it. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Learn about all the features Look at more examples","title":"Tutorial overview"},{"location":"tutorial/#train-and-deploy-a-sota-model","text":"Let's see great-ai in action by going over the life-cycle of a simple service.","title":"Train and deploy a SOTA model"},{"location":"tutorial/#objectives","text":"You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment.","title":"Objectives"},{"location":"tutorial/#overview","text":"You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it","title":"Overview"},{"location":"tutorial/#summary","text":"","title":"Summary"},{"location":"tutorial/#training-notebook","text":"We load and preprocess the dataset while relying on great_ai.utilities.clean for doing the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) For more info, checkout the configuration how-to page .","title":"Training notebook"},{"location":"tutorial/#deployment-notebook","text":"We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that the function is always provided with it. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Learn about all the features Look at more examples","title":"Deployment notebook"},{"location":"tutorial/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); 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const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Harden and deploy your app \u00b6 Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment best practices . In the past, this step was too often either the source of unexpected struggle, or worse, simply ignored. With GreatAI , it has become a matter of 4 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) File my-domain-predictor-9 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) predict_domain(\"Mountains are just big rocks.\") Out[2]: Trace[str]({'created': '2022-07-12T13:34:26.743292', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 9}], 'original_execution_time_ms': 6.9699, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'c80bdee3-602b-49dd-a84d-6eef80127e5a'}) Notice how the original return value is under the .output key. Additionally, a plethora of metadata has been added which will be useful later on. Running your app in development-mode is as easy as executing great-ai deploy.ipynb from your terminal. In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-12 15:34:28 | INFO | Converting notebook to Python script 2022-07-12 15:34:29 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-12 15:34:29 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-12 15:34:31 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-12 15:34:31 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-12 15:34:31 | INFO | GreatAI (v0.1.4): configured \u2705 2022-07-12 15:34:31 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-12 15:34:31 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-12 15:34:31 | INFO | \ud83d\udd29 is_production: False 2022-07-12 15:34:31 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-12 15:34:31 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-12 15:34:31 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-12 15:34:31 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-12 15:34:31 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-12 15:34:31 | INFO | Fetching cached versions of my-domain-predictor 2022-07-12 15:34:31 | INFO | Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) 2022-07-12 15:34:31 | INFO | File my-domain-predictor-9 found in cache 2022-07-12 15:34:31 | INFO | Started server process [199794] 2022-07-12 15:34:31 | INFO | Waiting for application startup. 2022-07-12 15:34:31 | INFO | Application startup complete. ^C 2022-07-12 15:34:33 | INFO | Shutting down 2022-07-12 15:34:33 | INFO | Waiting for application shutdown. 2022-07-12 15:34:33 | INFO | Application shutdown complete. 2022-07-12 15:34:33 | INFO | Finished server process [199794] Congrats, you've just created your first GreatAI service! \ud83c\udf89 Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services. There are three main approaches to deploy a GreatAI service: For more info about them, check out the deployment how-to . For more thorough examples, see the examples page . Go back to the summary \u00b6","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#harden-and-deploy-your-app","text":"Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment best practices . In the past, this step was too often either the source of unexpected struggle, or worse, simply ignored. With GreatAI , it has become a matter of 4 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) File my-domain-predictor-9 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) predict_domain(\"Mountains are just big rocks.\") Out[2]: Trace[str]({'created': '2022-07-12T13:34:26.743292', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 9}], 'original_execution_time_ms': 6.9699, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'c80bdee3-602b-49dd-a84d-6eef80127e5a'}) Notice how the original return value is under the .output key. Additionally, a plethora of metadata has been added which will be useful later on. Running your app in development-mode is as easy as executing great-ai deploy.ipynb from your terminal. In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-12 15:34:28 | INFO | Converting notebook to Python script 2022-07-12 15:34:29 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-12 15:34:29 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-12 15:34:31 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-12 15:34:31 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-12 15:34:31 | INFO | GreatAI (v0.1.4): configured \u2705 2022-07-12 15:34:31 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-12 15:34:31 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-12 15:34:31 | INFO | \ud83d\udd29 is_production: False 2022-07-12 15:34:31 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-12 15:34:31 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-12 15:34:31 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-12 15:34:31 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-12 15:34:31 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-12 15:34:31 | INFO | Fetching cached versions of my-domain-predictor 2022-07-12 15:34:31 | INFO | Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) 2022-07-12 15:34:31 | INFO | File my-domain-predictor-9 found in cache 2022-07-12 15:34:31 | INFO | Started server process [199794] 2022-07-12 15:34:31 | INFO | Waiting for application startup. 2022-07-12 15:34:31 | INFO | Application startup complete. ^C 2022-07-12 15:34:33 | INFO | Shutting down 2022-07-12 15:34:33 | INFO | Waiting for application shutdown. 2022-07-12 15:34:33 | INFO | Application shutdown complete. 2022-07-12 15:34:33 | INFO | Finished server process [199794] Congrats, you've just created your first GreatAI service! \ud83c\udf89 Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services. There are three main approaches to deploy a GreatAI service: For more info about them, check out the deployment how-to . For more thorough examples, see the examples page .","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#go-back-to-the-summary","text":"","title":"Go back to the summary"},{"location":"tutorial/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train your model \u00b6 The first step is, of course, to install great-ai in your Python environment. In [1]: Copied! % pip install great - ai > / dev / null %pip install great-ai > /dev/null Note: you may need to restart the kernel to use updated packages. First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a separate JSON document). Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [2]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. Open files in Python are iterable: in text mode, each iteration returns the next line. In [3]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f ) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8960.63it/s] Let's do the same for the test data. In [4]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f ) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:03<00:00, 6078.02it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [5]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [6]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [7]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\" . This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey zone for software engineering. In order to use this model in production, we have to make it available on some possibly shared infrastructure. In [8]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Copying file for my-domain-predictor-9 Compressing my-domain-predictor-9 Model my-domain-predictor uploaded with version 9 Out[8]: 'my-domain-predictor:9' Let's continue by finishing the deployment in the next notebook. \u00b6","title":"Train your model"},{"location":"tutorial/train/#train-your-model","text":"The first step is, of course, to install great-ai in your Python environment. In [1]: Copied! % pip install great - ai > / dev / null %pip install great-ai > /dev/null Note: you may need to restart the kernel to use updated packages. First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a separate JSON document). Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [2]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. Open files in Python are iterable: in text mode, each iteration returns the next line. In [3]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f ) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8960.63it/s] Let's do the same for the test data. In [4]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f ) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:03<00:00, 6078.02it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [5]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [6]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [7]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\" . This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey zone for software engineering. In order to use this model in production, we have to make it available on some possibly shared infrastructure. In [8]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Copying file for my-domain-predictor-9 Compressing my-domain-predictor-9 Model my-domain-predictor uploaded with version 9 Out[8]: 'my-domain-predictor:9'","title":"Train your model"},{"location":"tutorial/train/#lets-continue-by-finishing-the-deployment-in-the-next-notebook","text":"","title":"Let's continue by finishing the deployment in the next notebook."}]} \ No newline at end of file +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Overview of GreatAI Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing unintended biases . GreatAI helps fixing this by allowing you to easily transform your prototype AI code into production-ready software . Case studies \"There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature.\" \u2014 John et al. \"Finally, we have found that existing tools to aid Machine Learning development do not address the specificities of different projects, and thus, are seldom adopted by teams.\" \u2014 Haakman et al. \"Because a mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code, it may be less costly to create a clean native solution rather than re-use a generic package.\" \u2014 Sculley et al. \"For example, practice 25 is very important for \u201cTraceability\", yet relatively weakly adopted. We expect that the results from this type of analysis can, in the future, provide useful guidance for practitioners in terms of aiding them to assess their rate of adoption for each practice and to create roadmaps for improving their processes. \u2014 Serban et al. Features # Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Graceful error handling Seamless support for both synchronous and asynchronous inference methods Easy integration with remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for online monitoring and analysing traces Active support for Python 3.7, 3.8, 3.9, and 3.10 Roadmap # Prometheus & Grafana integration Well-tested feature extraction code for non-NLP data Support for direct file input Support for PostgreSQL Hello world # pip install great-ai demo.py from great_ai import GreatAI @GreatAI . create #(1) def greeter ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your greeter function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, greeter could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either way, it is imperative to have continuous oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai demo.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation. Why is this GREAT? # GreatAI fits between the prototype and deployment phases of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, several best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust Why GreatAI? # There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub Check out the tutorial Production use # GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Overview"},{"location":"#features","text":"Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Graceful error handling Seamless support for both synchronous and asynchronous inference methods Easy integration with remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for online monitoring and analysing traces Active support for Python 3.7, 3.8, 3.9, and 3.10","title":"Features"},{"location":"#roadmap","text":"Prometheus & Grafana integration Well-tested feature extraction code for non-NLP data Support for direct file input Support for PostgreSQL","title":"Roadmap"},{"location":"#hello-world","text":"pip install great-ai demo.py from great_ai import GreatAI @GreatAI . create #(1) def greeter ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your greeter function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, greeter could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either way, it is imperative to have continuous oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai demo.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation.","title":"Hello world"},{"location":"#why-is-this-great","text":"GreatAI fits between the prototype and deployment phases of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, several best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust","title":"Why is this GREAT?"},{"location":"#why-greatai","text":"There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub Check out the tutorial","title":"Why GreatAI?"},{"location":"#production-use","text":"GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Production use"},{"location":"explanation/","text":"Explanation # A lot more details and discussion about the problem context and approaches of GreatAI along with its evaluation can be found in my thesis. : Download","title":"Explanation"},{"location":"explanation/#explanation","text":"A lot more details and discussion about the problem context and approaches of GreatAI along with its evaluation can be found in my thesis. : Download","title":"Explanation"},{"location":"examples/scibert/","text":"Summarising scientific publications from a tech-transfer perspective # This is a simplified example illustrating how great-ai is used in practice at ScoutinScience . The subpages show great-ai in action by going over the life-cycle of finetuning and deploying a BERT-based software service. Propriety data The purpose of this example is to show you different ways in which great-ai can assist you. The exact NLP task being solved is not central. Stemming from this, and from the difficult nature of obtaining appropriate training data, the propriety dataset used for the experiments is not shared. Objectives # You will see how the great_ai.utilities can integrate into your Data Science workflow. You will see how great_ai.large_file can be used to version and store your trained model. You will see how GreatAI should be used prepare your model for a robust and responsible deployment. You will see multiple ways of customising your deployment. Overview # One of the core features of the ScoutinScience platform is summarising research papers from a tech-transfer perspective. In short, extractive summarisation is preferred using a binary classifier trained on clients' judgement of sentence interestingness. Thus, documents are sentences and the expected output is a binary label showing whether a sentence is \"worthy\" of being in the tech-transfer summary. Providing an explanation for each decision is imperative since ScoutinScience embraces applying only explainable AI (XAI) methods wherever feasible. Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Examine data Train model Deploy service Summary # Data notebook # We load and analyse the data by calculating inter-rater reliability and checking the feasibility of using an AI-based approach by testing the accuracy of a trivial baseline method. Training notebook # We simply fine-tune SciBERT. After training and evaluating a model, it is exported using great_ai.save_model . For more info, checkout the configuration how-to page . Deployment notebook # We customise the GreatAI configuration, create custom cahcing for the model and implement an inference function that can be hardened by wrapping it in a GreatAI instance. We also extract the attention weights as a quasi-explanation. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Additional files # There are some other files required for deploying the notebook. For example, the config file for S3 and MongoDB or a Dockerfile for building a custom image. These are compiled and shown on a separate page.","title":"Summarising scientific publications from a tech-transfer perspective"},{"location":"examples/scibert/#summarising-scientific-publications-from-a-tech-transfer-perspective","text":"This is a simplified example illustrating how great-ai is used in practice at ScoutinScience . 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You will see multiple ways of customising your deployment.","title":"Objectives"},{"location":"examples/scibert/#overview","text":"One of the core features of the ScoutinScience platform is summarising research papers from a tech-transfer perspective. In short, extractive summarisation is preferred using a binary classifier trained on clients' judgement of sentence interestingness. Thus, documents are sentences and the expected output is a binary label showing whether a sentence is \"worthy\" of being in the tech-transfer summary. Providing an explanation for each decision is imperative since ScoutinScience embraces applying only explainable AI (XAI) methods wherever feasible. Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Examine data Train model Deploy service","title":"Overview"},{"location":"examples/scibert/#summary","text":"","title":"Summary"},{"location":"examples/scibert/#data-notebook","text":"We load and analyse the data by calculating inter-rater reliability and checking the feasibility of using an AI-based approach by testing the accuracy of a trivial baseline method.","title":"Data notebook"},{"location":"examples/scibert/#training-notebook","text":"We simply fine-tune SciBERT. After training and evaluating a model, it is exported using great_ai.save_model . For more info, checkout the configuration how-to page .","title":"Training notebook"},{"location":"examples/scibert/#deployment-notebook","text":"We customise the GreatAI configuration, create custom cahcing for the model and implement an inference function that can be hardened by wrapping it in a GreatAI instance. We also extract the attention weights as a quasi-explanation. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly.","title":"Deployment notebook"},{"location":"examples/scibert/#additional-files","text":"There are some other files required for deploying the notebook. For example, the config file for S3 and MongoDB or a Dockerfile for building a custom image. These are compiled and shown on a separate page.","title":"Additional files"},{"location":"examples/scibert/additional-files/","text":"Additional files in the repository # In order to give you a smooth experience while comprehending this example, all non-notebook files are presented on this page in one place. In reality, these files should be in your project's top-level directory. config.ini # config.ini ENVIRONMENT = DEVELOPMENT ENVIRONMENT = ENV:ENVIRONMENT MONGO_CONNECTION_STRING = ENV:MONGO_CONNECTION_STRING MONGO_DATABASE = highlights AWS_REGION_NAME = eu-west-2 AWS_ACCESS_KEY_ID = MY_DEFAULT_AWS_ACCESS_KEY_ID_FOR_DEVELOPMENT AWS_SECRET_ACCESS_KEY = MY_DEFAULT_AWS_SECRET_ACCESS_KEY_FOR_DEVELOPMENT LARGE_FILES_BUCKET_NAME = my-orgs-large-files AWS_REGION_NAME = ENV:AWS_REGION_NAME AWS_ACCESS_KEY_ID = ENV:AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY = ENV:AWS_SECRET_ACCESS_KEY LARGE_FILES_BUCKET_NAME = ENV:LARGE_FILES_BUCKET_NAME All necessary configuration which is read by great_ai.utilities.ConfigFile . This will resolve values starting with ENV: from your environment variables. requirements.txt # requirements.txt torch==1.12.0 transformers==4.20.1 numpy==1.23.0 Usually, it is recommended to pin (freeze) the library versions on which we depend. This file is referenced by the Dockerfile . Dockerfile # Dockerfile FROM schmelczera/great-ai:v0.1.6 COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt COPY . ./ RUN large-file --backend s3 --secrets s3.ini --cache scibert-highlights CMD [ \"deploy.ipynb\" ] This is used by the CD pipeline to create the production deployment of the service. .dockerignore # .dockerignore .cache .git data .gitignore .env .vscode .dockerignore Dockerfile .mypy_cache .gitignore **/__pycache__ **/.DS_Store README.md It is useful not to send, for example, the .cache folder used by LargeFile to the docker daemon; this will speed up your local build times substantially. .gitignore A very similar looking .gitignore file should also be present.","title":"Additional files in the repository"},{"location":"examples/scibert/additional-files/#additional-files-in-the-repository","text":"In order to give you a smooth experience while comprehending this example, all non-notebook files are presented on this page in one place. In reality, these files should be in your project's top-level directory.","title":"Additional files in the repository"},{"location":"examples/scibert/additional-files/#configini","text":"config.ini ENVIRONMENT = DEVELOPMENT ENVIRONMENT = ENV:ENVIRONMENT MONGO_CONNECTION_STRING = ENV:MONGO_CONNECTION_STRING MONGO_DATABASE = highlights AWS_REGION_NAME = eu-west-2 AWS_ACCESS_KEY_ID = MY_DEFAULT_AWS_ACCESS_KEY_ID_FOR_DEVELOPMENT AWS_SECRET_ACCESS_KEY = MY_DEFAULT_AWS_SECRET_ACCESS_KEY_FOR_DEVELOPMENT LARGE_FILES_BUCKET_NAME = my-orgs-large-files AWS_REGION_NAME = ENV:AWS_REGION_NAME AWS_ACCESS_KEY_ID = ENV:AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY = ENV:AWS_SECRET_ACCESS_KEY LARGE_FILES_BUCKET_NAME = ENV:LARGE_FILES_BUCKET_NAME All necessary configuration which is read by great_ai.utilities.ConfigFile . This will resolve values starting with ENV: from your environment variables.","title":"config.ini"},{"location":"examples/scibert/additional-files/#requirementstxt","text":"requirements.txt torch==1.12.0 transformers==4.20.1 numpy==1.23.0 Usually, it is recommended to pin (freeze) the library versions on which we depend. This file is referenced by the Dockerfile .","title":"requirements.txt"},{"location":"examples/scibert/additional-files/#dockerfile","text":"Dockerfile FROM schmelczera/great-ai:v0.1.6 COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt COPY . ./ RUN large-file --backend s3 --secrets s3.ini --cache scibert-highlights CMD [ \"deploy.ipynb\" ] This is used by the CD pipeline to create the production deployment of the service.","title":"Dockerfile"},{"location":"examples/scibert/additional-files/#dockerignore","text":".dockerignore .cache .git data .gitignore .env .vscode .dockerignore Dockerfile .mypy_cache .gitignore **/__pycache__ **/.DS_Store README.md It is useful not to send, for example, the .cache folder used by LargeFile to the docker daemon; this will speed up your local build times substantially. .gitignore A very similar looking .gitignore file should also be present.","title":".dockerignore"},{"location":"examples/scibert/data/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Explore data and feasibility of approach \u00b6 We had asked our clients and in-house experts to annotate sentences using a rigorous guideline. The aim is to decide on which sentences they would like to see in a summary for a paper. The results are in JSON format, each annotator has a separate file. Let's load them. In [1]: Copied! from pathlib import Path import json annotations = [] for p in Path ( \"data\" ) . glob ( \"*.json\" ): with open ( p , encoding = \"utf-8\" ) as f : print ( p ) annotations . append ( json . load ( f )) evaluations = { sentence : [ annotation [ sentence ] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation . keys () } } X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] from pathlib import Path import json annotations = [] for p in Path(\"data\").glob(\"*.json\"): with open(p, encoding=\"utf-8\") as f: print(p) annotations.append(json.load(f)) evaluations = { sentence: [ annotation[sentence] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation.keys() } } X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] data/evaluation-experiment-2-stage #1-sa6a0y.json data/evaluation-experiment-2-stage #1-2m6dmb.json Save the compiled and processed data for later use using LargeFileS3. In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , \"w\" , encoding = \"utf-8\" ) as f : json . dump (( X , y ), f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", \"w\", encoding=\"utf-8\") as f: json.dump((X, y), f) Copying file for summary-train-dataset-small-0 Compressing summary-train-dataset-small-0 Uploading summary-train-dataset-small-0 to S3 as summary-train-dataset-small/0 Uploading summary-train-dataset-small-0.tar.gz 0.04/0.04 MB (100.0%) Filter out sentences which don't have enough annotations. In [3]: Copied! y1 = [ e [ 0 ] for e in evaluations . values () if len ( e ) == 2 ] y2 = [ e [ 1 ] for e in evaluations . values () if len ( e ) == 2 ] y1 = [e[0] for e in evaluations.values() if len(e) == 2] y2 = [e[1] for e in evaluations.values() if len(e) == 2] Calculate Cohen's kappa . It's a bit low but the task itself is pretty subjective so it's not all that surprising. In [4]: Copied! import sklearn.metrics sklearn . metrics . cohen_kappa_score ( y1 , y2 ) import sklearn.metrics sklearn.metrics.cohen_kappa_score(y1, y2) Out[4]: 0.3546448712421808 Can we train anything on this data? Let's try with a trivial SVM. In [5]: Copied! X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] In [6]: Copied! from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True , min_df = 3 , max_df = 0.3 )), ( \"classifier\" , LinearSVC ()), ] ) # baseline model cross_val_score ( model , X , y , cv = 5 ) from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True, min_df=3, max_df=0.3)), (\"classifier\", LinearSVC()), ] ) # baseline model cross_val_score(model, X, y, cv=5) Out[6]: array([0.79, 0.75, 0.77, 0.69, 0.77]) The cross-validation shows promising accuracies. But accuracy isn't everything, therefore, we should investigate the accuracy metrics. In [7]: Copied! X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.2 , random_state = 0 ) model . fit ( X_train , y_train ) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) model.fit(X_train, y_train) Out[7]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) TfidfVectorizer TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True) LinearSVC LinearSVC() In [8]: Copied! y_predicted = model . predict ( X_test ) print ( sklearn . metrics . classification_report ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ] ) ) sklearn . metrics . ConfusionMatrixDisplay . from_predictions ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ], xticks_rotation = \"vertical\" , values_format = \".2f\" , ) None y_predicted = model.predict(X_test) print( sklearn.metrics.classification_report( [y > 0 for y in y_test], [y > 0 for y in y_predicted] ) ) sklearn.metrics.ConfusionMatrixDisplay.from_predictions( [y > 0 for y in y_test], [y > 0 for y in y_predicted], xticks_rotation=\"vertical\", values_format=\".2f\", ) None precision recall f1-score support False 0.78 0.78 0.78 51 True 0.78 0.78 0.78 49 accuracy 0.78 100 macro avg 0.78 0.78 0.78 100 weighted avg 0.78 0.78 0.78 100 We get an F1-score of 0.78 without any hyperparameter-optimisation; this task may be feasible to solve with AI. Next: Part 2","title":"Explore data and feasibility of approach"},{"location":"examples/scibert/data/#explore-data-and-feasibility-of-approach","text":"We had asked our clients and in-house experts to annotate sentences using a rigorous guideline. The aim is to decide on which sentences they would like to see in a summary for a paper. The results are in JSON format, each annotator has a separate file. Let's load them. In [1]: Copied! from pathlib import Path import json annotations = [] for p in Path ( \"data\" ) . glob ( \"*.json\" ): with open ( p , encoding = \"utf-8\" ) as f : print ( p ) annotations . append ( json . load ( f )) evaluations = { sentence : [ annotation [ sentence ] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation . keys () } } X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] from pathlib import Path import json annotations = [] for p in Path(\"data\").glob(\"*.json\"): with open(p, encoding=\"utf-8\") as f: print(p) annotations.append(json.load(f)) evaluations = { sentence: [ annotation[sentence] for annotation in annotations if sentence in annotation ] for sentence in { sentence for annotation in annotations for sentence in annotation.keys() } } X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] data/evaluation-experiment-2-stage #1-sa6a0y.json data/evaluation-experiment-2-stage #1-2m6dmb.json Save the compiled and processed data for later use using LargeFileS3. In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , \"w\" , encoding = \"utf-8\" ) as f : json . dump (( X , y ), f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", \"w\", encoding=\"utf-8\") as f: json.dump((X, y), f) Copying file for summary-train-dataset-small-0 Compressing summary-train-dataset-small-0 Uploading summary-train-dataset-small-0 to S3 as summary-train-dataset-small/0 Uploading summary-train-dataset-small-0.tar.gz 0.04/0.04 MB (100.0%) Filter out sentences which don't have enough annotations. In [3]: Copied! y1 = [ e [ 0 ] for e in evaluations . values () if len ( e ) == 2 ] y2 = [ e [ 1 ] for e in evaluations . values () if len ( e ) == 2 ] y1 = [e[0] for e in evaluations.values() if len(e) == 2] y2 = [e[1] for e in evaluations.values() if len(e) == 2] Calculate Cohen's kappa . It's a bit low but the task itself is pretty subjective so it's not all that surprising. In [4]: Copied! import sklearn.metrics sklearn . metrics . cohen_kappa_score ( y1 , y2 ) import sklearn.metrics sklearn.metrics.cohen_kappa_score(y1, y2) Out[4]: 0.3546448712421808 Can we train anything on this data? Let's try with a trivial SVM. In [5]: Copied! X = [ s for s in evaluations . keys ()] y = [ int ( sum ( e ) > 0 ) for e in evaluations . values ()] X = [s for s in evaluations.keys()] y = [int(sum(e) > 0) for e in evaluations.values()] In [6]: Copied! from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True , min_df = 3 , max_df = 0.3 )), ( \"classifier\" , LinearSVC ()), ] ) # baseline model cross_val_score ( model , X , y , cv = 5 ) from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import cross_val_score model = Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True, min_df=3, max_df=0.3)), (\"classifier\", LinearSVC()), ] ) # baseline model cross_val_score(model, X, y, cv=5) Out[6]: array([0.79, 0.75, 0.77, 0.69, 0.77]) The cross-validation shows promising accuracies. But accuracy isn't everything, therefore, we should investigate the accuracy metrics. In [7]: Copied! X_train , X_test , y_train , y_test = train_test_split ( X , y , test_size = 0.2 , random_state = 0 ) model . fit ( X_train , y_train ) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) model.fit(X_train, y_train) Out[7]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True)), ('classifier', LinearSVC())]) TfidfVectorizer TfidfVectorizer(max_df=0.3, min_df=3, sublinear_tf=True) LinearSVC LinearSVC() In [8]: Copied! y_predicted = model . predict ( X_test ) print ( sklearn . metrics . classification_report ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ] ) ) sklearn . metrics . ConfusionMatrixDisplay . from_predictions ( [ y > 0 for y in y_test ], [ y > 0 for y in y_predicted ], xticks_rotation = \"vertical\" , values_format = \".2f\" , ) None y_predicted = model.predict(X_test) print( sklearn.metrics.classification_report( [y > 0 for y in y_test], [y > 0 for y in y_predicted] ) ) sklearn.metrics.ConfusionMatrixDisplay.from_predictions( [y > 0 for y in y_test], [y > 0 for y in y_predicted], xticks_rotation=\"vertical\", values_format=\".2f\", ) None precision recall f1-score support False 0.78 0.78 0.78 51 True 0.78 0.78 0.78 49 accuracy 0.78 100 macro avg 0.78 0.78 0.78 100 weighted avg 0.78 0.78 0.78 100 We get an F1-score of 0.78 without any hyperparameter-optimisation; this task may be feasible to solve with AI. Next: Part 2","title":"Explore data and feasibility of approach"},{"location":"examples/scibert/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); 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background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Create an inference function \u00b6 Everything is ready to wrap the previously trained model and deploy it. First, we need to configure the LargeFileBackend, the TracingDatabase and GreatAI. In [1]: Copied! from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure , MongoDbDriver configuration = ConfigFile ( \"config.ini\" ) LargeFileS3 . configure_credentials_from_file ( configuration ) MongoDbDriver . configure_credentials_from_file ( configuration ) configure ( dashboard_table_size = 100 , # traces are small, we can show many prediction_cache_size = 4096 , # predictions are expensive, cache them ) from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure, MongoDbDriver configuration = ConfigFile(\"config.ini\") LargeFileS3.configure_credentials_from_file(configuration) MongoDbDriver.configure_credentials_from_file(configuration) configure( dashboard_table_size=100, # traces are small, we can show many prediction_cache_size=4096, # predictions are expensive, cache them ) The value of `ENVIRONMENT` contains the \"ENV` prefix but `ENVIRONMENT` is not defined as an environment variable, using the default value defined above (`DEVELOPMENT`) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f MongoDbDriver has been already configured: skipping initialisation LargeFileS3 has been already configured: skipping initialisation GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: MongoDbDriver \ud83d\udd29 large_file_implementation: LargeFileS3 \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 4096 \ud83d\udd29 dashboard_table_size: 100 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices For a pleasant developer experience, we create some typed models that will show up in the automatically generated OpenAPI schema specification and will also provide runtime type validation. In [2]: Copied! from typing import List from pydantic import BaseModel class Attention ( BaseModel ): weight : float token : str class EvaluatedSentence ( BaseModel ): score : float text : str explanation : List [ Attention ] from typing import List from pydantic import BaseModel class Attention(BaseModel): weight: float token: str class EvaluatedSentence(BaseModel): score: float text: str explanation: List[Attention] Even though @use_model caches the remote files locally and it also handles deserialising objects, we only use it to store a directory. In this case, it gives back a path, the path to that directory. So, we need to load the files from that folder ourselves. In order to only load it once per process, we create a small model loader helper function. This is usually not needed, however, when we can outsmart dill so for optimisation purposes, we do it. In [3]: Copied! from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel , PreTrainedTokenizer , ) from transformers import ( AutoConfig , AutoModelForSequenceClassification , AutoTokenizer , ) _tokenizer : PreTrainedTokenizer = None _loaded_model : PreTrainedModel = None @use_model ( \"scibert-highlights\" , version = \"latest\" , model_kwarg_name = \"model_path\" ) def get_tokenizer_and_model ( model_path : Path , original_model : str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple [ PreTrainedTokenizer , PreTrainedModel ]: global _tokenizer , _loaded_model if _tokenizer is None : _tokenizer = AutoTokenizer . from_pretrained ( original_model ) if _loaded_model is None : config = AutoConfig . from_pretrained ( model_path , output_hidden_states = True , output_attentions = True ) _loaded_model = AutoModelForSequenceClassification . from_pretrained ( model_path , config = config ) return _tokenizer , _loaded_model from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel, PreTrainedTokenizer, ) from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, ) _tokenizer: PreTrainedTokenizer = None _loaded_model: PreTrainedModel = None @use_model(\"scibert-highlights\", version=\"latest\", model_kwarg_name=\"model_path\") def get_tokenizer_and_model( model_path: Path, original_model: str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple[PreTrainedTokenizer, PreTrainedModel]: global _tokenizer, _loaded_model if _tokenizer is None: _tokenizer = AutoTokenizer.from_pretrained(original_model) if _loaded_model is None: config = AutoConfig.from_pretrained( model_path, output_hidden_states=True, output_attentions=True ) _loaded_model = AutoModelForSequenceClassification.from_pretrained( model_path, config=config ) return _tokenizer, _loaded_model Latest version of scibert-highlights is 0 (from versions: 0) File scibert-highlights-0 found in cache Finally, implement the inference function. In [4]: Copied! from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI . create def find_highlights ( sentence : str ) -> EvaluatedSentence : \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer , loaded_model = get_tokenizer_and_model () sentence = clean ( sentence , convert_to_ascii = True , remove_brackets = True ) tensors = tokenizer ( sentence , return_tensors = \"pt\" , truncation = True , max_length = 512 ) with torch . inference_mode (): result : SequenceClassifierOutput = loaded_model ( ** tensors ) positive_likelihood = torch . nn . Softmax ( dim = 1 )( result . logits )[ 0 ][ 1 ] tokens = tensors [ \"input_ids\" ][ 0 ] attentions = np . sum ( result . attentions [ - 1 ] . numpy ()[ 0 ], axis = 0 )[ 0 ][ 1 : - 1 ] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list ( zip ( attentions , tokens [ 1 : - 1 ])) for token in re . split ( r \"([ .,])\" , sentence ): token = token . strip () if not token : continue bert_tokens = tokenizer ( token , return_tensors = \"pt\" , truncation = True , max_length = 512 )[ \"input_ids\" ][ 0 ][ 1 : - 1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens : if not token_attentions : break a , t2 = token_attentions . pop ( 0 ) assert t1 == t2 , sentence weight += a explanation . append ( Attention ( token = token if token in \".,\" else \" \" + token , weight = round ( weight , 4 ) ) ) if not token_attentions : break return EvaluatedSentence ( score = positive_likelihood , text = sentence , explanation = explanation ) from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI.create def find_highlights(sentence: str) -> EvaluatedSentence: \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer, loaded_model = get_tokenizer_and_model() sentence = clean(sentence, convert_to_ascii=True, remove_brackets=True) tensors = tokenizer(sentence, return_tensors=\"pt\", truncation=True, max_length=512) with torch.inference_mode(): result: SequenceClassifierOutput = loaded_model(**tensors) positive_likelihood = torch.nn.Softmax(dim=1)(result.logits)[0][1] tokens = tensors[\"input_ids\"][0] attentions = np.sum(result.attentions[-1].numpy()[0], axis=0)[0][1:-1] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list(zip(attentions, tokens[1:-1])) for token in re.split(r\"([ .,])\", sentence): token = token.strip() if not token: continue bert_tokens = tokenizer( token, return_tensors=\"pt\", truncation=True, max_length=512 )[\"input_ids\"][0][ 1:-1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens: if not token_attentions: break a, t2 = token_attentions.pop(0) assert t1 == t2, sentence weight += a explanation.append( Attention( token=token if token in \".,\" else \" \" + token, weight=round(weight, 4) ) ) if not token_attentions: break return EvaluatedSentence( score=positive_likelihood, text=sentence, explanation=explanation ) A simple test to see everything works. Note that the models list is filled by the @use_model call even though it's not on the main inference function. In [5]: Copied! if __name__ == \"__main__\" : find_highlights ( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights ( \"Their solution did not perform well.