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<li class="md-nav__item md-nav__item--nested">
|
||
|
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|
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<input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
|
||
|
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|
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|
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|
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<label class="md-nav__link" for="__nav_4">
|
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Reference
|
||
<span class="md-nav__icon md-icon"></span>
|
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</label>
|
||
|
||
<nav class="md-nav" aria-label="Reference" data-md-level="1">
|
||
<label class="md-nav__title" for="__nav_4">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
Reference
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
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|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../../reference/" class="md-nav__link">
|
||
GreatAI reference
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../../reference/utilities/" class="md-nav__link">
|
||
Utilities
|
||
</a>
|
||
</li>
|
||
|
||
|
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|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../../reference/large-file/" class="md-nav__link">
|
||
LargeFile
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../../reference/views/" class="md-nav__link">
|
||
View models
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
</li>
|
||
|
||
|
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|
||
|
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|
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|
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|
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|
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|
||
|
||
|
||
|
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|
||
<li class="md-nav__item md-nav__item--active md-nav__item--nested">
|
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|
||
|
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<input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" checked>
|
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|
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|
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|
||
|
||
<label class="md-nav__link" for="__nav_5">
|
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Examples
|
||
<span class="md-nav__icon md-icon"></span>
|
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</label>
|
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<nav class="md-nav" aria-label="Examples" data-md-level="1">
|
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<span class="md-nav__icon md-icon"></span>
|
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Examples
|
||
</label>
|
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<ul class="md-nav__list" data-md-scrollfix>
|
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|
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|
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<li class="md-nav__item md-nav__item--active md-nav__item--nested">
|
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<input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5_1" type="checkbox" id="__nav_5_1" checked>
|
||
|
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|
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|
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|
||
<label class="md-nav__link" for="__nav_5_1">
|
||
Explainable Naive Bayes
|
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<span class="md-nav__icon md-icon"></span>
|
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</label>
|
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|
||
<nav class="md-nav" aria-label="Explainable Naive Bayes" data-md-level="2">
|
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<label class="md-nav__title" for="__nav_5_1">
|
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<span class="md-nav__icon md-icon"></span>
|
||
Explainable Naive Bayes
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
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|
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|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../data/" class="md-nav__link">
|
||
Simple example: data engineering
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../train/" class="md-nav__link">
|
||
Optimise and train a model
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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|
||
|
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|
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<li class="md-nav__item md-nav__item--active">
|
||
|
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<input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
|
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|
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|
||
|
||
|
||
|
||
<label class="md-nav__link md-nav__link--active" for="__toc">
|
||
Hardening and deployment
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<a href="./" class="md-nav__link md-nav__link--active">
|
||
Hardening and deployment
|
||
</a>
|
||
|
||
|
||
|
||
<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
|
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|
||
|
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|
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|
||
|
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|
||
<label class="md-nav__title" for="__toc">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
Table of contents
|
||
</label>
|
||
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#create-the-inference-function" class="md-nav__link">
|
||
Create the inference function
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#check-accuracy-on-the-test-split" class="md-nav__link">
|
||
Check accuracy on the test split
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../../explanation/" class="md-nav__link">
|
||
Explanation
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
|
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<div class="md-sidebar__scrollwrap">
|
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<div class="md-sidebar__inner">
|
||
|
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|
||
<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
|
||
|
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|
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|
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|
||
|
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|
||
<label class="md-nav__title" for="__toc">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
Table of contents
|
||
</label>
|
||
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#create-the-inference-function" class="md-nav__link">
|
||
Create the inference function
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#check-accuracy-on-the-test-split" class="md-nav__link">
|
||
Check accuracy on the test split
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
<div class="md-content" data-md-component="content">
|
||
<article class="md-content__inner md-typeset">
|
||
|
||
|
||
|
||
<a href="deploy.ipynb" title="Download Notebook" class="md-content__button md-icon">
|
||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M5 20h14v-2H5m14-9h-4V3H9v6H5l7 7 7-7Z"/></svg>
|
||
</a>
|
||
|
||
|
||
|
||
|
||
<a href="https://github.com/schmelczer/great-ai/edit/main/docs/examples/simple/deploy.ipynb" title="Edit this page" class="md-content__button md-icon">
|
||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
|
||
</a>
|
||
|
||
|
||
|
||
<script>
|
||
(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;
|
||
|
||
}));
|
||
</script>
|
||
<script>
|
||
document.addEventListener('clipboard-copy', function(event) {
|
||
const notice = event.target.querySelector('.notice')
|
||
notice.hidden = false
|
||
setTimeout(function() {
|
||
notice.hidden = true
|
||
}, 1000)
|
||
})
|
||
</script>
|
||
|
||
|
||
|
||
|
||
<style type="text/css">
|
||
|
||
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 */
|
||
</style>
|
||
|
||
|
||
|
||
|
||
<style type="text/css">
|
||
/*
|
||
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: 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Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
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Licensed under the Apache License, Version 2.0.
