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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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|
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|
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|
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|
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|
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|
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<li class="md-nav__item">
|
||
<a href="../../../reference/" class="md-nav__link">
|
||
GreatAI reference
|
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</a>
|
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|
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|
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|
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|
||
<li class="md-nav__item">
|
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<a href="../../../reference/utilities/" class="md-nav__link">
|
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Utilities
|
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</a>
|
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|
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|
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|
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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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|
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|
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<li class="md-nav__item">
|
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<a href="../../../reference/views/" class="md-nav__link">
|
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View models
|
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|
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|
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</ul>
|
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<li class="md-nav__item md-nav__item--active md-nav__item--nested">
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<li class="md-nav__item md-nav__item--active md-nav__item--nested">
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<input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5_1" type="checkbox" id="__nav_5_1" checked>
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<label class="md-nav__link" for="__nav_5_1">
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Explainable Naive Bayes
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<span class="md-nav__icon md-icon"></span>
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Explainable Naive Bayes
|
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</label>
|
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<ul class="md-nav__list" data-md-scrollfix>
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|
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<li class="md-nav__item">
|
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<a href="../data/" class="md-nav__link">
|
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Simple example: data engineering
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|
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<li class="md-nav__item">
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<a href="../train/" class="md-nav__link">
|
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Optimise and train a model
|
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</a>
|
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|
||
|
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|
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<input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
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<label class="md-nav__link md-nav__link--active" for="__toc">
|
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Hardening and deployment
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<span class="md-nav__icon md-icon"></span>
|
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</label>
|
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|
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<a href="./" class="md-nav__link md-nav__link--active">
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Hardening and deployment
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</a>
|
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|
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<span class="md-nav__icon md-icon"></span>
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Table of contents
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<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
|
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|
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<li class="md-nav__item">
|
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<a href="#create-the-inference-function" class="md-nav__link">
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Create the inference function
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</a>
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|
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|
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<li class="md-nav__item">
|
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<a href="#check-accuracy-on-the-test-split" class="md-nav__link">
|
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Check accuracy on the test split
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|
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</li>
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|
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</ul>
|
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|
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</nav>
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|
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</ul>
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|
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|
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<li class="md-nav__item md-nav__item--nested">
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<input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5_2" type="checkbox" id="__nav_5_2" >
