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<li><a class="reference internal" href="#">Selecting dimensionality reduction with Pipeline and GridSearchCV</a><ul>
<li><a class="reference internal" href="#illustration-of-pipeline-and-gridsearchcv">Illustration of <code class="docutils literal notranslate"><span class="pre">Pipeline</span></code> and <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a></li>
<li><a class="reference internal" href="#caching-transformers-within-a-pipeline">Caching transformers within a <code class="docutils literal notranslate"><span class="pre">Pipeline</span></code></a></li>
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to download the full example code or to run this example in your browser via Binder</p>
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<section class="sphx-glr-example-title" id="selecting-dimensionality-reduction-with-pipeline-and-gridsearchcv">
<span id="sphx-glr-auto-examples-compose-plot-compare-reduction-py"></span><h1>Selecting dimensionality reduction with Pipeline and GridSearchCV<a class="headerlink" href="#selecting-dimensionality-reduction-with-pipeline-and-gridsearchcv" title="Permalink to this heading">¶</a></h1>
<p>This example constructs a pipeline that does dimensionality
reduction followed by prediction with a support vector
classifier. It demonstrates the use of <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code> and
<code class="docutils literal notranslate"><span class="pre">Pipeline</span></code> to optimize over different classes of estimators in a
single CV run – unsupervised <code class="docutils literal notranslate"><span class="pre">PCA</span></code> and <code class="docutils literal notranslate"><span class="pre">NMF</span></code> dimensionality
reductions are compared to univariate feature selection during
the grid search.</p>
<p>Additionally, <code class="docutils literal notranslate"><span class="pre">Pipeline</span></code> can be instantiated with the <code class="docutils literal notranslate"><span class="pre">memory</span></code>
argument to memoize the transformers within the pipeline, avoiding to fit
again the same transformers over and over.</p>
<p>Note that the use of <code class="docutils literal notranslate"><span class="pre">memory</span></code> to enable caching becomes interesting when the
fitting of a transformer is costly.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Robert McGibbon</span>
<span class="c1"># Joel Nothman</span>
<span class="c1"># Guillaume Lemaitre</span>
</pre></div>
</div>
<section id="illustration-of-pipeline-and-gridsearchcv">
<h2>Illustration of <code class="docutils literal notranslate"><span class="pre">Pipeline</span></code> and <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code><a class="headerlink" href="#illustration-of-pipeline-and-gridsearchcv" title="Permalink to this heading">¶</a></h2>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</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">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="sklearn.datasets.load_digits" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_digits</span></a>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GridSearchCV</span></a>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Pipeline</span></a>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LinearSVC</span></a>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PCA</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF" title="sklearn.decomposition.NMF" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">NMF</span></a>
<span class="kn">from</span> <span class="nn">sklearn.feature_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest" title="sklearn.feature_selection.SelectKBest" class="sphx-glr-backref-module-sklearn-feature_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SelectKBest</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.feature_selection.mutual_info_classif.html#sklearn.feature_selection.mutual_info_classif" title="sklearn.feature_selection.mutual_info_classif" class="sphx-glr-backref-module-sklearn-feature_selection sphx-glr-backref-type-py-function"><span class="n">mutual_info_classif</span></a>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler" title="sklearn.preprocessing.MinMaxScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MinMaxScaler</span></a>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="sklearn.datasets.load_digits" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_digits</span></a><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">pipe</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Pipeline</span></a><span class="p">(</span>
<span class="p">[</span>
<span class="p">(</span><span class="s2">"scaling"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler" title="sklearn.preprocessing.MinMaxScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MinMaxScaler</span></a><span class="p">()),</span>
<span class="c1"># the reduce_dim stage is populated by the param_grid</span>
<span class="p">(</span><span class="s2">"reduce_dim"</span><span class="p">,</span> <span class="s2">"passthrough"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"classify"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LinearSVC</span></a><span class="p">(</span><span class="n">dual</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">)),</span>
<span class="p">]</span>
<span class="p">)</span>
<span class="n">N_FEATURES_OPTIONS</span> <span class="o">=</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">8</span><span class="p">]</span>
<span class="n">C_OPTIONS</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">]</span>
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">{</span>
<span class="s2">"reduce_dim"</span><span class="p">:</span> <span class="p">[</span><a href="../../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PCA</span></a><span class="p">(</span><span class="n">iterated_power</span><span class="o">=</span><span class="mi">7</span><span class="p">),</span> <a href="../../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF" title="sklearn.decomposition.NMF" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">NMF</span></a><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">1_000</span><span class="p">)],</span>
