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---
title: Helper functions for processing data and basic transforms
keywords: fastai
sidebar: home_sidebar
summary: "Functions for getting, splitting, and labeling data, as well as generic transforms"
description: "Functions for getting, splitting, and labeling data, as well as generic transforms"
nb_path: "nbs/05_data.transforms.ipynb"
---
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<h2 id="Get,-split,-and-label">Get, split, and label<a class="anchor-link" href="#Get,-split,-and-label"> </a></h2>
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<p>For most data source creation we need functions to get a list of items, split them in to train/valid sets, and label them. fastai provides functions to make each of these steps easy (especially when combined with <code>fastai.data.blocks</code>).</p>
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<h3 id="Get">Get<a class="anchor-link" href="#Get"> </a></h3>
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<p>First we'll look at functions that <em>get</em> a list of items (generally file names).</p>
<p>We'll use <em>tiny MNIST</em> (a subset of MNIST with just two classes, <code>7</code>s and <code>3</code>s) for our examples/tests throughout this page.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">path</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">MNIST_TINY</span><span class="p">)</span>
<span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="s1">'train'</span><span class="p">)</span><span class="o">.</span><span class="n">ls</span><span class="p">()</span>
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<pre>(#2) [Path('/home/jhoward/.fastai/data/mnist_tiny/train/7'),Path('/home/jhoward/.fastai/data/mnist_tiny/train/3')]</pre>
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<h4 id="get_files" class="doc_header"><code>get_files</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L26" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_files</code>(<strong><code>path</code></strong>, <strong><code>extensions</code></strong>=<em><code>None</code></em>, <strong><code>recurse</code></strong>=<em><code>True</code></em>, <strong><code>folders</code></strong>=<em><code>None</code></em>, <strong><code>followlinks</code></strong>=<em><code>True</code></em>)</p>
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<p>Get all the files in <code>path</code> with optional <code>extensions</code>, optionally with <code>recurse</code>, only in <code>folders</code>, if specified.</p>
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<p>This is the most general way to grab a bunch of file names from disk. If you pass <code>extensions</code> (including the <code>.</code>) then returned file names are filtered by that list. Only those files directly in <code>path</code> are included, unless you pass <code>recurse</code>, in which case all child folders are also searched recursively. <code>folders</code> is an optional list of directories to limit the search to.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">t3</span> <span class="o">=</span> <span class="n">get_files</span><span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="s1">'train'</span><span class="o">/</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">extensions</span><span class="o">=</span><span class="s1">'.png'</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">t7</span> <span class="o">=</span> <span class="n">get_files</span><span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="s1">'train'</span><span class="o">/</span><span class="s1">'7'</span><span class="p">,</span> <span class="n">extensions</span><span class="o">=</span><span class="s1">'.png'</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">get_files</span><span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="s1">'train'</span><span class="p">,</span> <span class="n">extensions</span><span class="o">=</span><span class="s1">'.png'</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">t</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">t3</span><span class="p">)</span><span class="o">+</span><span class="nb">len</span><span class="p">(</span><span class="n">t7</span><span class="p">))</span>
<span class="n">test_eq</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">get_files</span><span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="s1">'train'</span><span class="o">/</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">extensions</span><span class="o">=</span><span class="s1">'.jpg'</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">False</span><span class="p">)),</span><span class="mi">0</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">t</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">get_files</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">extensions</span><span class="o">=</span><span class="s1">'.png'</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">folders</span><span class="o">=</span><span class="s1">'train'</span><span class="p">)))</span>
<span class="n">t</span>
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<pre>(#709) [Path('/home/jhoward/.fastai/data/mnist_tiny/train/7/723.png'),Path('/home/jhoward/.fastai/data/mnist_tiny/train/7/7446.png'),Path('/home/jhoward/.fastai/data/mnist_tiny/train/7/8566.png'),Path('/home/jhoward/.fastai/data/mnist_tiny/train/7/9200.png'),Path('/home/jhoward/.fastai/data/mnist_tiny/train/7/7085.png'),Path('/home/jhoward/.fastai/data/mnist_tiny/train/7/8665.png'),Path('/home/jhoward/.fastai/data/mnist_tiny/train/7/7348.png'),Path('/home/jhoward/.fastai/data/mnist_tiny/train/7/9283.png'),Path('/home/jhoward/.fastai/data/mnist_tiny/train/7/9854.png'),Path('/home/jhoward/.fastai/data/mnist_tiny/train/7/9548.png')...]</pre>