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<label class="md-nav__link" for="__nav_5_2">
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Explainable SciBERT
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<span class="md-nav__icon md-icon"></span>
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</label>
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<nav class="md-nav" aria-label="Explainable SciBERT" data-md-level="2">
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<span class="md-nav__icon md-icon"></span>
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Explainable SciBERT
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</label>
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<ul class="md-nav__list" data-md-scrollfix>
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|
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|
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|
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<li class="md-nav__item">
|
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<a href="../../scibert/" class="md-nav__link">
|
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Summarising scientific publications from a tech-transfer perspective
|
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</a>
|
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</li>
|
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|
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|
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|
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|
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|
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|
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|
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|
||
<li class="md-nav__item">
|
||
<a href="../../scibert/data/" class="md-nav__link">
|
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Explore data and feasibility of approach
|
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</a>
|
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</li>
|
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|
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|
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|
||
|
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|
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|
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|
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|
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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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</a>
|
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</li>
|
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|
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|
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|
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|
||
|
||
|
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|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../scibert/deploy/" class="md-nav__link">
|
||
Create an inference function
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
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|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../scibert/additional-files/" class="md-nav__link">
|
||
Additional files in the repository
|
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</a>
|
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</li>
|
||
|
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|
||
|
||
|
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</ul>
|
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</nav>
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|
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|
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|
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</ul>
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</nav>
|
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</li>
|
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|
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|
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|
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|
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|
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|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../../../explanation/" class="md-nav__link">
|
||
Explanation
|
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</a>
|
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</li>
|
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|
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|
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|
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</ul>
|
||
</nav>
|
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</div>
|
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</div>
|
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</div>
|
||
|
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|
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|
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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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|
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|
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<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
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<label class="md-nav__title" for="__toc">
|
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<span class="md-nav__icon md-icon"></span>
|
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Table of contents
|
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</label>
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<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
|
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|
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<li class="md-nav__item">
|
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<a href="#create-the-inference-function" class="md-nav__link">
|
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Create the inference function
|
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</a>
|