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*//*!
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Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
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Licensed under the Apache License, Version 2.0.
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<h1 id="hardening-and-deployment">Hardening and deployment<a class="anchor-link" href="#hardening-and-deployment">¶</a></h1><blockquote><p>Part 3: Create production inference function</p>
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</blockquote>
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<p><img src="/media/scope-deploy.svg" alt="position of this step in the lifecycle"></p>
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<blockquote><p>The blue boxes show the steps implemented in this notebook.</p>
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<p>In <a href="/examples/simple/train">Part 2</a>, we trained our AI model. Now, it's time to create <strong>G</strong>eneral <strong>R</strong>obust <strong>E</strong>nd-to-end <strong>A</strong>utomated <strong>T</strong>rustworthy deployment.</p>
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<h2 id="create-the-inference-function">Create the inference function<a class="anchor-link" href="#create-the-inference-function">¶</a></h2><p>Next to the prediction, we also return the top-n most influential words based on their weights.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">re</span>
|
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
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<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
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<span class="kn">from</span> <span class="nn">great_ai.utilities</span> <span class="kn">import</span> <span class="n">clean</span>
|
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<span class="kn">from</span> <span class="nn">great_ai</span> <span class="kn">import</span> <span class="p">(</span>
|
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<span class="n">MultiLabelClassificationOutput</span><span class="p">,</span>
|
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<span class="n">ClassificationOutput</span><span class="p">,</span>
|
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<span class="n">GreatAI</span><span class="p">,</span>
|
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<span class="n">use_model</span><span class="p">,</span>
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<span class="n">parameter</span><span class="p">,</span>
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<span class="p">)</span>
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<span class="nd">@GreatAI</span><span class="o">.</span><span class="n">create</span>
|
||
<span class="nd">@use_model</span><span class="p">(</span><span class="s2">"small-domain-prediction"</span><span class="p">,</span> <span class="n">version</span><span class="o">=</span><span class="s2">"latest"</span><span class="p">)</span>
|
||
<span class="nd">@parameter</span><span class="p">(</span><span class="s2">"target_confidence"</span><span class="p">,</span> <span class="n">validator</span><span class="o">=</span><span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="mi">0</span> <span class="o"><=</span> <span class="n">c</span> <span class="o"><=</span> <span class="mi">100</span><span class="p">)</span>
|
||
<span class="k">def</span> <span class="nf">predict_domain</span><span class="p">(</span>
|
||
<span class="n">text</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">model</span><span class="p">:</span> <span class="n">Pipeline</span><span class="p">,</span> <span class="n">target_confidence</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">50</span>
|
||
<span class="p">)</span> <span class="o">-></span> <span class="n">MultiLabelClassificationOutput</span><span class="p">:</span>
|
||
<span class="sd">"""</span>
|
||
<span class="sd"> Predict the scientific domain of the input text.</span>
|
||
<span class="sd"> Return labels until their sum likelihood is larger than `target_confidence`.</span>
|
||
<span class="sd"> """</span>
|
||
|
||
<span class="n">preprocessed</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="sa">r</span><span class="s2">"[^a-zA-Z\s]"</span><span class="p">,</span> <span class="s2">""</span><span class="p">,</span> <span class="n">clean</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">convert_to_ascii</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
|
||
<span class="n">features</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">"vectorizer"</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">([</span><span class="n">preprocessed</span><span class="p">])</span>
|
||
<span class="n">prediction</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">"classifier"</span><span class="p">]</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">features</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||
|
||
<span class="n">best_classes</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">prediction</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
|
||
<span class="n">results</span> <span class="o">=</span> <span class="n">MultiLabelClassificationOutput</span><span class="p">()</span>
|
||
<span class="k">for</span> <span class="n">class_index</span><span class="p">,</span> <span class="n">probability</span> <span class="ow">in</span> <span class="n">best_classes</span><span class="p">:</span>
|
||
<span class="n">results</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
|
||
<span class="n">get_label</span><span class="p">(</span>
|
||
<span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
|
||
<span class="n">features</span><span class="o">=</span><span class="n">features</span><span class="p">,</span>
|
||
<span class="n">class_index</span><span class="o">=</span><span class="n">class_index</span><span class="p">,</span>
