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<li><a class="reference internal" href="#">Visualizing cross-validation behavior in scikit-learn</a><ul>
<li><a class="reference internal" href="#visualize-our-data">Visualize our data</a></li>
<li><a class="reference internal" href="#define-a-function-to-visualize-cross-validation-behavior">Define a function to visualize cross-validation behavior</a></li>
<li><a class="reference internal" href="#visualize-cross-validation-indices-for-many-cv-objects">Visualize cross-validation indices for many CV objects</a></li>
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<section class="sphx-glr-example-title" id="visualizing-cross-validation-behavior-in-scikit-learn">
<span id="sphx-glr-auto-examples-model-selection-plot-cv-indices-py"></span><h1>Visualizing cross-validation behavior in scikit-learn<a class="headerlink" href="#visualizing-cross-validation-behavior-in-scikit-learn" title="Link to this heading">¶</a></h1>
<p>Choosing the right cross-validation object is a crucial part of fitting a
model properly. There are many ways to split data into training and test
sets in order to avoid model overfitting, to standardize the number of
groups in test sets, etc.</p>
<p>This example visualizes the behavior of several common scikit-learn objects
for comparison.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Patch</span></a>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="p">(</span>
<a href="../../modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GroupKFold</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.GroupShuffleSplit.html#sklearn.model_selection.GroupShuffleSplit" title="sklearn.model_selection.GroupShuffleSplit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GroupShuffleSplit</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KFold</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.ShuffleSplit.html#sklearn.model_selection.ShuffleSplit" title="sklearn.model_selection.ShuffleSplit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ShuffleSplit</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.StratifiedGroupKFold.html#sklearn.model_selection.StratifiedGroupKFold" title="sklearn.model_selection.StratifiedGroupKFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StratifiedGroupKFold</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold" title="sklearn.model_selection.StratifiedKFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StratifiedKFold</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html#sklearn.model_selection.StratifiedShuffleSplit" title="sklearn.model_selection.StratifiedShuffleSplit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StratifiedShuffleSplit</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.TimeSeriesSplit.html#sklearn.model_selection.TimeSeriesSplit" title="sklearn.model_selection.TimeSeriesSplit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TimeSeriesSplit</span></a><span class="p">,</span>
<span class="p">)</span>
<span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">1338</span><span class="p">)</span>
<span class="n">cmap_data</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Paired</span>
<span class="n">cmap_cv</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">coolwarm</span>
<span class="n">n_splits</span> <span class="o">=</span> <span class="mi">4</span>
</pre></div>
</div>
<section id="visualize-our-data">
<h2>Visualize our data<a class="headerlink" href="#visualize-our-data" title="Link to this heading">¶</a></h2>
<p>First, we must understand the structure of our data. It has 100 randomly
generated input datapoints, 3 classes split unevenly across datapoints,
and 10 “groups” split evenly across datapoints.</p>
<p>As we’ll see, some cross-validation objects do specific things with
labeled data, others behave differently with grouped data, and others
do not use this information.</p>
<p>To begin, we’ll visualize our data.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Generate the class/group data</span>
<span class="n">n_points</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">percentiles_classes</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.hstack.html#numpy.hstack" title="numpy.hstack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">hstack</span></a><span class="p">([[</span><span class="n">ii</span><span class="p">]</span> <span class="o">*</span> <span class="nb">int</span><span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">perc</span><span class="p">)</span> <span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="n">perc</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">percentiles_classes</span><span class="p">)])</span>
<span class="c1"># Generate uneven groups</span>
<span class="n">group_prior</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">dirichlet</span><span class="p">([</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">groups</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.repeat.html#numpy.repeat" title="numpy.repeat" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">repeat</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">10</span><span class="p">),</span> <span class="n">rng</span><span class="o">.</span><span class="n">multinomial</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">group_prior</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">visualize_groups</span><span class="p">(</span><span class="n">classes</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="c1"># Visualize dataset groups</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
<span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">groups</span><span class="p">)),</span>
<span class="p">[</span><span class="mf">0.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">groups</span><span class="p">),</span>
<span class="n">c</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span>
<span class="n">marker</span><span class="o">=</span><span class="s2">"_"</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
<span class="n">cmap</span><span class="o">=</span><span class="n">cmap_data</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
<span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">groups</span><span class="p">)),</span>
<span class="p">[</span><span class="mf">3.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">groups</span><span class="p">),</span>
<span class="n">c</span><span class="o">=</span><span class="n">classes</span><span class="p">,</span>
<span class="n">marker</span><span class="o">=</span><span class="s2">"_"</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
<span class="n">cmap</span><span class="o">=</span><span class="n">cmap_data</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span>
<span class="n">ylim</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
<span class="n">yticks</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">],</span>
<span class="n">yticklabels</span><span class="o">=</span><span class="p">[</span><span class="s2">"Data</span><span class="se">\n</span><span class="s2">group"</span><span class="p">,</span> <span class="s2">"Data</span><span class="se">\n</span><span class="s2">class"</span><span class="p">],</span>