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|
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</li>
|
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|
||
<li class="md-nav__item">
|
||
<a href="#check-accuracy-on-the-test-split" class="md-nav__link">
|
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Check accuracy on the test split
|
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</a>
|
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|
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</li>
|
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|
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</ul>
|
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|
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</nav>
|
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</div>
|
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</div>
|
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</div>
|
||
|
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|
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<div class="md-content" data-md-component="content">
|
||
<article class="md-content__inner md-typeset">
|
||
|
||
|
||
|
||
<a href="deploy.ipynb" title="Download Notebook" class="md-content__button md-icon">
|
||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M5 20h14v-2H5m14-9h-4V3H9v6H5l7 7 7-7Z"/></svg>
|
||
</a>
|
||
|
||
|
||
|
||
|
||
<a href="https://github.com/schmelczer/great-ai/edit/main/docs/examples/simple/deploy.ipynb" title="Edit this page" class="md-content__button md-icon">
|
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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>
|
||
|
||
|
||
|
||
<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());
|
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}(this, function () { 'use strict';
|
||
|
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function createNode(text) {
|
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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;
|
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if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
|
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const node = root.getElementById(id);
|
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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');
|
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}
|
||
}
|
||
|
||
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
|
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setTimeout(function() {
|
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notice.hidden = true
|
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}, 1000)
|
||
})
|
||
</script>
|
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|
||
|
||
|
||
|
||
<style type="text/css">
|
||
|
||
pre { line-height: 125%; }
|
||
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
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span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
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td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
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span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
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.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
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.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
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.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
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.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
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.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
|
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.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
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.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
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.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
|
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.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
|
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.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
|
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.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: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: 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#3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{/*!
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Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
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Licensed under the Apache License, Version 2.0.
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*//*!
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Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
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Licensed under the Apache License, Version 2.0.
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<h1 id="hardening-and-deployment">Hardening and deployment<a class="anchor-link" href="#hardening-and-deployment">¶</a></h1><blockquote><p>Part 3: Create production inference function</p>
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</blockquote>
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<p><img src="/media/scope-deploy.svg" alt="position of this step in the lifecycle"></p>
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<blockquote><p>The blue boxes show the steps implemented in this notebook.</p>
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<p>In <a href="/examples/simple/train">Part 2</a>, we trained our AI model. Now, it's time to create <strong>G</strong>eneral <strong>R</strong>obust <strong>E</strong>nd-to-end <strong>A</strong>utomated <strong>T</strong>rustworthy deployment.</p>
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<h2 id="create-the-inference-function">Create the inference function<a class="anchor-link" href="#create-the-inference-function">¶</a></h2><p>Next to the prediction, we also return the top-n most influential words based on their weights.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">re</span>
|
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
