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<span class="md-nav__icon md-icon"></span>
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Reference
|
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</label>
|
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<ul class="md-nav__list" data-md-scrollfix>
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<li class="md-nav__item">
|
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<a href="../../../reference/" class="md-nav__link">
|
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GreatAI reference
|
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</a>
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<a href="../../../reference/utilities/" class="md-nav__link">
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<li class="md-nav__item">
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<a href="../../../reference/large-file/" class="md-nav__link">
|
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LargeFile
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Explainable Naive Bayes
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Simple example: data engineering
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Hardening and deployment
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Explainable SciBERT
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<ul class="md-nav__list" data-md-scrollfix>
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<a href="../../scibert/data/" class="md-nav__link">
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Explore data and feasibility of approach
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<li class="md-nav__item">
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<a href="../../scibert/train/" class="md-nav__link">
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Fine-tune SciBERT
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<li class="md-nav__item">
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<a href="../../scibert/deploy/" class="md-nav__link">
|
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Create an inference function
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</a>
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<a href="../../scibert/additional-files/" class="md-nav__link">
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<a href="../../../explanation/" class="md-nav__link">
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</nav>
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</div>
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</div>
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<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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</div>
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</div>
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<div class="md-content" data-md-component="content">
|
||
<article class="md-content__inner md-typeset">
|
||
|
||
|
||
|
||
<a href="train.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>
|
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</a>
|
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|
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|
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|
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|
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<a href="https://github.com/schmelczer/great-ai/edit/main/docs/examples/simple-mag/train.ipynb" title="Edit this page" class="md-content__button md-icon">
|
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|
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<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>
|
||
|
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|
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<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) }
|
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.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
|
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.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
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.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
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.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
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.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
|
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.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 .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
|
||
.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="train-a-field-of-study-domain-classification-of-sentences-on-the-mag-dataset">Train a field of study (domain) classification of sentences on the <a href="https://github.com/allenai/scibert/tree/master/data/text_classification/mag">MAG</a> dataset<a class="anchor-link" href="#train-a-field-of-study-domain-classification-of-sentences-on-the-mag-dataset">¶</a></h1><p><a href="https://paperswithcode.com/sota/sentence-classification-on-paper-field">SciBERT achieves an F1-score of 0.6571 on this dataset.</a>