|
||
<span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
<span class="p">)</span>
|
||
|
||
<span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">r</span><span class="o">.</span><span class="n">confidence</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">results</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span> <span class="o">>=</span> <span class="n">target_confidence</span><span class="p">:</span>
|
||
<span class="k">break</span>
|
||
|
||
<span class="k">return</span> <span class="n">results</span>
|
||
|
||
|
||
<span class="k">def</span> <span class="nf">get_label</span><span class="p">(</span>
|
||
<span class="n">model</span><span class="p">:</span> <span class="n">Pipeline</span><span class="p">,</span> <span class="n">features</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">class_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">probability</span><span class="p">:</span> <span class="nb">float</span>
|
||
<span class="p">)</span> <span class="o">-></span> <span class="n">ClassificationOutput</span><span class="p">:</span>
|
||
<span class="k">return</span> <span class="n">ClassificationOutput</span><span class="p">(</span>
|
||
<span class="n">label</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">"classifier"</span><span class="p">]</span><span class="o">.</span><span class="n">classes_</span><span class="p">[</span><span class="n">class_index</span><span class="p">],</span>
|
||
<span class="n">confidence</span><span class="o">=</span><span class="nb">round</span><span class="p">(</span><span class="n">probability</span> <span class="o">*</span> <span class="mi">100</span><span class="p">),</span>
|
||
<span class="n">explanation</span><span class="o">=</span><span class="p">[</span>
|
||
<span class="n">word</span>
|
||
<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">word</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span>
|
||
<span class="p">(</span>
|
||
<span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">word</span><span class="p">)</span>
|
||
<span class="k">for</span> <span class="n">weight</span><span class="p">,</span> <span class="n">word</span><span class="p">,</span> <span class="n">count</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
|
||
<span class="n">model</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">"classifier"</span><span class="p">]</span><span class="o">.</span><span class="n">feature_log_prob_</span><span class="p">[</span><span class="n">class_index</span><span class="p">],</span>
|
||
<span class="n">model</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">"vectorizer"</span><span class="p">]</span><span class="o">.</span><span class="n">get_feature_names_out</span><span class="p">(),</span>
|
||
<span class="n">features</span><span class="o">.</span><span class="n">A</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
|
||
<span class="p">)</span>
|
||
<span class="k">if</span> <span class="n">count</span> <span class="o">></span> <span class="mi">0</span>
|
||
<span class="p">),</span>
|
||
<span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
<span class="p">][:</span><span class="mi">5</span><span class="p">],</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
<div id="cell-1" class="clipboard-copy-txt">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", validator=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],
|
||
)</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
|
||
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
|
||
<div class="jp-OutputArea-child">
|
||
|
||
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
|
||
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
|
||
<pre><span style="color: rgb(255,255,0)">Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️</span>
|
||
<span style="color: rgb(255,255,0)">Cannot find credentials files, defaulting to using ParallelTinyDbDriver</span>
|
||
<span style="color: rgb(255,255,0)">The selected tracing database (ParallelTinyDbDriver) is not recommended for production</span>
|
||
<span style="color: rgb(255,255,0)">Cannot find credentials files, defaulting to using LargeFileLocal</span>
|
||
<span style="color: rgb(0,175,255)">GreatAI (v0.1.4): configured ✅</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 tracing_database: ParallelTinyDbDriver</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 large_file_implementation: LargeFileLocal</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 is_production: False</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 should_log_exception_stack: True</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 prediction_cache_size: 512</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 dashboard_table_size: 50</span>
|
||
<span style="color: rgb(255,255,0)">You still need to check whether you follow all best practices before trusting your deployment.</span>
|
||
<span style="color: rgb(255,255,0)">> Find out more at https://se-ml.github.io/practices</span>
|
||
<span style="color: rgb(0,175,255)">Fetching cached versions of small-domain-prediction</span>
|
||
<span style="color: rgb(0,175,255)">Latest version of small-domain-prediction is 0 (from versions: 0)</span>
|
||
<span style="color: rgb(0,175,255)">File small-domain-prediction-0 found in cache</span>
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
|
||
<h2 id="check-accuracy-on-the-test-split">Check accuracy on the test split<a class="anchor-link" href="#check-accuracy-on-the-test-split">¶</a></h2><p>Anything under <code>if __name__ == "__main__":</code> will not be run when the script is executed by the <code>great-ai</code> CLI app. This, combined with <code>query_ground_truth</code> and the <code>/traces/{trace_id}/feedback</code> endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment.</p>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell ">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell ">
|
||