\" ) if __name__ == \"__main__\": find_highlights( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights(\"Their solution did not perform well.\") Out[5]: (Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:29.581701', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 51, 'arg:sentence:value': 'Our solution has outperformed the ' 'state-of-the-art.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 7127.2063, 'output': { 'explanation': [ {'token': ' Our', 'weight': 0.3993}, {'token': ' solution', 'weight': 0.3481}, {'token': ' has', 'weight': 0.2945}, {'token': ' outperformed', 'weight': 0.4011}, {'token': ' the', 'weight': 0.1484}, {'token': ' state-of-the-art', 'weight': 0.5727}, {'token': '.', 'weight': 7.775}], 'score': 0.9991180300712585, 'text': 'Our solution has outperformed the state-of-the-art.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '56e20e94-79df-4793-ae61-d20820ebe2d3'}), Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:37.020275', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 36, 'arg:sentence:value': 'Their solution did not perform ' 'well.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 170.7057, 'output': { 'explanation': [ {'token': ' Their', 'weight': 1.1475}, {'token': ' solution', 'weight': 0.8205}, {'token': ' did', 'weight': 0.3254}, {'token': ' not', 'weight': 0.2921}, {'token': ' perform', 'weight': 0.4293}, {'token': ' well', 'weight': 0.2772}, {'token': '.', 'weight': 4.4723}], 'score': 0.12305451184511185, 'text': 'Their solution did not perform well.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '7fcf8271-1738-4025-8305-d5a1e5100aea'})) In this case, the service is built as a docker image, pushed to our image registry and subsequent rolling update is performed in the production cluster. To check out the Dockerimage, go to the additional files page .","title":"Create an inference function"},{"location":"examples/scibert/deploy/#create-an-inference-function","text":"Everything is ready to wrap the previously trained model and deploy it. First, we need to configure the LargeFileBackend, the TracingDatabase and GreatAI. In [1]: Copied! from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure , MongoDbDriver configuration = ConfigFile ( \"config.ini\" ) LargeFileS3 . configure_credentials_from_file ( configuration ) MongoDbDriver . configure_credentials_from_file ( configuration ) configure ( dashboard_table_size = 100 , # traces are small, we can show many prediction_cache_size = 4096 , # predictions are expensive, cache them ) from great_ai.utilities import ConfigFile from great_ai.large_file import LargeFileS3 from great_ai import configure, MongoDbDriver configuration = ConfigFile(\"config.ini\") LargeFileS3.configure_credentials_from_file(configuration) MongoDbDriver.configure_credentials_from_file(configuration) configure( dashboard_table_size=100, # traces are small, we can show many prediction_cache_size=4096, # predictions are expensive, cache them ) The value of `ENVIRONMENT` contains the \"ENV` prefix but `ENVIRONMENT` is not defined as an environment variable, using the default value defined above (`DEVELOPMENT`) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f MongoDbDriver has been already configured: skipping initialisation LargeFileS3 has been already configured: skipping initialisation GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: MongoDbDriver \ud83d\udd29 large_file_implementation: LargeFileS3 \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 4096 \ud83d\udd29 dashboard_table_size: 100 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices For a pleasant developer experience, we create some typed models that will show up in the automatically generated OpenAPI schema specification and will also provide runtime type validation. In [2]: Copied! from typing import List from pydantic import BaseModel class Attention ( BaseModel ): weight : float token : str class EvaluatedSentence ( BaseModel ): score : float text : str explanation : List [ Attention ] from typing import List from pydantic import BaseModel class Attention(BaseModel): weight: float token: str class EvaluatedSentence(BaseModel): score: float text: str explanation: List[Attention] Even though @use_model caches the remote files locally and it also handles deserialising objects, we only use it to store a directory. In this case, it gives back a path, the path to that directory. So, we need to load the files from that folder ourselves. In order to only load it once per process, we create a small model loader helper function. This is usually not needed, however, when we can outsmart dill so for optimisation purposes, we do it. In [3]: Copied! from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel , PreTrainedTokenizer , ) from transformers import ( AutoConfig , AutoModelForSequenceClassification , AutoTokenizer , ) _tokenizer : PreTrainedTokenizer = None _loaded_model : PreTrainedModel = None @use_model ( \"scibert-highlights\" , version = \"latest\" , model_kwarg_name = \"model_path\" ) def get_tokenizer_and_model ( model_path : Path , original_model : str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple [ PreTrainedTokenizer , PreTrainedModel ]: global _tokenizer , _loaded_model if _tokenizer is None : _tokenizer = AutoTokenizer . from_pretrained ( original_model ) if _loaded_model is None : config = AutoConfig . from_pretrained ( model_path , output_hidden_states = True , output_attentions = True ) _loaded_model = AutoModelForSequenceClassification . from_pretrained ( model_path , config = config ) return _tokenizer , _loaded_model from great_ai import use_model from pathlib import Path from typing import Tuple from transformers import ( PreTrainedModel, PreTrainedTokenizer, ) from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, ) _tokenizer: PreTrainedTokenizer = None _loaded_model: PreTrainedModel = None @use_model(\"scibert-highlights\", version=\"latest\", model_kwarg_name=\"model_path\") def get_tokenizer_and_model( model_path: Path, original_model: str = \"allenai/scibert_scivocab_uncased\" ) -> Tuple[PreTrainedTokenizer, PreTrainedModel]: global _tokenizer, _loaded_model if _tokenizer is None: _tokenizer = AutoTokenizer.from_pretrained(original_model) if _loaded_model is None: config = AutoConfig.from_pretrained( model_path, output_hidden_states=True, output_attentions=True ) _loaded_model = AutoModelForSequenceClassification.from_pretrained( model_path, config=config ) return _tokenizer, _loaded_model Latest version of scibert-highlights is 0 (from versions: 0) File scibert-highlights-0 found in cache Finally, implement the inference function. In [4]: Copied! from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI . create def find_highlights ( sentence : str ) -> EvaluatedSentence : \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer , loaded_model = get_tokenizer_and_model () sentence = clean ( sentence , convert_to_ascii = True , remove_brackets = True ) tensors = tokenizer ( sentence , return_tensors = \"pt\" , truncation = True , max_length = 512 ) with torch . inference_mode (): result : SequenceClassifierOutput = loaded_model ( ** tensors ) positive_likelihood = torch . nn . Softmax ( dim = 1 )( result . logits )[ 0 ][ 1 ] tokens = tensors [ \"input_ids\" ][ 0 ] attentions = np . sum ( result . attentions [ - 1 ] . numpy ()[ 0 ], axis = 0 )[ 0 ][ 1 : - 1 ] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list ( zip ( attentions , tokens [ 1 : - 1 ])) for token in re . split ( r \"([ .,])\" , sentence ): token = token . strip () if not token : continue bert_tokens = tokenizer ( token , return_tensors = \"pt\" , truncation = True , max_length = 512 )[ \"input_ids\" ][ 0 ][ 1 : - 1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens : if not token_attentions : break a , t2 = token_attentions . pop ( 0 ) assert t1 == t2 , sentence weight += a explanation . append ( Attention ( token = token if token in \".,\" else \" \" + token , weight = round ( weight , 4 ) ) ) if not token_attentions : break return EvaluatedSentence ( score = positive_likelihood , text = sentence , explanation = explanation ) from great_ai import GreatAI from great_ai.utilities import clean import re import numpy as np import torch from transformers.modeling_outputs import SequenceClassifierOutput @GreatAI.create def find_highlights(sentence: str) -> EvaluatedSentence: \"\"\"Get the interestingness prediction of the input sentence using SciBERT. Run the SciBERT model in inference mode and evaluate the sentence. Additionally, provide explanation in the form of the last layer's sum attention between `[CLS]` and the other tokens. \"\"\" tokenizer, loaded_model = get_tokenizer_and_model() sentence = clean(sentence, convert_to_ascii=True, remove_brackets=True) tensors = tokenizer(sentence, return_tensors=\"pt\", truncation=True, max_length=512) with torch.inference_mode(): result: SequenceClassifierOutput = loaded_model(**tensors) positive_likelihood = torch.nn.Softmax(dim=1)(result.logits)[0][1] tokens = tensors[\"input_ids\"][0] attentions = np.sum(result.attentions[-1].numpy()[0], axis=0)[0][1:-1] # Tuple of `torch.FloatTensor` (one for each layer) of shape # `(batch_size, num_heads, sequence_length, sequence_length)`. explanation = [] token_attentions = list(zip(attentions, tokens[1:-1])) for token in re.split(r\"([ .,])\", sentence): token = token.strip() if not token: continue bert_tokens = tokenizer( token, return_tensors=\"pt\", truncation=True, max_length=512 )[\"input_ids\"][0][ 1:-1 ] # truncation=True needed to fix `RuntimeError: Already borrowed` weight = 0 for t1 in bert_tokens: if not token_attentions: break a, t2 = token_attentions.pop(0) assert t1 == t2, sentence weight += a explanation.append( Attention( token=token if token in \".,\" else \" \" + token, weight=round(weight, 4) ) ) if not token_attentions: break return EvaluatedSentence( score=positive_likelihood, text=sentence, explanation=explanation ) A simple test to see everything works. Note that the models list is filled by the @use_model call even though it's not on the main inference function. In [5]: Copied! if __name__ == \"__main__\" : find_highlights ( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights ( \"Their solution did not perform well.\" ) if __name__ == \"__main__\": find_highlights( \"Our solution has outperformed the state-of-the-art.\" ), find_highlights(\"Their solution did not perform well.\") Out[5]: (Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:29.581701', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 51, 'arg:sentence:value': 'Our solution has outperformed the ' 'state-of-the-art.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 7127.2063, 'output': { 'explanation': [ {'token': ' Our', 'weight': 0.3993}, {'token': ' solution', 'weight': 0.3481}, {'token': ' has', 'weight': 0.2945}, {'token': ' outperformed', 'weight': 0.4011}, {'token': ' the', 'weight': 0.1484}, {'token': ' state-of-the-art', 'weight': 0.5727}, {'token': '.', 'weight': 7.775}], 'score': 0.9991180300712585, 'text': 'Our solution has outperformed the state-of-the-art.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '56e20e94-79df-4793-ae61-d20820ebe2d3'}), Trace[EvaluatedSentence]({'created': '2022-07-16T18:47:37.020275', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 36, 'arg:sentence:value': 'Their solution did not perform ' 'well.'}, 'models': [{'key': 'scibert-highlights', 'version': 0}], 'original_execution_time_ms': 170.7057, 'output': { 'explanation': [ {'token': ' Their', 'weight': 1.1475}, {'token': ' solution', 'weight': 0.8205}, {'token': ' did', 'weight': 0.3254}, {'token': ' not', 'weight': 0.2921}, {'token': ' perform', 'weight': 0.4293}, {'token': ' well', 'weight': 0.2772}, {'token': '.', 'weight': 4.4723}], 'score': 0.12305451184511185, 'text': 'Their solution did not perform well.'}, 'tags': ['find_highlights', 'online', 'development'], 'trace_id': '7fcf8271-1738-4025-8305-d5a1e5100aea'})) In this case, the service is built as a docker image, pushed to our image registry and subsequent rolling update is performed in the production cluster. To check out the Dockerimage, go to the additional files page .","title":"Create an inference function"},{"location":"examples/scibert/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); 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font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Fine-tune SciBERT \u00b6 We are planning to do a simple classification task on scientific text. For that, SciBERT is an ideal model to fine-tune since it has been pretrained of academic publications. This notebook was updated so that it can run in Google Colab . First, we need to install the dependencies. In [1]: Copied! ! pip install transformers datasets great - ai > / dev / null !pip install transformers datasets great-ai > /dev/null Load the training data from S3. (We have uploaded this to S3 in the data notebook.) In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , encoding = \"utf-8\" ) as f : # splitting training and test data is done later by `datasets` X , y = json . load ( f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", encoding=\"utf-8\") as f: # splitting training and test data is done later by `datasets` X, y = json.load(f) Latest version of summary-train-dataset-small is 0 (from versions: 0) File summary-train-dataset-small-0 found in cache Finetune SciBERT, for more info about this step, check out HuggingFace . If you're only here for great-ai , feel free to skip the next cell. In [22]: Copied! from transformers import ( AutoModelForSequenceClassification , AutoTokenizer , DataCollatorWithPadding , Trainer , TrainingArguments , EarlyStoppingCallback , ) from pathlib import Path import numpy as np from datasets import Dataset , load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer . from_pretrained ( MODEL ) model = AutoModelForSequenceClassification . from_pretrained ( MODEL , num_labels = 2 ) data_collator = DataCollatorWithPadding ( tokenizer = tokenizer ) def tokenize_function ( v ): return tokenizer ( v [ \"text\" ]) dataset = ( Dataset . from_dict ({ \"text\" : X , \"label\" : y }) . map ( lambda v : tokenizer ( v [ \"text\" ], truncation = True ), batched = True ) . remove_columns ( \"text\" ) . train_test_split ( test_size = 0.2 , shuffle = True ) # test is actually validation ) f1_score = load_metric ( \"f1\" ) def compute_metrics ( p ): pred , labels = p pred = np . argmax ( pred , axis = 1 ) return f1_score . compute ( predictions = pred , references = labels ) training_args = TrainingArguments ( output_dir = Path ( \"models\" ), per_device_train_batch_size = BATCH_SIZE , per_device_eval_batch_size = BATCH_SIZE , save_total_limit = 5 , num_train_epochs = 50 , save_strategy = \"epoch\" , evaluation_strategy = \"epoch\" , logging_strategy = \"epoch\" , weight_decay = 0.01 , metric_for_best_model = \"f1\" , load_best_model_at_end = True , ) result = Trainer ( model = model , args = training_args , train_dataset = dataset [ \"train\" ], eval_dataset = dataset [ \"test\" ], data_collator = data_collator , compute_metrics = compute_metrics , callbacks = [ EarlyStoppingCallback ( early_stopping_patience = 5 )], ) . train () from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, Trainer, TrainingArguments, EarlyStoppingCallback, ) from pathlib import Path import numpy as np from datasets import Dataset, load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer.from_pretrained(MODEL) model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=2) data_collator = DataCollatorWithPadding(tokenizer=tokenizer) def tokenize_function(v): return tokenizer(v[\"text\"]) dataset = ( Dataset.from_dict({\"text\": X, \"label\": y}) .map(lambda v: tokenizer(v[\"text\"], truncation=True), batched=True) .remove_columns(\"text\") .train_test_split(test_size=0.2, shuffle=True) # test is actually validation ) f1_score = load_metric(\"f1\") def compute_metrics(p): pred, labels = p pred = np.argmax(pred, axis=1) return f1_score.compute(predictions=pred, references=labels) training_args = TrainingArguments( output_dir=Path(\"models\"), per_device_train_batch_size=BATCH_SIZE, per_device_eval_batch_size=BATCH_SIZE, save_total_limit=5, num_train_epochs=50, save_strategy=\"epoch\", evaluation_strategy=\"epoch\", logging_strategy=\"epoch\", weight_decay=0.01, metric_for_best_model=\"f1\", load_best_model_at_end=True, ) result = Trainer( model=model, args=training_args, train_dataset=dataset[\"train\"], eval_dataset=dataset[\"test\"], data_collator=data_collator, compute_metrics=compute_metrics, callbacks=[EarlyStoppingCallback(early_stopping_patience=5)], ).train() 0%| | 0/1 [00:00 / dev / null !pip install transformers datasets great-ai > /dev/null Load the training data from S3. (We have uploaded this to S3 in the data notebook.) In [2]: Copied! from great_ai.large_file import LargeFileS3 import json LargeFileS3 . configure_credentials_from_file ( \"config.ini\" ) with LargeFileS3 ( \"summary-train-dataset-small\" , encoding = \"utf-8\" ) as f : # splitting training and test data is done later by `datasets` X , y = json . load ( f ) from great_ai.large_file import LargeFileS3 import json LargeFileS3.configure_credentials_from_file(\"config.ini\") with LargeFileS3(\"summary-train-dataset-small\", encoding=\"utf-8\") as f: # splitting training and test data is done later by `datasets` X, y = json.load(f) Latest version of summary-train-dataset-small is 0 (from versions: 0) File summary-train-dataset-small-0 found in cache Finetune SciBERT, for more info about this step, check out HuggingFace . If you're only here for great-ai , feel free to skip the next cell. In [22]: Copied! from transformers import ( AutoModelForSequenceClassification , AutoTokenizer , DataCollatorWithPadding , Trainer , TrainingArguments , EarlyStoppingCallback , ) from pathlib import Path import numpy as np from datasets import Dataset , load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer . from_pretrained ( MODEL ) model = AutoModelForSequenceClassification . from_pretrained ( MODEL , num_labels = 2 ) data_collator = DataCollatorWithPadding ( tokenizer = tokenizer ) def tokenize_function ( v ): return tokenizer ( v [ \"text\" ]) dataset = ( Dataset . from_dict ({ \"text\" : X , \"label\" : y }) . map ( lambda v : tokenizer ( v [ \"text\" ], truncation = True ), batched = True ) . remove_columns ( \"text\" ) . train_test_split ( test_size = 0.2 , shuffle = True ) # test is actually validation ) f1_score = load_metric ( \"f1\" ) def compute_metrics ( p ): pred , labels = p pred = np . argmax ( pred , axis = 1 ) return f1_score . compute ( predictions = pred , references = labels ) training_args = TrainingArguments ( output_dir = Path ( \"models\" ), per_device_train_batch_size = BATCH_SIZE , per_device_eval_batch_size = BATCH_SIZE , save_total_limit = 5 , num_train_epochs = 50 , save_strategy = \"epoch\" , evaluation_strategy = \"epoch\" , logging_strategy = \"epoch\" , weight_decay = 0.01 , metric_for_best_model = \"f1\" , load_best_model_at_end = True , ) result = Trainer ( model = model , args = training_args , train_dataset = dataset [ \"train\" ], eval_dataset = dataset [ \"test\" ], data_collator = data_collator , compute_metrics = compute_metrics , callbacks = [ EarlyStoppingCallback ( early_stopping_patience = 5 )], ) . train () from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, Trainer, TrainingArguments, EarlyStoppingCallback, ) from pathlib import Path import numpy as np from datasets import Dataset, load_metric MODEL = \"allenai/scibert_scivocab_uncased\" BATCH_SIZE = 32 tokenizer = AutoTokenizer.from_pretrained(MODEL) model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=2) data_collator = DataCollatorWithPadding(tokenizer=tokenizer) def tokenize_function(v): return tokenizer(v[\"text\"]) dataset = ( Dataset.from_dict({\"text\": X, \"label\": y}) .map(lambda v: tokenizer(v[\"text\"], truncation=True), batched=True) .remove_columns(\"text\") .train_test_split(test_size=0.2, shuffle=True) # test is actually validation ) f1_score = load_metric(\"f1\") def compute_metrics(p): pred, labels = p pred = np.argmax(pred, axis=1) return f1_score.compute(predictions=pred, references=labels) training_args = TrainingArguments( output_dir=Path(\"models\"), per_device_train_batch_size=BATCH_SIZE, per_device_eval_batch_size=BATCH_SIZE, save_total_limit=5, num_train_epochs=50, save_strategy=\"epoch\", evaluation_strategy=\"epoch\", logging_strategy=\"epoch\", weight_decay=0.01, metric_for_best_model=\"f1\", load_best_model_at_end=True, ) result = Trainer( model=model, args=training_args, train_dataset=dataset[\"train\"], eval_dataset=dataset[\"test\"], data_collator=data_collator, compute_metrics=compute_metrics, callbacks=[EarlyStoppingCallback(early_stopping_patience=5)], ).train() 0%| | 0/1 [00:00.p-SplitPanel-handle,.jupyter-wrapper .lm-SplitPanel[data-orientation=horizontal]>.lm-SplitPanel-handle{cursor:ew-resize}.jupyter-wrapper .p-SplitPanel[data-orientation=vertical]>.p-SplitPanel-handle,.jupyter-wrapper .lm-SplitPanel[data-orientation=vertical]>.lm-SplitPanel-handle{cursor:ns-resize}.jupyter-wrapper .p-SplitPanel[data-orientation=horizontal]>.p-SplitPanel-handle:after,.jupyter-wrapper .lm-SplitPanel[data-orientation=horizontal]>.lm-SplitPanel-handle:after{left:50%;min-width:8px;transform:translateX(-50%)}.jupyter-wrapper .p-SplitPanel[data-orientation=vertical]>.p-SplitPanel-handle:after,.jupyter-wrapper 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Simple example: data engineering \u00b6 Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best practices. In short, we train a domain classifier on the semantic scholar dataset by taking full advantage of great-ai . Subsequently, we create a production-ready deployment. The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook. Since the true scope of great-ai is the phase between proof-of-concept code and production-ready service, it is predominantly used in the deployment notebook . Feel free to skip there, or continue reading if you'd like to see the full picture. Extract \u00b6 This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this example, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 4 MAX_CHUNK_COUNT = 4 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"\"\"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\"\"\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"\"\"Processing {len(chunks)} out of the { len(manifest.split()) } available chunks\"\"\" Out[2]: 'Processing 4 out of the 6002 available chunks' Transform \u00b6 Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.simple_parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map , clean , is_english , predict_language , unchunk , ) def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f \"open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } ' f ' { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if ( c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ]))) ] preprocessed_data = unchunk ( simple_parallel_map ( preprocess_chunk , chunks , concurrency = 4 ) ) from typing import List, Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map, clean, is_english, predict_language, unchunk, ) def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f\"open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} ' f'{c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if (c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))) ] preprocessed_data = unchunk( simple_parallel_map(preprocess_chunk, chunks, concurrency=4) ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [04:22<00:00, 65.51s/it] In [4]: Copied! X , y = zip ( * preprocessed_data ) # X is the input, y is the expected output X, y = zip(*preprocessed_data) # X is the input, y is the expected output Load \u00b6 Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each data-point with a split label according to the ratios we set. Additional tags can be also given. Production-ready backend \u00b6 The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service Otherwise, TinyDB is used which is just a local JSON file. In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Next: Part 2 \u00b6","title":"Simple example: data engineering"},{"location":"examples/simple/data/#simple-example-data-engineering","text":"Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best practices. In short, we train a domain classifier on the semantic scholar dataset by taking full advantage of great-ai . Subsequently, we create a production-ready deployment. The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook. Since the true scope of great-ai is the phase between proof-of-concept code and production-ready service, it is predominantly used in the deployment notebook . Feel free to skip there, or continue reading if you'd like to see the full picture.","title":"Simple example: data engineering"},{"location":"examples/simple/data/#extract","text":"This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this example, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 4 MAX_CHUNK_COUNT = 4 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"\"\"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\"\"\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"\"\"Processing {len(chunks)} out of the { len(manifest.split()) } available chunks\"\"\" Out[2]: 'Processing 4 out of the 6002 available chunks'","title":"Extract"},{"location":"examples/simple/data/#transform","text":"Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.simple_parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map , clean , is_english , predict_language , unchunk , ) def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f \"open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } ' f ' { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if ( c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ]))) ] preprocessed_data = unchunk ( simple_parallel_map ( preprocess_chunk , chunks , concurrency = 4 ) ) from typing import List, Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map, clean, is_english, predict_language, unchunk, ) def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f\"open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} ' f'{c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if (c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))) ] preprocessed_data = unchunk( simple_parallel_map(preprocess_chunk, chunks, concurrency=4) ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [04:22<00:00, 65.51s/it] In [4]: Copied! X , y = zip ( * preprocessed_data ) # X is the input, y is the expected output X, y = zip(*preprocessed_data) # X is the input, y is the expected output","title":"Transform"},{"location":"examples/simple/data/#load","text":"Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each data-point with a split label according to the ratios we set. Additional tags can be also given.","title":"Load"},{"location":"examples/simple/data/#production-ready-backend","text":"The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service Otherwise, TinyDB is used which is just a local JSON file. In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices","title":"Production-ready backend"},{"location":"examples/simple/data/#next-part-2","text":"","title":"Next: Part 2"},{"location":"examples/simple/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Hardening and deployment \u00b6 Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment. Create the inference function \u00b6 Next to the prediction, we also return the top-n most influential words based on their weights. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validate = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validate=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of small-domain-prediction Latest version of small-domain-prediction is 2 (from versions: 0, 1, 2) File small-domain-prediction-2 found in cache Check accuracy on the test split \u00b6 Anything under if __name__ == \"__main__\": will not be run when the script is executed by the great-ai CLI app. This, combined with query_ground_truth and the /traces/{trace_id}/feedback endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment. In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 19 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 16 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"ss-confusion.png\" , dpi = 600 ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (19, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 16 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"ss-confusion.png\", dpi=600) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12272/12272 [01:10<00:00, 174.77it/s] precision recall f1-score support Art 0.54 0.38 0.45 126 Biology 0.77 0.84 0.80 1215 Business 0.47 0.73 0.57 311 Chemistry 0.82 0.67 0.74 1205 Computer Science 0.77 0.76 0.76 1277 Economics 0.69 0.55 0.61 270 Engineering 0.55 0.52 0.53 754 Environmental Science 0.56 0.55 0.55 227 Geography 0.54 0.39 0.45 276 Geology 0.74 0.67 0.70 265 History 0.35 0.18 0.24 140 Materials Science 0.72 0.81 0.76 1011 Mathematics 0.77 0.70 0.74 498 Medicine 0.96 0.77 0.86 2835 Philosophy 0.57 0.06 0.10 71 Physics 0.66 0.75 0.70 611 Political Science 0.44 0.61 0.51 291 Psychology 0.52 0.84 0.64 574 Sociology 0.33 0.59 0.42 315 accuracy 0.71 12272 macro avg 0.62 0.60 0.59 12272 weighted avg 0.74 0.71 0.71 12272","title":"Hardening and deployment"},{"location":"examples/simple/deploy/#hardening-and-deployment","text":"Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment.","title":"Hardening and deployment"},{"location":"examples/simple/deploy/#create-the-inference-function","text":"Next to the prediction, we also return the top-n most influential words based on their weights. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validate = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validate=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of small-domain-prediction Latest version of small-domain-prediction is 2 (from versions: 0, 1, 2) File small-domain-prediction-2 found in cache","title":"Create the inference function"},{"location":"examples/simple/deploy/#check-accuracy-on-the-test-split","text":"Anything under if __name__ == \"__main__\": will not be run when the script is executed by the great-ai CLI app. This, combined with query_ground_truth and the /traces/{trace_id}/feedback endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment. In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 19 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 16 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"ss-confusion.png\" , dpi = 600 ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (19, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 16 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"ss-confusion.png\", dpi=600) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12272/12272 [01:10<00:00, 174.77it/s] precision recall f1-score support Art 0.54 0.38 0.45 126 Biology 0.77 0.84 0.80 1215 Business 0.47 0.73 0.57 311 Chemistry 0.82 0.67 0.74 1205 Computer Science 0.77 0.76 0.76 1277 Economics 0.69 0.55 0.61 270 Engineering 0.55 0.52 0.53 754 Environmental Science 0.56 0.55 0.55 227 Geography 0.54 0.39 0.45 276 Geology 0.74 0.67 0.70 265 History 0.35 0.18 0.24 140 Materials Science 0.72 0.81 0.76 1011 Mathematics 0.77 0.70 0.74 498 Medicine 0.96 0.77 0.86 2835 Philosophy 0.57 0.06 0.10 71 Physics 0.66 0.75 0.70 611 Political Science 0.44 0.61 0.51 291 Psychology 0.52 0.84 0.64 574 Sociology 0.33 0.59 0.42 315 accuracy 0.71 12272 macro avg 0.62 0.60 0.59 12272 weighted avg 0.74 0.71 0.71 12272","title":"Check accuracy on the test split"},{"location":"examples/simple/stacked-bars/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) from great_ai import query_ground_truth data = query_ground_truth(\"train\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [9]: Copied! from collections import Counter import matplotlib.pyplot as plt from great_ai.utilities import unique import numpy as np import matplotlib.ticker as mtick first = Counter ( d . feedback [ 0 ] for d in data if len ( d . feedback ) > 0 ) second = Counter ( d . feedback [ 1 ] for d in data if len ( d . feedback ) > 1 ) third = Counter ( d . feedback [ 2 ] for d in data if len ( d . feedback ) > 2 ) domains = sorted ( unique ( domain for d in data for domain in d . feedback )) first = [ first [ d ] / len ( data ) for d in domains ] second = [ second [ d ] / len ( data ) for d in domains ] third = [ third [ d ] / len ( data ) for d in domains ] # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 20 plt . rcParams [ \"figure.figsize\" ] = ( 14 , 10 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , first , label = \"Primary domain\" ) ax . bar ( domains , second , label = \"Secondary domain\" , bottom = first ) ax . bar ( domains , third , label = \"Tertiary domain\" , bottom = np . add ( first , second )) ax . yaxis . set_major_formatter ( mtick . PercentFormatter ( 1 )) ax . legend () ax . set_ylabel ( \"Ratio of all publications\" ) fig . tight_layout () fig . savefig ( \"ss-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt from great_ai.utilities import unique import numpy as np import matplotlib.ticker as mtick first = Counter(d.feedback[0] for d in data if len(d.feedback) > 0) second = Counter(d.feedback[1] for d in data if len(d.feedback) > 1) third = Counter(d.feedback[2] for d in data if len(d.feedback) > 2) domains = sorted(unique(domain for d in data for domain in d.feedback)) first = [first[d] / len(data) for d in domains] second = [second[d] / len(data) for d in domains] third = [third[d] / len(data) for d in domains] # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 20 plt.rcParams[\"figure.figsize\"] = (14, 10) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, first, label=\"Primary domain\") ax.bar(domains, second, label=\"Secondary domain\", bottom=first) ax.bar(domains, third, label=\"Tertiary domain\", bottom=np.add(first, second)) ax.yaxis.set_major_formatter(mtick.PercentFormatter(1)) ax.legend() ax.set_ylabel(\"Ratio of all publications\") fig.tight_layout() fig.savefig(\"ss-distribution.png\", dpi=500) None","title":"Stacked bars"},{"location":"examples/simple/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Optimise and train a model \u00b6 The blue boxes show the steps implemented in this notebook. In the first part, we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using great_ai.save_model . Load data that has been extracted in part 1 \u00b6 In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 5 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . xticks ( rotation = 90 ) plt . bar ( domains , counts ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 5) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.xticks(rotation=90) plt.bar(domains, counts) None Optimise and train Multinomial Naive Bayes classifier \u00b6 In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 14.549476 0.361685 8.476837 0.222398 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.518061 0.514842 0.511599 0.514834 0.002638 1 10 11.235289 0.130426 4.092868 0.082518 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513897 0.515661 0.507867 0.512475 0.003337 2 19 7.383645 0.138110 4.130709 0.250048 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.496825 0.501045 0.496854 0.498241 0.001983 3 11 10.435154 0.305144 3.882101 0.128886 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.493247 0.497814 0.502245 0.497769 0.003674 4 8 13.643193 0.310696 4.173707 0.142980 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.489609 0.495207 0.498154 0.494323 0.003544 5 22 7.048340 0.050070 3.172948 0.152418 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.487456 0.493865 0.491157 0.490826 0.002627 6 23 7.564685 0.146092 2.374111 0.285026 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.485160 0.494039 0.490127 0.489776 0.003633 7 20 7.172353 0.212599 3.747219 0.130217 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.481303 0.490002 0.488269 0.486524 0.003759 8 6 14.276345 0.456576 8.318859 0.268701 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482429 0.487735 0.484888 0.485017 0.002168 9 2 14.902358 0.737693 5.975091 0.171150 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.469598 0.474490 0.473637 0.472575 0.002134 10 9 12.677349 0.145143 4.374204 0.175674 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.468872 0.476451 0.470921 0.472082 0.003201 11 5 13.423686 0.482872 8.008324 0.442975 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.465726 0.474548 0.471879 0.470718 0.003694 12 1 13.819117 0.838347 6.161175 0.336590 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.463395 0.473982 0.471262 0.469546 0.004489 13 4 13.281476 0.588822 8.335852 0.254627 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458734 0.468053 0.464418 0.463735 0.003835 14 14 7.282247 0.444940 3.567094 0.044519 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.438189 0.450160 0.446180 0.444843 0.004978 15 17 7.098797 0.196241 3.838628 0.091128 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.436488 0.444503 0.445900 0.442297 0.004147 16 18 7.492791 0.288889 3.843224 0.073438 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428196 0.431945 0.430160 0.430100 0.001531 17 21 7.229826 0.099823 3.656332 0.073780 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.403130 0.410170 0.409801 0.407700 0.003235 18 13 7.158370 0.169818 3.765632 0.082924 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.399237 0.412872 0.407982 0.406697 0.005640 19 16 7.064643 0.119529 3.810983 0.125897 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.388060 0.399247 0.396325 0.394544 0.004737 20 0 13.749660 0.465174 6.407841 0.549166 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.384852 0.386487 0.386796 0.386045 0.000853 21 3 15.954147 0.318013 6.337361 0.261697 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.369785 0.375645 0.375858 0.373763 0.002814 22 12 7.120198 0.050452 3.833905 0.069540 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.277741 0.280564 0.285337 0.281214 0.003135 23 15 7.497707 0.183054 3.870714 0.062888 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.255578 0.263381 0.266184 0.261714 0.004487 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False) Export the model using GreatAI \u00b6 In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) Fetching cached versions of small-domain-prediction Copying file for small-domain-prediction-2 Compressing small-domain-prediction-2 Model small-domain-prediction uploaded with version 2 Out[6]: 'small-domain-prediction:2' Next: Part 3 \u00b6","title":"Optimise and train a model"},{"location":"examples/simple/train/#optimise-and-train-a-model","text":"The blue boxes show the steps implemented in this notebook. In the first part, we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using great_ai.save_model .","title":"Optimise and train a model"},{"location":"examples/simple/train/#load-data-that-has-been-extracted-in-part-1","text":"In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 5 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . xticks ( rotation = 90 ) plt . bar ( domains , counts ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 5) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.xticks(rotation=90) plt.bar(domains, counts) None","title":"Load data that has been extracted in part 1"},{"location":"examples/simple/train/#optimise-and-train-multinomial-naive-bayes-classifier","text":"In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 14.549476 0.361685 8.476837 0.222398 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.518061 0.514842 0.511599 0.514834 0.002638 1 10 11.235289 0.130426 4.092868 0.082518 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513897 0.515661 0.507867 0.512475 0.003337 2 19 7.383645 0.138110 4.130709 0.250048 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.496825 0.501045 0.496854 0.498241 0.001983 3 11 10.435154 0.305144 3.882101 0.128886 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.493247 0.497814 0.502245 0.497769 0.003674 4 8 13.643193 0.310696 4.173707 0.142980 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.489609 0.495207 0.498154 0.494323 0.003544 5 22 7.048340 0.050070 3.172948 0.152418 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.487456 0.493865 0.491157 0.490826 0.002627 6 23 7.564685 0.146092 2.374111 0.285026 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.485160 0.494039 0.490127 0.489776 0.003633 7 20 7.172353 0.212599 3.747219 0.130217 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.481303 0.490002 0.488269 0.486524 0.003759 8 6 14.276345 0.456576 8.318859 0.268701 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482429 0.487735 0.484888 0.485017 0.002168 9 2 14.902358 0.737693 5.975091 0.171150 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.469598 0.474490 0.473637 0.472575 0.002134 10 9 12.677349 0.145143 4.374204 0.175674 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.468872 0.476451 0.470921 0.472082 0.003201 11 5 13.423686 0.482872 8.008324 0.442975 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.465726 0.474548 0.471879 0.470718 0.003694 12 1 13.819117 0.838347 6.161175 0.336590 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.463395 0.473982 0.471262 0.469546 0.004489 13 4 13.281476 0.588822 8.335852 0.254627 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458734 0.468053 0.464418 0.463735 0.003835 14 14 7.282247 0.444940 3.567094 0.044519 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.438189 0.450160 0.446180 0.444843 0.004978 15 17 7.098797 0.196241 3.838628 0.091128 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.436488 0.444503 0.445900 0.442297 0.004147 16 18 7.492791 0.288889 3.843224 0.073438 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428196 0.431945 0.430160 0.430100 0.001531 17 21 7.229826 0.099823 3.656332 0.073780 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.403130 0.410170 0.409801 0.407700 0.003235 18 13 7.158370 0.169818 3.765632 0.082924 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.399237 0.412872 0.407982 0.406697 0.005640 19 16 7.064643 0.119529 3.810983 0.125897 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.388060 0.399247 0.396325 0.394544 0.004737 20 0 13.749660 0.465174 6.407841 0.549166 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.384852 0.386487 0.386796 0.386045 0.000853 21 3 15.954147 0.318013 6.337361 0.261697 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.369785 0.375645 0.375858 0.373763 0.002814 22 12 7.120198 0.050452 3.833905 0.069540 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.277741 0.280564 0.285337 0.281214 0.003135 23 15 7.497707 0.183054 3.870714 0.062888 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.255578 0.263381 0.266184 0.261714 0.004487 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False)","title":"Optimise and train Multinomial Naive Bayes classifier"},{"location":"examples/simple/train/#export-the-model-using-greatai","text":"In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) Fetching cached versions of small-domain-prediction Copying file for small-domain-prediction-2 Compressing small-domain-prediction-2 Model small-domain-prediction uploaded with version 2 Out[6]: 'small-domain-prediction:2'","title":"Export the model using GreatAI"},{"location":"examples/simple/train/#next-part-3","text":"","title":"Next: Part 3"},{"location":"examples/simple-mag/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a field of study (domain) classification of sentences on the MAG dataset \u00b6 SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , simple_parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"../../tutorial/data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"../../tutorial/data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, simple_parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"../../tutorial/data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"../../tutorial/data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:16<00:00, 4964.08it/s] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:05<00:00, 3847.04it/s] In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y_train ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 18 plt . rcParams [ \"figure.figsize\" ] = ( 8 , 5 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , counts ) ax . set_ylabel ( \"Count\" ) fig . tight_layout () fig . savefig ( \"mag-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y_train).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 18 plt.rcParams[\"figure.figsize\"] = (8, 5) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, counts) ax.set_ylabel(\"Count\") fig.tight_layout() fig.savefig(\"mag-distribution.png\", dpi=500) None In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 2.352721 0.257936 1.357399 0.094580 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 2.544958 0.051976 1.245658 0.137108 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 2.472888 0.053978 1.447181 0.051170 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 2.467637 0.005358 1.216254 0.048319 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 2.477612 0.134880 1.428521 0.017417 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 2.484810 0.067836 1.249979 0.209959 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 2.452453 0.049376 1.279694 0.089200 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 2.748628 0.098077 1.255213 0.101334 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 2.371907 0.204820 1.375143 0.193984 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 2.557486 0.184332 1.272137 0.133980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 2.362093 0.109796 1.344960 0.083562 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 2.668012 0.131751 1.383441 0.049157 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 2.517576 0.154192 1.374269 0.026773 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 2.386240 0.069718 0.967638 0.076766 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 2.416211 0.138001 1.316785 0.036402 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 2.397768 0.244951 1.330007 0.008983 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 2.590127 0.054396 1.155976 0.187602 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 2.497556 0.085952 1.169808 0.061336 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 2.467611 0.168399 1.313936 0.109695 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 2.339322 0.092595 1.209515 0.132825 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 2.644488 0.119967 1.238554 0.048871 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 2.456030 0.072136 1.240463 0.092383 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 2.335796 0.082338 0.786187 0.073499 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 2.530331 0.107199 1.247842 0.078229 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [12]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 22 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"mag-confusion.png\" , dpi = 600 ) from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 22 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"mag-confusion.png\", dpi=600) precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"examples/simple-mag/train/#train-a-field-of-study-domain-classification-of-sentences-on-the-mag-dataset","text":"SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , simple_parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"../../tutorial/data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"../../tutorial/data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, simple_parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"../../tutorial/data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"../../tutorial/data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:16<00:00, 4964.08it/s] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:05<00:00, 3847.04it/s] In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y_train ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 18 plt . rcParams [ \"figure.figsize\" ] = ( 8 , 5 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , counts ) ax . set_ylabel ( \"Count\" ) fig . tight_layout () fig . savefig ( \"mag-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y_train).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 18 plt.rcParams[\"figure.figsize\"] = (8, 5) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, counts) ax.set_ylabel(\"Count\") fig.tight_layout() fig.savefig(\"mag-distribution.png\", dpi=500) None In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 2.352721 0.257936 1.357399 0.094580 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 2.544958 0.051976 1.245658 0.137108 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 2.472888 0.053978 1.447181 0.051170 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 2.467637 0.005358 1.216254 0.048319 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 2.477612 0.134880 1.428521 0.017417 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 2.484810 0.067836 1.249979 0.209959 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 2.452453 0.049376 1.279694 0.089200 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 2.748628 0.098077 1.255213 0.101334 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 2.371907 0.204820 1.375143 0.193984 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 2.557486 0.184332 1.272137 0.133980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 2.362093 0.109796 1.344960 0.083562 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 2.668012 0.131751 1.383441 0.049157 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 2.517576 0.154192 1.374269 0.026773 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 2.386240 0.069718 0.967638 0.076766 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 2.416211 0.138001 1.316785 0.036402 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 2.397768 0.244951 1.330007 0.008983 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 2.590127 0.054396 1.155976 0.187602 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 2.497556 0.085952 1.169808 0.061336 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 2.467611 0.168399 1.313936 0.109695 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 2.339322 0.092595 1.209515 0.132825 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 2.644488 0.119967 1.238554 0.048871 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 2.456030 0.072136 1.240463 0.092383 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 2.335796 0.082338 0.786187 0.073499 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 2.530331 0.107199 1.247842 0.078229 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [12]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 22 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"mag-confusion.png\" , dpi = 600 ) from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 22 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"mag-confusion.png\", dpi=600) precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"how-to-guides/call-remote/","text":"How to call remote GreatAI instances # Microservices architecture (or SOA ) work well with ML applications. This is because their interfaces are usually very narrow, while the functionality provided quite comprehensive. Hence, drawing the boundaries of responsibilities is more straightforward in the case of ML services than in the case of more traditional business applications. For this reason, it is common to have a tree of models (preferably wrapped in GreatAI instances) communicating with each other. Although regular HTTP POST requests could be sent to each service's /predict endpoint, great-ai comes with two convenience functions: call_remote_great_ai and call_remote_great_ai_async to wrap this request. These provide you with some level of robustness and deserialisation. Inside notebooks The async variant, call_remote_great_ai_async , requires a running event loop while the synchronous variant disallows other running event-loops. Therefore, when running inside a Jupyter Notebook, always call call_remote_great_ai_async . Simple example # Let's create two processes: a server and a client. Server # server.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def slow_greeter ( your_name ): await sleep ( 2 ) return f 'Hi { your_name } !' Run this in development mode by executing great-ai server.py or python3 -m great_ai server.py if you're on Windows and great-ai is not in your PATH . Client # client.py from great_ai import call_remote_great_ai names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] results = [ call_remote_great_ai ( 'http://localhost:6060' , { 'your_name' : name } ) . output #(1) for name in names ] print ( results ) Only return the outputs so we don't clutter up the terminal. Run this script as a regular Python script by executing python3 client.py . As you can see, everything worked as expected. There is one way to improve it though. An async example # Let's send multiple requests at the same time to speed up the overall execution time. To do this, we will use the call_remote_great_ai_async function. Why is this possible? Note, that in server.py , the inference function is declared async . This means that multiple \"copies\" of it can run at the same time in the same thread. Since, there is no CPU bottleneck, the server has a quite large throughput (requests responded to per second), but its latency will stay around 2 seconds due to the async sleep command. If your great-ai server is not async , higher throughput can be achieved by running multiple instances of it, either manually, or by running it with multiple uvicorn workers like this: ENVIRONMENT=production great-ai server.py --worker_count 4 Async client # async-client.py from great_ai import call_remote_great_ai_async import asyncio names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] async def main (): futures = [ call_remote_great_ai_async ( 'http://localhost:6060' , { 'your_name' : name } ) for name in names ] results = await asyncio . gather ( * futures ) print ([ r . output for r in results ]) asyncio . run ( main ()) Replace client.py with this async client. Note that even though async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart. This also works, and in some use-cases might be considerably quicker.","title":"How to call remote GreatAI instances"},{"location":"how-to-guides/call-remote/#how-to-call-remote-greatai-instances","text":"Microservices architecture (or SOA ) work well with ML applications. This is because their interfaces are usually very narrow, while the functionality provided quite comprehensive. Hence, drawing the boundaries of responsibilities is more straightforward in the case of ML services than in the case of more traditional business applications. For this reason, it is common to have a tree of models (preferably wrapped in GreatAI instances) communicating with each other. Although regular HTTP POST requests could be sent to each service's /predict endpoint, great-ai comes with two convenience functions: call_remote_great_ai and call_remote_great_ai_async to wrap this request. These provide you with some level of robustness and deserialisation. Inside notebooks The async variant, call_remote_great_ai_async , requires a running event loop while the synchronous variant disallows other running event-loops. Therefore, when running inside a Jupyter Notebook, always call call_remote_great_ai_async .","title":"How to call remote GreatAI instances"},{"location":"how-to-guides/call-remote/#simple-example","text":"Let's create two processes: a server and a client.","title":"Simple example"},{"location":"how-to-guides/call-remote/#server","text":"server.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def slow_greeter ( your_name ): await sleep ( 2 ) return f 'Hi { your_name } !' Run this in development mode by executing great-ai server.py or python3 -m great_ai server.py if you're on Windows and great-ai is not in your PATH .","title":"Server"},{"location":"how-to-guides/call-remote/#client","text":"client.py from great_ai import call_remote_great_ai names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] results = [ call_remote_great_ai ( 'http://localhost:6060' , { 'your_name' : name } ) . output #(1) for name in names ] print ( results ) Only return the outputs so we don't clutter up the terminal. Run this script as a regular Python script by executing python3 client.py . As you can see, everything worked as expected. There is one way to improve it though.","title":"Client"},{"location":"how-to-guides/call-remote/#an-async-example","text":"Let's send multiple requests at the same time to speed up the overall execution time. To do this, we will use the call_remote_great_ai_async function. Why is this possible? Note, that in server.py , the inference function is declared async . This means that multiple \"copies\" of it can run at the same time in the same thread. Since, there is no CPU bottleneck, the server has a quite large throughput (requests responded to per second), but its latency will stay around 2 seconds due to the async sleep command. If your great-ai server is not async , higher throughput can be achieved by running multiple instances of it, either manually, or by running it with multiple uvicorn workers like this: ENVIRONMENT=production great-ai server.py --worker_count 4","title":"An async example"},{"location":"how-to-guides/call-remote/#async-client","text":"async-client.py from great_ai import call_remote_great_ai_async import asyncio names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] async def main (): futures = [ call_remote_great_ai_async ( 'http://localhost:6060' , { 'your_name' : name } ) for name in names ] results = await asyncio . gather ( * futures ) print ([ r . output for r in results ]) asyncio . run ( main ()) Replace client.py with this async client. Note that even though async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart. This also works, and in some use-cases might be considerably quicker.","title":"Async client"},{"location":"how-to-guides/configure-service/","text":"How to configure GreatAI # GreatAI aims to provide reasonable defaults wherever possible. The current configuration is always prominently displayed (and updated) on the dashboard and in the command-line start-up banner. Using great_ai.configure # You can override any of the default settings by calling great_ai.configure . If you don't call configure , the default settings are applied on the first call to most great-ai functions. Warning You must call great_ai.configure before calling (or decorating with) any other great-ai function. However, importing other functions before calling great_ai.configure is permitted. configure-demo.py from great_ai import configure , RouteConfig import logging configure ( version = '1.0.0' , log_level = logging . INFO , seed = 2 , should_log_exception_stack = False , prediction_cache_size = 0 , #(1) disable_se4ml_banner = True , dashboard_table_size = 200 , route_config = RouteConfig ( #(2) feedback_endpoints_enabled = False , dashboard_enabled = False ) ) Completely disable caching. The unspecified routes are enabled by default. Using remote storage # The only aspect that cannot be automated is choosing the backing storage for the database and file storage. Right now, you have 3 options for storing the models and large datasets: LargeFileLocal , LargeFileMongo , and LargeFileS3 . Without explicit configuration, LargeFileLocal is selected by default. This one still version-controls your files but it only stores them in a local path (which of course can be a remote volume attached by NFS , HDFS , etc.). Important If your working directory contains a mongo.ini or s3.ini file, an attempt is made to auto-configure LargeFileMongo or LargeFileS3 respectively. To use LargeFileMongo or LargeFileS3 explicitly, configure them before calling any other great-ai function. S3-compatible # s3.ini aws_region_name = eu-west-2 aws_access_key_id = MY_AWS_ACCESS_KEY # ENV:MY_AWS_ACCESS_KEY would also work aws_secret_access_key = MY_AWS_SECRET_KEY large_files_bucket_name = bucket-for-models use-s3.py from great_ai.large_file import LargeFileS3 from great_ai import save_model LargeFileS3 . configure_credentials_from_file ( 's3.ini' ) #(1) model = [ 4 , 3 ] save_model ( model , 'my-model' ) This line isn't strictly necessary because if s3.ini (or mongo.ini ) is available in the current working directory, they are automatically used to configure their respective LargeFile implementations/databases. Departing from AWS With the aws_endpoint_url argument, it is possible to use any other S3-compatible service such as Backblaze . In that case, it would be aws_endpoint_url=https://s3.us-west-002.backblazeb2.com . GridFS # GridFS specifies how to store files in MongoDB. The official MongoDB server and many compatible implementations support it. mongo.ini MONGO_CONNECTION_STRING = mongodb : // localhost : 27017 # this is the default value # if `MONGO_CONNECTION_STRING` is specified, this default is overridden MONGO_CONNECTION_STRING = ENV : MONGO_CONNECTION_STRING MONGO_DATABASE = my-database # it is automatically created if doesn't exist use-mongo.py from great_ai.large_file import LargeFileMongo from great_ai import save_model LargeFileMongo . configure_credentials_from_file ( 'mongo.ini' ) model = [ 4 , 3 ] save_model ( model , 'my-model' ) Simplifying config files You can combine mongo.ini or s3.ini with your application's config file because the unneeded keys are ignored by the configure_credentials_from_file method. Using a database # By default, a thread-safe version of TinyDB is utilised for saving the prediction traces into a local file. Unfortunately, for most production needs, this method is not suitable. MongoDB # At the moment, only MongoDB is supported as a production-ready TracingDatabase . In order to use it, you have to either place a file named mongo.ini in your working directory, or explicitly call either MongoDbDriver.configure_credentials_from_file or MongoDbDriver.configure_credentials .","title":"How to configure GreatAI"},{"location":"how-to-guides/configure-service/#how-to-configure-greatai","text":"GreatAI aims to provide reasonable defaults wherever possible. The current configuration is always prominently displayed (and updated) on the dashboard and in the command-line start-up banner.","title":"How to configure GreatAI"},{"location":"how-to-guides/configure-service/#using-great_aiconfigure","text":"You can override any of the default settings by calling great_ai.configure . If you don't call configure , the default settings are applied on the first call to most great-ai functions. Warning You must call great_ai.configure before calling (or decorating with) any other great-ai function. However, importing other functions before calling great_ai.configure is permitted. configure-demo.py from great_ai import configure , RouteConfig import logging configure ( version = '1.0.0' , log_level = logging . INFO , seed = 2 , should_log_exception_stack = False , prediction_cache_size = 0 , #(1) disable_se4ml_banner = True , dashboard_table_size = 200 , route_config = RouteConfig ( #(2) feedback_endpoints_enabled = False , dashboard_enabled = False ) ) Completely disable caching. The unspecified routes are enabled by default.","title":"Using great_ai.configure"},{"location":"how-to-guides/configure-service/#using-remote-storage","text":"The only aspect that cannot be automated is choosing the backing storage for the database and file storage. Right now, you have 3 options for storing the models and large datasets: LargeFileLocal , LargeFileMongo , and LargeFileS3 . Without explicit configuration, LargeFileLocal is selected by default. This one still version-controls your files but it only stores them in a local path (which of course can be a remote volume attached by NFS , HDFS , etc.). Important If your working directory contains a mongo.ini or s3.ini file, an attempt is made to auto-configure LargeFileMongo or LargeFileS3 respectively. To use LargeFileMongo or LargeFileS3 explicitly, configure them before calling any other great-ai function.","title":"Using remote storage"},{"location":"how-to-guides/configure-service/#s3-compatible","text":"s3.ini aws_region_name = eu-west-2 aws_access_key_id = MY_AWS_ACCESS_KEY # ENV:MY_AWS_ACCESS_KEY would also work aws_secret_access_key = MY_AWS_SECRET_KEY large_files_bucket_name = bucket-for-models use-s3.py from great_ai.large_file import LargeFileS3 from great_ai import save_model LargeFileS3 . configure_credentials_from_file ( 's3.ini' ) #(1) model = [ 4 , 3 ] save_model ( model , 'my-model' ) This line isn't strictly necessary because if s3.ini (or mongo.ini ) is available in the current working directory, they are automatically used to configure their respective LargeFile implementations/databases. Departing from AWS With the aws_endpoint_url argument, it is possible to use any other S3-compatible service such as Backblaze . In that case, it would be aws_endpoint_url=https://s3.us-west-002.backblazeb2.com .","title":"S3-compatible"},{"location":"how-to-guides/configure-service/#gridfs","text":"GridFS specifies how to store files in MongoDB. The official MongoDB server and many compatible implementations support it. mongo.ini MONGO_CONNECTION_STRING = mongodb : // localhost : 27017 # this is the default value # if `MONGO_CONNECTION_STRING` is specified, this default is overridden MONGO_CONNECTION_STRING = ENV : MONGO_CONNECTION_STRING MONGO_DATABASE = my-database # it is automatically created if doesn't exist use-mongo.py from great_ai.large_file import LargeFileMongo from great_ai import save_model LargeFileMongo . configure_credentials_from_file ( 'mongo.ini' ) model = [ 4 , 3 ] save_model ( model , 'my-model' ) Simplifying config files You can combine mongo.ini or s3.ini with your application's config file because the unneeded keys are ignored by the configure_credentials_from_file method.","title":"GridFS"},{"location":"how-to-guides/configure-service/#using-a-database","text":"By default, a thread-safe version of TinyDB is utilised for saving the prediction traces into a local file. Unfortunately, for most production needs, this method is not suitable.","title":"Using a database"},{"location":"how-to-guides/configure-service/#mongodb","text":"At the moment, only MongoDB is supported as a production-ready TracingDatabase . In order to use it, you have to either place a file named mongo.ini in your working directory, or explicitly call either MongoDbDriver.configure_credentials_from_file or MongoDbDriver.configure_credentials .","title":"MongoDB"},{"location":"how-to-guides/create-service/","text":"How to create a GreatAI service # The core value of great-ai lies in its GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function using the @GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of @GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead. With types # Even though it's not required by GreatAI, type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard . With async # Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-bound operation return f 'Hi { your_name } !' With decorators # GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create must come last. There are two built-in decorators that you can use to customise your function but you can you use any third-party decorator as well. Using @use_model # If you have previously saved a model with save_model , you can inject it into your function by calling @use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @use_model in other places of the codebase, it works equally well outside of GreatAI services. Using @parameter # If you wish to turn off logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model are automatically decorated with @parameter when @GreatAI.create is called. greeter_with_validation.py from great_ai import GreatAI , use_model @GreatAI . create @parameter ( 'your_name' , disable_logging = True ) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' Important You must call @parameter before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @parameter in other places of the codebase, it works equally well outside of GreatAI services. Complex example # Refer to the following example summarising the options you have when instantiating a GreatAI service. complex.py from great_ai import save_model , GreatAI , parameter , use_model , log_metric save_model ( 4 , 'secret-number' ) #(1) @GreatAI . create @parameter ( 'positive_number' , validate = lambda n : n > 0 , disable_logging = True ) @use_model ( 'secret-number' , version = 'latest' , model_kwarg_name = 'secret' ) def add_number ( positive_number : int , secret : int ) -> int : log_metric ( 'log directly into the returned Trace' , positive_number * 2 ) return positive_number + secret assert add_number ( 1 ) . output == 5 Refer to the configuration page for specifying where to store your models.","title":"How to create a GreatAI service"},{"location":"how-to-guides/create-service/#how-to-create-a-greatai-service","text":"The core value of great-ai lies in its GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function using the @GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of @GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead.","title":"How to create a GreatAI service"},{"location":"how-to-guides/create-service/#with-types","text":"Even though it's not required by GreatAI, type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard .","title":"With types"},{"location":"how-to-guides/create-service/#with-async","text":"Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-bound operation return f 'Hi { your_name } !'","title":"With async"},{"location":"how-to-guides/create-service/#with-decorators","text":"GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create must come last. There are two built-in decorators that you can use to customise your function but you can you use any third-party decorator as well.","title":"With decorators"},{"location":"how-to-guides/create-service/#using-use_model","text":"If you have previously saved a model with save_model , you can inject it into your function by calling @use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @use_model in other places of the codebase, it works equally well outside of GreatAI services.","title":"Using @use_model"},{"location":"how-to-guides/create-service/#using-parameter","text":"If you wish to turn off logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model are automatically decorated with @parameter when @GreatAI.create is called. greeter_with_validation.py from great_ai import GreatAI , use_model @GreatAI . create @parameter ( 'your_name' , disable_logging = True ) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' Important You must call @parameter before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @parameter in other places of the codebase, it works equally well outside of GreatAI services.","title":"Using @parameter"},{"location":"how-to-guides/create-service/#complex-example","text":"Refer to the following example summarising the options you have when instantiating a GreatAI service. complex.py from great_ai import save_model , GreatAI , parameter , use_model , log_metric save_model ( 4 , 'secret-number' ) #(1) @GreatAI . create @parameter ( 'positive_number' , validate = lambda n : n > 0 , disable_logging = True ) @use_model ( 'secret-number' , version = 'latest' , model_kwarg_name = 'secret' ) def