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<p>It's often useful to be able to create functions with customized behavior. <code>fastai.data</code> generally uses functions named as CamelCase verbs ending in <code>er</code> to create these functions. <a href="/data.transforms.html#FileGetter"><code>FileGetter</code></a> is a simple example of such a function creator.</p>
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<h4 id="FileGetter" class="doc_header"><code>FileGetter</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L45" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>FileGetter</code>(<strong><code>suf</code></strong>=<em><code>''</code></em>, <strong><code>extensions</code></strong>=<em><code>None</code></em>, <strong><code>recurse</code></strong>=<em><code>True</code></em>, <strong><code>folders</code></strong>=<em><code>None</code></em>)</p>
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<p>Create <a href="/data.transforms.html#get_files"><code>get_files</code></a> partial function that searches path suffix <code>suf</code>, only in <code>folders</code>, if specified, and passes along args</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fpng</span> <span class="o">=</span> <span class="n">FileGetter</span><span class="p">(</span><span class="n">extensions</span><span class="o">=</span><span class="s1">'.png'</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">t7</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">fpng</span><span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="s1">'train'</span><span class="o">/</span><span class="s1">'7'</span><span class="p">)))</span>
<span class="n">test_eq</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">t</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">fpng</span><span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="s1">'train'</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">True</span><span class="p">)))</span>
<span class="n">fpng_r</span> <span class="o">=</span> <span class="n">FileGetter</span><span class="p">(</span><span class="n">extensions</span><span class="o">=</span><span class="s1">'.png'</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">t</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">fpng_r</span><span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="s1">'train'</span><span class="p">)))</span>
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<h4 id="get_image_files" class="doc_header"><code>get_image_files</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L55" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_image_files</code>(<strong><code>path</code></strong>, <strong><code>recurse</code></strong>=<em><code>True</code></em>, <strong><code>folders</code></strong>=<em><code>None</code></em>)</p>
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<p>Get image files in <code>path</code> recursively, only in <code>folders</code>, if specified.</p>
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<p>This is simply <a href="/data.transforms.html#get_files"><code>get_files</code></a> called with a list of standard image extensions.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">t</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">get_image_files</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">folders</span><span class="o">=</span><span class="s1">'train'</span><span class="p">)))</span>
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<h4 id="ImageGetter" class="doc_header"><code>ImageGetter</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L60" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>ImageGetter</code>(<strong><code>suf</code></strong>=<em><code>''</code></em>, <strong><code>recurse</code></strong>=<em><code>True</code></em>, <strong><code>folders</code></strong>=<em><code>None</code></em>)</p>
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<p>Create <a href="/data.transforms.html#get_image_files"><code>get_image_files</code></a> partial function that searches path suffix <code>suf</code> and passes along <code>kwargs</code>, only in <code>folders</code>, if specified.</p>
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<p>Same as <a href="/data.transforms.html#FileGetter"><code>FileGetter</code></a>, but for image extensions.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">get_files</span><span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="s1">'train'</span><span class="p">,</span> <span class="n">extensions</span><span class="o">=</span><span class="s1">'.png'</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">folders</span><span class="o">=</span><span class="s1">'3'</span><span class="p">)),</span>
<span class="nb">len</span><span class="p">(</span><span class="n">ImageGetter</span><span class="p">(</span> <span class="s1">'train'</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">folders</span><span class="o">=</span><span class="s1">'3'</span><span class="p">)(</span><span class="n">path</span><span class="p">)))</span>
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<h4 id="get_text_files" class="doc_header"><code>get_text_files</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L66" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_text_files</code>(<strong><code>path</code></strong>, <strong><code>recurse</code></strong>=<em><code>True</code></em>, <strong><code>folders</code></strong>=<em><code>None</code></em>)</p>
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<p>Get text files in <code>path</code> recursively, only in <code>folders</code>, if specified.</p>
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<h2 id="ItemGetter" class="doc_header"><code>class</code> <code>ItemGetter</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L71" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>ItemGetter</code>(<strong><code>i</code></strong>) :: <a href="https://fastcore.fast.ai/transform#ItemTransform"><code>ItemTransform</code></a></p>