<span class="s2">"reduce_dim__n_components"</span><span class="p">:</span> <span class="n">N_FEATURES_OPTIONS</span><span class="p">,</span>
<span class="s2">"classify__C"</span><span class="p">:</span> <span class="n">C_OPTIONS</span><span class="p">,</span>
<span class="p">},</span>
<span class="p">{</span>
<span class="s2">"reduce_dim"</span><span class="p">:</span> <span class="p">[</span><a href="../../modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest" title="sklearn.feature_selection.SelectKBest" class="sphx-glr-backref-module-sklearn-feature_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SelectKBest</span></a><span class="p">(</span><a href="../../modules/generated/sklearn.feature_selection.mutual_info_classif.html#sklearn.feature_selection.mutual_info_classif" title="sklearn.feature_selection.mutual_info_classif" class="sphx-glr-backref-module-sklearn-feature_selection sphx-glr-backref-type-py-function"><span class="n">mutual_info_classif</span></a><span class="p">)],</span>
<span class="s2">"reduce_dim__k"</span><span class="p">:</span> <span class="n">N_FEATURES_OPTIONS</span><span class="p">,</span>
<span class="s2">"classify__C"</span><span class="p">:</span> <span class="n">C_OPTIONS</span><span class="p">,</span>
<span class="p">},</span>
<span class="p">]</span>
<span class="n">reducer_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"PCA"</span><span class="p">,</span> <span class="s2">"NMF"</span><span class="p">,</span> <span class="s2">"KBest(mutual_info_classif)"</span><span class="p">]</span>
<span class="n">grid</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GridSearchCV</span></a><span class="p">(</span><span class="n">pipe</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">param_grid</span><span class="p">)</span>
<span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
<div class="output_subarea output_html rendered_html output_result">
<style>#sk-container-id-59 {color: black;background-color: white;}#sk-container-id-59 pre{padding: 0;}#sk-container-id-59 div.sk-toggleable {background-color: white;}#sk-container-id-59 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-59 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-59 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-59 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-59 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-59 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-59 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-59 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-59 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-59 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-59 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-59 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-59 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-59 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-59 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-59 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-59 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-59 div.sk-item {position: relative;z-index: 1;}#sk-container-id-59 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-59 div.sk-item::before, #sk-container-id-59 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-59 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-59 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-59 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-59 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-59 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-59 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-59 div.sk-label-container {text-align: center;}#sk-container-id-59 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-59 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-59" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[('scaling', MinMaxScaler()),
('reduce_dim', 'passthrough'),
('classify',
LinearSVC(dual=False,
max_iter=10000))]),
n_jobs=1,
param_grid=[{'classify__C': [1, 10, 100, 1000],
'reduce_dim': [PCA(iterated_power=7, n_components=8),
NMF(max_iter=1000)],
'reduce_dim__n_components': [2, 4, 8]},
{'classify__C': [1, 10, 100, 1000],
'reduce_dim': [SelectKBest(score_func=<function mutual_info_classif at 0x7fd08312f280>)],
'reduce_dim__k': [2, 4, 8]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-264" type="checkbox" ><label for="sk-estimator-id-264" class="sk-toggleable__label sk-toggleable__label-arrow">GridSearchCV</label><div class="sk-toggleable__content"><pre>GridSearchCV(estimator=Pipeline(steps=[('scaling', MinMaxScaler()),
('reduce_dim', 'passthrough'),
('classify',
LinearSVC(dual=False,
max_iter=10000))]),
n_jobs=1,
param_grid=[{'classify__C': [1, 10, 100, 1000],
'reduce_dim': [PCA(iterated_power=7, n_components=8),
NMF(max_iter=1000)],
'reduce_dim__n_components': [2, 4, 8]},
{'classify__C': [1, 10, 100, 1000],
'reduce_dim': [SelectKBest(score_func=<function mutual_info_classif at 0x7fd08312f280>)],
'reduce_dim__k': [2, 4, 8]}])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-265" type="checkbox" ><label for="sk-estimator-id-265" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('scaling', MinMaxScaler()), ('reduce_dim', 'passthrough'),
('classify', LinearSVC(dual=False, max_iter=10000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-266" type="checkbox" ><label for="sk-estimator-id-266" class="sk-toggleable__label sk-toggleable__label-arrow">MinMaxScaler</label><div class="sk-toggleable__content"><pre>MinMaxScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-267" type="checkbox" ><label for="sk-estimator-id-267" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-268" type="checkbox" ><label for="sk-estimator-id-268" class="sk-toggleable__label sk-toggleable__label-arrow">LinearSVC</label><div class="sk-toggleable__content"><pre>LinearSVC(dual=False, max_iter=10000)</pre></div></div></div></div></div></div></div></div></div></div></div></div>
</div>
<br />
<br /><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="n">mean_scores</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><span class="n">grid</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="s2">"mean_test_score"</span><span class="p">])</span>