add_number ( positive_number : int , secret : int ) -> int : log_metric ( 'log directly into the returned Trace' , positive_number * 2 ) return positive_number + secret assert add_number ( 1 ) . output == 5 Refer to the configuration page for specifying where to store your models.","title":"Complex example"},{"location":"how-to-guides/handle-training-data/","text":"How to manage training data # In order to simplify your training data management, great-ai provide two complementing approaches for inputting new data-points. Upload data # At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call great_ai.add_ground_truth . This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, great_ai.query_ground_truth can be called to get a filtered view of the training data. Train-test-validation splits It is a best practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it. With great_ai.add_ground_truth , there is an option to tag the samples with train , test , and validation randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags. The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces. from great_ai import add_ground_truth add_ground_truth ( [ 1 , 2 ], [ 'odd' , 'even' ], tags = 'my_tag' , train_split_ratio = 1 , #(1) test_split_ratio = 1 ) Note that the ratios don't have to add up to 1. They are just weights. There is also a validation_split_ratio which is 0 by default. >>> from great_ai import query_ground_truth >>> query_ground_truth ( 'my_tag' ) [ Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'odd' , #(1) 'logged_values' : { 'input' : 1 }, #(2) 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'odd' , 'tags' : [ 'ground_truth' , 'test' , 'my_tag' ], #(3) 'trace_id' : '4fcf2ce6-a172-469d-94b2-874577655814' }), Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'even' , 'logged_values' : { 'input' : 2 }, 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'even' , 'tags' : [ 'ground_truth' , 'train' , 'my_tag' ], 'trace_id' : 'abee0671-beb9-4284-8c3b-c65e5836ce38' })] Expected output. This can be also accessed through the .output property. The input value is stored here. Notice how ground_truth is always included as a tag when using great_ai.add_ground_truth . Get feedback # After the initial data gathering, end-to-end feedback can be also integrated into the dataset. The scaffolded REST API contains endpoints for managing traces and their feedbacks. When great_ai.query_ground_truth is executed, it implicitly filters for traces that have feedback. Therefore, both the ground_truth and the online traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API. Remove clutter # Traces can be deleted either through the REST API or by calling great_ai.delete_ground_truth . The latter provides the same interface as great_ai.query_ground_truth except it deletes the matched points.","title":"How to manage training data"},{"location":"how-to-guides/handle-training-data/#how-to-manage-training-data","text":"In order to simplify your training data management, great-ai provide two complementing approaches for inputting new data-points.","title":"How to manage training data"},{"location":"how-to-guides/handle-training-data/#upload-data","text":"At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call great_ai.add_ground_truth . This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, great_ai.query_ground_truth can be called to get a filtered view of the training data. Train-test-validation splits It is a best practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it. With great_ai.add_ground_truth , there is an option to tag the samples with train , test , and validation randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags. The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces. from great_ai import add_ground_truth add_ground_truth ( [ 1 , 2 ], [ 'odd' , 'even' ], tags = 'my_tag' , train_split_ratio = 1 , #(1) test_split_ratio = 1 ) Note that the ratios don't have to add up to 1. They are just weights. There is also a validation_split_ratio which is 0 by default. >>> from great_ai import query_ground_truth >>> query_ground_truth ( 'my_tag' ) [ Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'odd' , #(1) 'logged_values' : { 'input' : 1 }, #(2) 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'odd' , 'tags' : [ 'ground_truth' , 'test' , 'my_tag' ], #(3) 'trace_id' : '4fcf2ce6-a172-469d-94b2-874577655814' }), Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'even' , 'logged_values' : { 'input' : 2 }, 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'even' , 'tags' : [ 'ground_truth' , 'train' , 'my_tag' ], 'trace_id' : 'abee0671-beb9-4284-8c3b-c65e5836ce38' })] Expected output. This can be also accessed through the .output property. The input value is stored here. Notice how ground_truth is always included as a tag when using great_ai.add_ground_truth .","title":"Upload data"},{"location":"how-to-guides/handle-training-data/#get-feedback","text":"After the initial data gathering, end-to-end feedback can be also integrated into the dataset. The scaffolded REST API contains endpoints for managing traces and their feedbacks. When great_ai.query_ground_truth is executed, it implicitly filters for traces that have feedback. Therefore, both the ground_truth and the online traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API.","title":"Get feedback"},{"location":"how-to-guides/handle-training-data/#remove-clutter","text":"Traces can be deleted either through the REST API or by calling great_ai.delete_ground_truth . The latter provides the same interface as great_ai.query_ground_truth except it deletes the matched points.","title":"Remove clutter"},{"location":"how-to-guides/install/","text":"Installation guide # Provided you already have Python3 (and pip) installed, simply execute: pip install great-ai Python 3.7 or later is required. This will work on all major operating systems. Google Colab # In order to use GreatAI in Google Colab , you need to downgrade pyyaml to a Colab compatible version. See related StackOverflow question . pip install great-ai pyyaml == 5 .4.1 This will make GreatAI work in Colab. Command-line tools # After installation, great-ai and large-file are available as commands. The former is required for deploying your application while the latter lets you manage models and datasets from your terminal. Snakes & kebabs The library is called great-ai , therefore, its command-line entrypoint is also called great-ai . However, Python module names cannot contain hyphens, that's why you have to import great_ai with an underscore. The great-ai CLI tool is also available as python3 -m great_ai . To help with the confusion, a CLI executable called great_ai (and large_file ) are also installed, thus, if you prefer, you can always refer to GreatAI using its underscored name variant ( great_ai ). Windows On Windows, you might encounter a similar warning from pip : WARNING: The scripts great-ai.exe, great_ai.exe, large-file.exe and large_file.exe are installed in 'C:\\Users\\...\\Scripts' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. This means that great-ai.exe and large-file.exe are not in your PATH . Either add their containing directory ('C:\\Users...\\Scripts' in this case) to your PATH or use python3 -m great_ai and python3 -m great_ai.large_file instead of the exe-s. Update # If you wish to update to the latest version execute: pip install --upgrade great-ai Bleeding edge # You can also install the latest (usually unreleased) version from GitHub. pip install --upgrade git+https://github.com/schmelczer/great-ai.git Python 3.7 or later is required.","title":"Installation guide"},{"location":"how-to-guides/install/#installation-guide","text":"Provided you already have Python3 (and pip) installed, simply execute: pip install great-ai Python 3.7 or later is required. This will work on all major operating systems.","title":"Installation guide"},{"location":"how-to-guides/install/#google-colab","text":"In order to use GreatAI in Google Colab , you need to downgrade pyyaml to a Colab compatible version. See related StackOverflow question . pip install great-ai pyyaml == 5 .4.1 This will make GreatAI work in Colab.","title":"Google Colab"},{"location":"how-to-guides/install/#command-line-tools","text":"After installation, great-ai and large-file are available as commands. The former is required for deploying your application while the latter lets you manage models and datasets from your terminal. Snakes & kebabs The library is called great-ai , therefore, its command-line entrypoint is also called great-ai . However, Python module names cannot contain hyphens, that's why you have to import great_ai with an underscore. The great-ai CLI tool is also available as python3 -m great_ai . To help with the confusion, a CLI executable called great_ai (and large_file ) are also installed, thus, if you prefer, you can always refer to GreatAI using its underscored name variant ( great_ai ). Windows On Windows, you might encounter a similar warning from pip : WARNING: The scripts great-ai.exe, great_ai.exe, large-file.exe and large_file.exe are installed in 'C:\\Users\\...\\Scripts' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. This means that great-ai.exe and large-file.exe are not in your PATH . Either add their containing directory ('C:\\Users...\\Scripts' in this case) to your PATH or use python3 -m great_ai and python3 -m great_ai.large_file instead of the exe-s.","title":"Command-line tools"},{"location":"how-to-guides/install/#update","text":"If you wish to update to the latest version execute: pip install --upgrade great-ai","title":"Update"},{"location":"how-to-guides/install/#bleeding-edge","text":"You can also install the latest (usually unreleased) version from GitHub. pip install --upgrade git+https://github.com/schmelczer/great-ai.git Python 3.7 or later is required.","title":"Bleeding edge"},{"location":"how-to-guides/large-file/","text":"How to use LargeFiles # The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile , users have few reasons to use LargeFiles directly. Motivation # Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support deleting large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. DVC is a viable alternative, however, it requires users to learn to use one more CLI tool. Using LargeFile-s directly (usually not needed) LargeFile doesn't require users to learn too much new. It is a nearly exact copy of the built-in open() function of Python with which users are certainly already familiar. Simple example # from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version # leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # The latest version is returned by default # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : #(1) print ( f . readlines ()[ 0 ]) In this case, the latest version is already in the local cache, no download is required. More details # LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reads and writes are supported along with the different keywords open() accepts . The local cache can be configured with these properties: LargeFileS3 . cache_path = Path ( '.cache' ) LargeFileS3 . max_cache_size = \"30 GB\" I only need a path # In case you only need a path to the (proxy of the) remote file, this pattern can be applied: path_to_model = LargeFileS3 ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFileS3 ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFileS3(\"my-file\").delete() . It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned. From the command-line # The main reason for using the large-file or python3 -m great_ai.large_file commands is to upload or download models from the terminal. For example, when building a docker image, it is best practice to cache the referred models. Setup # Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files Upload some files # large-file --backend s3 --secrets secrets.ini \\ --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Using MongoDB The possible values for --backend are s3 , mongo , and local . The latter doesn't need credentials, it only versions and stores your files in a local folder. MongoDB on the other hand requires a mongo_connection_string and a mongo_database to be specified. For storing large files, it uses the GridFS specification. Download some files to the local cache # This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. large-file --backend s3 --secrets ~/.aws/credentials \\ --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s. Delete remote files # large-file --backend s3 --secrets ~/.aws/credentials \\ --delete my_first_file.json","title":"How to use LargeFiles"},{"location":"how-to-guides/large-file/#how-to-use-largefiles","text":"The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile , users have few reasons to use LargeFiles directly.","title":"How to use LargeFiles"},{"location":"how-to-guides/large-file/#motivation","text":"Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support deleting large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. DVC is a viable alternative, however, it requires users to learn to use one more CLI tool. Using LargeFile-s directly (usually not needed) LargeFile doesn't require users to learn too much new. It is a nearly exact copy of the built-in open() function of Python with which users are certainly already familiar.","title":"Motivation"},{"location":"how-to-guides/large-file/#simple-example","text":"from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version # leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # The latest version is returned by default # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : #(1) print ( f . readlines ()[ 0 ]) In this case, the latest version is already in the local cache, no download is required.","title":"Simple example"},{"location":"how-to-guides/large-file/#more-details","text":"LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reads and writes are supported along with the different keywords open() accepts . The local cache can be configured with these properties: LargeFileS3 . cache_path = Path ( '.cache' ) LargeFileS3 . max_cache_size = \"30 GB\"","title":"More details"},{"location":"how-to-guides/large-file/#i-only-need-a-path","text":"In case you only need a path to the (proxy of the) remote file, this pattern can be applied: path_to_model = LargeFileS3 ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFileS3 ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFileS3(\"my-file\").delete() . It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned.","title":"I only need a path"},{"location":"how-to-guides/large-file/#from-the-command-line","text":"The main reason for using the large-file or python3 -m great_ai.large_file commands is to upload or download models from the terminal. For example, when building a docker image, it is best practice to cache the referred models.","title":"From the command-line"},{"location":"how-to-guides/large-file/#setup","text":"Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files","title":"Setup"},{"location":"how-to-guides/large-file/#upload-some-files","text":"large-file --backend s3 --secrets secrets.ini \\ --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Using MongoDB The possible values for --backend are s3 , mongo , and local . The latter doesn't need credentials, it only versions and stores your files in a local folder. MongoDB on the other hand requires a mongo_connection_string and a mongo_database to be specified. For storing large files, it uses the GridFS specification.","title":"Upload some files"},{"location":"how-to-guides/large-file/#download-some-files-to-the-local-cache","text":"This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. large-file --backend s3 --secrets ~/.aws/credentials \\ --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s.","title":"Download some files to the local cache"},{"location":"how-to-guides/large-file/#delete-remote-files","text":"large-file --backend s3 --secrets ~/.aws/credentials \\ --delete my_first_file.json","title":"Delete remote files"},{"location":"how-to-guides/use-service/","text":"How to perform prediction with GreatAI # After creating a GreatAI service by wrapping your prediction function, and optionally configuring it , it's time to do some prediction. Let's take the following example: greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' One-off prediction # Even though greeter is now an instance of GreatAI , you can continue using it as a regular function. >>> greeter ( 'Bob' ) Trace [ str ]({ 'created' : '2022-07-11T14:31:46.183764' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0381 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '7c284fd7-7f0d-4464-b5f8-3ef126df34af' }) As you can see, the original return value is wrapped in a Trace object (which is also persisted in your database of choice). You can access the original value under the output property. Online prediction # Likely, the main way you would like to expose your model is through an HTTP API. @GreatAI.create scaffolds many REST API endpoints for your model and creates a FastAPI app available under GreatAI.app . This can be served using uvicorn or any other ASGI server . Since most ML code lives in Jupyter notebooks, therefore, deploying a notebook containing the inference function is supported. To achieve this, uvicorn is wrapped by the great-ai command-line utility which \u2014 among others \u2014 takes care of feeding a notebook into uvicorn . It also supports auto-reloading. In development # great-ai greeter.py Success Your model is accessible at localhost:6060 . Some configuration options are also supported. great-ai greeter.py --port 8000 --host 127 .0.0.1 --timeout_keep_alive 10 More options For more options (but no Notebook support), simply use uvicorn for starting your app (available at greeter.app ). In production # There are three main approaches for deploying a GreatAI service. Manual deployment # The app is run in production-mode if the value of the ENVIRONMENT environment variable is set to production . ENVIRONMENT = production great-ai greeter.py Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach; however, it might be fitting for some contexts. Containerised deployment # Run the notebook directly in a container or create a service for it using your favourite container orchestrator. docker run -p 6060 :6060 --volume ` pwd ` :/app --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder. Use a Platform-as-a-Service # Similar to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as AWS ECS , DO App platform , MLEM , Streamlit ) that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as PyTorch or TensorFlow . FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to cache them in the image so that you don't # have to download it each time a container starts RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor # Add you application code to the image COPY . . # The default ENTRYPOINT is great-ai, specify its argument using CMD CMD [ \"deploy.ipynb\" ] Batch prediction # Processing larger amounts of data on a single machine is made easy by the GreatAI 's process_batch method. This relies on multiprocessing ( parallel_map ) to take full advantage of all available CPU-cores. >>> greeter . process_batch ([ 'Alice' , 'Bob' ]) [ Trace [ str ]({ 'created' : '2022-07-11T14:36:37.119183' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 5 , 'arg:your_name:value' : 'Alice' }, 'models' : [], 'original_execution_time_ms' : 0.1251 , 'output' : 'Hi Alice!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '90ffa15f-e839-41c4-8e7a-3211168bc138' }), Trace [ str ]({ 'created' : '2022-07-11T14:36:37.166659' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0571 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : 'f48e94c7-0815-48b3-a864-41349d3dae84' })]","title":"How to perform prediction with GreatAI"},{"location":"how-to-guides/use-service/#how-to-perform-prediction-with-greatai","text":"After creating a GreatAI service by wrapping your prediction function, and optionally configuring it , it's time to do some prediction. Let's take the following example: greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f 'Hi { your_name } !'","title":"How to perform prediction with GreatAI"},{"location":"how-to-guides/use-service/#one-off-prediction","text":"Even though greeter is now an instance of GreatAI , you can continue using it as a regular function. >>> greeter ( 'Bob' ) Trace [ str ]({ 'created' : '2022-07-11T14:31:46.183764' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0381 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '7c284fd7-7f0d-4464-b5f8-3ef126df34af' }) As you can see, the original return value is wrapped in a Trace object (which is also persisted in your database of choice). You can access the original value under the output property.","title":"One-off prediction"},{"location":"how-to-guides/use-service/#online-prediction","text":"Likely, the main way you would like to expose your model is through an HTTP API. @GreatAI.create scaffolds many REST API endpoints for your model and creates a FastAPI app available under GreatAI.app . This can be served using uvicorn or any other ASGI server . Since most ML code lives in Jupyter notebooks, therefore, deploying a notebook containing the inference function is supported. To achieve this, uvicorn is wrapped by the great-ai command-line utility which \u2014 among others \u2014 takes care of feeding a notebook into uvicorn . It also supports auto-reloading.","title":"Online prediction"},{"location":"how-to-guides/use-service/#in-development","text":"great-ai greeter.py Success Your model is accessible at localhost:6060 . Some configuration options are also supported. great-ai greeter.py --port 8000 --host 127 .0.0.1 --timeout_keep_alive 10 More options For more options (but no Notebook support), simply use uvicorn for starting your app (available at greeter.app ).","title":"In development"},{"location":"how-to-guides/use-service/#in-production","text":"There are three main approaches for deploying a GreatAI service.","title":"In production"},{"location":"how-to-guides/use-service/#manual-deployment","text":"The app is run in production-mode if the value of the ENVIRONMENT environment variable is set to production . ENVIRONMENT = production great-ai greeter.py Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach; however, it might be fitting for some contexts.","title":"Manual deployment"},{"location":"how-to-guides/use-service/#containerised-deployment","text":"Run the notebook directly in a container or create a service for it using your favourite container orchestrator. docker run -p 6060 :6060 --volume ` pwd ` :/app --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder.","title":"Containerised deployment"},{"location":"how-to-guides/use-service/#use-a-platform-as-a-service","text":"Similar to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as AWS ECS , DO App platform , MLEM , Streamlit ) that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as PyTorch or TensorFlow . FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to cache them in the image so that you don't # have to download it each time a container starts RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor # Add you application code to the image COPY . . # The default ENTRYPOINT is great-ai, specify its argument using CMD CMD [ \"deploy.ipynb\" ]","title":"Use a Platform-as-a-Service"},{"location":"how-to-guides/use-service/#batch-prediction","text":"Processing larger amounts of data on a single machine is made easy by the GreatAI 's process_batch method. This relies on multiprocessing ( parallel_map ) to take full advantage of all available CPU-cores. >>> greeter . process_batch ([ 'Alice' , 'Bob' ]) [ Trace [ str ]({ 'created' : '2022-07-11T14:36:37.119183' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 5 , 'arg:your_name:value' : 'Alice' }, 'models' : [], 'original_execution_time_ms' : 0.1251 , 'output' : 'Hi Alice!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '90ffa15f-e839-41c4-8e7a-3211168bc138' }), Trace [ str ]({ 'created' : '2022-07-11T14:36:37.166659' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0571 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : 'f48e94c7-0815-48b3-a864-41349d3dae84' })]","title":"Batch prediction"},{"location":"reference/","text":"GreatAI reference # from great_ai import * Core # GreatAI # Bases: Generic [ T , V ] Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using configure(prediction_cache_size=0) . Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: Name Type Description app FastAPI instance wrapping the scaffolded endpoints and the Dash app. version SemVer derived from the app's version and the model names and versions registered through use_model. Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 class GreatAI ( Generic [ T , V ]): \"\"\"Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using `configure(prediction_cache_size=0)`. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: app: FastAPI instance wrapping the scaffolded endpoints and the Dash app. version: SemVer derived from the app's version and the model names and versions registered through use_model. \"\"\" __name__ : str # help for MyPy __doc__ : str # help for MyPy def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), ) create ( func ) staticmethod # Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure . Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) process_batch ( batch , * , concurrency = None , unpack_arguments = False , do_not_persist_traces = False ) # Map the wrapped function over a list of input_values ( batch ). A wrapper over parallel_map providing type-safety and a progressbar through tqdm. Parameters: Name Type Description Default batch Sequence A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set unpack_arguments=True . required concurrency Optional [ int ] Number of processes to start. Don't set it too much higher than the number of available CPU cores. None unpack_arguments bool Expect a list of tuples and unpack the tuples before giving them to the wrapped function. False do_not_persist_traces bool Don't save the traces in the database. Useful for evaluations run part of the CI. False Source code in great_ai/deploy/great_ai.py 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) configure ( * , version = '0.0.1' , log_level = DEBUG , seed = 42 , tracing_database_factory = None , large_file_implementation = None , should_log_exception_stack = None , prediction_cache_size = 512 , disable_se4ml_banner = False , dashboard_table_size = 50 , route_config = RouteConfig ()) # Set the global configuration used by the great-ai library. You must call configure before calling (or decorating with) any other great-ai function. If tracing_database_factory or large_file_implementation is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure ( prediction_cache_size = 0 ) Parameters: Name Type Description Default version Union [ int , str ] The version of your application (using SemVer is recommended). '0.0.1' log_level int Set the default logging level of logging . DEBUG seed int Set seed of random (and numpy if installed) for reproducibility. 42 tracing_database_factory Optional [ Type [ TracingDatabaseDriver ]] Specify a different TracingDatabaseDriver than the one already configured. None large_file_implementation Optional [ Type [ LargeFileBase ]] Specify a different LargeFile than the one already configured. None should_log_exception_stack Optional [ bool ] Log the traces of unhandled exceptions. None prediction_cache_size int Size of the LRU cache applied over the prediction functions. 512 disable_se4ml_banner bool Turn off the warning about the importance of SE4ML best- practices. False dashboard_table_size int Number of rows to display in the dashboard's table. 50 route_config RouteConfig Enable or disable specific HTTP API endpoints. RouteConfig() Source code in great_ai/context.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 50 , route_config : RouteConfig = RouteConfig (), ) -> None : \"\"\"Set the global configuration used by the great-ai library. You must call `configure` before calling (or decorating with) any other great-ai function. If `tracing_database_factory` or `large_file_implementation` is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure(prediction_cache_size=0) Arguments: version: The version of your application (using SemVer is recommended). log_level: Set the default logging level of `logging`. seed: Set seed of `random` (and `numpy` if installed) for reproducibility. tracing_database_factory: Specify a different TracingDatabaseDriver than the one already configured. large_file_implementation: Specify a different LargeFile than the one already configured. should_log_exception_stack: Log the traces of unhandled exceptions. prediction_cache_size: Size of the LRU cache applied over the prediction functions. disable_se4ml_banner: Turn off the warning about the importance of SE4ML best- practices. dashboard_table_size: Number of rows to display in the dashboard's table. route_config: Enable or disable specific HTTP API endpoints. \"\"\" global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"\"\"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\"\"\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before \" \"trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" ) save_model ( model , key , * , keep_last_n = None ) # Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \" use_model ( key , * , version = 'latest' , model_kwarg_name = 'model' ) # Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default key str The model's name as stored by the LargeFile implementation. required version Union [ int , Literal [ latest ]] The model's version as stored by the LargeFile implementation. 'latest' model_kwarg_name str the parameter to use for injecting the loaded model 'model' Returns: Type Description Callable [[ F ], F ] A decorator for model injection. Source code in great_ai/models/use_model.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator parameter ( parameter_name , * , validate = lambda _ : True , disable_logging = False ) # Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validate = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider. required validate Callable [[ Any ], bool ] Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def parameter ( parameter_name : str , * , validate : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validate=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider. validate: Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validate ( argument ): raise ArgumentValidationError ( f \"\"\"Argument { parameter_name } in { func . __name__ } did not pass validation\"\"\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator log_metric ( argument_name , value , disable_logging = False ) # Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Parameters: Name Type Description Default argument_name str The key for storing the value. required value Any Value to log. Must be JSON-serialisable. required disable_logging bool If True, only persist in trace but don't show in console False Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 def log_metric ( argument_name : str , value : Any , disable_logging : bool = False ) -> None : \"\"\"Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Args: argument_name: The key for storing the value. value: Value to log. Must be JSON-serialisable. disable_logging: If True, only persist in trace but don't show in console \"\"\" tracing_context = TracingContext . get_current_tracing_context () try : caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" except : # inspect might not work in notebooks actual_name = f \"metric: { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) if not disable_logging : get_context () . logger . info ( f \" { actual_name } = { value } \" ) Remote calls # call_remote_great_ai ( base_uri , data , retry_count = 4 , timeout_in_seconds = 300 , model_class = None ) # Communicate with a GreatAI object through an HTTP request. Wrapper over call_remote_great_ai_async making it synchronous. For more info, see call_remote_great_ai_async . Source code in great_ai/remote/call_remote_great_ai.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def call_remote_great_ai ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Wrapper over `call_remote_great_ai_async` making it synchronous. For more info, see `call_remote_great_ai_async`. \"\"\" try : asyncio . get_running_loop () raise Exception ( f \"Already running in an event loop, you have to call ` { call_remote_great_ai_async . __name__ } `\" ) except RuntimeError : pass future = call_remote_great_ai_async ( base_uri = base_uri , data = data , retry_count = retry_count , timeout_in_seconds = timeout_in_seconds , model_class = model_class , ) return asyncio . run ( future ) call_remote_great_ai_async ( base_uri , data , retry_count = 4 , timeout_in_seconds = 300 , model_class = None ) async # Communicate with a GreatAI object through an HTTP request. Send a POST request using httpx to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If model_class is specified, the original output is deserialised. Parameters: Name Type Description Default base_uri str Address of the remote instance, example: 'http://localhost:6060' required data Mapping [ str , Any ] The input sent as a json to the remote instance. required retry_count int Retry on any HTTP communication failure. 4 timeout_in_seconds Optional [ int ] Overall permissible max length of the request. None means no timeout. 300 model_class Optional [ Type [ T ]] A subtype of BaseModel to be used for deserialising the .output of the trace. None Source code in great_ai/remote/call_remote_great_ai_async.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 async def call_remote_great_ai_async ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Send a POST request using [httpx](https://www.python-httpx.org/) to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If `model_class` is specified, the original output is deserialised. Args: base_uri: Address of the remote instance, example: 'http://localhost:6060' data: The input sent as a json to the remote instance. retry_count: Retry on any HTTP communication failure. timeout_in_seconds: Overall permissible max length of the request. `None` means no timeout. model_class: A subtype of BaseModel to be used for deserialising the `.output` of the trace. \"\"\" if base_uri . endswith ( \"/\" ): base_uri = base_uri [: - 1 ] if not base_uri . endswith ( \"/predict\" ): base_uri = f \" { base_uri } /predict\" transport = httpx . AsyncHTTPTransport ( retries = retry_count ) try : async with httpx . AsyncClient ( transport = transport , timeout = timeout_in_seconds ) as client : response = await client . post ( base_uri , json = data ) try : response . raise_for_status () except Exception : raise RemoteCallError ( f \"Unexpected status code, reason: { response . text } \" ) except Exception as e : raise RemoteCallError from e try : trace = response . json () except Exception : raise RemoteCallError ( f \"JSON parsing failed { response . text } \" , ) try : if model_class is not None : trace [ \"output\" ] = model_class . parse_obj ( trace [ \"output\" ]) return Trace . parse_obj ( trace ) except Exception : raise RemoteCallError ( \"Could not parse Trace\" ) Ground-truth # add_ground_truth ( inputs , expected_outputs , * , tags = [], train_split_ratio = 1 , test_split_ratio = 0 , validation_split_ratio = 0 ) # Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The inputs are available via the .input property, while expected_outputs under both the .output and .feedback properties. All generated traces are tagged with ground_truth by default. Additional tags can be also provided. Using the split_ratio arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth ( ... [ 1 , 2 , 3 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) >>> add_ground_truth ( ... [ 1 , 2 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) Traceback (most recent call last): ... AssertionError : The length of the inputs and expected_outputs must be equal Parameters: Name Type Description Default inputs Iterable [ Any ] The inputs. (X in scikit-learn) required expected_outputs Iterable [ T ] The ground-truth values corresponding to the inputs. (y in scikit-learn) required tags Union [ List [ str ], str ] A single tag or a list of tags to append to each generated trace's tags. [] train_split_ratio float The probability-weight of giving each trace the train tag. 1 test_split_ratio float The probability-weight of giving each trace the test tag. 0 validation_split_ratio float The probability-weight of giving each trace the validation tag. 0 Source code in great_ai/tracing/add_ground_truth.