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<p>Creates a proper transform that applies <code>itemgetter(i)</code> (even on a tuple)</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="n">ItemGetter</span><span class="p">(</span><span class="mi">1</span><span class="p">)((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)),</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">ItemGetter</span><span class="p">(</span><span class="mi">1</span><span class="p">)(</span><span class="n">L</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)),</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">ItemGetter</span><span class="p">(</span><span class="mi">1</span><span class="p">)([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">ItemGetter</span><span class="p">(</span><span class="mi">1</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])),</span> <span class="mi">2</span><span class="p">)</span>
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<h2 id="AttrGetter" class="doc_header"><code>class</code> <code>AttrGetter</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L78" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>AttrGetter</code>(<strong><code>nm</code></strong>, <strong><code>default</code></strong>=<em><code>None</code></em>) :: <a href="https://fastcore.fast.ai/transform#ItemTransform"><code>ItemTransform</code></a></p>
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<p>Creates a proper transform that applies <code>attrgetter(nm)</code> (even on a tuple)</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_eq</span><span class="p">(</span><span class="n">AttrGetter</span><span class="p">(</span><span class="s1">'shape'</span><span class="p">)(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">])),</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">])</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">AttrGetter</span><span class="p">(</span><span class="s1">'shape'</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">])([</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">]),</span> <span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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<h3 id="Split">Split<a class="anchor-link" href="#Split"> </a></h3>
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<p>The next set of functions are used to <em>split</em> data into training and validation sets. The functions return two lists - a list of indices or masks for each of training and validation sets.</p>
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<h4 id="RandomSplitter" class="doc_header"><code>RandomSplitter</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L85" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>RandomSplitter</code>(<strong><code>valid_pct</code></strong>=<em><code>0.2</code></em>, <strong><code>seed</code></strong>=<em><code>None</code></em>)</p>
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<p>Create function that splits <code>items</code> between train/val with <code>valid_pct</code> randomly.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">src</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">30</span><span class="p">))</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">RandomSplitter</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="n">trn</span><span class="p">,</span><span class="n">val</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">src</span><span class="p">)</span>
<span class="k">assert</span> <span class="mi">0</span><span class="o"><</span><span class="nb">len</span><span class="p">(</span><span class="n">trn</span><span class="p">)</span><span class="o"><</span><span class="nb">len</span><span class="p">(</span><span class="n">src</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">o</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">val</span> <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">trn</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">trn</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">src</span><span class="p">)</span><span class="o">-</span><span class="nb">len</span><span class="p">(</span><span class="n">val</span><span class="p">))</span>
<span class="c1"># test random seed consistency</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">f</span><span class="p">(</span><span class="n">src</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span> <span class="n">trn</span><span class="p">)</span>
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<p>Use scikit-learn train_test_split. This allow to <em>split</em> items in a stratified fashion (uniformely according to the ‘labels‘ distribution)</p>
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<h4 id="TrainTestSplitter" class="doc_header"><code>TrainTestSplitter</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L95" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>TrainTestSplitter</code>(<strong><code>test_size</code></strong>=<em><code>0.2</code></em>, <strong><code>random_state</code></strong>=<em><code>None</code></em>, <strong><code>stratify</code></strong>=<em><code>None</code></em>, <strong><code>train_size</code></strong>=<em><code>None</code></em>, <strong><code>shuffle</code></strong>=<em><code>True</code></em>)</p>
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<p>Split <code>items</code> into random train and test subsets using sklearn train_test_split utility.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">src</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">30</span><span class="p">))</span>
<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mi">20</span> <span class="o">+</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span>
<span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.2</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">TrainTestSplitter</span><span class="p">(</span><span class="n">test_size</span><span class="o">=</span><span class="n">test_size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">labels</span><span class="p">)</span>