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<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
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<span class="kn">from</span> <span class="nn">great_ai.utilities</span> <span class="kn">import</span> <span class="n">clean</span>
|
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<span class="kn">from</span> <span class="nn">great_ai</span> <span class="kn">import</span> <span class="p">(</span>
|
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<span class="n">MultiLabelClassificationOutput</span><span class="p">,</span>
|
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<span class="n">ClassificationOutput</span><span class="p">,</span>
|
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<span class="n">GreatAI</span><span class="p">,</span>
|
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<span class="n">use_model</span><span class="p">,</span>
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<span class="n">parameter</span><span class="p">,</span>
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<span class="p">)</span>
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<span class="nd">@GreatAI</span><span class="o">.</span><span class="n">create</span>
|
||
<span class="nd">@use_model</span><span class="p">(</span><span class="s2">"small-domain-prediction"</span><span class="p">,</span> <span class="n">version</span><span class="o">=</span><span class="s2">"latest"</span><span class="p">)</span>
|
||
<span class="nd">@parameter</span><span class="p">(</span><span class="s2">"target_confidence"</span><span class="p">,</span> <span class="n">validate</span><span class="o">=</span><span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="mi">0</span> <span class="o"><=</span> <span class="n">c</span> <span class="o"><=</span> <span class="mi">100</span><span class="p">)</span>
|
||
<span class="k">def</span> <span class="nf">predict_domain</span><span class="p">(</span>
|
||
<span class="n">text</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">model</span><span class="p">:</span> <span class="n">Pipeline</span><span class="p">,</span> <span class="n">target_confidence</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">50</span>
|
||
<span class="p">)</span> <span class="o">-></span> <span class="n">MultiLabelClassificationOutput</span><span class="p">:</span>
|
||
<span class="sd">"""</span>
|
||
<span class="sd"> Predict the scientific domain of the input text.</span>
|
||
<span class="sd"> Return labels until their sum likelihood is larger than `target_confidence`.</span>
|
||
<span class="sd"> """</span>
|
||
|
||
<span class="n">preprocessed</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="sa">r</span><span class="s2">"[^a-zA-Z\s]"</span><span class="p">,</span> <span class="s2">""</span><span class="p">,</span> <span class="n">clean</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">convert_to_ascii</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
|
||
<span class="n">features</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">"vectorizer"</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">([</span><span class="n">preprocessed</span><span class="p">])</span>
|
||
<span class="n">prediction</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">"classifier"</span><span class="p">]</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">features</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||
|
||
<span class="n">best_classes</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">prediction</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
|
||
<span class="n">results</span> <span class="o">=</span> <span class="n">MultiLabelClassificationOutput</span><span class="p">()</span>
|
||
<span class="k">for</span> <span class="n">class_index</span><span class="p">,</span> <span class="n">probability</span> <span class="ow">in</span> <span class="n">best_classes</span><span class="p">:</span>
|
||
<span class="n">results</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
|
||
<span class="n">get_label</span><span class="p">(</span>
|
||
<span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
|
||
<span class="n">features</span><span class="o">=</span><span class="n">features</span><span class="p">,</span>
|
||
<span class="n">class_index</span><span class="o">=</span><span class="n">class_index</span><span class="p">,</span>
|
||
<span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
<span class="p">)</span>
|
||
|
||
<span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">r</span><span class="o">.</span><span class="n">confidence</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">results</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span> <span class="o">>=</span> <span class="n">target_confidence</span><span class="p">:</span>
|
||
<span class="k">break</span>
|
||
|
||
<span class="k">return</span> <span class="n">results</span>
|
||
|
||
|
||
<span class="k">def</span> <span class="nf">get_label</span><span class="p">(</span>
|
||
<span class="n">model</span><span class="p">:</span> <span class="n">Pipeline</span><span class="p">,</span> <span class="n">features</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">class_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">probability</span><span class="p">:</span> <span class="nb">float</span>
|
||
<span class="p">)</span> <span class="o">-></span> <span class="n">ClassificationOutput</span><span class="p">:</span>
|
||
<span class="k">return</span> <span class="n">ClassificationOutput</span><span class="p">(</span>
|
||
<span class="n">label</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">"classifier"</span><span class="p">]</span><span class="o">.</span><span class="n">classes_</span><span class="p">[</span><span class="n">class_index</span><span class="p">],</span>
|
||
<span class="n">confidence</span><span class="o">=</span><span class="nb">round</span><span class="p">(</span><span class="n">probability</span> <span class="o">*</span> <span class="mi">100</span><span class="p">),</span>
|
||
<span class="n">explanation</span><span class="o">=</span><span class="p">[</span>
|
||
<span class="n">word</span>
|
||
<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">word</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span>