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def add_ground_truth ( inputs : Iterable [ Any ], expected_outputs : Iterable [ T ], * , tags : Union [ List [ str ], str ] = [], train_split_ratio : float = 1 , test_split_ratio : float = 0 , validation_split_ratio : float = 0 ) -> None : \"\"\"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The `inputs` are available via the `.input` property, while `expected_outputs` under both the `.output` and `.feedback` properties. All generated traces are tagged with `ground_truth` by default. Additional tags can be also provided. Using the `split_ratio` arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth( ... [1, 2, 3], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) >>> add_ground_truth( ... [1, 2], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) Traceback (most recent call last): ... AssertionError: The length of the inputs and expected_outputs must be equal Args: inputs: The inputs. (X in scikit-learn) expected_outputs: The ground-truth values corresponding to the inputs. (y in scikit-learn) tags: A single tag or a list of tags to append to each generated trace's tags. train_split_ratio: The probability-weight of giving each trace the `train` tag. test_split_ratio: The probability-weight of giving each trace the `test` tag. validation_split_ratio: The probability-weight of giving each trace the `validation` tag. \"\"\" inputs = list ( inputs ) expected_outputs = list ( expected_outputs ) assert len ( inputs ) == len ( expected_outputs ), \"The length of the inputs and expected_outputs must be equal\" tags = tags if isinstance ( tags , list ) else [ tags ] sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio assert sum_ratio > 0 , \"The sum of the split ratios must be a positive number\" train_split_ratio /= sum_ratio test_split_ratio /= sum_ratio validation_split_ratio /= sum_ratio values = list ( zip ( inputs , expected_outputs )) shuffle ( values ) split_tags = ( [ TRAIN_SPLIT_TAG_NAME ] * ceil ( train_split_ratio * len ( inputs )) + [ TEST_SPLIT_TAG_NAME ] * ceil ( test_split_ratio * len ( inputs )) + [ VALIDATION_SPLIT_TAG_NAME ] * ceil ( validation_split_ratio * len ( inputs )) ) shuffle ( split_tags ) created = datetime . utcnow () . isoformat () traces = [ cast ( Trace [ T ], Trace ( # avoid ValueError: \"Trace\" object has no field \"__orig_class__\" trace_id = str ( uuid4 ()), created = created , original_execution_time_ms = 0 , logged_values = X if isinstance ( X , dict ) else { \"input\" : X }, models = [], output = y , feedback = y , exception = None , tags = [ GROUND_TRUTH_TAG_NAME , split_tag , * tags ], ), ) for (( X , y ), split_tag ) in zip ( values , split_tags ) ] get_context () . tracing_database . save_batch ( traces ) query_ground_truth ( conjunctive_tags = [], * , since = None , until = None , return_max_count = None ) # Return training samples. Combines, filters, and returns data-points that have been either added by add_ground_truth or were the result of a prediction after which their trace got feedback through the RESP API-s /traces/{trace_id}/feedback endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth () [...] Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only return traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only return traces created before the given timestamp. None means no filtering. None return_max_count Optional [ int ] Return at-most this many traces. (take, limit) None Source code in great_ai/tracing/query_ground_truth.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def query_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , return_max_count : Optional [ int ] = None ) -> List [ Trace ]: \"\"\"Return training samples. Combines, filters, and returns data-points that have been either added by `add_ground_truth` or were the result of a prediction after which their trace got feedback through the RESP API-s `/traces/{trace_id}/feedback` endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth() [...] Args: conjunctive_tags: Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). since: Only return traces created after the given timestamp. `None` means no filtering. until: Only return traces created before the given timestamp. `None` means no filtering. return_max_count: Return at-most this many traces. (take, limit) \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , since = since , until = until , take = return_max_count , has_feedback = True , ) return items delete_ground_truth ( conjunctive_tags = [], * , since = None , until = None ) # Delete traces matching the given criteria. Takes the same arguments as query_ground_truth but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth ([ 'train' , 'test' , 'validation' ]) Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only delete traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only delete traces created before the given timestamp. None means no filtering. None Source code in great_ai/tracing/delete_ground_truth.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 def delete_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , ) -> None : \"\"\"Delete traces matching the given criteria. Takes the same arguments as `query_ground_truth` but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth(['train', 'test', 'validation']) Args: conjunctive_tags: Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). since: Only delete traces created after the given timestamp. `None` means no filtering. until: Only delete traces created before the given timestamp. `None` means no filtering. \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , until = until , since = since , has_feedback = True ) db . delete_batch ([ i . trace_id for i in items ]) Tracing databases # TracingDatabaseDriver # Bases: ABC Interface expected from a database to be used for storing traces. Source code in great_ai/persistence/tracing_database_driver.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 class TracingDatabaseDriver ( ABC ): \"\"\"Interface expected from a database to be used for storing traces.\"\"\" is_production_ready : bool initialized : bool = False @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ) -> None : cls . initialized = True @abstractmethod def save ( self , document : Trace ) -> str : pass @abstractmethod def save_batch ( self , documents : List [ Trace ], ) -> List [ str ]: pass @abstractmethod def get ( self , id : str ) -> Optional [ Trace ]: pass @abstractmethod def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], until : Optional [ datetime ] = None , since : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: pass @abstractmethod def update ( self , id : str , new_version : Trace ) -> None : pass @abstractmethod def delete ( self , id : str ) -> None : pass @abstractmethod def delete_batch ( self , ids : List [ str ], ) -> None : pass MongoDbDriver # Bases: TracingDatabaseDriver TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout MongoDB Atlas for a hosted MongoDB solution. Source code in great_ai/persistence/mongodb_driver.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 class MongoDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register) for a hosted MongoDB solution. \"\"\" is_production_ready = True mongo_connection_string : str mongo_database : str def __init__ ( self ) -> None : super () . __init__ () if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling \" \"MongoDbDriver.configure_credentials\" ) with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . create_index ( [( \"tags\" , ASCENDING ), ( \"created\" , DESCENDING )], background = True ) @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () def save ( self , trace : Trace ) -> str : serialized = trace . to_flat_dict () serialized [ \"_id\" ] = trace . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : return client [ self . mongo_database ] . traces . insert_one ( serialized ) . inserted_id def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: serialized = [ d . to_flat_dict () for d in documents ] for s in serialized : s [ \"_id\" ] = s [ \"trace_id\" ] with MongoClient [ Any ]( self . mongo_connection_string ) as client : return ( client [ self . mongo_database ] . traces . insert_many ( serialized , ordered = False ) . inserted_ids ) def get ( self , id : str ) -> Optional [ Trace ]: with MongoClient [ Any ]( self . mongo_connection_string ) as client : value = client [ self . mongo_database ] . traces . find_one ( id ) if value : value = Trace . parse_obj ( value ) return value def _get_operator ( self , filter : Filter ) -> str : if filter . operator == \"contains\" and not isinstance ( filter . value , str ): return operator_mapping [ \"=\" ] return operator_mapping [ filter . operator ] def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [], ) -> Tuple [ List [ Trace ], int ]: query : Dict [ str , Any ] = { \"filter\" : {}, } and_query : List [ Dict [ str , Any ]] = [] and_query . extend ({ \"tags\" : tag } for tag in conjunctive_tags ) and_query . extend ( { f . property : { self . _get_operator ( f ): f . value }} for f in conjunctive_filters ) if not and_query : and_query . append ({}) if since : and_query . append ({ \"created\" : { \"$gte\" : since }}) if until : and_query . append ({ \"created\" : { \"$lte\" : until }}) if has_feedback is not None : and_query . append ( { \"feedback\" : { \"$ne\" : None }} if has_feedback else { \"feedback\" : None } ) query [ \"filter\" ][ \"$and\" ] = and_query with MongoClient [ Any ]( self . mongo_connection_string ) as client : count = client [ self . mongo_database ] . traces . count_documents ( ** query ) if skip : query [ \"skip\" ] = skip if take : query [ \"limit\" ] = take query [ \"sort\" ] = [ ( col . column_id , 1 if col . direction == \"asc\" else - 1 ) for col in sort_by ] with client [ self . mongo_database ] . traces . find ( ** query ) as cursor : documents = [ Trace [ Any ] . parse_obj ( t ) for t in cursor ] return documents , count def update ( self , id : str , new_version : Trace ) -> None : serialized = new_version . to_flat_dict () serialized [ \"_id\" ] = new_version . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . update_one ({ \"_id\" : id }, serialized ) def delete ( self , id : str ) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . delete_one ({ \"_id\" : id }) def delete_batch ( self , ids : List [ str ]) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : for c in chunk ( ids , chunk_size = 10000 ): # avoid: 'delete' command document too large delete_filter = { \"_id\" : { \"$in\" : c }} client [ self . mongo_database ] . traces . delete_many ( delete_filter ) configure_credentials ( * , mongo_connection_string , mongo_database , ** _ ) classmethod # Configure the connection details for MongoDB. Parameters: Name Type Description Default mongo_connection_string str For example: 'mongodb://my_user:my_pass@localhost:27017' required mongo_database str Name of the database to use. If doesn't exist, it is created and initialised. required Source code in great_ai/persistence/mongodb_driver.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () ParallelTinyDbDriver # Bases: TracingDatabaseDriver TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. Source code in great_ai/persistence/parallel_tinydb_driver.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 class ParallelTinyDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. \"\"\" is_production_ready = False path_to_db = Path ( DEFAULT_TRACING_DB_FILENAME ) def save ( self , trace : Trace ) -> str : return self . _safe_execute ( lambda db : db . insert ( trace . dict ())) def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: traces = [ d . dict () for d in documents ] return self . _safe_execute ( lambda db : db . insert_multiple ( traces )) def get ( self , id : str ) -> Optional [ Trace ]: value = self . _safe_execute ( lambda db : db . get ( lambda d : d [ \"trace_id\" ] == id )) if value : value = Trace . parse_obj ( value ) return value def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: def does_match ( d : Dict [ str , Any ]) -> bool : return ( not set ( conjunctive_tags ) - set ( d [ \"tags\" ]) and ( since is None or datetime . fromisoformat ( d [ \"created\" ]) >= since ) and ( until is None or datetime . fromisoformat ( d [ \"created\" ]) <= until ) and ( has_feedback is None or has_feedback == ( d [ \"feedback\" ] is not None ) ) ) documents = self . _safe_execute ( lambda db : db . search ( does_match )) if not documents : return [], 0 df = pd . DataFrame ([ Trace . parse_obj ( d ) . to_flat_dict () for d in documents ]) for f in conjunctive_filters : operator = f . operator . lower () if operator in operator_mapping : df = df . loc [ getattr ( df [ f . property ], operator_mapping [ f . operator ])( f . value ) ] elif operator == \"contains\" : df = df . loc [ df [ f . property ] . str . contains ( str ( int ( f . value )) if isinstance ( f . value , float ) else f . value , case = False , ) ] if sort_by : df . sort_values ( [ col . column_id for col in sort_by ], ascending = [ col . direction == \"asc\" for col in sort_by ], inplace = True , ) count = len ( df ) result = df . iloc [ skip :] if take is None else df . iloc [ skip : skip + take ] return [ Trace . parse_obj ( trace ) for _ , trace in result . iterrows ()], count def update ( self , id : str , new_version : Trace ) -> None : self . _safe_execute ( lambda db : db . update ( new_version . dict (), lambda d : d [ \"trace_id\" ] == id ) ) def delete ( self , id : str ) -> None : self . _safe_execute ( lambda db : db . remove ( lambda d : d [ \"trace_id\" ] == id )) def delete_batch ( self , ids : List [ str ]) -> None : with lock : with TinyDB ( self . path_to_db ) as db : for id in ids : db . remove ( lambda d : d [ \"trace_id\" ] == id ) def _safe_execute ( self , func : Callable [[ TinyDB ], Any ]) -> Any : with lock : with TinyDB ( self . path_to_db ) as db : return func ( db )","title":"GreatAI reference"},{"location":"reference/#greatai-reference","text":"from great_ai import *","title":"GreatAI reference"},{"location":"reference/#core","text":"","title":"Core"},{"location":"reference/#great_ai.GreatAI","text":"Bases: Generic [ T , V ] Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using configure(prediction_cache_size=0) . Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: Name Type Description app FastAPI instance wrapping the scaffolded endpoints and the Dash app. version SemVer derived from the app's version and the model names and versions registered through use_model. Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 class GreatAI ( Generic [ T , V ]): \"\"\"Wrapper for a prediction function providing the implementation of SE4ML best practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. IMPORTANT: when a request is served from cache, no new trace is created. Thus, the same trace can be returned multiple times. If this is undesirable turn off caching using `configure(prediction_cache_size=0)`. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: app: FastAPI instance wrapping the scaffolded endpoints and the Dash app. version: SemVer derived from the app's version and the model names and versions registered through use_model. \"\"\" __name__ : str # help for MyPy __doc__ : str # help for MyPy def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), )","title":"GreatAI"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.create","text":"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure . Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , )","title":"create()"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.process_batch","text":"Map the wrapped function over a list of input_values ( batch ). A wrapper over parallel_map providing type-safety and a progressbar through tqdm. Parameters: Name Type Description Default batch Sequence A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set unpack_arguments=True . required concurrency Optional [ int ] Number of processes to start. Don't set it too much higher than the number of available CPU cores. None unpack_arguments bool Expect a list of tuples and unpack the tuples before giving them to the wrapped function. False do_not_persist_traces bool Don't save the traces in the database. Useful for evaluations run part of the CI. False Source code in great_ai/deploy/great_ai.py 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) )","title":"process_batch()"},{"location":"reference/#great_ai.configure","text":"Set the global configuration used by the great-ai library. You must call configure before calling (or decorating with) any other great-ai function. If tracing_database_factory or large_file_implementation is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure ( prediction_cache_size = 0 ) Parameters: Name Type Description Default version Union [ int , str ] The version of your application (using SemVer is recommended). '0.0.1' log_level int Set the default logging level of logging . DEBUG seed int Set seed of random (and numpy if installed) for reproducibility. 42 tracing_database_factory Optional [ Type [ TracingDatabaseDriver ]] Specify a different TracingDatabaseDriver than the one already configured. None large_file_implementation Optional [ Type [ LargeFileBase ]] Specify a different LargeFile than the one already configured. None should_log_exception_stack Optional [ bool ] Log the traces of unhandled exceptions. None prediction_cache_size int Size of the LRU cache applied over the prediction functions. 512 disable_se4ml_banner bool Turn off the warning about the importance of SE4ML best- practices. False dashboard_table_size int Number of rows to display in the dashboard's table. 50 route_config RouteConfig Enable or disable specific HTTP API endpoints. RouteConfig() Source code in great_ai/context.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 50 , route_config : RouteConfig = RouteConfig (), ) -> None : \"\"\"Set the global configuration used by the great-ai library. You must call `configure` before calling (or decorating with) any other great-ai function. If `tracing_database_factory` or `large_file_implementation` is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure(prediction_cache_size=0) Arguments: version: The version of your application (using SemVer is recommended). log_level: Set the default logging level of `logging`. seed: Set seed of `random` (and `numpy` if installed) for reproducibility. tracing_database_factory: Specify a different TracingDatabaseDriver than the one already configured. large_file_implementation: Specify a different LargeFile than the one already configured. should_log_exception_stack: Log the traces of unhandled exceptions. prediction_cache_size: Size of the LRU cache applied over the prediction functions. disable_se4ml_banner: Turn off the warning about the importance of SE4ML best- practices. dashboard_table_size: Number of rows to display in the dashboard's table. route_config: Enable or disable specific HTTP API endpoints. \"\"\" global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"\"\"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\"\"\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before \" \"trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" )","title":"configure()"},{"location":"reference/#great_ai.save_model","text":"Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \"","title":"save_model()"},{"location":"reference/#great_ai.use_model","text":"Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default key str The model's name as stored by the LargeFile implementation. required version Union [ int , Literal [ latest ]] The model's version as stored by the LargeFile implementation. 'latest' model_kwarg_name str the parameter to use for injecting the loaded model 'model' Returns: Type Description Callable [[ F ], F ] A decorator for model injection. Source code in great_ai/models/use_model.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator","title":"use_model()"},{"location":"reference/#great_ai.parameter","text":"Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validate = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider. required validate Callable [[ Any ], bool ] Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def parameter ( parameter_name : str , * , validate : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Basically, a parameter decorator. Unfortunately, Python does not have that concept, thus, it's a method decorator that expects the name of the to-be-decorated parameter. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validate=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider. validate: Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validate ( argument ): raise ArgumentValidationError ( f \"\"\"Argument { parameter_name } in { func . __name__ } did not pass validation\"\"\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator","title":"parameter()"},{"location":"reference/#great_ai.log_metric","text":"Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Parameters: Name Type Description Default argument_name str The key for storing the value. required value Any Value to log. Must be JSON-serialisable. required disable_logging bool If True, only persist in trace but don't show in console False Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 def log_metric ( argument_name : str , value : Any , disable_logging : bool = False ) -> None : \"\"\"Log a key (argument_name)-value pair that is persisted inside the trace. The name of the function from where this is called is also stored. Args: argument_name: The key for storing the value. value: Value to log. Must be JSON-serialisable. disable_logging: If True, only persist in trace but don't show in console \"\"\" tracing_context = TracingContext . get_current_tracing_context () try : caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" except : # inspect might not work in notebooks actual_name = f \"metric: { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) if not disable_logging : get_context () . logger . info ( f \" { actual_name } = { value } \" )","title":"log_metric()"},{"location":"reference/#remote-calls","text":"","title":"Remote calls"},{"location":"reference/#great_ai.call_remote_great_ai","text":"Communicate with a GreatAI object through an HTTP request. Wrapper over call_remote_great_ai_async making it synchronous. For more info, see call_remote_great_ai_async . Source code in great_ai/remote/call_remote_great_ai.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def call_remote_great_ai ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Wrapper over `call_remote_great_ai_async` making it synchronous. For more info, see `call_remote_great_ai_async`. \"\"\" try : asyncio . get_running_loop () raise Exception ( f \"Already running in an event loop, you have to call ` { call_remote_great_ai_async . __name__ } `\" ) except RuntimeError : pass future = call_remote_great_ai_async ( base_uri = base_uri , data = data , retry_count = retry_count , timeout_in_seconds = timeout_in_seconds , model_class = model_class , ) return asyncio . run ( future )","title":"call_remote_great_ai()"},{"location":"reference/#great_ai.call_remote_great_ai_async","text":"Communicate with a GreatAI object through an HTTP request. Send a POST request using httpx to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If model_class is specified, the original output is deserialised. Parameters: Name Type Description Default base_uri str Address of the remote instance, example: 'http://localhost:6060' required data Mapping [ str , Any ] The input sent as a json to the remote instance. required retry_count int Retry on any HTTP communication failure. 4 timeout_in_seconds Optional [ int ] Overall permissible max length of the request. None means no timeout. 300 model_class Optional [ Type [ T ]] A subtype of BaseModel to be used for deserialising the .output of the trace. None Source code in great_ai/remote/call_remote_great_ai_async.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 async def call_remote_great_ai_async ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Send a POST request using [httpx](https://www.python-httpx.org/) to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If `model_class` is specified, the original output is deserialised. Args: base_uri: Address of the remote instance, example: 'http://localhost:6060' data: The input sent as a json to the remote instance. retry_count: Retry on any HTTP communication failure. timeout_in_seconds: Overall permissible max length of the request. `None` means no timeout. model_class: A subtype of BaseModel to be used for deserialising the `.output` of the trace. \"\"\" if base_uri . endswith ( \"/\" ): base_uri = base_uri [: - 1 ] if not base_uri . endswith ( \"/predict\" ): base_uri = f \" { base_uri } /predict\" transport = httpx . AsyncHTTPTransport ( retries = retry_count ) try : async with httpx . AsyncClient ( transport = transport , timeout = timeout_in_seconds ) as client : response = await client . post ( base_uri , json = data ) try : response . raise_for_status () except Exception : raise RemoteCallError ( f \"Unexpected status code, reason: { response . text } \" ) except Exception as e : raise RemoteCallError from e try : trace = response . json () except Exception : raise RemoteCallError ( f \"JSON parsing failed { response . text } \" , ) try : if model_class is not None : trace [ \"output\" ] = model_class . parse_obj ( trace [ \"output\" ]) return Trace . parse_obj ( trace ) except Exception : raise RemoteCallError ( \"Could not parse Trace\" )","title":"call_remote_great_ai_async()"},{"location":"reference/#ground-truth","text":"","title":"Ground-truth"},{"location":"reference/#great_ai.add_ground_truth","text":"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The inputs are available via the .input property, while expected_outputs under both the .output and .feedback properties. All generated traces are tagged with ground_truth by default. Additional tags can be also provided. Using the split_ratio arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth ( ... [ 1 , 2 , 3 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) >>> add_ground_truth ( ... [ 1 , 2 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) Traceback (most recent call last): ... AssertionError : The length of the inputs and expected_outputs must be equal Parameters: Name Type Description Default inputs Iterable [ Any ] The inputs. (X in scikit-learn) required expected_outputs Iterable [ T ] The ground-truth values corresponding to the inputs. (y in scikit-learn) required tags Union [ List [ str ], str ] A single tag or a list of tags to append to each generated trace's tags. [] train_split_ratio float The probability-weight of giving each trace the train tag. 1 test_split_ratio float The probability-weight of giving each trace the test tag. 0 validation_split_ratio float The probability-weight of giving each trace the validation tag. 0 Source code in great_ai/tracing/add_ground_truth.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def add_ground_truth ( inputs : Iterable [ Any ], expected_outputs : Iterable [ T ], * , tags : Union [ List [ str ], str ] = [], train_split_ratio : float = 1 , test_split_ratio : float = 0 , validation_split_ratio : float = 0 ) -> None : \"\"\"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The `inputs` are available via the `.input` property, while `expected_outputs` under both the `.output` and `.feedback` properties. All generated traces are tagged with `ground_truth` by default. Additional tags can be also provided. Using the `split_ratio` arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth( ... [1, 2, 3], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) >>> add_ground_truth( ... [1, 2], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) Traceback (most recent call last): ... AssertionError: The length of the inputs and expected_outputs must be equal Args: inputs: The inputs. (X in scikit-learn) expected_outputs: The ground-truth values corresponding to the inputs. (y in scikit-learn) tags: A single tag or a list of tags to append to each generated trace's tags. train_split_ratio: The probability-weight of giving each trace the `train` tag. test_split_ratio: The probability-weight of giving each trace the `test` tag. validation_split_ratio: The probability-weight of giving each trace the `validation` tag. \"\"\" inputs = list ( inputs ) expected_outputs = list ( expected_outputs ) assert len ( inputs ) == len ( expected_outputs ), \"The length of the inputs and expected_outputs must be equal\" tags = tags if isinstance ( tags , list ) else [ tags ] sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio assert sum_ratio > 0 , \"The sum of the split ratios must be a positive number\" train_split_ratio /= sum_ratio test_split_ratio /= sum_ratio validation_split_ratio /= sum_ratio values = list ( zip ( inputs , expected_outputs )) shuffle ( values ) split_tags = ( [ TRAIN_SPLIT_TAG_NAME ] * ceil ( train_split_ratio * len ( inputs )) + [ TEST_SPLIT_TAG_NAME ] * ceil ( test_split_ratio * len ( inputs )) + [ VALIDATION_SPLIT_TAG_NAME ] * ceil ( validation_split_ratio * len ( inputs )) ) shuffle ( split_tags ) created = datetime . utcnow () . isoformat () traces = [ cast ( Trace [ T ], Trace ( # avoid ValueError: \"Trace\" object has no field \"__orig_class__\" trace_id = str ( uuid4 ()), created = created , original_execution_time_ms = 0 , logged_values = X if isinstance ( X , dict ) else { \"input\" : X }, models = [], output = y , feedback = y , exception = None , tags = [ GROUND_TRUTH_TAG_NAME , split_tag , * tags ], ), ) for (( X , y ), split_tag ) in zip ( values , split_tags ) ] get_context () . tracing_database . save_batch ( traces )","title":"add_ground_truth()"},{"location":"reference/#great_ai.query_ground_truth","text":"Return training samples. Combines, filters, and returns data-points that have been either added by add_ground_truth or were the result of a prediction after which their trace got feedback through the RESP API-s /traces/{trace_id}/feedback endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth () [...] Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only return traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only return traces created before the given timestamp. None means no filtering. None return_max_count Optional [ int ] Return at-most this many traces. (take, limit) None Source code in great_ai/tracing/query_ground_truth.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def query_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , return_max_count : Optional [ int ] = None ) -> List [ Trace ]: \"\"\"Return training samples. Combines, filters, and returns data-points that have been either added by `add_ground_truth` or were the result of a prediction after which their trace got feedback through the RESP API-s `/traces/{trace_id}/feedback` endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth() [...] Args: conjunctive_tags: Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). since: Only return traces created after the given timestamp. `None` means no filtering. until: Only return traces created before the given timestamp. `None` means no filtering. return_max_count: Return at-most this many traces. (take, limit) \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , since = since , until = until , take = return_max_count , has_feedback = True , ) return items","title":"query_ground_truth()"},{"location":"reference/#great_ai.delete_ground_truth","text":"Delete traces matching the given criteria. Takes the same arguments as query_ground_truth but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth ([ 'train' , 'test' , 'validation' ]) Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only delete traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only delete traces created before the given timestamp. None means no filtering. None Source code in great_ai/tracing/delete_ground_truth.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 def delete_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , ) -> None : \"\"\"Delete traces matching the given criteria. Takes the same arguments as `query_ground_truth` but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth(['train', 'test', 'validation']) Args: conjunctive_tags: Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). since: Only delete traces created after the given timestamp. `None` means no filtering. until: Only delete traces created before the given timestamp. `None` means no filtering. \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , until = until , since = since , has_feedback = True ) db . delete_batch ([ i . trace_id for i in items ])","title":"delete_ground_truth()"},{"location":"reference/#tracing-databases","text":"","title":"Tracing databases"},{"location":"reference/#great_ai.TracingDatabaseDriver","text":"Bases: ABC Interface expected from a database to be used for storing traces. Source code in great_ai/persistence/tracing_database_driver.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 class TracingDatabaseDriver ( ABC ): \"\"\"Interface expected from a database to be used for storing traces.\"\"\" is_production_ready : bool initialized : bool = False @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ) -> None : cls . initialized = True @abstractmethod def save ( self , document : Trace ) -> str : pass @abstractmethod def save_batch ( self , documents : List [ Trace ], ) -> List [ str ]: pass @abstractmethod def get ( self , id : str ) -> Optional [ Trace ]: pass @abstractmethod def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], until : Optional [ datetime ] = None , since : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: pass @abstractmethod def update ( self , id : str , new_version : Trace ) -> None : pass @abstractmethod def delete ( self , id : str ) -> None : pass @abstractmethod def delete_batch ( self , ids : List [ str ], ) -> None : pass","title":"TracingDatabaseDriver"},{"location":"reference/#great_ai.MongoDbDriver","text":"Bases: TracingDatabaseDriver TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout MongoDB Atlas for a hosted MongoDB solution. Source code in great_ai/persistence/mongodb_driver.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 class MongoDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register) for a hosted MongoDB solution. \"\"\" is_production_ready = True mongo_connection_string : str mongo_database : str def __init__ ( self ) -> None : super () . __init__ () if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling \" \"MongoDbDriver.configure_credentials\" ) with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . create_index ( [( \"tags\" , ASCENDING ), ( \"created\" , DESCENDING )], background = True ) @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () def save ( self , trace : Trace ) -> str : serialized = trace . to_flat_dict () serialized [ \"_id\" ] = trace . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : return client [ self . mongo_database ] . traces . insert_one ( serialized ) . inserted_id def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: serialized = [ d . to_flat_dict () for d in documents ] for s in serialized : s [ \"_id\" ] = s [ \"trace_id\" ] with MongoClient [ Any ]( self . mongo_connection_string ) as client : return ( client [ self . mongo_database ] . traces . insert_many ( serialized , ordered = False ) . inserted_ids ) def get ( self , id : str ) -> Optional [ Trace ]: with MongoClient [ Any ]( self . mongo_connection_string ) as client : value = client [ self . mongo_database ] . traces . find_one ( id ) if value : value = Trace . parse_obj ( value ) return value def _get_operator ( self , filter : Filter ) -> str : if filter . operator == \"contains\" and not isinstance ( filter . value , str ): return operator_mapping [ \"=\" ] return operator_mapping [ filter . operator ] def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [], ) -> Tuple [ List [ Trace ], int ]: query : Dict [ str , Any ] = { \"filter\" : {}, } and_query : List [ Dict [ str , Any ]] = [] and_query . extend ({ \"tags\" : tag } for tag in conjunctive_tags ) and_query . extend ( { f . property : { self . _get_operator ( f ): f . value }} for f in conjunctive_filters ) if not and_query : and_query . append ({}) if since : and_query . append ({ \"created\" : { \"$gte\" : since }}) if until : and_query . append ({ \"created\" : { \"$lte\" : until }}) if has_feedback is not None : and_query . append ( { \"feedback\" : { \"$ne\" : None }} if has_feedback else { \"feedback\" : None } ) query [ \"filter\" ][ \"$and\" ] = and_query with MongoClient [ Any ]( self . mongo_connection_string ) as client : count = client [ self . mongo_database ] . traces . count_documents ( ** query ) if skip : query [ \"skip\" ] = skip if take : query [ \"limit\" ] = take query [ \"sort\" ] = [ ( col . column_id , 1 if col . direction == \"asc\" else - 1 ) for col in sort_by ] with client [ self . mongo_database ] . traces . find ( ** query ) as cursor : documents = [ Trace [ Any ] . parse_obj ( t ) for t in cursor ] return documents , count def update ( self , id : str , new_version : Trace ) -> None : serialized = new_version . to_flat_dict () serialized [ \"_id\" ] = new_version . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . update_one ({ \"_id\" : id }, serialized ) def delete ( self , id : str ) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . delete_one ({ \"_id\" : id }) def delete_batch ( self , ids : List [ str ]) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : for c in chunk ( ids , chunk_size = 10000 ): # avoid: 'delete' command document too large delete_filter = { \"_id\" : { \"$in\" : c }} client [ self . mongo_database ] . traces . delete_many ( delete_filter )","title":"MongoDbDriver"},{"location":"reference/#great_ai.persistence.mongodb_driver.MongoDbDriver.configure_credentials","text":"Configure the connection details for MongoDB. Parameters: Name Type Description Default mongo_connection_string str For example: 'mongodb://my_user:my_pass@localhost:27017' required mongo_database str Name of the database to use. If doesn't exist, it is created and initialised. required Source code in great_ai/persistence/mongodb_driver.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials ()","title":"configure_credentials()"},{"location":"reference/#great_ai.ParallelTinyDbDriver","text":"Bases: TracingDatabaseDriver TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. Source code in great_ai/persistence/parallel_tinydb_driver.