<span class="n">trn</span><span class="p">,</span><span class="n">val</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">src</span><span class="p">)</span>
<span class="k">assert</span> <span class="mi">0</span><span class="o"><</span><span class="nb">len</span><span class="p">(</span><span class="n">trn</span><span class="p">)</span><span class="o"><</span><span class="nb">len</span><span class="p">(</span><span class="n">src</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">o</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">val</span> <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">trn</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">trn</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">src</span><span class="p">)</span><span class="o">-</span><span class="nb">len</span><span class="p">(</span><span class="n">val</span><span class="p">))</span>
<span class="c1"># test random seed consistency</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">f</span><span class="p">(</span><span class="n">src</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span> <span class="n">trn</span><span class="p">)</span>
<span class="c1"># test labels distribution consistency</span>
<span class="c1"># there should be test_size % of zeroes and ones respectively in the validation set</span>
<span class="n">test_eq</span><span class="p">(</span><span class="nb">len</span><span class="p">([</span><span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">val</span> <span class="k">if</span> <span class="n">t</span> <span class="o"><</span> <span class="mi">20</span><span class="p">])</span> <span class="o">/</span> <span class="mi">20</span><span class="p">,</span> <span class="n">test_size</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="nb">len</span><span class="p">([</span><span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">val</span> <span class="k">if</span> <span class="n">t</span> <span class="o">></span> <span class="mi">20</span><span class="p">])</span> <span class="o">/</span> <span class="mi">10</span><span class="p">,</span> <span class="n">test_size</span><span class="p">)</span>
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<h4 id="IndexSplitter" class="doc_header"><code>IndexSplitter</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L103" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>IndexSplitter</code>(<strong><code>valid_idx</code></strong>)</p>
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<p>Split <code>items</code> so that <code>val_idx</code> are in the validation set and the others in the training set</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">items</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
<span class="n">splitter</span> <span class="o">=</span> <span class="n">IndexSplitter</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">9</span><span class="p">])</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">splitter</span><span class="p">(</span><span class="n">items</span><span class="p">),[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">8</span><span class="p">],[</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">9</span><span class="p">]])</span>
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<h4 id="GrandparentSplitter" class="doc_header"><code>GrandparentSplitter</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L116" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>GrandparentSplitter</code>(<strong><code>train_name</code></strong>=<em><code>'train'</code></em>, <strong><code>valid_name</code></strong>=<em><code>'valid'</code></em>)</p>
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<p>Split <code>items</code> from the grand parent folder names (<code>train_name</code> and <code>valid_name</code>).</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fnames</span> <span class="o">=</span> <span class="p">[</span><span class="n">path</span><span class="o">/</span><span class="s1">'train/3/9932.png'</span><span class="p">,</span> <span class="n">path</span><span class="o">/</span><span class="s1">'valid/7/7189.png'</span><span class="p">,</span>
<span class="n">path</span><span class="o">/</span><span class="s1">'valid/7/7320.png'</span><span class="p">,</span> <span class="n">path</span><span class="o">/</span><span class="s1">'train/7/9833.png'</span><span class="p">,</span>
<span class="n">path</span><span class="o">/</span><span class="s1">'train/3/7666.png'</span><span class="p">,</span> <span class="n">path</span><span class="o">/</span><span class="s1">'valid/3/925.png'</span><span class="p">,</span>
<span class="n">path</span><span class="o">/</span><span class="s1">'train/7/724.png'</span><span class="p">,</span> <span class="n">path</span><span class="o">/</span><span class="s1">'valid/3/93055.png'</span><span class="p">]</span>
<span class="n">splitter</span> <span class="o">=</span> <span class="n">GrandparentSplitter</span><span class="p">()</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">splitter</span><span class="p">(</span><span class="n">fnames</span><span class="p">),[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">7</span><span class="p">]])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fnames2</span> <span class="o">=</span> <span class="n">fnames</span> <span class="o">+</span> <span class="p">[</span><span class="n">path</span><span class="o">/</span><span class="s1">'test/3/4256.png'</span><span class="p">,</span> <span class="n">path</span><span class="o">/</span><span class="s1">'test/7/2345.png'</span><span class="p">,</span> <span class="n">path</span><span class="o">/</span><span class="s1">'valid/7/6467.png'</span><span class="p">]</span>