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This notebook shows that better results can be achieved without even using transformers.</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">json</span>
|
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<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
|
||
<span class="kn">from</span> <span class="nn">great_ai.utilities</span> <span class="kn">import</span> <span class="n">clean</span><span class="p">,</span> <span class="n">simple_parallel_map</span>
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<span class="kn">from</span> <span class="nn">tqdm.cli</span> <span class="kn">import</span> <span class="n">tqdm</span>
|
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<span class="k">def</span> <span class="nf">preprocess</span><span class="p">(</span><span class="n">line</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]:</span>
|
||
<span class="n">data_point</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
|
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||
<span class="k">return</span> <span class="p">(</span><span class="n">clean</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="s2">"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">data_point</span><span class="p">[</span><span class="s2">"label"</span><span class="p">])</span>
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|
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<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"../../tutorial/data/train.txt"</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
|
||
<span class="n">training_data</span> <span class="o">=</span> <span class="n">simple_parallel_map</span><span class="p">(</span><span class="n">preprocess</span><span class="p">,</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">())</span>
|
||
|
||
<span class="n">X_train</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">training_data</span><span class="p">]</span>
|
||
<span class="n">y_train</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">training_data</span><span class="p">]</span>
|
||
|
||
|
||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"../../tutorial/data/test.txt"</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
|
||
<span class="n">test_data</span> <span class="o">=</span> <span class="n">simple_parallel_map</span><span class="p">(</span><span class="n">preprocess</span><span class="p">,</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">())</span>
|
||
|
||
<span class="n">X_test</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">test_data</span><span class="p">]</span>
|
||
<span class="n">y_test</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">test_data</span><span class="p">]</span>
|
||
</pre></div>
|
||
<div id="cell-1" class="clipboard-copy-txt">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]</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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||
</div>
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||
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||
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
|
||
<div class="jp-OutputArea-child">
|
||
|
||
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||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
|
||
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
|
||
<pre>100%|██████████| 84000/84000 [00:16<00:00, 4964.08it/s]
|
||
100%|██████████| 22399/22399 [00:05<00:00, 3847.04it/s]
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
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<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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</div>
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<div class="jp-InputArea jp-Cell-inputArea">
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<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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||
|
||
|
||
<div class="CodeMirror cm-s-jupyter">
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||
<div class="zeroclipboard-container">
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||
<clipboard-copy for="cell-2">
|
||
<div>
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||
<span class="notice" hidden>Copied!</span>
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||
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||
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||
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|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
|
||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||
|
||
<span class="n">domains</span><span class="p">,</span> <span class="n">counts</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">Counter</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span><span class="o">.</span><span class="n">most_common</span><span class="p">())</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.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">18</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">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