<span class="n">xlabel</span><span class="o">=</span><span class="s2">"Sample index"</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">visualize_groups</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="s2">"no groups"</span><span class="p">)</span>
</pre></div>
</div>
<img src="../../_images/sphx_glr_plot_cv_indices_001.png" srcset="../../_images/sphx_glr_plot_cv_indices_001.png" alt="plot cv indices" class = "sphx-glr-single-img"/></section>
<section id="define-a-function-to-visualize-cross-validation-behavior">
<h2>Define a function to visualize cross-validation behavior<a class="headerlink" href="#define-a-function-to-visualize-cross-validation-behavior" title="Link to this heading">¶</a></h2>
<p>We’ll define a function that lets us visualize the behavior of each
cross-validation object. We’ll perform 4 splits of the data. On each
split, we’ll visualize the indices chosen for the training set
(in blue) and the test set (in red).</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_cv_indices</span><span class="p">(</span><span class="n">cv</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">group</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">n_splits</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Create a sample plot for indices of a cross-validation object."""</span>
<span class="c1"># Generate the training/testing visualizations for each CV split</span>
<span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="p">(</span><span class="n">tr</span><span class="p">,</span> <span class="n">tt</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">cv</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="n">group</span><span class="p">)):</span>
<span class="c1"># Fill in indices with the training/test groups</span>
<span class="n">indices</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><a href="https://numpy.org/doc/stable/reference/constants.html#numpy.nan" title="numpy.nan" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">nan</span></a><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
<span class="n">indices</span><span class="p">[</span><span class="n">tt</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">indices</span><span class="p">[</span><span class="n">tr</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># Visualize the results</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
<span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">)),</span>
<span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">),</span>
<span class="n">c</span><span class="o">=</span><span class="n">indices</span><span class="p">,</span>
<span class="n">marker</span><span class="o">=</span><span class="s2">"_"</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span>
<span class="n">cmap</span><span class="o">=</span><span class="n">cmap_cv</span><span class="p">,</span>
<span class="n">vmin</span><span class="o">=-</span><span class="mf">0.2</span><span class="p">,</span>
<span class="n">vmax</span><span class="o">=</span><span class="mf">1.2</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># Plot the data classes and groups at the end</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
<span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)),</span> <span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="mf">1.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"_"</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_data</span>
<span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
<span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)),</span> <span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="mf">2.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">c</span><span class="o">=</span><span class="n">group</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"_"</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_data</span>
<span class="p">)</span>
<span class="c1"># Formatting</span>
<span class="n">yticklabels</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="n">n_splits</span><span class="p">))</span> <span class="o">+</span> <span class="p">[</span><span class="s2">"class"</span><span class="p">,</span> <span class="s2">"group"</span><span class="p">]</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span>
<span class="n">yticks</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="n">n_splits</span> <span class="o">+</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span>
<span class="n">yticklabels</span><span class="o">=</span><span class="n">yticklabels</span><span class="p">,</span>
<span class="n">xlabel</span><span class="o">=</span><span class="s2">"Sample index"</span><span class="p">,</span>
<span class="n">ylabel</span><span class="o">=</span><span class="s2">"CV iteration"</span><span class="p">,</span>
<span class="n">ylim</span><span class="o">=</span><span class="p">[</span><span class="n">n_splits</span> <span class="o">+</span> <span class="mf">2.2</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2</span><span class="p">],</span>
<span class="n">xlim</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span>
<span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">cv</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ax</span>
</pre></div>
</div>
<p>Let’s see how it looks for the <a class="reference internal" href="../../modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">KFold</span></code></a>
cross-validation object:</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
<span class="n">cv</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="p">)</span>
<span class="n">plot_cv_indices</span><span class="p">(</span><span class="n">cv</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">n_splits</span><span class="p">)</span>
</pre></div>
</div>
<img src="../../_images/sphx_glr_plot_cv_indices_002.png" srcset="../../_images/sphx_glr_plot_cv_indices_002.png" alt="KFold" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span><Axes: title={'center': 'KFold'}, xlabel='Sample index', ylabel='CV iteration'>
</pre></div>
</div>
<p>As you can see, by default the KFold cross-validation iterator does not
take either datapoint class or group into consideration. We can change this
by using either:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">StratifiedKFold</span></code> to preserve the percentage of samples for each class.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">GroupKFold</span></code> to ensure that the same group will not appear in two
different folds.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">StratifiedGroupKFold</span></code> to keep the constraint of <code class="docutils literal notranslate"><span class="pre">GroupKFold</span></code> while
attempting to return stratified folds.</p></li>