|
||
<span class="p">(</span>
|
||
<span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">word</span><span class="p">)</span>
|
||
<span class="k">for</span> <span class="n">weight</span><span class="p">,</span> <span class="n">word</span><span class="p">,</span> <span class="n">count</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
|
||
<span class="n">model</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">"classifier"</span><span class="p">]</span><span class="o">.</span><span class="n">feature_log_prob_</span><span class="p">[</span><span class="n">class_index</span><span class="p">],</span>
|
||
<span class="n">model</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">"vectorizer"</span><span class="p">]</span><span class="o">.</span><span class="n">get_feature_names_out</span><span class="p">(),</span>
|
||
<span class="n">features</span><span class="o">.</span><span class="n">A</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
|
||
<span class="p">)</span>
|
||
<span class="k">if</span> <span class="n">count</span> <span class="o">></span> <span class="mi">0</span>
|
||
<span class="p">),</span>
|
||
<span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
<span class="p">][:</span><span class="mi">5</span><span class="p">],</span>
|
||
<span class="p">)</span>
|
||
</pre></div>
|
||
<div id="cell-1" class="clipboard-copy-txt">import re
|
||
import numpy as np
|
||
from sklearn.pipeline import Pipeline
|
||
from great_ai.utilities import clean
|
||
from great_ai import (
|
||
MultiLabelClassificationOutput,
|
||
ClassificationOutput,
|
||
GreatAI,
|
||
use_model,
|
||
parameter,
|
||
)
|
||
|
||
|
||
@GreatAI.create
|
||
@use_model("small-domain-prediction", version="latest")
|
||
@parameter("target_confidence", validate=lambda c: 0 <= c <= 100)
|
||
def predict_domain(
|
||
text: str, model: Pipeline, target_confidence: int = 50
|
||
) -> MultiLabelClassificationOutput:
|
||
"""
|
||
Predict the scientific domain of the input text.
|
||
Return labels until their sum likelihood is larger than `target_confidence`.
|
||
"""
|
||
|
||
preprocessed = re.sub(r"[^a-zA-Z\s]", "", clean(text, convert_to_ascii=True))
|
||
features = model.named_steps["vectorizer"].transform([preprocessed])
|
||
prediction = model.named_steps["classifier"].predict_proba(features)[0]
|
||
|
||
best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True)
|
||
|
||
results = MultiLabelClassificationOutput()
|
||
for class_index, probability in best_classes:
|
||
results.labels.append(
|
||
get_label(
|
||
model=model,
|
||
features=features,
|
||
class_index=class_index,
|
||
probability=probability,
|
||
)
|
||
)
|
||
|
||
if sum(r.confidence for r in results.labels) >= target_confidence:
|
||
break
|
||
|
||
return results
|
||
|
||
|
||
def get_label(
|
||
model: Pipeline, features: np.ndarray, class_index: int, probability: float
|
||
) -> ClassificationOutput:
|
||
return ClassificationOutput(
|
||
label=model.named_steps["classifier"].classes_[class_index],
|
||
confidence=round(probability * 100),
|
||
explanation=[
|
||
word
|
||
for _, word in sorted(
|
||
(
|
||
(weight, word)
|
||
for weight, word, count in zip(
|
||
model.named_steps["classifier"].feature_log_prob_[class_index],
|
||
model.named_steps["vectorizer"].get_feature_names_out(),
|
||
features.A[0],
|
||
)
|
||
if count > 0
|
||
),
|
||
reverse=True,
|
||
)
|
||
][:5],
|
||
)</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
|
||
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
|
||
<div class="jp-OutputArea-child">
|
||
|
||
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
|
||
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
|
||
<pre><span style="color: rgb(255,255,0)">Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️</span>
|
||
<span style="color: rgb(255,255,0)">Cannot find credentials files, defaulting to using ParallelTinyDbDriver</span>
|
||
<span style="color: rgb(255,255,0)">The selected tracing database (ParallelTinyDbDriver) is not recommended for production</span>
|
||
<span style="color: rgb(255,255,0)">Cannot find credentials files, defaulting to using LargeFileLocal</span>
|
||
<span style="color: rgb(0,175,255)">GreatAI (v0.1.6): configured ✅</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 tracing_database: ParallelTinyDbDriver</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 large_file_implementation: LargeFileLocal</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 is_production: False</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 should_log_exception_stack: True</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 prediction_cache_size: 512</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 dashboard_table_size: 50</span>
|
||
<span style="color: rgb(255,255,0)">You still need to check whether you follow all best practices before trusting your deployment.</span>
|
||
<span style="color: rgb(255,255,0)">> Find out more at https://se-ml.github.io/practices</span>
|
||
<span style="color: rgb(0,175,255)">Fetching cached versions of small-domain-prediction</span>
|
||
<span style="color: rgb(0,175,255)">Latest version of small-domain-prediction is 2 (from versions: 0, 1, 2)</span>
|
||
<span style="color: rgb(0,175,255)">File small-domain-prediction-2 found in cache</span>
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
|
||
<h2 id="check-accuracy-on-the-test-split">Check accuracy on the test split<a class="anchor-link" href="#check-accuracy-on-the-test-split">¶</a></h2><p>Anything under <code>if __name__ == "__main__":</code> will not be run when the script is executed by the <code>great-ai</code> CLI app. This, combined with <code>query_ground_truth</code> and the <code>/traces/{trace_id}/feedback</code> endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment.</p>
|
||
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell ">
|
||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell ">
|
||
<div class="jp-Cell-inputWrapper">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
|
||
|
||
<div class="CodeMirror cm-s-jupyter">
|
||
<div class="zeroclipboard-container">
|
||
<clipboard-copy for="cell-2">
|
||
<div>
|
||
<span class="notice" hidden>Copied!</span>
|
||
<svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1" data-view-component="true" class="clipboard-copy-icon">