|
||
|
||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
|
||
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">domains</span><span class="p">,</span> <span class="n">counts</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Count"</span><span class="p">)</span>
|
||
<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||
<span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s2">"mag-distribution.png"</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
|
||
<span class="kc">None</span>
|
||
</pre></div>
|
||
<div id="cell-2" class="clipboard-copy-txt">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</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-Cell-outputWrapper">
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"
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|
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<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs ">
|
||
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<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
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|
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|
||
<div class="CodeMirror cm-s-jupyter">
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|
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|
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|
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</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">MultinomialNB</span>
|
||
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
|
||
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">TfidfVectorizer</span>
|
||
|
||
|
||
<span class="k">def</span> <span class="nf">create_pipeline</span><span class="p">()</span> <span class="o">-></span> <span class="n">Pipeline</span><span class="p">:</span>
|
||
<span class="k">return</span> <span class="n">Pipeline</span><span class="p">(</span>
|
||
<span class="n">steps</span><span class="o">=</span><span class="p">[</span>
|
||
<span class="p">(</span><span class="s2">"vectorizer"</span><span class="p">,</span> <span class="n">TfidfVectorizer</span><span class="p">(</span><span class="n">sublinear_tf</span><span class="o">=</span><span class="kc">True</span><span class="p">)),</span>
|
||
<span class="p">(</span><span class="s2">"classifier"</span><span class="p">,</span> <span class="n">MultinomialNB</span><span class="p">()),</span>
|
||
<span class="p">]</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
<div id="cell-3" class="clipboard-copy-txt">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()),
|
||
]
|
||
)</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</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 [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
|
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|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy for="cell-4">
|
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<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">
|
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<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="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
|
||
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||
|
||
<span class="n">optimisation_pipeline</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span>
|
||
<span class="n">create_pipeline</span><span class="p">(),</span>
|
||
<span class="p">{</span>
|
||
<span class="s2">"vectorizer__min_df"</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span>
|
||
<span class="s2">"vectorizer__max_df"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span>
|
||
<span class="s2">"classifier__alpha"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
|
||
<span class="s2">"classifier__fit_prior"</span><span class="p">:</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">],</span>
|
||
<span class="p">},</span>
|
||
<span class="n">scoring</span><span class="o">=</span><span class="s2">"f1_macro"</span><span class="p">,</span>
|
||
<span class="n">cv</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
|
||
<span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
|
||
<span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
<span class="n">optimisation_pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
|
||
|
||
<span class="n">results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">optimisation_pipeline</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">)</span>
|
||
<span class="n">results</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">"rank_test_score"</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div id="cell-4" class="clipboard-copy-txt">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")</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="text/plain">
|
||
<pre>Fitting 3 folds for each of 24 candidates, totalling 72 fits
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-OutputArea-child">