<div class="jp-Cell-inputWrapper">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
|
||
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy for="cell-2">
|
||
<div>
|
||
<span class="notice" hidden>Copied!</span>
|
||
<svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1" data-view-component="true" class="clipboard-copy-icon">
|
||
<path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
|
||
<path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span>
|
||
<span class="kn">from</span> <span class="nn">great_ai</span> <span class="kn">import</span> <span class="n">query_ground_truth</span>
|
||
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
|
||
|
||
<span class="n">data</span> <span class="o">=</span> <span class="n">query_ground_truth</span><span class="p">(</span><span class="s2">"test"</span><span class="p">)</span>
|
||
|
||
<span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="o">.</span><span class="n">input</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
|
||
<span class="n">y_actual</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="o">.</span><span class="n">feedback</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
|
||
|
||
<span class="n">y_predicted</span> <span class="o">=</span> <span class="p">[</span>
|
||
<span class="n">d</span><span class="o">.</span><span class="n">output</span><span class="o">.</span><span class="n">labels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">label</span>
|
||
<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">predict_domain</span><span class="o">.</span><span class="n">process_batch</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">do_not_persist_traces</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
<span class="p">]</span>
|
||
<span class="n">y_actual_aligned</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span> <span class="k">if</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">a</span> <span class="k">else</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span><span class="p">,</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">y_predicted</span><span class="p">,</span> <span class="n">y_actual</span><span class="p">)]</span>
|
||
|
||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||
|
||
<span class="c1"># Configure matplotlib to have nice, high-resolution charts</span>
|
||
<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"figure.figsize"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">15</span><span class="p">)</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"figure.facecolor"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"white"</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"font.size"</span><span class="p">]</span> <span class="o">=</span> <span class="mi">12</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"axes.xmargin"</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
|
||
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_actual_aligned</span><span class="p">,</span> <span class="n">y_predicted</span><span class="p">))</span>
|
||
<span class="n">metrics</span><span class="o">.</span><span class="n">ConfusionMatrixDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span>
|
||
<span class="n">y_true</span><span class="o">=</span><span class="n">y_actual_aligned</span><span class="p">,</span>
|
||
<span class="n">y_pred</span><span class="o">=</span><span class="n">y_predicted</span><span class="p">,</span>
|
||
<span class="n">xticks_rotation</span><span class="o">=</span><span class="s2">"vertical"</span><span class="p">,</span>
|
||
<span class="n">normalize</span><span class="o">=</span><span class="s2">"pred"</span><span class="p">,</span>
|
||
<span class="n">values_format</span><span class="o">=</span><span class="s2">".2f"</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
<div id="cell-2" class="clipboard-copy-txt">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"] = (30, 15)
|
||
plt.rcParams["figure.facecolor"] = "white"
|
||
plt.rcParams["font.size"] = 12
|
||
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",
|
||
)</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
|
||
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
|
||
<div class="jp-OutputArea-child">
|
||
|
||
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
|
||
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
|
||
<pre>100%|██████████| 12332/12332 [01:27<00:00, 141.08it/s]
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-OutputArea-child">
|
||
|
||
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
|
||
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
|
||
<pre> precision recall f1-score support
|
||
|
||
Art 0.43 0.35 0.39 125
|
||
Biology 0.77 0.83 0.80 1209
|
||
Business 0.50 0.71 0.59 312
|
||
Chemistry 0.80 0.67 0.73 1194
|
||
Computer Science 0.77 0.76 0.77 1293
|
||
Economics 0.64 0.58 0.61 251
|
||
Engineering 0.55 0.53 0.54 815
|
||
Environmental Science 0.54 0.56 0.55 230
|
||
Geography 0.54 0.44 0.48 277
|
||
Geology 0.76 0.67 0.71 228
|
||
History 0.29 0.21 0.24 102
|
||
Materials Science 0.73 0.81 0.77 1053
|
||
Mathematics 0.80 0.70 0.75 551
|
||
Medicine 0.95 0.77 0.85 2794
|
||
Philosophy 0.61 0.12 0.21 88
|
||
Physics 0.67 0.76 0.72 605
|
||
Political Science 0.43 0.52 0.47 297
|
||
Psychology 0.53 0.80 0.63 600
|
||
Sociology 0.32 0.60 0.42 308
|
||
|
||
accuracy 0.71 12332
|
||
macro avg 0.61 0.60 0.59 12332
|
||
weighted avg 0.73 0.71 0.71 12332
|
||
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-OutputArea-child">
|
||
|
||
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
|
||
|
||
|
||
|
||
<div class="jp-RenderedImage jp-OutputArea-output ">
|
||
<img 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"
|
||
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|
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|
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Last update:
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