</ul>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">cvs</span> <span class="o">=</span> <span class="p">[</span><a href="../../modules/generated/sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold" title="sklearn.model_selection.StratifiedKFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StratifiedKFold</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GroupKFold</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.model_selection.StratifiedGroupKFold.html#sklearn.model_selection.StratifiedGroupKFold" title="sklearn.model_selection.StratifiedGroupKFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StratifiedGroupKFold</span></a><span class="p">]</span>
<span class="k">for</span> <span class="n">cv</span> <span class="ow">in</span> <span class="n">cvs</span><span class="p">:</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">plot_cv_indices</span><span class="p">(</span><span class="n">cv</span><span class="p">(</span><span class="n">n_splits</span><span class="p">),</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">n_splits</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
<span class="p">[</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">cmap_cv</span><span class="p">(</span><span class="mf">0.8</span><span class="p">)),</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">cmap_cv</span><span class="p">(</span><span class="mf">0.02</span><span class="p">))],</span>
<span class="p">[</span><span class="s2">"Testing set"</span><span class="p">,</span> <span class="s2">"Training set"</span><span class="p">],</span>
<span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="mf">1.02</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">),</span>
<span class="p">)</span>
<span class="c1"># Make the legend fit</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">right</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
</pre></div>
</div>
<ul class="sphx-glr-horizontal">
<li><img src="../../_images/sphx_glr_plot_cv_indices_003.png" srcset="../../_images/sphx_glr_plot_cv_indices_003.png" alt="StratifiedKFold" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_plot_cv_indices_004.png" srcset="../../_images/sphx_glr_plot_cv_indices_004.png" alt="GroupKFold" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_plot_cv_indices_005.png" srcset="../../_images/sphx_glr_plot_cv_indices_005.png" alt="StratifiedGroupKFold" class = "sphx-glr-multi-img"/></li>
</ul>
<p>Next we’ll visualize this behavior for a number of CV iterators.</p>
</section>
<section id="visualize-cross-validation-indices-for-many-cv-objects">
<h2>Visualize cross-validation indices for many CV objects<a class="headerlink" href="#visualize-cross-validation-indices-for-many-cv-objects" title="Link to this heading">¶</a></h2>
<p>Let’s visually compare the cross validation behavior for many
scikit-learn cross-validation objects. Below we will loop through several
common cross-validation objects, visualizing the behavior of each.</p>
<p>Note how some use the group/class information while others do not.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">cvs</span> <span class="o">=</span> <span class="p">[</span>
<a href="../../modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KFold</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GroupKFold</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.ShuffleSplit.html#sklearn.model_selection.ShuffleSplit" title="sklearn.model_selection.ShuffleSplit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ShuffleSplit</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold" title="sklearn.model_selection.StratifiedKFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StratifiedKFold</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.StratifiedGroupKFold.html#sklearn.model_selection.StratifiedGroupKFold" title="sklearn.model_selection.StratifiedGroupKFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StratifiedGroupKFold</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.GroupShuffleSplit.html#sklearn.model_selection.GroupShuffleSplit" title="sklearn.model_selection.GroupShuffleSplit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GroupShuffleSplit</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html#sklearn.model_selection.StratifiedShuffleSplit" title="sklearn.model_selection.StratifiedShuffleSplit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StratifiedShuffleSplit</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.TimeSeriesSplit.html#sklearn.model_selection.TimeSeriesSplit" title="sklearn.model_selection.TimeSeriesSplit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TimeSeriesSplit</span></a><span class="p">,</span>
<span class="p">]</span>
<span class="k">for</span> <span class="n">cv</span> <span class="ow">in</span> <span class="n">cvs</span><span class="p">:</span>
<span class="n">this_cv</span> <span class="o">=</span> <span class="n">cv</span><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="n">n_splits</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">plot_cv_indices</span><span class="p">(</span><span class="n">this_cv</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">n_splits</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
<span class="p">[</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">cmap_cv</span><span class="p">(</span><span class="mf">0.8</span><span class="p">)),</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">cmap_cv</span><span class="p">(</span><span class="mf">0.02</span><span class="p">))],</span>
<span class="p">[</span><span class="s2">"Testing set"</span><span class="p">,</span> <span class="s2">"Training set"</span><span class="p">],</span>
<span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="mf">1.02</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">),</span>
<span class="p">)</span>
<span class="c1"># Make the legend fit</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">right</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<ul class="sphx-glr-horizontal">
<li><img src="../../_images/sphx_glr_plot_cv_indices_006.png" srcset="../../_images/sphx_glr_plot_cv_indices_006.png" alt="KFold" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_plot_cv_indices_007.png" srcset="../../_images/sphx_glr_plot_cv_indices_007.png" alt="GroupKFold" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_plot_cv_indices_008.png" srcset="../../_images/sphx_glr_plot_cv_indices_008.png" alt="ShuffleSplit" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_plot_cv_indices_009.png" srcset="../../_images/sphx_glr_plot_cv_indices_009.png" alt="StratifiedKFold" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_plot_cv_indices_010.png" srcset="../../_images/sphx_glr_plot_cv_indices_010.png" alt="StratifiedGroupKFold" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_plot_cv_indices_011.png" srcset="../../_images/sphx_glr_plot_cv_indices_011.png" alt="GroupShuffleSplit" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_plot_cv_indices_012.png" srcset="../../_images/sphx_glr_plot_cv_indices_012.png" alt="StratifiedShuffleSplit" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_plot_cv_indices_013.png" srcset="../../_images/sphx_glr_plot_cv_indices_013.png" alt="TimeSeriesSplit" class = "sphx-glr-multi-img"/></li>
</ul>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.178 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-cv-indices-py">
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