|
||
|
||
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
|
||
|
||
|
||
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
|
||
<div>
|
||
<style scoped>
|
||
.dataframe tbody tr th:only-of-type {
|
||
vertical-align: middle;
|
||
}
|
||
|
||
.dataframe tbody tr th {
|
||
vertical-align: top;
|
||
}
|
||
|
||
.dataframe thead th {
|
||
text-align: right;
|
||
}
|
||
</style>
|
||
<table border="1" class="dataframe">
|
||
<thead>
|
||
<tr style="text-align: right;">
|
||
<th></th>
|
||
<th>mean_fit_time</th>
|
||
<th>std_fit_time</th>
|
||
<th>mean_score_time</th>
|
||
<th>std_score_time</th>
|
||
<th>param_classifier__alpha</th>
|
||
<th>param_classifier__fit_prior</th>
|
||
<th>param_vectorizer__max_df</th>
|
||
<th>param_vectorizer__min_df</th>
|
||
<th>params</th>
|
||
<th>split0_test_score</th>
|
||
<th>split1_test_score</th>
|
||
<th>split2_test_score</th>
|
||
<th>mean_test_score</th>
|
||
<th>std_test_score</th>
|
||
<th>rank_test_score</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<th>12</th>
|
||
<td>2.352721</td>
|
||
<td>0.257936</td>
|
||
<td>1.357399</td>
|
||
<td>0.094580</td>
|
||
<td>1</td>
|
||
<td>True</td>
|
||
<td>0.05</td>
|
||
<td>5</td>
|
||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||
<td>0.677283</td>
|
||
<td>0.670398</td>
|
||
<td>0.665671</td>
|
||
<td>0.671118</td>
|
||
<td>0.004768</td>
|
||
<td>1</td>
|
||
</tr>
|
||
<tr>
|
||
<th>15</th>
|
||
<td>2.544958</td>
|
||
<td>0.051976</td>
|
||
<td>1.245658</td>
|
||
<td>0.137108</td>
|
||
<td>1</td>
|
||
<td>True</td>
|
||
<td>0.1</td>
|
||
<td>5</td>
|
||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||
<td>0.676970</td>
|
||
<td>0.670244</td>
|
||
<td>0.666123</td>
|
||
<td>0.671112</td>
|
||
<td>0.004470</td>
|
||
<td>2</td>
|
||
</tr>
|
||
<tr>
|
||
<th>18</th>
|
||
<td>2.472888</td>
|
||
<td>0.053978</td>
|
||
<td>1.447181</td>
|
||
<td>0.051170</td>
|
||
<td>1</td>
|
||
<td>False</td>
|
||
<td>0.05</td>
|
||
<td>5</td>
|
||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||
<td>0.675678</td>
|
||
<td>0.669321</td>
|
||
<td>0.665305</td>
|
||
<td>0.670101</td>
|
||
<td>0.004271</td>
|
||
<td>3</td>
|
||
</tr>
|
||
<tr>
|
||
<th>21</th>
|
||
<td>2.467637</td>
|
||
<td>0.005358</td>
|
||
<td>1.216254</td>
|
||
<td>0.048319</td>
|
||
<td>1</td>
|
||
<td>False</td>
|
||
<td>0.1</td>
|
||
<td>5</td>
|
||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||
<td>0.675569</td>
|
||
<td>0.669482</td>
|
||
<td>0.665225</td>
|
||
<td>0.670092</td>
|
||
<td>0.004245</td>
|
||
<td>4</td>
|
||
</tr>
|
||
<tr>
|
||
<th>3</th>
|
||
<td>2.477612</td>
|
||
<td>0.134880</td>
|
||
<td>1.428521</td>
|
||
<td>0.017417</td>
|
||
<td>0.5</td>
|
||
<td>True</td>
|
||
<td>0.1</td>
|
||
<td>5</td>
|
||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||
<td>0.675225</td>
|
||
<td>0.668912</td>
|
||
<td>0.665652</td>
|
||
<td>0.669930</td>
|
||
<td>0.003974</td>
|
||
<td>5</td>
|
||
</tr>
|
||
<tr>
|
||
<th>0</th>
|
||
<td>2.484810</td>
|
||
<td>0.067836</td>
|
||
<td>1.249979</td>
|
||
<td>0.209959</td>
|
||
<td>0.5</td>
|
||
<td>True</td>
|
||
<td>0.05</td>
|
||
<td>5</td>
|
||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||
<td>0.675264</td>
|
||
<td>0.668781</td>
|
||
<td>0.665394</td>
|
||
<td>0.669813</td>
|
||
<td>0.004095</td>
|
||
<td>6</td>
|
||
</tr>
|
||
<tr>
|
||
<th>9</th>
|
||
<td>2.452453</td>
|
||
<td>0.049376</td>
|
||
<td>1.279694</td>
|
||
<td>0.089200</td>
|
||
<td>0.5</td>
|
||
<td>False</td>
|
||
<td>0.1</td>
|
||
<td>5</td>
|
||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||
<td>0.674199</td>
|
||
<td>0.668005</td>
|
||
<td>0.664456</td>
|
||
<td>0.668887</td>
|
||
<td>0.004026</td>
|
||
<td>7</td>
|
||
</tr>
|
||
<tr>
|
||
<th>6</th>
|
||
<td>2.748628</td>
|
||
<td>0.098077</td>
|
||
<td>1.255213</td>
|
||
<td>0.101334</td>
|
||
<td>0.5</td>
|
||
<td>False</td>
|
||
<td>0.05</td>
|
||
<td>5</td>
|
||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||
<td>0.673996</td>
|
||
<td>0.668048</td>
|
||
<td>0.664572</td>
|
||
<td>0.668872</td>
|
||
<td>0.003891</td>
|
||
<td>8</td>
|
||
</tr>
|
||
<tr>
|
||
<th>13</th>
|
||
<td>2.371907</td>
|
||
<td>0.204820</td>
|
||
<td>1.375143</td>
|
||
<td>0.193984</td>
|
||
<td>1</td>
|
||
<td>True</td>
|
||
<td>0.05</td>
|
||
<td>20</td>
|
||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||
<td>0.656459</td>
|
||
<td>0.647433</td>
|
||
<td>0.644864</td>
|
||
<td>0.649585</td>
|
||
<td>0.004972</td>
|
||
<td>9</td>
|
||
</tr>
|
||
<tr>
|
||
<th>16</th>
|
||
<td>2.557486</td>
|
||
<td>0.184332</td>
|
||
<td>1.272137</td>
|
||
<td>0.133980</td>
|
||
<td>1</td>
|
||
<td>True</td>
|
||
<td>0.1</td>
|
||
<td>20</td>
|
||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||
<td>0.656196</td>
|
||
<td>0.646960</td>
|
||
<td>0.645436</td>
|
||
<td>0.649530</td>
|
||
<td>0.004754</td>
|
||
<td>10</td>
|
||
</tr>
|
||
<tr>
|
||
<th>4</th>
|
||
<td>2.362093</td>
|
||
<td>0.109796</td>
|
||
<td>1.344960</td>
|
||
<td>0.083562</td>
|
||
<td>0.5</td>
|
||
<td>True</td>
|
||
<td>0.1</td>
|
||
<td>20</td>
|
||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||