<span class="n">splitter</span> <span class="o">=</span> <span class="n">GrandparentSplitter</span><span class="p">(</span><span class="n">train_name</span><span class="o">=</span><span class="p">(</span><span class="s1">'train'</span><span class="p">,</span> <span class="s1">'valid'</span><span class="p">),</span> <span class="n">valid_name</span><span class="o">=</span><span class="s1">'test'</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">splitter</span><span class="p">(</span><span class="n">fnames2</span><span class="p">),[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">10</span><span class="p">],[</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">]])</span>
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<h4 id="FuncSplitter" class="doc_header"><code>FuncSplitter</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L123" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>FuncSplitter</code>(<strong><code>func</code></strong>)</p>
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<p>Split <code>items</code> by result of <code>func</code> (<code>True</code> for validation, <code>False</code> for training set).</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">splitter</span> <span class="o">=</span> <span class="n">FuncSplitter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">o</span><span class="p">:</span> <span class="n">Path</span><span class="p">(</span><span class="n">o</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">name</span> <span class="o">==</span> <span class="s1">'valid'</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">splitter</span><span class="p">(</span><span class="n">fnames</span><span class="p">),[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">7</span><span class="p">]])</span>
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<h4 id="MaskSplitter" class="doc_header"><code>MaskSplitter</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L131" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>MaskSplitter</code>(<strong><code>mask</code></strong>)</p>
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<p>Split <code>items</code> depending on the value of <code>mask</code>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">items</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">6</span><span class="p">))</span>
<span class="n">splitter</span> <span class="o">=</span> <span class="n">MaskSplitter</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="kc">False</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="kc">True</span><span class="p">])</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">splitter</span><span class="p">(</span><span class="n">items</span><span class="p">),[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">]])</span>
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<h4 id="FileSplitter" class="doc_header"><code>FileSplitter</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L137" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>FileSplitter</code>(<strong><code>fname</code></strong>)</p>
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<p>Split <code>items</code> by providing file <code>fname</code> (contains names of valid items separated by newline).</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">TemporaryDirectory</span><span class="p">()</span> <span class="k">as</span> <span class="n">d</span><span class="p">:</span>
<span class="n">fname</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">d</span><span class="p">)</span><span class="o">/</span><span class="s1">'valid.txt'</span>
<span class="n">fname</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">Path</span><span class="p">(</span><span class="n">fnames</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]]))</span>
<span class="n">splitter</span> <span class="o">=</span> <span class="n">FileSplitter</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">splitter</span><span class="p">(</span><span class="n">fnames</span><span class="p">),[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]])</span>
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<h4 id="ColSplitter" class="doc_header"><code>ColSplitter</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L145" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>ColSplitter</code>(<strong><code>col</code></strong>=<em><code>'is_valid'</code></em>)</p>
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<p>Split <code>items</code> (supposed to be a dataframe) by value in <code>col</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</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="s1">'a'</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="s1">'b'</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="kc">True</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="n">splits</span> <span class="o">=</span> <span class="n">ColSplitter</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)(</span><span class="n">df</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">splits</span><span class="p">,</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]])</span>
<span class="c1">#Works with strings or index</span>
<span class="n">splits</span> <span class="o">=</span> <span class="n">ColSplitter</span><span class="p">(</span><span class="mi">1</span><span class="p">)(</span><span class="n">df</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">splits</span><span class="p">,</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]])</span>
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<h4 id="RandomSubsetSplitter" class="doc_header"><code>RandomSubsetSplitter</code><a href="https://github.com/fastai/fastai/tree/master/fastai/data/transforms.py#L154" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>RandomSubsetSplitter</code>(<strong><code>train_sz</code></strong>, <strong><code>valid_sz</code></strong>, <strong><code>seed</code></strong>=<em><code>None</code></em>)</p>
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<p>Take randoms subsets of <code>splits</code> with <code>train_sz</code> and <code>valid_sz</code></p>