<span class="c1"># scores are in the order of param_grid iteration, which is alphabetical</span>
<span class="n">mean_scores</span> <span class="o">=</span> <span class="n">mean_scores</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">C_OPTIONS</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">N_FEATURES_OPTIONS</span><span class="p">))</span>
<span class="c1"># select score for best C</span>
<span class="n">mean_scores</span> <span class="o">=</span> <span class="n">mean_scores</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># create a dataframe to ease plotting</span>
<span class="n">mean_scores</span> <span class="o">=</span> <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame" class="sphx-glr-backref-module-pandas sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span></a><span class="p">(</span>
<span class="n">mean_scores</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">N_FEATURES_OPTIONS</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">reducer_labels</span>
<span class="p">)</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">mean_scores</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</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">"Comparing feature reduction techniques"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Reduced number of features"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Digit classification accuracy"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</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="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"upper left"</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>
<img src="../../_images/sphx_glr_plot_compare_reduction_001.png" srcset="../../_images/sphx_glr_plot_compare_reduction_001.png" alt="Comparing feature reduction techniques" class = "sphx-glr-single-img"/></section>
<section id="caching-transformers-within-a-pipeline">
<h2>Caching transformers within a <code class="docutils literal notranslate"><span class="pre">Pipeline</span></code><a class="headerlink" href="#caching-transformers-within-a-pipeline" title="Permalink to this heading">¶</a></h2>
<blockquote>
<div><p>It is sometimes worthwhile storing the state of a specific transformer
since it could be used again. Using a pipeline in <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code> triggers
such situations. Therefore, we use the argument <code class="docutils literal notranslate"><span class="pre">memory</span></code> to enable caching.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Note that this example is, however, only an illustration since for this
specific case fitting PCA is not necessarily slower than loading the
cache. Hence, use the <code class="docutils literal notranslate"><span class="pre">memory</span></code> constructor parameter when the fitting
of a transformer is costly.</p>
</div>
</div></blockquote>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <a href="https://joblib.readthedocs.io/en/latest/generated/joblib.Memory.html#joblib.Memory" title="joblib.Memory" class="sphx-glr-backref-module-joblib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Memory</span></a>
<span class="kn">from</span> <span class="nn">shutil</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/shutil.html#shutil.rmtree" title="shutil.rmtree" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">rmtree</span></a>
<span class="c1"># Create a temporary folder to store the transformers of the pipeline</span>
<span class="n">location</span> <span class="o">=</span> <span class="s2">"cachedir"</span>
<span class="n">memory</span> <span class="o">=</span> <a href="https://joblib.readthedocs.io/en/latest/generated/joblib.Memory.html#joblib.Memory" title="joblib.Memory" class="sphx-glr-backref-module-joblib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Memory</span></a><span class="p">(</span><span class="n">location</span><span class="o">=</span><span class="n">location</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">cached_pipe</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Pipeline</span></a><span class="p">(</span>
<span class="p">[(</span><span class="s2">"reduce_dim"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">PCA</span></a><span class="p">()),</span> <span class="p">(</span><span class="s2">"classify"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LinearSVC</span></a><span class="p">(</span><span class="n">dual</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">))],</span>
<span class="n">memory</span><span class="o">=</span><span class="n">memory</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># This time, a cached pipeline will be used within the grid search</span>
<span class="c1"># Delete the temporary cache before exiting</span>
<span class="n">memory</span><span class="o">.</span><span class="n">clear</span><span class="p">(</span><span class="n">warn</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<a href="https://docs.python.org/3/library/shutil.html#shutil.rmtree" title="shutil.rmtree" class="sphx-glr-backref-module-shutil sphx-glr-backref-type-py-function"><span class="n">rmtree</span></a><span class="p">(</span><span class="n">location</span><span class="p">)</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">PCA</span></code> fitting is only computed at the evaluation of the first
configuration of the <code class="docutils literal notranslate"><span class="pre">C</span></code> parameter of the <code class="docutils literal notranslate"><span class="pre">LinearSVC</span></code> classifier. The
other configurations of <code class="docutils literal notranslate"><span class="pre">C</span></code> will trigger the loading of the cached <code class="docutils literal notranslate"><span class="pre">PCA</span></code>
estimator data, leading to save processing time. Therefore, the use of
caching the pipeline using <code class="docutils literal notranslate"><span class="pre">memory</span></code> is highly beneficial when fitting
a transformer is costly.</p>
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