<td>0.654424</td>
|
||
<td>0.644690</td>
|
||
<td>0.644991</td>
|
||
<td>0.648035</td>
|
||
<td>0.004519</td>
|
||
<td>11</td>
|
||
</tr>
|
||
<tr>
|
||
<th>1</th>
|
||
<td>2.668012</td>
|
||
<td>0.131751</td>
|
||
<td>1.383441</td>
|
||
<td>0.049157</td>
|
||
<td>0.5</td>
|
||
<td>True</td>
|
||
<td>0.05</td>
|
||
<td>20</td>
|
||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||
<td>0.654622</td>
|
||
<td>0.644901</td>
|
||
<td>0.644425</td>
|
||
<td>0.647983</td>
|
||
<td>0.004699</td>
|
||
<td>12</td>
|
||
</tr>
|
||
<tr>
|
||
<th>19</th>
|
||
<td>2.517576</td>
|
||
<td>0.154192</td>
|
||
<td>1.374269</td>
|
||
<td>0.026773</td>
|
||
<td>1</td>
|
||
<td>False</td>
|
||
<td>0.05</td>
|
||
<td>20</td>
|
||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||
<td>0.655125</td>
|
||
<td>0.645586</td>
|
||
<td>0.643166</td>
|
||
<td>0.647959</td>
|
||
<td>0.005163</td>
|
||
<td>13</td>
|
||
</tr>
|
||
<tr>
|
||
<th>22</th>
|
||
<td>2.386240</td>
|
||
<td>0.069718</td>
|
||
<td>0.967638</td>
|
||
<td>0.076766</td>
|
||
<td>1</td>
|
||
<td>False</td>
|
||
<td>0.1</td>
|
||
<td>20</td>
|
||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||
<td>0.654254</td>
|
||
<td>0.645409</td>
|
||
<td>0.643514</td>
|
||
<td>0.647726</td>
|
||
<td>0.004680</td>
|
||
<td>14</td>
|
||
</tr>
|
||
<tr>
|
||
<th>7</th>
|
||
<td>2.416211</td>
|
||
<td>0.138001</td>
|
||
<td>1.316785</td>
|
||
<td>0.036402</td>
|
||
<td>0.5</td>
|
||
<td>False</td>
|
||
<td>0.05</td>
|
||
<td>20</td>
|
||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||
<td>0.652727</td>
|
||
<td>0.643691</td>
|
||
<td>0.642749</td>
|
||
<td>0.646389</td>
|
||
<td>0.004498</td>
|
||
<td>15</td>
|
||
</tr>
|
||
<tr>
|
||
<th>10</th>
|
||
<td>2.397768</td>
|
||
<td>0.244951</td>
|
||
<td>1.330007</td>
|
||
<td>0.008983</td>
|
||
<td>0.5</td>
|
||
<td>False</td>
|
||
<td>0.1</td>
|
||
<td>20</td>
|
||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||
<td>0.652290</td>
|
||
<td>0.642975</td>
|
||
<td>0.643421</td>
|
||
<td>0.646229</td>
|
||
<td>0.004290</td>
|
||
<td>16</td>
|
||
</tr>
|
||
<tr>
|
||
<th>14</th>
|
||
<td>2.590127</td>
|
||
<td>0.054396</td>
|
||
<td>1.155976</td>
|
||
<td>0.187602</td>
|
||
<td>1</td>
|
||
<td>True</td>
|
||
<td>0.05</td>
|
||
<td>100</td>
|
||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||
<td>0.587097</td>
|
||
<td>0.577411</td>
|
||
<td>0.580170</td>
|
||
<td>0.581560</td>
|
||
<td>0.004075</td>
|
||
<td>17</td>
|
||
</tr>
|
||
<tr>
|
||
<th>17</th>
|
||
<td>2.497556</td>
|
||
<td>0.085952</td>
|
||
<td>1.169808</td>
|
||
<td>0.061336</td>
|
||
<td>1</td>
|
||
<td>True</td>
|
||
<td>0.1</td>
|
||
<td>100</td>
|
||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||
<td>0.585713</td>
|
||
<td>0.577197</td>
|
||
<td>0.580763</td>
|
||
<td>0.581224</td>
|
||
<td>0.003492</td>
|
||
<td>18</td>
|
||
</tr>
|
||
<tr>
|
||
<th>2</th>
|
||
<td>2.467611</td>
|
||
<td>0.168399</td>
|
||
<td>1.313936</td>
|
||
<td>0.109695</td>
|
||
<td>0.5</td>
|
||
<td>True</td>
|
||
<td>0.05</td>
|
||
<td>100</td>
|
||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||
<td>0.586346</td>
|
||
<td>0.576987</td>
|
||
<td>0.579852</td>
|
||
<td>0.581062</td>
|
||
<td>0.003915</td>
|
||
<td>19</td>
|
||
</tr>
|
||
<tr>
|
||
<th>5</th>
|
||
<td>2.339322</td>
|
||
<td>0.092595</td>
|
||
<td>1.209515</td>
|
||
<td>0.132825</td>
|
||
<td>0.5</td>
|
||
<td>True</td>
|
||
<td>0.1</td>
|
||
<td>100</td>
|
||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||
<td>0.585634</td>
|
||
<td>0.576217</td>
|
||
<td>0.580320</td>
|
||
<td>0.580723</td>
|
||
<td>0.003855</td>
|
||
<td>20</td>
|
||
</tr>
|
||
<tr>
|
||
<th>20</th>
|
||
<td>2.644488</td>
|
||
<td>0.119967</td>
|
||
<td>1.238554</td>
|
||
<td>0.048871</td>
|
||
<td>1</td>
|
||
<td>False</td>
|
||
<td>0.05</td>
|
||
<td>100</td>
|
||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||
<td>0.581096</td>
|
||
<td>0.572833</td>
|
||
<td>0.576280</td>
|
||
<td>0.576736</td>
|
||
<td>0.003389</td>
|
||
<td>21</td>
|
||
</tr>
|
||
<tr>
|
||
<th>8</th>
|
||
<td>2.456030</td>
|
||
<td>0.072136</td>
|
||
<td>1.240463</td>
|
||
<td>0.092383</td>
|
||
<td>0.5</td>
|
||
<td>False</td>
|
||
<td>0.05</td>
|
||
<td>100</td>
|
||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||
<td>0.580841</td>
|
||
<td>0.572391</td>
|
||
<td>0.575731</td>
|
||
<td>0.576321</td>
|
||
<td>0.003475</td>
|
||
<td>22</td>
|
||
</tr>
|
||
<tr>
|
||
<th>23</th>
|
||
<td>2.335796</td>
|
||
<td>0.082338</td>
|
||
<td>0.786187</td>
|
||
<td>0.073499</td>
|
||
<td>1</td>
|
||
<td>False</td>
|
||
<td>0.1</td>
|
||
<td>100</td>
|
||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||
<td>0.579769</td>
|
||
<td>0.572373</td>
|
||
<td>0.576699</td>
|
||
<td>0.576280</td>
|
||
<td>0.003034</td>
|
||
<td>23</td>
|
||
</tr>
|
||
<tr>
|
||
<th>11</th>
|
||
<td>2.530331</td>
|
||
<td>0.107199</td>
|
||
<td>1.247842</td>
|
||
<td>0.078229</td>
|
||
<td>0.5</td>
|
||
<td>False</td>
|
||
<td>0.1</td>
|
||
<td>100</td>
|
||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||
<td>0.579743</td>
|
||