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 class ParallelTinyDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. \"\"\" is_production_ready = False path_to_db = Path ( DEFAULT_TRACING_DB_FILENAME ) def save ( self , trace : Trace ) -> str : return self . _safe_execute ( lambda db : db . insert ( trace . dict ())) def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: traces = [ d . dict () for d in documents ] return self . _safe_execute ( lambda db : db . insert_multiple ( traces )) def get ( self , id : str ) -> Optional [ Trace ]: value = self . _safe_execute ( lambda db : db . get ( lambda d : d [ \"trace_id\" ] == id )) if value : value = Trace . parse_obj ( value ) return value def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: def does_match ( d : Dict [ str , Any ]) -> bool : return ( not set ( conjunctive_tags ) - set ( d [ \"tags\" ]) and ( since is None or datetime . fromisoformat ( d [ \"created\" ]) >= since ) and ( until is None or datetime . fromisoformat ( d [ \"created\" ]) <= until ) and ( has_feedback is None or has_feedback == ( d [ \"feedback\" ] is not None ) ) ) documents = self . _safe_execute ( lambda db : db . search ( does_match )) if not documents : return [], 0 df = pd . DataFrame ([ Trace . parse_obj ( d ) . to_flat_dict () for d in documents ]) for f in conjunctive_filters : operator = f . operator . lower () if operator in operator_mapping : df = df . loc [ getattr ( df [ f . property ], operator_mapping [ f . operator ])( f . value ) ] elif operator == \"contains\" : df = df . loc [ df [ f . property ] . str . contains ( str ( int ( f . value )) if isinstance ( f . value , float ) else f . value , case = False , ) ] if sort_by : df . sort_values ( [ col . column_id for col in sort_by ], ascending = [ col . direction == \"asc\" for col in sort_by ], inplace = True , ) count = len ( df ) result = df . iloc [ skip :] if take is None else df . iloc [ skip : skip + take ] return [ Trace . parse_obj ( trace ) for _ , trace in result . iterrows ()], count def update ( self , id : str , new_version : Trace ) -> None : self . _safe_execute ( lambda db : db . update ( new_version . dict (), lambda d : d [ \"trace_id\" ] == id ) ) def delete ( self , id : str ) -> None : self . _safe_execute ( lambda db : db . remove ( lambda d : d [ \"trace_id\" ] == id )) def delete_batch ( self , ids : List [ str ]) -> None : with lock : with TinyDB ( self . path_to_db ) as db : for id in ids : db . remove ( lambda d : d [ \"trace_id\" ] == id ) def _safe_execute ( self , func : Callable [[ TinyDB ], Any ]) -> Any : with lock : with TinyDB ( self . path_to_db ) as db : return func ( db )","title":"ParallelTinyDbDriver"},{"location":"reference/large-file/","text":"LargeFile # from great_ai.large_file import * For more details about using LargeFiles, check out the how to guide . LargeFileLocal # Bases: LargeFileBase LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal . cache_path = Path ( \".cache\" ) >>> LargeFileLocal . max_cache_size = \"30GB\" >>> with LargeFileLocal ( \"my_test.txt\" , \"w\" , keep_last_n = 2 ) as f : ... f . write ( 'test' ) 4 >>> with LargeFileLocal ( \"my_test.txt\" ) as f : ... print ( f . read ()) test Source code in great_ai/large_file/large_file/large_file_local.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 class LargeFileLocal ( LargeFileBase ): \"\"\"LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal.cache_path = Path(\".cache\") >>> LargeFileLocal.max_cache_size = \"30GB\" >>> with LargeFileLocal(\"my_test.txt\", \"w\", keep_last_n=2) as f: ... f.write('test') 4 >>> with LargeFileLocal(\"my_test.txt\") as f: ... print(f.read()) test \"\"\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , ): super () . __init__ ( name , mode , buffering = buffering , encoding = encoding , errors = errors , newline = newline , version = version , keep_last_n = keep_last_n , cache_only_mode = True , ) super () . configure_credentials () def _find_remote_instances ( self ) -> List [ DataInstance ]: return [] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : # This will never be called because the file must be in the cache raise NotImplementedError () def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass # the \"upload\" is already done py the parent's caching mechanism def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) i . remote_path . unlink () LargeFileS3 # Bases: LargeFileBase LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_s3.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 class LargeFileS3 ( LargeFileBase ): \"\"\"LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" region_name = None access_key_id = None secret_access_key = None bucket_name = None endpoint_url = None @classmethod def configure_credentials ( # type: ignore cls , * , aws_region_name : str , aws_access_key_id : str , aws_secret_access_key : str , large_files_bucket_name : str , aws_endpoint_url : Optional [ str ] = None , ** _ : Any , ) -> None : cls . region_name = aws_region_name cls . access_key_id = aws_access_key_id cls . secret_access_key = aws_secret_access_key cls . bucket_name = large_files_bucket_name cls . endpoint_url = aws_endpoint_url super () . configure_credentials () @cached_property def _client ( self ) -> boto3 . client : if ( self . region_name is None or self . access_key_id is None or self . secret_access_key is None or self . bucket_name is None ): raise ValueError ( \"Please configure the S3 access options by calling LargeFileS3.configure_credentials or set offline_mode=True in the constructor.\" ) return boto3 . client ( \"s3\" , aws_access_key_id = self . access_key_id , aws_secret_access_key = self . secret_access_key , region_name = self . region_name , endpoint_url = self . endpoint_url , ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching S3 versions of { self . _name } \" ) found_objects = self . _client . list_objects_v2 ( Bucket = self . bucket_name , Prefix = self . _name ) return ( [ DataInstance ( name = o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ], version = int ( o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = o [ \"Key\" ], ) for o in found_objects [ \"Contents\" ] if o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ] == self . _name ] if \"Contents\" in found_objects else [] ) def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path } from S3\" ) size = self . _client . head_object ( Bucket = self . bucket_name , Key = remote_path )[ \"ContentLength\" ] self . _client . download_file ( Bucket = self . bucket_name , Key = remote_path , Filename = str ( local_path ), Callback = None if hide_progress else DownloadProgressBar ( name = str ( remote_path ), size = size , logger = logger ), ) def _upload ( self , local_path : Path , hide_progress : bool ) -> None : key = f \" { self . _name } / { self . version } \" logger . info ( f \"Uploading { self . _local_name } to S3 as { key } \" ) self . _client . upload_file ( Filename = str ( local_path ), Bucket = self . bucket_name , Key = key , Callback = None if hide_progress else UploadProgressBar ( path = local_path , logger = logger ), ) def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from S3 (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) self . _client . delete_object ( Bucket = self . bucket_name , Key = i . remote_path ) LargeFileMongo # Bases: LargeFileBase LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_mongo.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 class LargeFileMongo ( LargeFileBase ): \"\"\"LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" mongo_connection_string = None mongo_database = None @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () @cached_property def _client ( self ) -> GridFSBucket : if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling LargeFileMongo.configure_credentials or set offline_mode=True in the constructor.\" ) db : Database = MongoClient ( self . mongo_connection_string )[ self . mongo_database ] return GridFSBucket ( db ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching Mongo (GridFS) versions of { self . _name } \" ) return [ DataInstance ( name = MONGO_NAME_VERSION_SEPARATOR . join ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = ( f . _id , f . length ), origin = \"mongodb\" , ) for f in self . _client . find ( { \"filename\" : re . compile ( re . escape ( self . _name + MONGO_NAME_VERSION_SEPARATOR ) + \".*\" ) } ) ] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path [ 0 ] } from Mongo (GridFS)\" ) progress = ( DownloadProgressBar ( name = str ( remote_path [ 0 ]), size = remote_path [ 1 ], logger = logger ) if not hide_progress else None ) with self . _client . open_download_stream ( remote_path [ 0 ]) as stream : with open ( local_path , \"wb\" ) as f : while True : content = stream . read ( DEFAULT_CHUNK_SIZE ) f . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _upload ( self , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Uploading { local_path } to Mongo (GridFS)\" ) progress = ( UploadProgressBar ( path = local_path , logger = logger ) if not hide_progress else None ) with self . _client . open_upload_stream ( f \" { self . _name }{ MONGO_NAME_VERSION_SEPARATOR }{ self . version } \" ) as stream : with open ( local_path , \"rb\" ) as f : while True : content = f . read ( DEFAULT_CHUNK_SIZE ) stream . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from MongoDB (GridFS) (keep_last_n= { self . _keep_last_n } ): { i . name }{ MONGO_NAME_VERSION_SEPARATOR }{ i . version } \" ) self . _client . delete ( i . remote_path [ 0 ]) LargeFileBase # Bases: ABC Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase . cache_path = Path ( \".cache\" ) >>> LargeFileBase . max_cache_size = \"30GB\" Attributes: Name Type Description initialized Tell whether configure_credentials or configure_credentials_from_file has been already called. cache_path Storage location for cached files. max_cache_size Optional [ str ] Delete files until the folder at cache_path is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to None for no automatic cache-pruning. Source code in great_ai/large_file/large_file/large_file_base.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 class LargeFileBase ( ABC ): \"\"\"Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase.cache_path = Path(\".cache\") >>> LargeFileBase.max_cache_size = \"30GB\" Attributes: initialized: Tell whether `configure_credentials` or `configure_credentials_from_file` has been already called. cache_path: Storage location for cached files. max_cache_size: Delete files until the folder at `cache_path` is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to `None` for no automatic cache-pruning. \"\"\" initialized = False cache_path = Path ( \".cache\" ) max_cache_size : Optional [ str ] = \"30GB\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , cache_only_mode : bool = False , ): self . _name = re . sub ( r \"[^a-zA-Z0-9._-]+\" , \"\" , name ) self . _version = version self . _mode = mode self . _keep_last_n = keep_last_n self . _cache_only_mode = cache_only_mode self . _buffering = buffering self . _encoding = encoding if errors is not None and sys . version_info [ 1 ] < 8 : raise RuntimeError ( \"The `errors` kwarg is only supported in 3.8 <= Python versions.\" ) self . _errors = errors self . _newline = newline LargeFileBase . cache_path . mkdir ( parents = True , exist_ok = True ) self . _find_instances () self . _check_mode_and_set_version () @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True def __enter__ ( self ) -> IO : params = dict ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , delete = False , prefix = \"large_file-\" , ) if sys . version_info [ 1 ] >= 8 : params [ \"errors\" ] = self . _errors self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( ** params ) # type: ignore if \"w\" in self . _mode else open ( self . get (), mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , ) ) return self . _file def __exit__ ( self , type : Optional [ Type [ BaseException ]], exc : Optional [ BaseException ], traceback : Optional [ TracebackType ], ) -> Literal [ False ]: self . _file . close () if type is None : if \"w\" in self . _mode : self . push ( Path ( self . _file . name )) os . unlink ( self . _file . name ) else : logger . exception ( \"Could not finish operation.\" ) return False @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions () @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache () @property def _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" def _find_instances ( self ) -> None : if self . _cache_only_mode : self . _instances = self . _find_instances_from_cache () else : self . _instances = self . _find_remote_instances () self . _instances = sorted ( self . _instances , key = lambda i : i . version ) def _find_instances_from_cache ( self ) -> List [ DataInstance ]: logger . info ( f \"Fetching cached versions of { self . _name } \" ) candidates = [ DataInstance ( name = CACHE_NAME_VERSION_SEPARATOR . join ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = f , ) for f in self . cache_path . glob ( f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR } *\" ) ] return [ c for c in candidates if c . name == self . _name ] def _check_mode_and_set_version ( self ) -> None : if \"+\" in self . _mode : raise ValueError ( f \"File mode ` { self . _mode } ` is not allowed3, remove the `+`.\" ) if \"w\" in self . _mode : if self . _version is not None : raise ValueError ( \"Providing a version is not allowed in write mode.\" ) self . _version = self . _instances [ - 1 ] . version + 1 if self . _instances else 0 elif \"r\" in self . _mode : if not self . _instances : raise FileNotFoundError ( f \"File { self . _name } not found. No versions are available.\" ) if self . _version is None : self . _version = self . _instances [ - 1 ] . version logger . info ( f \"Latest version of { self . _name } is { self . _version } \" + f \"(from versions: { self . versions_pretty } )\" ) elif self . _version not in [ i . version for i in self . _instances ]: raise FileNotFoundError ( f \"File { self . _name } not found with version { self . _version } . \" + f \"(from versions: { self . versions_pretty } )\" ) else : raise ValueError ( \"Unsupported file mode.\" ) def _prune_cache ( self ) -> None : self . cache_path . mkdir ( parents = True , exist_ok = True ) if self . max_cache_size is None : return allowed_size = human_readable_to_byte ( self . max_cache_size ) assert allowed_size >= 0 least_recently_read = sorted ( [ f for f in self . cache_path . glob ( \"*\" )], key = lambda f : f . stat () . st_atime ) while sum ( os . path . getsize ( f ) for f in least_recently_read ) > allowed_size : file = least_recently_read . pop ( 0 ) logger . info ( f \"Deleting file from cache to meet quota (max_cache_size= { self . max_cache_size } ): { file } \" ) os . unlink ( file ) @abstractmethod def _find_remote_instances ( self ) -> List [ DataInstance ]: pass @abstractmethod def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _delete_old_remote_versions ( self ) -> None : pass clean_up () # Delete local and remote versions according to currently set cache and retention policy. Source code in great_ai/large_file/large_file/large_file_base.py 251 252 253 254 255 def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache () configure_credentials ( ** kwargs ) classmethod # Configure required credentials for the LargeFile backend. Source code in great_ai/large_file/large_file/large_file_base.py 103 104 105 106 107 @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True configure_credentials_from_file ( secrets ) classmethod # Load file and feed its content to configure_credentials . Extra keys are ignored. Source code in great_ai/large_file/large_file/large_file_base.py 89 90 91 92 93 94 95 96 97 98 99 100 101 @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) delete () # Delete all versions of the files under this key . Source code in great_ai/large_file/large_file/large_file_base.py 240 241 242 243 244 def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions () get ( hide_progress = False ) cached # Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination push ( path , hide_progress = False ) # Upload a file (or directory) as a new version of key . The file/directory is compressed before upload. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () version () property # Numeric version of the file proxied by this LargeFile instance. Source code in great_ai/large_file/large_file/large_file_base.py 154 155 156 157 158 @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version ) versions_pretty () property # Formatted string of all available versions. Source code in great_ai/large_file/large_file/large_file_base.py 246 247 248 249 @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances ))","title":"LargeFile"},{"location":"reference/large-file/#largefile","text":"from great_ai.large_file import * For more details about using LargeFiles, check out the how to guide .","title":"LargeFile"},{"location":"reference/large-file/#great_ai.large_file.LargeFileLocal","text":"Bases: LargeFileBase LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal . cache_path = Path ( \".cache\" ) >>> LargeFileLocal . max_cache_size = \"30GB\" >>> with LargeFileLocal ( \"my_test.txt\" , \"w\" , keep_last_n = 2 ) as f : ... f . write ( 'test' ) 4 >>> with LargeFileLocal ( \"my_test.txt\" ) as f : ... print ( f . read ()) test Source code in great_ai/large_file/large_file/large_file_local.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 class LargeFileLocal ( LargeFileBase ): \"\"\"LargeFile implementation using local filesystem as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. IMPORTANT: If LargeFileLocal.max_cache_size is too small, it won't be enough to store all your files and they can end up deleted. See parent for more details. Examples: >>> LargeFileLocal.cache_path = Path(\".cache\") >>> LargeFileLocal.max_cache_size = \"30GB\" >>> with LargeFileLocal(\"my_test.txt\", \"w\", keep_last_n=2) as f: ... f.write('test') 4 >>> with LargeFileLocal(\"my_test.txt\") as f: ... print(f.read()) test \"\"\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , ): super () . __init__ ( name , mode , buffering = buffering , encoding = encoding , errors = errors , newline = newline , version = version , keep_last_n = keep_last_n , cache_only_mode = True , ) super () . configure_credentials () def _find_remote_instances ( self ) -> List [ DataInstance ]: return [] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : # This will never be called because the file must be in the cache raise NotImplementedError () def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass # the \"upload\" is already done py the parent's caching mechanism def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) i . remote_path . unlink ()","title":"LargeFileLocal"},{"location":"reference/large-file/#great_ai.large_file.LargeFileS3","text":"Bases: LargeFileBase LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_s3.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 class LargeFileS3 ( LargeFileBase ): \"\"\"LargeFile implementation using S3-compatible storage as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" region_name = None access_key_id = None secret_access_key = None bucket_name = None endpoint_url = None @classmethod def configure_credentials ( # type: ignore cls , * , aws_region_name : str , aws_access_key_id : str , aws_secret_access_key : str , large_files_bucket_name : str , aws_endpoint_url : Optional [ str ] = None , ** _ : Any , ) -> None : cls . region_name = aws_region_name cls . access_key_id = aws_access_key_id cls . secret_access_key = aws_secret_access_key cls . bucket_name = large_files_bucket_name cls . endpoint_url = aws_endpoint_url super () . configure_credentials () @cached_property def _client ( self ) -> boto3 . client : if ( self . region_name is None or self . access_key_id is None or self . secret_access_key is None or self . bucket_name is None ): raise ValueError ( \"Please configure the S3 access options by calling LargeFileS3.configure_credentials or set offline_mode=True in the constructor.\" ) return boto3 . client ( \"s3\" , aws_access_key_id = self . access_key_id , aws_secret_access_key = self . secret_access_key , region_name = self . region_name , endpoint_url = self . endpoint_url , ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching S3 versions of { self . _name } \" ) found_objects = self . _client . list_objects_v2 ( Bucket = self . bucket_name , Prefix = self . _name ) return ( [ DataInstance ( name = o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ], version = int ( o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = o [ \"Key\" ], ) for o in found_objects [ \"Contents\" ] if o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ] == self . _name ] if \"Contents\" in found_objects else [] ) def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path } from S3\" ) size = self . _client . head_object ( Bucket = self . bucket_name , Key = remote_path )[ \"ContentLength\" ] self . _client . download_file ( Bucket = self . bucket_name , Key = remote_path , Filename = str ( local_path ), Callback = None if hide_progress else DownloadProgressBar ( name = str ( remote_path ), size = size , logger = logger ), ) def _upload ( self , local_path : Path , hide_progress : bool ) -> None : key = f \" { self . _name } / { self . version } \" logger . info ( f \"Uploading { self . _local_name } to S3 as { key } \" ) self . _client . upload_file ( Filename = str ( local_path ), Bucket = self . bucket_name , Key = key , Callback = None if hide_progress else UploadProgressBar ( path = local_path , logger = logger ), ) def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from S3 (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) self . _client . delete_object ( Bucket = self . bucket_name , Key = i . remote_path )","title":"LargeFileS3"},{"location":"reference/large-file/#great_ai.large_file.LargeFileMongo","text":"Bases: LargeFileBase LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. See parent for more details. Source code in great_ai/large_file/large_file/large_file_mongo.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 class LargeFileMongo ( LargeFileBase ): \"\"\"LargeFile implementation using GridFS (MongoDB) as a backend. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. See parent for more details. \"\"\" mongo_connection_string = None mongo_database = None @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () @cached_property def _client ( self ) -> GridFSBucket : if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling LargeFileMongo.configure_credentials or set offline_mode=True in the constructor.\" ) db : Database = MongoClient ( self . mongo_connection_string )[ self . mongo_database ] return GridFSBucket ( db ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching Mongo (GridFS) versions of { self . _name } \" ) return [ DataInstance ( name = MONGO_NAME_VERSION_SEPARATOR . join ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = ( f . _id , f . length ), origin = \"mongodb\" , ) for f in self . _client . find ( { \"filename\" : re . compile ( re . escape ( self . _name + MONGO_NAME_VERSION_SEPARATOR ) + \".*\" ) } ) ] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path [ 0 ] } from Mongo (GridFS)\" ) progress = ( DownloadProgressBar ( name = str ( remote_path [ 0 ]), size = remote_path [ 1 ], logger = logger ) if not hide_progress else None ) with self . _client . open_download_stream ( remote_path [ 0 ]) as stream : with open ( local_path , \"wb\" ) as f : while True : content = stream . read ( DEFAULT_CHUNK_SIZE ) f . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _upload ( self , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Uploading { local_path } to Mongo (GridFS)\" ) progress = ( UploadProgressBar ( path = local_path , logger = logger ) if not hide_progress else None ) with self . _client . open_upload_stream ( f \" { self . _name }{ MONGO_NAME_VERSION_SEPARATOR }{ self . version } \" ) as stream : with open ( local_path , \"rb\" ) as f : while True : content = f . read ( DEFAULT_CHUNK_SIZE ) stream . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from MongoDB (GridFS) (keep_last_n= { self . _keep_last_n } ): { i . name }{ MONGO_NAME_VERSION_SEPARATOR }{ i . version } \" ) self . _client . delete ( i . remote_path [ 0 ])","title":"LargeFileMongo"},{"location":"reference/large-file/#great_ai.large_file.LargeFileBase","text":"Bases: ABC Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of open() . With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase . cache_path = Path ( \".cache\" ) >>> LargeFileBase . max_cache_size = \"30GB\" Attributes: Name Type Description initialized Tell whether configure_credentials or configure_credentials_from_file has been already called. cache_path Storage location for cached files. max_cache_size Optional [ str ] Delete files until the folder at cache_path is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to None for no automatic cache-pruning. Source code in great_ai/large_file/large_file/large_file_base.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 class LargeFileBase ( ABC ): \"\"\"Base for LargeFile implementations with different backends. Store large files remotely using the familiar API of `open()`. With built-in versioning, pruning and local cache. By default, files are stored in the \".cache\" folder and the least recently used is deleted after the overall size reaches 30 GBs. Examples: >>> LargeFileBase.cache_path = Path(\".cache\") >>> LargeFileBase.max_cache_size = \"30GB\" Attributes: initialized: Tell whether `configure_credentials` or `configure_credentials_from_file` has been already called. cache_path: Storage location for cached files. max_cache_size: Delete files until the folder at `cache_path` is smaller than this value. Examples: \"5 GB\", \"10MB\", \"0.3 TB\". Set to `None` for no automatic cache-pruning. \"\"\" initialized = False cache_path = Path ( \".cache\" ) max_cache_size : Optional [ str ] = \"30GB\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , cache_only_mode : bool = False , ): self . _name = re . sub ( r \"[^a-zA-Z0-9._-]+\" , \"\" , name ) self . _version = version self . _mode = mode self . _keep_last_n = keep_last_n self . _cache_only_mode = cache_only_mode self . _buffering = buffering self . _encoding = encoding if errors is not None and sys . version_info [ 1 ] < 8 : raise RuntimeError ( \"The `errors` kwarg is only supported in 3.8 <= Python versions.\" ) self . _errors = errors self . _newline = newline LargeFileBase . cache_path . mkdir ( parents = True , exist_ok = True ) self . _find_instances () self . _check_mode_and_set_version () @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True def __enter__ ( self ) -> IO : params = dict ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , delete = False , prefix = \"large_file-\" , ) if sys . version_info [ 1 ] >= 8 : params [ \"errors\" ] = self . _errors self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( ** params ) # type: ignore if \"w\" in self . _mode else open ( self . get (), mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , ) ) return self . _file def __exit__ ( self , type : Optional [ Type [ BaseException ]], exc : Optional [ BaseException ], traceback : Optional [ TracebackType ], ) -> Literal [ False ]: self . _file . close () if type is None : if \"w\" in self . _mode : self . push ( Path ( self . _file . name )) os . unlink ( self . _file . name ) else : logger . exception ( \"Could not finish operation.\" ) return False @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions () @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache () @property def _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" def _find_instances ( self ) -> None : if self . _cache_only_mode : self . _instances = self . _find_instances_from_cache () else : self . _instances = self . _find_remote_instances () self . _instances = sorted ( self . _instances , key = lambda i : i . version ) def _find_instances_from_cache ( self ) -> List [ DataInstance ]: logger . info ( f \"Fetching cached versions of { self . _name } \" ) candidates = [ DataInstance ( name = CACHE_NAME_VERSION_SEPARATOR . join ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = f , ) for f in self . cache_path . glob ( f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR } *\" ) ] return [ c for c in candidates if c . name == self . _name ] def _check_mode_and_set_version ( self ) -> None : if \"+\" in self . _mode : raise ValueError ( f \"File mode ` { self . _mode } ` is not allowed3, remove the `+`.\" ) if \"w\" in self . _mode : if self . _version is not None : raise ValueError ( \"Providing a version is not allowed in write mode.\" ) self . _version = self . _instances [ - 1 ] . version + 1 if self . _instances else 0 elif \"r\" in self . _mode : if not self . _instances : raise FileNotFoundError ( f \"File { self . _name } not found. No versions are available.\" ) if self . _version is None : self . _version = self . _instances [ - 1 ] . version logger . info ( f \"Latest version of { self . _name } is { self . _version } \" + f \"(from versions: { self . versions_pretty } )\" ) elif self . _version not in [ i . version for i in self . _instances ]: raise FileNotFoundError ( f \"File { self . _name } not found with version { self . _version } . \" + f \"(from versions: { self . versions_pretty } )\" ) else : raise ValueError ( \"Unsupported file mode.\" ) def _prune_cache ( self ) -> None : self . cache_path . mkdir ( parents = True , exist_ok = True ) if self . max_cache_size is None : return allowed_size = human_readable_to_byte ( self . max_cache_size ) assert allowed_size >= 0 least_recently_read = sorted ( [ f for f in self . cache_path . glob ( \"*\" )], key = lambda f : f . stat () . st_atime ) while sum ( os . path . getsize ( f ) for f in least_recently_read ) > allowed_size : file = least_recently_read . pop ( 0 ) logger . info ( f \"Deleting file from cache to meet quota (max_cache_size= { self . max_cache_size } ): { file } \" ) os . unlink ( file ) @abstractmethod def _find_remote_instances ( self ) -> List [ DataInstance ]: pass @abstractmethod def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _delete_old_remote_versions ( self ) -> None : pass","title":"LargeFileBase"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.clean_up","text":"Delete local and remote versions according to currently set cache and retention policy. Source code in great_ai/large_file/large_file/large_file_base.py 251 252 253 254 255 def clean_up ( self ) -> None : \"\"\"Delete local and remote versions according to currently set cache and retention policy.\"\"\" self . _delete_old_remote_versions () self . _prune_cache ()","title":"clean_up()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.configure_credentials","text":"Configure required credentials for the LargeFile backend. Source code in great_ai/large_file/large_file/large_file_base.py 103 104 105 106 107 @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : \"\"\"Configure required credentials for the LargeFile backend.\"\"\" cls . initialized = True","title":"configure_credentials()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.configure_credentials_from_file","text":"Load file and feed its content to configure_credentials . Extra keys are ignored. Source code in great_ai/large_file/large_file/large_file_base.py 89 90 91 92 93 94 95 96 97 98 99 100 101 @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : \"\"\"Load file and feed its content to `configure_credentials`. Extra keys are ignored. \"\"\" if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()})","title":"configure_credentials_from_file()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.delete","text":"Delete all versions of the files under this key . Source code in great_ai/large_file/large_file/large_file_base.py 240 241 242 243 244 def delete ( self ) -> None : \"\"\"Delete all versions of the files under this `key`.\"\"\" self . _keep_last_n = 0 self . _delete_old_remote_versions ()","title":"delete()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.get","text":"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : \"\"\"Return path to the proxy of a file (or directory). If not available in the local cache, an attempt is made to download it. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination","title":"get()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.push","text":"Upload a file (or directory) as a new version of key . The file/directory is compressed before upload. Parameters: Name Type Description Default hide_progress bool Do not show a progress update after each 10% of progress. False Source code in great_ai/large_file/large_file/large_file_base.py 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : \"\"\"Upload a file (or directory) as a new version of `key`. The file/directory is compressed before upload. Args: hide_progress: Do not show a progress update after each 10% of progress. \"\"\" if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up ()","title":"push()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.version","text":"Numeric version of the file proxied by this LargeFile instance. Source code in great_ai/large_file/large_file/large_file_base.py 154 155 156 157 158 @property def version ( self ) -> int : \"\"\"Numeric version of the file proxied by this LargeFile instance.\"\"\" return cast ( int , self . _version )","title":"version()"},{"location":"reference/large-file/#great_ai.large_file.large_file.large_file_base.LargeFileBase.versions_pretty","text":"Formatted string of all available versions. Source code in great_ai/large_file/large_file/large_file_base.py 246 247 248 249 @property def versions_pretty ( self ) -> str : \"\"\"Formatted string of all available versions.\"\"\" return \", \" . join (( str ( i . version ) for i in self . _instances ))","title":"versions_pretty()"},{"location":"reference/utilities/","text":"Utilities # from great_ai.utilities import * NLP tools # Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well. clean ( text , ignore_xml = False , ignore_latex = False , remove_brackets = False , convert_to_ascii = False ) # Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... ignore_xml = True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... ignore_xml=True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text get_sentences ( text , ignore_partial = False , true_case = False , remove_punctuation = False ) # Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a punctuation. False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences predict_language ( text ) # Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag () english_name_of_language ( language_code ) # Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.language.predict_language.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name () is_english ( language_code ) # Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.language.predict_language.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15 evaluate_ranking ( expected , actual_scores , target_recall , title = '' , disable_interpolation = False , axes = None , output_svg = None , reverse_order = False , plot = True ) # Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for plotting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for plotting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 10 , 10 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] sorted_expected_actual = sorted ( zip ( binarized_expected , actual_scores ), key = lambda v : v [ 1 ], reverse = True ) precision = [] recall = [] correct = 0 for all , ( e , score ) in enumerate ( sorted_expected_actual , start = 1 ): correct += int ( e ) precision . append ( correct / all ) recall . append ( all / len ( sorted_expected_actual )) if not disable_interpolation : for j in range ( len ( precision ) - 2 , - 1 , - 1 ): precision [ j ] = max ( precision [ j ], precision [ j + 1 ]) closest_recall_index = np . argmin ( np . abs ( np . array ( recall ) - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results Parallel processing # Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess . simple_parallel_map ( func , input_values , * , chunk_size = None , concurrency = None ) # Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence generator = parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) return list ( tqdm ( generator , total = len ( input_values ), ) ) parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown () threaded_parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 ) Composable parallel processing # Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions. chunk ( values , chunk_size ) # Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result unchunk ( chunks ) # Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Similar to itertools.chain but ignores None chunks. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Similar to itertools.chain but ignores `None` chunks. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk Operations # ConfigFile # Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"\"\"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\"\"\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path . as_posix () } ) { self . _key_values } \" __init__ ( path , * , ignore_missing = False ) # Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () path () property # Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path get_logger ( name , level = logging . INFO , disable_colors = False ) # Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. \"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) try : get_ipython () # type: ignore log_format = \" %(message)s \" except NameError : # will fail outside of a notebook https://ipython.org/ log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"Utilities"},{"location":"reference/utilities/#utilities","text":"from great_ai.utilities import *","title":"Utilities"},{"location":"reference/utilities/#nlp-tools","text":"Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well.","title":"NLP tools"},{"location":"reference/utilities/#great_ai.utilities.clean.clean","text":"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '

    Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 <3

    ' , ... ignore_xml = True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '

    Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 <3

    ', ... ignore_xml=True ... ) '

    Bj\u00f6rn is happy \ud83d\ude42 lt;3

    ' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text","title":"clean()"},{"location":"reference/utilities/#great_ai.utilities.get_sentences.get_sentences","text":"Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a punctuation. False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences","title":"get_sentences()"},{"location":"reference/utilities/#great_ai.utilities.language.predict_language.predict_language","text":"Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag ()","title":"predict_language()"},{"location":"reference/utilities/#great_ai.utilities.language.english_name_of_language.english_name_of_language","text":"Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.language.predict_language.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name ()","title":"english_name_of_language()"},{"location":"reference/utilities/#great_ai.utilities.language.is_english.is_english","text":"Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.language.predict_language.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15","title":"is_english()"},{"location":"reference/utilities/#great_ai.utilities.evaluate_ranking.evaluate_ranking.evaluate_ranking","text":"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for plotting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for plotting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 10 , 10 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] sorted_expected_actual = sorted ( zip ( binarized_expected , actual_scores ), key = lambda v : v [ 1 ], reverse = True ) precision = [] recall = [] correct = 0 for all , ( e , score ) in enumerate ( sorted_expected_actual , start = 1 ): correct += int ( e ) precision . append ( correct / all ) recall . append ( all / len ( sorted_expected_actual )) if not disable_interpolation : for j in range ( len ( precision ) - 2 , - 1 , - 1 ): precision [ j ] = max ( precision [ j ], precision [ j + 1 ]) closest_recall_index = np . argmin ( np . abs ( np . array ( recall ) - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results","title":"evaluate_ranking()"},{"location":"reference/utilities/#parallel-processing","text":"Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess .","title":"Parallel processing"},{"location":"reference/utilities/#great_ai.utilities.simple_parallel_map","text":"Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence generator = parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) return list ( tqdm ( generator , total = len ( input_values ), ) )","title":"simple_parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.parallel_map.parallel_map.parallel_map","text":"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some start-up cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown ()","title":"parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.threaded_parallel_map","text":"Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 )","title":"threaded_parallel_map()"},{"location":"reference/utilities/#composable-parallel-processing","text":"Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions.","title":"Composable parallel processing"},{"location":"reference/utilities/#great_ai.utilities.chunk.chunk","text":"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result","title":"chunk()"},{"location":"reference/utilities/#great_ai.utilities.unchunk.unchunk","text":"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Similar to itertools.chain but ignores None chunks. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Similar to itertools.chain but ignores `None` chunks. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk","title":"unchunk()"},{"location":"reference/utilities/#operations","text":"","title":"Operations"},{"location":"reference/utilities/#great_ai.utilities.ConfigFile","text":"Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"\"\"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\"\"\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path . as_posix () } ) { self . _key_values } \"","title":"ConfigFile"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.__init__","text":"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse ()","title":"__init__()"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.path","text":"Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path","title":"path()"},{"location":"reference/utilities/#great_ai.utilities.get_logger","text":"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. \"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) try : get_ipython () # type: ignore log_format = \" %(message)s \" except NameError : # will fail outside of a notebook https://ipython.org/ log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"get_logger()"},{"location":"reference/views/","text":"View models # Trace # Bases: Generic [ T ] , HashableBaseModel Universal structure for storing prediction traces and training data. Attributes: Name Type Description trace_id str UUID4 identifier for uniquely referring to a trace. created str Timestamp of its (original) construction. original_execution_time_ms float Wall-time elapsed while its generating TracingContext was alive. logged_values Dict [ str , Any ] Values persisted through using @parameter or log_metric() . models List [ Model ] Marks left by each encountered @use_model decorated function. exception Optional [ str ] Exception description if any was encountered. output Optional [ T ] Return value of the function wrapped by GreatAI. feedback Any Feedback obtained using the REST API of add_ground_truth . tags List [ str ] Tags used for filtering traces. Contains the name of the original function, value of ENVIRONMENT , its split if has any, and either ground_truth or online depending on the origin of the Trace. Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 class Trace ( Generic [ T ], HashableBaseModel ): \"\"\"Universal structure for storing prediction traces and training data. Attributes: trace_id: UUID4 identifier for uniquely referring to a trace. created: Timestamp of its (original) construction. original_execution_time_ms: Wall-time elapsed while its generating TracingContext was alive. logged_values: Values persisted through using `@parameter` or `log_metric()`. models: Marks left by each encountered `@use_model` decorated function. exception: Exception description if any was encountered. output: Return value of the function wrapped by GreatAI. feedback: Feedback obtained using the REST API of `add_ground_truth`. tags: Tags used for filtering traces. Contains the name of the original function, value of `ENVIRONMENT`, its split if has any, and either `ground_truth` or `online` depending on the origin of the Trace. \"\"\" trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : v if ( isinstance ( v , float ) or isinstance ( v , int )) else pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"\"\"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) . replace ( '{ ' , '{' , 1 ) } )\"\"\" RouteConfig # Bases: BaseModel Source code in great_ai/views/route_config.py 4 5 6 7 8 9 10 class RouteConfig ( BaseModel ): prediction_endpoint_enabled : bool = True docs_endpoints_enabled : bool = True dashboard_enabled : bool = True feedback_endpoints_enabled : bool = True trace_endpoints_enabled : bool = True meta_endpoints_enabled : bool = True ClassificationOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/classification_output.py 6 7 8 9 class ClassificationOutput ( HashableBaseModel ): label : Union [ str , int ] confidence : float explanation : Optional [ Any ] MultiLabelClassificationOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/multi_label_classification_output.py 7 8 class MultiLabelClassificationOutput ( HashableBaseModel ): labels : List [ ClassificationOutput ] = [] RegressionOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/regression_output.py 6 7 8 class RegressionOutput ( HashableBaseModel ): value : Union [ int , float ] explanation : Optional [ Any ] SequenceLabelingOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/sequence_labeling_output.py 15 16 17 class SequenceLabelingOutput ( HashableBaseModel ): labeled_tokens : List [ LabeledToken ] explanation : Optional [ Any ]","title":"View models"},{"location":"reference/views/#view-models","text":"","title":"View models"},{"location":"reference/views/#great_ai.Trace","text":"Bases: Generic [ T ] , HashableBaseModel Universal structure for storing prediction traces and training data. Attributes: Name Type Description trace_id str UUID4 identifier for uniquely referring to a trace. created str Timestamp of its (original) construction. original_execution_time_ms float Wall-time elapsed while its generating TracingContext was alive. logged_values Dict [ str , Any ] Values persisted through using @parameter or log_metric() . models List [ Model ] Marks left by each encountered @use_model decorated function. exception Optional [ str ] Exception description if any was encountered. output Optional [ T ] Return value of the function wrapped by GreatAI. feedback Any Feedback obtained using the REST API of add_ground_truth . tags List [ str ] Tags used for filtering traces. Contains the name of the original function, value of ENVIRONMENT , its split if has any, and either ground_truth or online depending on the origin of the Trace. Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 class Trace ( Generic [ T ], HashableBaseModel ): \"\"\"Universal structure for storing prediction traces and training data. Attributes: trace_id: UUID4 identifier for uniquely referring to a trace. created: Timestamp of its (original) construction. original_execution_time_ms: Wall-time elapsed while its generating TracingContext was alive. logged_values: Values persisted through using `@parameter` or `log_metric()`. models: Marks left by each encountered `@use_model` decorated function. exception: Exception description if any was encountered. output: Return value of the function wrapped by GreatAI. feedback: Feedback obtained using the REST API of `add_ground_truth`. tags: Tags used for filtering traces. Contains the name of the original function, value of `ENVIRONMENT`, its split if has any, and either `ground_truth` or `online` depending on the origin of the Trace. \"\"\" trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : v if ( isinstance ( v , float ) or isinstance ( v , int )) else pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"\"\"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) . replace ( '{ ' , '{' , 1 ) } )\"\"\"","title":"Trace"},{"location":"reference/views/#great_ai.RouteConfig","text":"Bases: BaseModel Source code in great_ai/views/route_config.py 4 5 6 7 8 9 10 class RouteConfig ( BaseModel ): prediction_endpoint_enabled : bool = True docs_endpoints_enabled : bool = True dashboard_enabled : bool = True feedback_endpoints_enabled : bool = True trace_endpoints_enabled : bool = True meta_endpoints_enabled : bool = True","title":"RouteConfig"},{"location":"reference/views/#great_ai.ClassificationOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/classification_output.py 6 7 8 9 class ClassificationOutput ( HashableBaseModel ): label : Union [ str , int ] confidence : float explanation : Optional [ Any ]","title":"ClassificationOutput"},{"location":"reference/views/#great_ai.MultiLabelClassificationOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/multi_label_classification_output.py 7 8 class MultiLabelClassificationOutput ( HashableBaseModel ): labels : List [ ClassificationOutput ] = []","title":"MultiLabelClassificationOutput"},{"location":"reference/views/#great_ai.RegressionOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/regression_output.py 6 7 8 class RegressionOutput ( HashableBaseModel ): value : Union [ int , float ] explanation : Optional [ Any ]","title":"RegressionOutput"},{"location":"reference/views/#great_ai.SequenceLabelingOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/sequence_labeling_output.py 15 16 17 class SequenceLabelingOutput ( HashableBaseModel ): labeled_tokens : List [ LabeledToken ] explanation : Optional [ Any ]","title":"SequenceLabelingOutput"},{"location":"surveys/surveys/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); In [1]: Copied! import pandas as pd best_practices = pd . read_csv ( \"Best practices assessment.csv\" ) best_practices import pandas as pd best_practices = pd.read_csv(\"Best practices assessment.csv\") best_practices Out[1]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } How many years of software engineering experience do you have? How many years of data science experience do you have? Write reusable scripts for data cleaning and merging Make datasets available on shared infrastructure Use versioning for data, model, configurations and training scripts Continuously monitor the behaviour of deployed models Log production predictions with the model\u2019s version and input data Store models in a single format for ease of use Equip with web interface, package image, provide REST API Provide simple API for serving batch and real-time requests Integration with existing data infrastructure Querying, visualising and understanding metrics and event logging Allow experimentation with the inference code Keep the model\u2019s API and documentation together Parallelise feature extraction Cache predictions Async support for top-down chaining models 0 3 1 Strongly agree Agree Strongly agree Neither agree nor disagree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Neither agree nor disagree Strongly agree 1 6 2 Neither agree nor disagree Agree Neither agree nor disagree Agree Agree Neither agree nor disagree Disagree Strongly disagree Disagree Strongly disagree Disagree Strongly disagree Disagree Strongly agree Strongly disagree 2 1 5 Strongly disagree Disagree Disagree Strongly disagree Disagree Strongly disagree Strongly disagree Neither agree nor disagree Strongly disagree Disagree Neither agree nor disagree Agree Strongly disagree Strongly disagree Disagree 3 3 3 Agree Agree Disagree Neither agree nor disagree Agree Not applicable Not applicable Neither agree nor disagree Agree Disagree Agree Strongly agree Not applicable Neither agree nor disagree Agree 4 1 7 Neither agree nor disagree Disagree Neither agree nor disagree Disagree Strongly disagree Not applicable Not applicable Disagree Strongly disagree Disagree Disagree Neither agree nor disagree Strongly disagree Strongly agree Not applicable 5 6 0 Strongly agree Strongly agree Agree Neither agree nor disagree Agree Strongly agree Agree Strongly agree Agree Strongly agree Disagree Strongly agree Agree Strongly agree Not applicable 6 2 2 Disagree Neither agree nor disagree Agree Strongly agree Neither agree nor disagree Disagree Strongly agree Disagree Disagree Agree Neither agree nor disagree Neither agree nor disagree Not applicable Disagree Strongly agree 7 1 2 Disagree Neither agree nor disagree Disagree Agree Disagree Strongly disagree Strongly agree Strongly agree Strongly disagree Disagree Agree Strongly disagree Not applicable Strongly disagree Strongly disagree 8 0 1 Strongly disagree Disagree Strongly disagree Disagree Strongly disagree Disagree Agree Strongly disagree Disagree Strongly disagree Disagree Strongly disagree Disagree Disagree Strongly disagree 9 7 1 Strongly agree Strongly agree Agree Strongly agree Agree Agree Strongly agree Strongly disagree Strongly agree Not applicable Agree Agree Strongly agree Strongly agree Agree In [2]: Copied! m = { \"Strongly agree\" : 4 / 4 , \"Agree\" : 3 / 4 , \"Neither agree nor disagree\" : 2 / 4 , \"Disagree\" : 1 / 4 , \"Strongly disagree\" : 0 / 4 , } best_practices_dicts = [ v . to_dict () for _ , v in best_practices . iterrows ()] best_practice_score_ds = [] best_practice_score_se = [] for d in best_practices_dicts : ds_experience = int ( d [ \"How many years of data science experience do you have?\" ]) del d [ \"How many years of data science experience do you have?\" ] se_experience = int ( d [ \"How many years of software engineering experience do you have?\" ] ) del d [ \"How many years of software engineering experience do you have?\" ] scores = [ m [ v ] for v in d . values () if v != \"Not applicable\" ] score = sum ( scores ) / len ( scores ) best_practice_score_ds . append (( ds_experience , score )) best_practice_score_se . append (( se_experience , score )) best_practices [ \"How many years of software engineering experience do you have?\" ] . median (), best_practices [ \"How many years of data science experience do you have?\" ] . median () m = { \"Strongly agree\": 4 / 4, \"Agree\": 3 / 4, \"Neither agree nor disagree\": 2 / 4, \"Disagree\": 1 / 4, \"Strongly disagree\": 0 / 4, } best_practices_dicts = [v.to_dict() for _, v in best_practices.iterrows()] best_practice_score_ds = [] best_practice_score_se = [] for d in best_practices_dicts: ds_experience = int(d[\"How many years of data science experience do you have?\"]) del d[\"How many years of data science experience do you have?\"] se_experience = int( d[\"How many years of software engineering experience do you have?\"] ) del d[\"How many years of software engineering experience do you have?\"] scores = [m[v] for v in d.values() if v != \"Not applicable\"] score = sum(scores) / len(scores) best_practice_score_ds.append((ds_experience, score)) best_practice_score_se.append((se_experience, score)) best_practices[ \"How many years of software engineering experience do you have?\" ].median(), best_practices[ \"How many years of data science experience do you have?\" ].median() Out[2]: (2.5, 2.0) In [3]: Copied! import matplotlib.pyplot as plt from scipy import stats best_practice_score_ds = sorted ( best_practice_score_ds ) plt . scatter ( [ x for x , y in best_practice_score_ds ], [ y for x , y in best_practice_score_ds ] ) stats . pearsonr ( [ x for x , y in best_practice_score_ds ], [ y for x , y in best_practice_score_ds ] ) import matplotlib.pyplot as plt from scipy import stats best_practice_score_ds = sorted(best_practice_score_ds) plt.scatter( [x for x, y in best_practice_score_ds], [y for x, y in best_practice_score_ds] ) stats.pearsonr( [x for x, y in best_practice_score_ds], [y for x, y in best_practice_score_ds] ) Out[3]: (-0.5440527060340572, 0.10399880919437814) In [4]: Copied! import re best_practice_score_se = sorted ( best_practice_score_se ) % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 24 plt . rcParams [ \"figure.figsize\" ] = ( 14 , 10 ) X = [ x for x , y in best_practice_score_se ] Y = [ y for x , y in best_practice_score_se ] sc = plt . scatter ( X , Y , c = \"black\" , s = [ x * 50 for x , y in best_practice_score_ds ]) plt . ylabel ( \"Ratio of implemented deployment best practices\" ) plt . xlabel ( \"Years of professional software engineering experience\" ) def best_fit ( X , Y ): xbar = sum ( X ) / len ( X ) ybar = sum ( Y ) / len ( Y ) n = len ( X ) numer = sum ([ xi * yi for xi , yi in zip ( X , Y )]) - n * xbar * ybar denum = sum ([ xi ** 2 for xi in X ]) - n * xbar ** 2 b = numer / denum a = ybar - b * xbar return a , b a , b = best_fit ( X , Y ) yfit = [ a + b * xi for xi in X ] plt . plot ( X , yfit , \"b\" , label = \"Best fit\" ) plt . legend ( sc . legend_elements ( \"sizes\" , num = 5 )[ 0 ], [ re . sub ( r \"[^\\d]*(\\d+)[^\\d]*\" , lambda v : f \" { int ( v . group ( 1 )) // 40 } years (DS)\" , t ) for t in sc . legend_elements ( \"sizes\" , num = 5 )[ 1 ] ], ) plt . tight_layout () plt . savefig ( \"best-practices\" , dpi = 150 ) stats . pearsonr ( [ x for x , y in best_practice_score_se ], [ y for x , y in best_practice_score_se ] ) import re best_practice_score_se = sorted(best_practice_score_se) %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 24 plt.rcParams[\"figure.figsize\"] = (14, 10) X = [x for x, y in best_practice_score_se] Y = [y for x, y in best_practice_score_se] sc = plt.scatter(X, Y, c=\"black\", s=[x * 50 for x, y in best_practice_score_ds]) plt.ylabel(\"Ratio of implemented deployment best practices\") plt.xlabel(\"Years of professional software engineering experience\") def best_fit(X, Y): xbar = sum(X) / len(X) ybar = sum(Y) / len(Y) n = len(X) numer = sum([xi * yi for xi, yi in zip(X, Y)]) - n * xbar * ybar denum = sum([xi**2 for xi in X]) - n * xbar**2 b = numer / denum a = ybar - b * xbar return a, b a, b = best_fit(X, Y) yfit = [a + b * xi for xi in X] plt.plot(X, yfit, \"b\", label=\"Best fit\") plt.legend( sc.legend_elements(\"sizes\", num=5)[0], [ re.sub(r\"[^\\d]*(\\d+)[^\\d]*\", lambda v: f\"{int(v.group(1)) // 40} years (DS)\", t) for t in sc.legend_elements(\"sizes\", num=5)[1] ], ) plt.tight_layout() plt.savefig(\"best-practices\", dpi=150) stats.pearsonr( [x for x, y in best_practice_score_se], [y for x, y in best_practice_score_se] ) Out[4]: (0.6722543326178704, 0.03321124881554773) In [5]: Copied! tam = pd . read_csv ( \"Technology acceptance model questionnaire.csv\" ) tam tam = pd.read_csv(\"Technology acceptance model questionnaire.csv\") tam Out[5]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } I believe the use of GreatAI improves the quality of AI deployments. I believe the use of GreatAI would increase my productivity. I believe the use of GreatAI can lead to robust and trustworthy deployments. Overall, I found GreatAI useful when working with AI. I found the GreatAI easy to learn. I found it is easy to employ GreatAI in practice. I found it is easy to integrate GreatAI into an existing project. Overall, I found GreatAI easy to use. Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future. Overall, I intend to use the GreatAI in my personal or professional projects. 0 7 7 7 7 7 7 7 7 7 7 1 7 7 6 7 5 6 7 6 7 7 2 4 4 5 6 6 5 4 4 5 4 3 6 7 6 7 7 6 7 6 7 6 4 7 7 6 7 4 5 5 5 6 6 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 4 5 7 6 6 7 7 7 7 6 6 6 4 5 3 5 5 6 8 4 5 5 5 7 2 3 3 3 3 9 7 7 7 7 5 6 5 6 7 7 In [6]: Copied! import pingouin pu = [ \"I believe the use of GreatAI improves the quality of AI deployments.\" , \"I believe the use of GreatAI would increase my productivity.\" , \"I believe the use of GreatAI can lead to robust and trustworthy deployments.\" , \"Overall, I found GreatAI useful when working with AI.\" , ] pingouin . cronbach_alpha ( tam [ pu ]) import pingouin pu = [ \"I believe the use of GreatAI improves the quality of AI deployments.\", \"I believe the use of GreatAI would increase my productivity.\", \"I believe the use of GreatAI can lead to robust and trustworthy deployments.\", \"Overall, I found GreatAI useful when working with AI.\", ] pingouin.cronbach_alpha(tam[pu]) Out[6]: (0.8813771517996869, array([0.688, 0.967])) In [7]: Copied! peou = [ \"I found the GreatAI easy to learn.\" , \"I found it is easy to employ GreatAI in practice.\" , \"I found it is easy to integrate GreatAI into an existing project.\" , \"Overall, I found GreatAI easy to use.\" , ] pingouin . cronbach_alpha ( tam [ peou ]) peou = [ \"I found the GreatAI easy to learn.\", \"I found it is easy to employ GreatAI in practice.\", \"I found it is easy to integrate GreatAI into an existing project.\", \"Overall, I found GreatAI easy to use.\", ] pingouin.cronbach_alpha(tam[peou]) Out[7]: (0.7729220222793487, array([0.403, 0.937])) In [8]: Copied! itu = [ \"Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future.\" , \"Overall, I intend to use the GreatAI in my personal or professional projects.\" , ] pingouin . cronbach_alpha ( tam [ itu ]) itu = [ \"Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future.\", \"Overall, I intend to use the GreatAI in my personal or professional projects.\", ] pingouin.cronbach_alpha(tam[itu]) Out[8]: (0.9538950715421304, array([0.814, 0.989])) In [9]: Copied! tam [ \"pu\" ] = tam [ pu ] . mean ( 1 ) tam [ \"peou\" ] = tam [ peou ] . mean ( 1 ) tam [ \"itu\" ] = tam [ itu ] . mean ( 1 ) tam [[ \"pu\" , \"peou\" , \"itu\" ]] tam[\"pu\"] = tam[pu].mean(1) tam[\"peou\"] = tam[peou].mean(1) tam[\"itu\"] = tam[itu].mean(1) tam[[\"pu\", \"peou\", \"itu\"]] Out[9]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } pu peou itu 0 7.00 7.00 7.0 1 6.75 6.00 7.0 2 4.75 4.75 4.5 3 6.50 6.50 6.5 4 6.75 4.75 6.0 5 6.00 6.00 6.0 6 5.50 6.00 7.0 7 6.25 4.25 5.5 8 4.75 3.75 3.0 9 7.00 5.50 7.0 In [10]: Copied! stats . pearsonr ( tam [ \"peou\" ], tam [ \"pu\" ]) stats.pearsonr(tam[\"peou\"], tam[\"pu\"]) Out[10]: (0.5515422017785757, 0.09838124227663879) In [11]: Copied! stats . pearsonr ( tam [ \"peou\" ], tam [ \"itu\" ]) stats.pearsonr(tam[\"peou\"], tam[\"itu\"]) Out[11]: (0.8066270322592023, 0.004809023073123024) In [12]: Copied! stats . pearsonr ( tam [ \"pu\" ], tam [ \"itu\" ]) stats.pearsonr(tam[\"pu\"], tam[\"itu\"]) Out[12]: (0.7880605510627579, 0.006774486564715021)","title":"Surveys"},{"location":"tutorial/","text":"Train and deploy a SOTA model # Let's see great-ai in action by going over the life-cycle of a simple service. Objectives # You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment. Overview # You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it Summary # Training notebook # We load and preprocess the dataset while relying on great_ai.utilities.clean for doing the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) For more info, checkout the configuration how-to page . Deployment notebook # We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that the function is always provided with it. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Learn about all the features Look at more examples","title":"Tutorial overview"},{"location":"tutorial/#train-and-deploy-a-sota-model","text":"Let's see great-ai in action by going over the life-cycle of a simple service.","title":"Train and deploy a SOTA model"},{"location":"tutorial/#objectives","text":"You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment.","title":"Objectives"},{"location":"tutorial/#overview","text":"You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it","title":"Overview"},{"location":"tutorial/#summary","text":"","title":"Summary"},{"location":"tutorial/#training-notebook","text":"We load and preprocess the dataset while relying on great_ai.utilities.clean for doing the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) For more info, checkout the configuration how-to page .","title":"Training notebook"},{"location":"tutorial/#deployment-notebook","text":"We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that the function is always provided with it. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Learn about all the features Look at more examples","title":"Deployment notebook"},{"location":"tutorial/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Harden and deploy your app \u00b6 Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment best practices . In the past, this step was too often either the source of unexpected struggle, or worse, simply ignored. With GreatAI , it has become a matter of 4 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) File my-domain-predictor-9 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) predict_domain(\"Mountains are just big rocks.\") Out[2]: Trace[str]({'created': '2022-07-12T13:34:26.743292', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 9}], 'original_execution_time_ms': 6.9699, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'c80bdee3-602b-49dd-a84d-6eef80127e5a'}) Notice how the original return value is under the .output key. Additionally, a plethora of metadata has been added which will be useful later on. Running your app in development-mode is as easy as executing great-ai deploy.ipynb from your terminal. In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-12 15:34:28 | INFO | Converting notebook to Python script 2022-07-12 15:34:29 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-12 15:34:29 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-12 15:34:31 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-12 15:34:31 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-12 15:34:31 | INFO | GreatAI (v0.1.4): configured \u2705 2022-07-12 15:34:31 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-12 15:34:31 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-12 15:34:31 | INFO | \ud83d\udd29 is_production: False 2022-07-12 15:34:31 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-12 15:34:31 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-12 15:34:31 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-12 15:34:31 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-12 15:34:31 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-12 15:34:31 | INFO | Fetching cached versions of my-domain-predictor 2022-07-12 15:34:31 | INFO | Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) 2022-07-12 15:34:31 | INFO | File my-domain-predictor-9 found in cache 2022-07-12 15:34:31 | INFO | Started server process [199794] 2022-07-12 15:34:31 | INFO | Waiting for application startup. 2022-07-12 15:34:31 | INFO | Application startup complete. ^C 2022-07-12 15:34:33 | INFO | Shutting down 2022-07-12 15:34:33 | INFO | Waiting for application shutdown. 2022-07-12 15:34:33 | INFO | Application shutdown complete. 2022-07-12 15:34:33 | INFO | Finished server process [199794] Congrats, you've just created your first GreatAI service! \ud83c\udf89 Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services. There are three main approaches to deploy a GreatAI service: For more info about them, check out the deployment how-to . For more thorough examples, see the examples page . Go back to the summary \u00b6","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#harden-and-deploy-your-app","text":"Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment best practices . In the past, this step was too often either the source of unexpected struggle, or worse, simply ignored. With GreatAI , it has become a matter of 4 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) File my-domain-predictor-9 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) predict_domain(\"Mountains are just big rocks.\") Out[2]: Trace[str]({'created': '2022-07-12T13:34:26.743292', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 9}], 'original_execution_time_ms': 6.9699, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'c80bdee3-602b-49dd-a84d-6eef80127e5a'}) Notice how the original return value is under the .output key. Additionally, a plethora of metadata has been added which will be useful later on. Running your app in development-mode is as easy as executing great-ai deploy.ipynb from your terminal. In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-12 15:34:28 | INFO | Converting notebook to Python script 2022-07-12 15:34:29 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-12 15:34:29 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-12 15:34:31 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-12 15:34:31 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-12 15:34:31 | INFO | GreatAI (v0.1.4): configured \u2705 2022-07-12 15:34:31 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-12 15:34:31 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-12 15:34:31 | INFO | \ud83d\udd29 is_production: False 2022-07-12 15:34:31 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-12 15:34:31 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-12 15:34:31 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-12 15:34:31 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-12 15:34:31 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-12 15:34:31 | INFO | Fetching cached versions of my-domain-predictor 2022-07-12 15:34:31 | INFO | Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) 2022-07-12 15:34:31 | INFO | File my-domain-predictor-9 found in cache 2022-07-12 15:34:31 | INFO | Started server process [199794] 2022-07-12 15:34:31 | INFO | Waiting for application startup. 2022-07-12 15:34:31 | INFO | Application startup complete. ^C 2022-07-12 15:34:33 | INFO | Shutting down 2022-07-12 15:34:33 | INFO | Waiting for application shutdown. 2022-07-12 15:34:33 | INFO | Application shutdown complete. 2022-07-12 15:34:33 | INFO | Finished server process [199794] Congrats, you've just created your first GreatAI service! \ud83c\udf89 Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services. There are three main approaches to deploy a GreatAI service: For more info about them, check out the deployment how-to . For more thorough examples, see the examples page .","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#go-back-to-the-summary","text":"","title":"Go back to the summary"},{"location":"tutorial/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train your model \u00b6 The first step is, of course, to install great-ai in your Python environment. In [1]: Copied! % pip install great - ai > / dev / null %pip install great-ai > /dev/null Note: you may need to restart the kernel to use updated packages. First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a separate JSON document). Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [2]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. Open files in Python are iterable: in text mode, each iteration returns the next line. In [3]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f ) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8960.63it/s] Let's do the same for the test data. In [4]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f ) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:03<00:00, 6078.02it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [5]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [6]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [7]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\" . This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey zone for software engineering. In order to use this model in production, we have to make it available on some possibly shared infrastructure. In [8]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Copying file for my-domain-predictor-9 Compressing my-domain-predictor-9 Model my-domain-predictor uploaded with version 9 Out[8]: 'my-domain-predictor:9' Let's continue by finishing the deployment in the next notebook. \u00b6","title":"Train your model"},{"location":"tutorial/train/#train-your-model","text":"The first step is, of course, to install great-ai in your Python environment. In [1]: Copied! % pip install great - ai > / dev / null %pip install great-ai > /dev/null Note: you may need to restart the kernel to use updated packages. First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a separate JSON document). Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [2]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. Open files in Python are iterable: in text mode, each iteration returns the next line. In [3]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f ) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8960.63it/s] Let's do the same for the test data. In [4]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f ) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:03<00:00, 6078.02it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [5]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [6]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [7]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\" . This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey zone for software engineering. In order to use this model in production, we have to make it available on some possibly shared infrastructure. In [8]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Copying file for my-domain-predictor-9 Compressing my-domain-predictor-9 Model my-domain-predictor uploaded with version 9 Out[8]: 'my-domain-predictor:9'","title":"Train your model"},{"location":"tutorial/train/#lets-continue-by-finishing-the-deployment-in-the-next-notebook","text":"","title":"Let's continue by finishing the deployment in the next notebook."}]} \ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml index c11dc88..60799c4 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,132 +2,137 @@ None - 2022-08-11 + 2022-08-13 daily None - 2022-08-11 + 2022-08-13 daily None - 2022-08-11 + 2022-08-13 daily None - 2022-08-11 + 2022-08-13 daily None - 2022-08-11 + 2022-08-13 daily None - 2022-08-11 + 2022-08-13 daily None - 2022-08-11 + 2022-08-13 daily None - 2022-08-11 + 2022-08-13 daily None - 2022-08-11 + 2022-08-13 daily None - 2022-08-11 + 2022-08-13 daily None - 2022-08-11 + 2022-08-13 daily None - 2022-08-11 + 2022-08-13 daily None - 2022-08-11 + 2022-08-13 daily None - 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    Surveys

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+        
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    @property
-def versions_pretty(self) -> str:
-    """Formatted string of all available versions."""