|
||
<path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
|
||
<path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
|
||
</svg>
|
||
</div>
|
||
</clipboard-copy>
|
||
</div>
|
||
<div class="highlight-ipynb hl-python"><pre><span></span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span>
|
||
<span class="kn">from</span> <span class="nn">great_ai</span> <span class="kn">import</span> <span class="n">query_ground_truth</span>
|
||
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
|
||
|
||
<span class="n">data</span> <span class="o">=</span> <span class="n">query_ground_truth</span><span class="p">(</span><span class="s2">"test"</span><span class="p">)</span>
|
||
|
||
<span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="o">.</span><span class="n">input</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
|
||
<span class="n">y_actual</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="o">.</span><span class="n">feedback</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
|
||
|
||
<span class="n">y_predicted</span> <span class="o">=</span> <span class="p">[</span>
|
||
<span class="n">d</span><span class="o">.</span><span class="n">output</span><span class="o">.</span><span class="n">labels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">label</span>
|
||
<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">predict_domain</span><span class="o">.</span><span class="n">process_batch</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">do_not_persist_traces</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
<span class="p">]</span>
|
||
<span class="n">y_actual_aligned</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span> <span class="k">if</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">a</span> <span class="k">else</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span><span class="p">,</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">y_predicted</span><span class="p">,</span> <span class="n">y_actual</span><span class="p">)]</span>
|
||
|
||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||
|
||
<span class="c1"># Configure matplotlib to have nice, high-resolution charts</span>
|
||
<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"figure.figsize"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">19</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">16</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"axes.xmargin"</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
|
||
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_actual_aligned</span><span class="p">,</span> <span class="n">y_predicted</span><span class="p">))</span>
|
||
<span class="n">metrics</span><span class="o">.</span><span class="n">ConfusionMatrixDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span>
|
||
<span class="n">y_true</span><span class="o">=</span><span class="n">y_actual_aligned</span><span class="p">,</span>
|
||
<span class="n">y_pred</span><span class="o">=</span><span class="n">y_predicted</span><span class="p">,</span>
|
||
<span class="n">xticks_rotation</span><span class="o">=</span><span class="s2">"vertical"</span><span class="p">,</span>
|
||
<span class="n">normalize</span><span class="o">=</span><span class="s2">"pred"</span><span class="p">,</span>
|
||
<span class="n">values_format</span><span class="o">=</span><span class="s2">".2f"</span><span class="p">,</span>
|
||
<span class="p">)</span>
|
||
|
||
<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">"ss-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-2" class="clipboard-copy-txt">if __name__ == "__main__":
|
||
from great_ai import query_ground_truth
|
||
from sklearn import metrics
|
||
|
||
data = query_ground_truth("test")
|
||
|
||
X = [d.input for d in data]
|
||
y_actual = [d.feedback for d in data]
|
||
|
||
y_predicted = [
|
||
d.output.labels[0].label
|
||
for d in predict_domain.process_batch(X, do_not_persist_traces=True)
|
||
]
|
||
y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)]
|
||
|
||
import matplotlib.pyplot as plt
|
||
|
||
# Configure matplotlib to have nice, high-resolution charts
|
||
%matplotlib inline
|
||
plt.rcParams["figure.figsize"] = (19, 18)
|
||
plt.rcParams["figure.facecolor"] = "white"
|
||
plt.rcParams["font.size"] = 16
|
||
plt.rcParams["axes.xmargin"] = 0
|
||
|
||
print(metrics.classification_report(y_actual_aligned, y_predicted))
|
||
metrics.ConfusionMatrixDisplay.from_predictions(
|
||
y_true=y_actual_aligned,
|
||
y_pred=y_predicted,
|
||
xticks_rotation="vertical",
|
||
normalize="pred",
|
||
values_format=".2f",
|
||
)
|
||
|
||
plt.tight_layout()
|
||
plt.savefig("ss-confusion.png", dpi=600)</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
|
||
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
|
||
<div class="jp-OutputArea-child">
|
||
|
||
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
|
||
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
|
||
<pre>100%|██████████| 12272/12272 [01:10<00:00, 174.77it/s]
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-OutputArea-child">
|
||
|
||
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
|
||
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
|
||
<pre> precision recall f1-score support
|
||
|
||
Art 0.54 0.38 0.45 126
|
||
Biology 0.77 0.84 0.80 1215
|
||
Business 0.47 0.73 0.57 311
|
||
Chemistry 0.82 0.67 0.74 1205
|
||
Computer Science 0.77 0.76 0.76 1277
|
||
Economics 0.69 0.55 0.61 270
|
||
Engineering 0.55 0.52 0.53 754
|
||
Environmental Science 0.56 0.55 0.55 227
|
||
Geography 0.54 0.39 0.45 276
|
||
Geology 0.74 0.67 0.70 265
|
||
History 0.35 0.18 0.24 140
|
||
Materials Science 0.72 0.81 0.76 1011
|
||
Mathematics 0.77 0.70 0.74 498
|
||
Medicine 0.96 0.77 0.86 2835
|
||
Philosophy 0.57 0.06 0.10 71
|
||
Physics 0.66 0.75 0.70 611
|
||
Political Science 0.44 0.61 0.51 291
|
||
Psychology 0.52 0.84 0.64 574
|
||
Sociology 0.33 0.59 0.42 315
|
||
|
||
accuracy 0.71 12272
|
||
macro avg 0.62 0.60 0.59 12272
|
||
weighted avg 0.74 0.71 0.71 12272
|
||
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="jp-OutputArea-child">
|
||
|
||
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
|
||
|
||
|
||
|
||
<div class="jp-RenderedImage jp-OutputArea-output ">
|
||
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