<td>0.571607</td>
|
||
<td>0.576352</td>
|
||
<td>0.575901</td>
|
||
<td>0.003337</td>
|
||
<td>24</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</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 [5]:</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-5">
|
||
<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="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">set_config</span>
|
||
|
||
<span class="n">set_config</span><span class="p">(</span><span class="n">display</span><span class="o">=</span><span class="s2">"diagram"</span><span class="p">)</span>
|
||
|
||
<span class="n">classifier</span> <span class="o">=</span> <span class="n">create_pipeline</span><span class="p">()</span>
|
||
<span class="n">classifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">optimisation_pipeline</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
|
||
<span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div id="cell-5" class="clipboard-copy-txt">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)</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">Out[5]:</div>
|
||
|
||
|
||
|
||
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
|
||
<style>#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: "▸";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: "▾";}#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;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('vectorizer',
|
||
TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)),
|
||
('classifier', MultinomialNB(alpha=1))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('vectorizer',
|
||
TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)),
|
||
('classifier', MultinomialNB(alpha=1))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">TfidfVectorizer</label><div class="sk-toggleable__content"><pre>TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB(alpha=1)</pre></div></div></div></div></div></div></div>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</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 [12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
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|
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|
||
<div class="CodeMirror cm-s-jupyter">
|
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<div class="zeroclipboard-container">
|
||
<clipboard-copy for="cell-6">
|
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<div>
|
||
<span class="notice" hidden>Copied!</span>
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</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
|
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<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
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|
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<span class="n">y_predicted</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
|
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|
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<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">20</span><span class="p">,</span> <span class="mi">18</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">22</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_test</span><span class="p">,</span> <span class="n">y_predicted</span><span class="p">,</span> <span class="n">digits</span><span class="o">=</span><span class="mi">4</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_test</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>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s2">"mag-confusion.png"</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">600</span><span class="p">)</span>
|
||
</pre></div>
|
||
<div id="cell-6" class="clipboard-copy-txt">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)</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
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</div>
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|
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<div class="jp-OutputArea jp-Cell-outputArea">
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<div class="jp-OutputArea-child">
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<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
|
||
<pre> 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
|
||
|
||
</pre>
|
||
</div>
|
||
</div>
|
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<div class="jp-RenderedImage jp-OutputArea-output ">
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"
|
||
class="
|
||
"
|
||
>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
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||
|
||
<hr>
|
||
<div class="md-source-file">
|
||
<small>
|
||
|
||
Last update:
|
||
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 16, 2022</span>
|
||
|
||
|
||
</small>
|
||
</div>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
</article>
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</div>
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</div>
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