-    return ", ".join((str(i.version) for i in self._instances))
+248
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    @property
+def versions_pretty(self) -> str:
+    """Formatted string of all available versions."""
+    return ", ".join((str(i.version) for i in self._instances))
    @@ -3026,6 +3026,9 @@ automatic cache-pruning.
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    How many years of software engineering experience do you have?How many years of data science experience do you have?Write reusable scripts for data cleaning and mergingMake datasets available on shared infrastructureUse versioning for data, model, configurations and training scriptsContinuously monitor the behaviour of deployed modelsLog production predictions with the model’s version and input dataStore models in a single format for ease of useEquip with web interface, package image, provide REST APIProvide simple API for serving batch and real-time requestsIntegration with existing data infrastructureQuerying, visualising and understanding metrics and event loggingAllow experimentation with the inference codeKeep the model’s API and documentation togetherParallelise feature extractionCache predictionsAsync support for top-down chaining models
    031Strongly agreeAgreeStrongly agreeNeither agree nor disagreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeNeither agree nor disagreeStrongly agree
    162Neither agree nor disagreeAgreeNeither agree nor disagreeAgreeAgreeNeither agree nor disagreeDisagreeStrongly disagreeDisagreeStrongly disagreeDisagreeStrongly disagreeDisagreeStrongly agreeStrongly disagree
    215Strongly disagreeDisagreeDisagreeStrongly disagreeDisagreeStrongly disagreeStrongly disagreeNeither agree nor disagreeStrongly disagreeDisagreeNeither agree nor disagreeAgreeStrongly disagreeStrongly disagreeDisagree
    333AgreeAgreeDisagreeNeither agree nor disagreeAgreeNot applicableNot applicableNeither agree nor disagreeAgreeDisagreeAgreeStrongly agreeNot applicableNeither agree nor disagreeAgree
    417Neither agree nor disagreeDisagreeNeither agree nor disagreeDisagreeStrongly disagreeNot applicableNot applicableDisagreeStrongly disagreeDisagreeDisagreeNeither agree nor disagreeStrongly disagreeStrongly agreeNot applicable
    560Strongly agreeStrongly agreeAgreeNeither agree nor disagreeAgreeStrongly agreeAgreeStrongly agreeAgreeStrongly agreeDisagreeStrongly agreeAgreeStrongly agreeNot applicable
    622DisagreeNeither agree nor disagreeAgreeStrongly agreeNeither agree nor disagreeDisagreeStrongly agreeDisagreeDisagreeAgreeNeither agree nor disagreeNeither agree nor disagreeNot applicableDisagreeStrongly agree
    712DisagreeNeither agree nor disagreeDisagreeAgreeDisagreeStrongly disagreeStrongly agreeStrongly agreeStrongly disagreeDisagreeAgreeStrongly disagreeNot applicableStrongly disagreeStrongly disagree
    801Strongly disagreeDisagreeStrongly disagreeDisagreeStrongly disagreeDisagreeAgreeStrongly disagreeDisagreeStrongly disagreeDisagreeStrongly disagreeDisagreeDisagreeStrongly disagree
    971Strongly agreeStrongly agreeAgreeStrongly agreeAgreeAgreeStrongly agreeStrongly disagreeStrongly agreeNot applicableAgreeAgreeStrongly agreeStrongly agreeAgree
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    + + + + + + + + + \ No newline at end of file diff --git a/surveys/surveys/surveys.ipynb b/surveys/surveys/surveys.ipynb new file mode 100644 index 0000000..f9133aa --- /dev/null +++ b/surveys/surveys/surveys.ipynb @@ -0,0 +1,1219 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    How many years of software engineering experience do you have?How many years of data science experience do you have?Write reusable scripts for data cleaning and mergingMake datasets available on shared infrastructureUse versioning for data, model, configurations and training scriptsContinuously monitor the behaviour of deployed modelsLog production predictions with the model’s version and input dataStore models in a single format for ease of useEquip with web interface, package image, provide REST APIProvide simple API for serving batch and real-time requestsIntegration with existing data infrastructureQuerying, visualising and understanding metrics and event loggingAllow experimentation with the inference codeKeep the model’s API and documentation togetherParallelise feature extractionCache predictionsAsync support for top-down chaining models
    031Strongly agreeAgreeStrongly agreeNeither agree nor disagreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeStrongly agreeNeither agree nor disagreeStrongly agree
    162Neither agree nor disagreeAgreeNeither agree nor disagreeAgreeAgreeNeither agree nor disagreeDisagreeStrongly disagreeDisagreeStrongly disagreeDisagreeStrongly disagreeDisagreeStrongly agreeStrongly disagree
    215Strongly disagreeDisagreeDisagreeStrongly disagreeDisagreeStrongly disagreeStrongly disagreeNeither agree nor disagreeStrongly disagreeDisagreeNeither agree nor disagreeAgreeStrongly disagreeStrongly disagreeDisagree
    333AgreeAgreeDisagreeNeither agree nor disagreeAgreeNot applicableNot applicableNeither agree nor disagreeAgreeDisagreeAgreeStrongly agreeNot applicableNeither agree nor disagreeAgree
    417Neither agree nor disagreeDisagreeNeither agree nor disagreeDisagreeStrongly disagreeNot applicableNot applicableDisagreeStrongly disagreeDisagreeDisagreeNeither agree nor disagreeStrongly disagreeStrongly agreeNot applicable
    560Strongly agreeStrongly agreeAgreeNeither agree nor disagreeAgreeStrongly agreeAgreeStrongly agreeAgreeStrongly agreeDisagreeStrongly agreeAgreeStrongly agreeNot applicable
    622DisagreeNeither agree nor disagreeAgreeStrongly agreeNeither agree nor disagreeDisagreeStrongly agreeDisagreeDisagreeAgreeNeither agree nor disagreeNeither agree nor disagreeNot applicableDisagreeStrongly agree
    712DisagreeNeither agree nor disagreeDisagreeAgreeDisagreeStrongly disagreeStrongly agreeStrongly agreeStrongly disagreeDisagreeAgreeStrongly disagreeNot applicableStrongly disagreeStrongly disagree
    801Strongly disagreeDisagreeStrongly disagreeDisagreeStrongly disagreeDisagreeAgreeStrongly disagreeDisagreeStrongly disagreeDisagreeStrongly disagreeDisagreeDisagreeStrongly disagree
    971Strongly agreeStrongly agreeAgreeStrongly agreeAgreeAgreeStrongly agreeStrongly disagreeStrongly agreeNot applicableAgreeAgreeStrongly agreeStrongly agreeAgree
    \n", + "
    " + ], + "text/plain": [ + " How many years of software engineering experience do you have? \\\n", + "0 3 \n", + "1 6 \n", + "2 1 \n", + "3 3 \n", + "4 1 \n", + "5 6 \n", + "6 2 \n", + "7 1 \n", + "8 0 \n", + "9 7 \n", + "\n", + " How many years of data science experience do you have? \\\n", + "0 1 \n", + "1 2 \n", + "2 5 \n", + "3 3 \n", + "4 7 \n", + "5 0 \n", + "6 2 \n", + "7 2 \n", + "8 1 \n", + "9 1 \n", + "\n", + " Write reusable scripts for data cleaning and merging \\\n", + "0 Strongly agree \n", + "1 Neither agree nor disagree \n", + "2 Strongly disagree \n", + "3 Agree \n", + "4 Neither agree nor disagree \n", + "5 Strongly agree \n", + "6 Disagree \n", + "7 Disagree \n", + "8 Strongly disagree \n", + "9 Strongly agree \n", + "\n", + " Make datasets available on shared infrastructure \\\n", + "0 Agree \n", + "1 Agree \n", + "2 Disagree \n", + "3 Agree \n", + "4 Disagree \n", + "5 Strongly agree \n", + "6 Neither agree nor disagree \n", + "7 Neither agree nor disagree \n", + "8 Disagree \n", + "9 Strongly agree \n", + "\n", + " Use versioning for data, model, configurations and training scripts \\\n", + "0 Strongly agree \n", + "1 Neither agree nor disagree \n", + "2 Disagree \n", + "3 Disagree \n", + "4 Neither agree nor disagree \n", + "5 Agree \n", + "6 Agree \n", + "7 Disagree \n", + "8 Strongly disagree \n", + "9 Agree \n", + "\n", + " Continuously monitor the behaviour of deployed models \\\n", + "0 Neither agree nor disagree \n", + "1 Agree \n", + "2 Strongly disagree \n", + "3 Neither agree nor disagree \n", + "4 Disagree \n", + "5 Neither agree nor disagree \n", + "6 Strongly agree \n", + "7 Agree \n", + "8 Disagree \n", + "9 Strongly agree \n", + "\n", + " Log production predictions with the model’s version and input data \\\n", + "0 Strongly agree \n", + "1 Agree \n", + "2 Disagree \n", + "3 Agree \n", + "4 Strongly disagree \n", + "5 Agree \n", + "6 Neither agree nor disagree \n", + "7 Disagree \n", + "8 Strongly disagree \n", + "9 Agree \n", + "\n", + " Store models in a single format for ease of use \\\n", + "0 Strongly agree \n", + "1 Neither agree nor disagree \n", + "2 Strongly disagree \n", + "3 Not applicable \n", + "4 Not applicable \n", + "5 Strongly agree \n", + "6 Disagree \n", + "7 Strongly disagree \n", + "8 Disagree \n", + "9 Agree \n", + "\n", + " Equip with web interface, package image, provide REST API \\\n", + "0 Strongly agree \n", + "1 Disagree \n", + "2 Strongly disagree \n", + "3 Not applicable \n", + "4 Not applicable \n", + "5 Agree \n", + "6 Strongly agree \n", + "7 Strongly agree \n", + "8 Agree \n", + "9 Strongly agree \n", + "\n", + " Provide simple API for serving batch and real-time requests \\\n", + "0 Strongly agree \n", + "1 Strongly disagree \n", + "2 Neither agree nor disagree \n", + "3 Neither agree nor disagree \n", + "4 Disagree \n", + "5 Strongly agree \n", + "6 Disagree \n", + "7 Strongly agree \n", + "8 Strongly disagree \n", + "9 Strongly disagree \n", + "\n", + " Integration with existing data infrastructure \\\n", + "0 Strongly agree \n", + "1 Disagree \n", + "2 Strongly disagree \n", + "3 Agree \n", + "4 Strongly disagree \n", + "5 Agree \n", + "6 Disagree \n", + "7 Strongly disagree \n", + "8 Disagree \n", + "9 Strongly agree \n", + "\n", + " Querying, visualising and understanding metrics and event logging \\\n", + "0 Strongly agree \n", + "1 Strongly disagree \n", + "2 Disagree \n", + "3 Disagree \n", + "4 Disagree \n", + "5 Strongly agree \n", + "6 Agree \n", + "7 Disagree \n", + "8 Strongly disagree \n", + "9 Not applicable \n", + "\n", + " Allow experimentation with the inference code \\\n", + "0 Strongly agree \n", + "1 Disagree \n", + "2 Neither agree nor disagree \n", + "3 Agree \n", + "4 Disagree \n", + "5 Disagree \n", + "6 Neither agree nor disagree \n", + "7 Agree \n", + "8 Disagree \n", + "9 Agree \n", + "\n", + " Keep the model’s API and documentation together \\\n", + "0 Strongly agree \n", + "1 Strongly disagree \n", + "2 Agree \n", + "3 Strongly agree \n", + "4 Neither agree nor disagree \n", + "5 Strongly agree \n", + "6 Neither agree nor disagree \n", + "7 Strongly disagree \n", + "8 Strongly disagree \n", + "9 Agree \n", + "\n", + " Parallelise feature extraction Cache predictions \\\n", + "0 Strongly agree Neither agree nor disagree \n", + "1 Disagree Strongly agree \n", + "2 Strongly disagree Strongly disagree \n", + "3 Not applicable Neither agree nor disagree \n", + "4 Strongly disagree Strongly agree \n", + "5 Agree Strongly agree \n", + "6 Not applicable Disagree \n", + "7 Not applicable Strongly disagree \n", + "8 Disagree Disagree \n", + "9 Strongly agree Strongly agree \n", + "\n", + " Async support for top-down chaining models \n", + "0 Strongly agree \n", + "1 Strongly disagree \n", + "2 Disagree \n", + "3 Agree \n", + "4 Not applicable \n", + "5 Not applicable \n", + "6 Strongly agree \n", + "7 Strongly disagree \n", + "8 Strongly disagree \n", + "9 Agree " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "best_practices = pd.read_csv(\"Best practices assessment.csv\")\n", + "best_practices" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.5, 2.0)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = {\n", + " \"Strongly agree\": 4 / 4,\n", + " \"Agree\": 3 / 4,\n", + " \"Neither agree nor disagree\": 2 / 4,\n", + " \"Disagree\": 1 / 4,\n", + " \"Strongly disagree\": 0 / 4,\n", + "}\n", + "\n", + "best_practices_dicts = [v.to_dict() for _, v in best_practices.iterrows()]\n", + "\n", + "best_practice_score_ds = []\n", + "best_practice_score_se = []\n", + "\n", + "for d in best_practices_dicts:\n", + " ds_experience = int(d[\"How many years of data science experience do you have?\"])\n", + " del d[\"How many years of data science experience do you have?\"]\n", + "\n", + " se_experience = int(\n", + " d[\"How many years of software engineering experience do you have?\"]\n", + " )\n", + " del d[\"How many years of software engineering experience do you have?\"]\n", + "\n", + " scores = [m[v] for v in d.values() if v != \"Not applicable\"]\n", + " score = sum(scores) / len(scores)\n", + " best_practice_score_ds.append((ds_experience, score))\n", + " best_practice_score_se.append((se_experience, score))\n", + "\n", + "best_practices[\n", + " \"How many years of software engineering experience do you have?\"\n", + "].median(), best_practices[\n", + " \"How many years of data science experience do you have?\"\n", + "].median()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.5440527060340572, 0.10399880919437814)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from scipy import stats\n", + "\n", + "best_practice_score_ds = sorted(best_practice_score_ds)\n", + "plt.scatter(\n", + " [x for x, y in best_practice_score_ds], [y for x, y in best_practice_score_ds]\n", + ")\n", + "stats.pearsonr(\n", + " [x for x, y in best_practice_score_ds], [y for x, y in best_practice_score_ds]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.6722543326178704, 0.03321124881554773)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import re\n", + "\n", + "best_practice_score_se = sorted(best_practice_score_se)\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams[\"figure.facecolor\"] = \"white\"\n", + "plt.rcParams[\"font.size\"] = 24\n", + "plt.rcParams[\"figure.figsize\"] = (14, 10)\n", + "\n", + "X = [x for x, y in best_practice_score_se]\n", + "Y = [y for x, y in best_practice_score_se]\n", + "sc = plt.scatter(X, Y, c=\"black\", s=[x * 50 for x, y in best_practice_score_ds])\n", + "plt.ylabel(\"Ratio of implemented deployment best practices\")\n", + "plt.xlabel(\"Years of professional software engineering experience\")\n", + "\n", + "\n", + "def best_fit(X, Y):\n", + " xbar = sum(X) / len(X)\n", + " ybar = sum(Y) / len(Y)\n", + " n = len(X)\n", + "\n", + " numer = sum([xi * yi for xi, yi in zip(X, Y)]) - n * xbar * ybar\n", + " denum = sum([xi**2 for xi in X]) - n * xbar**2\n", + "\n", + " b = numer / denum\n", + " a = ybar - b * xbar\n", + " return a, b\n", + "\n", + "\n", + "a, b = best_fit(X, Y)\n", + "yfit = [a + b * xi for xi in X]\n", + "plt.plot(X, yfit, \"b\", label=\"Best fit\")\n", + "plt.legend(\n", + " sc.legend_elements(\"sizes\", num=5)[0],\n", + " [\n", + " re.sub(r\"[^\\d]*(\\d+)[^\\d]*\", lambda v: f\"{int(v.group(1)) // 40} years (DS)\", t)\n", + " for t in sc.legend_elements(\"sizes\", num=5)[1]\n", + " ],\n", + ")\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig(\"best-practices\", dpi=150)\n", + "\n", + "stats.pearsonr(\n", + " [x for x, y in best_practice_score_se], [y for x, y in best_practice_score_se]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    I believe the use of GreatAI improves the quality of AI deployments.I believe the use of GreatAI would increase my productivity.I believe the use of GreatAI can lead to robust and trustworthy deployments.Overall, I found GreatAI useful when working with AI.I found the GreatAI easy to learn.I found it is easy to employ GreatAI in practice.I found it is easy to integrate GreatAI into an existing project.Overall, I found GreatAI easy to use.Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future.Overall, I intend to use the GreatAI in my personal or professional projects.
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    " + ], + "text/plain": [ + " I believe the use of GreatAI improves the quality of AI deployments. \\\n", + "0 7 \n", + "1 7 \n", + "2 4 \n", + "3 6 \n", + "4 7 \n", + "5 6 \n", + "6 6 \n", + "7 7 \n", + "8 4 \n", + "9 7 \n", + "\n", + " I believe the use of GreatAI would increase my productivity. \\\n", + "0 7 \n", + "1 7 \n", + "2 4 \n", + "3 7 \n", + "4 7 \n", + "5 6 \n", + "6 6 \n", + "7 6 \n", + "8 5 \n", + "9 7 \n", + "\n", + " I believe the use of GreatAI can lead to robust and trustworthy deployments. \\\n", + "0 7 \n", + "1 6 \n", + "2 5 \n", + "3 6 \n", + "4 6 \n", + "5 6 \n", + "6 6 \n", + "7 6 \n", + "8 5 \n", + "9 7 \n", + "\n", + " Overall, I found GreatAI useful when working with AI. \\\n", + "0 7 \n", + "1 7 \n", + "2 6 \n", + "3 7 \n", + "4 7 \n", + "5 6 \n", + "6 4 \n", + "7 6 \n", + "8 5 \n", + "9 7 \n", + "\n", + " I found the GreatAI easy to learn. \\\n", + "0 7 \n", + "1 5 \n", + "2 6 \n", + "3 7 \n", + "4 4 \n", + "5 6 \n", + "6 5 \n", + "7 4 \n", + "8 7 \n", + "9 5 \n", + "\n", + " I found it is easy to employ GreatAI in practice. \\\n", + "0 7 \n", + "1 6 \n", + "2 5 \n", + "3 6 \n", + "4 5 \n", + "5 6 \n", + "6 7 \n", + "7 5 \n", + "8 2 \n", + "9 6 \n", + "\n", + " I found it is easy to integrate GreatAI into an existing project. \\\n", + "0 7 \n", + "1 7 \n", + "2 4 \n", + "3 7 \n", + "4 5 \n", + "5 6 \n", + "6 6 \n", + "7 3 \n", + "8 3 \n", + "9 5 \n", + "\n", + " Overall, I found GreatAI easy to use. \\\n", + "0 7 \n", + "1 6 \n", + "2 4 \n", + "3 6 \n", + "4 5 \n", + "5 6 \n", + "6 6 \n", + "7 5 \n", + "8 3 \n", + "9 6 \n", + "\n", + " Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future. \\\n", + "0 7 \n", + "1 7 \n", + "2 5 \n", + "3 7 \n", + "4 6 \n", + "5 6 \n", + "6 7 \n", + "7 5 \n", + "8 3 \n", + "9 7 \n", + "\n", + " Overall, I intend to use the GreatAI in my personal or professional projects. \n", + "0 7 \n", + "1 7 \n", + "2 4 \n", + "3 6 \n", + "4 6 \n", + "5 6 \n", + "6 7 \n", + "7 6 \n", + "8 3 \n", + "9 7 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tam = pd.read_csv(\"Technology acceptance model questionnaire.csv\")\n", + "tam" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.8813771517996869, array([0.688, 0.967]))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pingouin\n", + "\n", + "pu = [\n", + " \"I believe the use of GreatAI improves the quality of AI deployments.\",\n", + " \"I believe the use of GreatAI would increase my productivity.\",\n", + " \"I believe the use of GreatAI can lead to robust and trustworthy deployments.\",\n", + " \"Overall, I found GreatAI useful when working with AI.\",\n", + "]\n", + "pingouin.cronbach_alpha(tam[pu])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.7729220222793487, array([0.403, 0.937]))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "peou = [\n", + " \"I found the GreatAI easy to learn.\",\n", + " \"I found it is easy to employ GreatAI in practice.\",\n", + " \"I found it is easy to integrate GreatAI into an existing project.\",\n", + " \"Overall, I found GreatAI easy to use.\",\n", + "]\n", + "pingouin.cronbach_alpha(tam[peou])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.9538950715421304, array([0.814, 0.989]))" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "itu = [\n", + " \"Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future.\",\n", + " \"Overall, I intend to use the GreatAI in my personal or professional projects.\",\n", + "]\n", + "pingouin.cronbach_alpha(tam[itu])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " pu peou itu\n", + "0 7.00 7.00 7.0\n", + "1 6.75 6.00 7.0\n", + "2 4.75 4.75 4.5\n", + "3 6.50 6.50 6.5\n", + "4 6.75 4.75 6.0\n", + "5 6.00 6.00 6.0\n", + "6 5.50 6.00 7.0\n", + "7 6.25 4.25 5.5\n", + "8 4.75 3.75 3.0\n", + "9 7.00 5.50 7.0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tam[\"pu\"] = tam[pu].mean(1)\n", + "tam[\"peou\"] = tam[peou].mean(1)\n", + "tam[\"itu\"] = tam[itu].mean(1)\n", + "tam[[\"pu\", \"peou\", \"itu\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.5515422017785757, 0.09838124227663879)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.pearsonr(tam[\"peou\"], tam[\"pu\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.8066270322592023, 0.004809023073123024)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.pearsonr(tam[\"peou\"], tam[\"itu\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.7880605510627579, 0.006774486564715021)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.pearsonr(tam[\"pu\"], tam[\"itu\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('.env': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "02dd6d3afbfa9fbbe1037d64ad9014965528a1ccad21929d6e72f466389a68ad" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial/deploy/index.html b/tutorial/deploy/index.html index 8b38a36..377e833 100644 --- a/tutorial/deploy/index.html +++ b/tutorial/deploy/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -2203,6 +2203,9 @@ def predict_domain(sentence, model):
    + + + @@ -2266,25 +2269,25 @@ def predict_domain(sentence, model):     - + - + - + - +
    @@ -2297,10 +2300,11 @@ def predict_domain(sentence, model):
    - + + - + diff --git a/tutorial/index.html b/tutorial/index.html index ec29bf9..e9c5b3b 100644 --- a/tutorial/index.html +++ b/tutorial/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -962,7 +962,7 @@

    You are ready to start the tutorial. Feel free to come back to the summary section once you're finished.

    Summary#

    @@ -1020,6 +1020,9 @@
    + + + @@ -1083,25 +1086,25 @@ - + - + - + - + @@ -1114,10 +1117,11 @@
    - + + - + diff --git a/tutorial/train/index.html b/tutorial/train/index.html index 2431059..22e0758 100644 --- a/tutorial/train/index.html +++ b/tutorial/train/index.html @@ -13,7 +13,7 @@ - + @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@ - + @@ -185,7 +185,7 @@
    - +
    schmelczer/great-ai @@ -230,7 +230,7 @@
    - +
    schmelczer/great-ai @@ -2579,6 +2579,9 @@ save_model(model, key="my-domain-predictor")
    + + + @@ -2642,25 +2645,25 @@ save_model(model, key="my-domain-predictor") - + - + - + - + @@ -2673,10 +2676,11 @@ save_model(model, key="my-domain-predictor")
    - + + - +