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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"><span class="pre">Pipeline</span></code> and <code class="docutils literal"><span class="pre">GridSearchCV</span></code></a></li>
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<div class="sphx-glr-example-title section" id="selecting-dimensionality-reduction-with-pipeline-and-gridsearchcv">
<span id="sphx-glr-auto-examples-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 headline">¶</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"><span class="pre">GridSearchCV</span></code> and
<code class="docutils literal"><span class="pre">Pipeline</span></code> to optimize over different classes of estimators in a
single CV run – unsupervised <code class="docutils literal"><span class="pre">PCA</span></code> and <code class="docutils literal"><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"><span class="pre">Pipeline</span></code> can be instantiated with the <code class="docutils literal"><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"><span class="pre">memory</span></code> to enable caching becomes interesting when the
fitting of a transformer is costly.</p>
<div class="section" id="illustration-of-pipeline-and-gridsearchcv">
<h2>Illustration of <code class="docutils literal"><span class="pre">Pipeline</span></code> and <code class="docutils literal"><span class="pre">GridSearchCV</span></code><a class="headerlink" href="#illustration-of-pipeline-and-gridsearchcv" title="Permalink to this headline">¶</a></h2>
<blockquote>
<div>This section illustrates the use of a <code class="docutils literal"><span class="pre">Pipeline</span></code> with
<code class="docutils literal"><span class="pre">GridSearchCV</span></code></div></blockquote>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Authors: Robert McGibbon, Joel Nothman, Guillaume Lemaitre</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">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="View documentation for sklearn.datasets.load_digits"><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="View documentation for sklearn.model_selection.GridSearchCV"><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="View documentation for sklearn.pipeline.Pipeline"><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="View documentation for sklearn.svm.LinearSVC"><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="View documentation for sklearn.decomposition.PCA"><span class="n">PCA</span></a><span class="p">,</span> <a href="../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF" title="View documentation for sklearn.decomposition.NMF"><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="View documentation for sklearn.feature_selection.SelectKBest"><span class="n">SelectKBest</span></a><span class="p">,</span> <a href="../modules/generated/sklearn.feature_selection.chi2.html#sklearn.feature_selection.chi2" title="View documentation for sklearn.feature_selection.chi2"><span class="n">chi2</span></a>
<span class="k">print</span><span class="p">(</span><span class="vm">__doc__</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="View documentation for sklearn.pipeline.Pipeline"><span class="n">Pipeline</span></a><span class="p">([</span>
<span class="p">(</span><span class="s1">'reduce_dim'</span><span class="p">,</span> <a href="../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="View documentation for sklearn.decomposition.PCA"><span class="n">PCA</span></a><span class="p">()),</span>
<span class="p">(</span><span class="s1">'classify'</span><span class="p">,</span> <a href="../modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="View documentation for sklearn.svm.LinearSVC"><span class="n">LinearSVC</span></a><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="s1">'reduce_dim'</span><span class="p">:</span> <span class="p">[</span><a href="../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="View documentation for sklearn.decomposition.PCA"><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="View documentation for sklearn.decomposition.NMF"><span class="n">NMF</span></a><span class="p">()],</span>
<span class="s1">'reduce_dim__n_components'</span><span class="p">:</span> <span class="n">N_FEATURES_OPTIONS</span><span class="p">,</span>
<span class="s1">'classify__C'</span><span class="p">:</span> <span class="n">C_OPTIONS</span>
<span class="p">},</span>
<span class="p">{</span>
<span class="s1">'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="View documentation for sklearn.feature_selection.SelectKBest"><span class="n">SelectKBest</span></a><span class="p">(</span><a href="../modules/generated/sklearn.feature_selection.chi2.html#sklearn.feature_selection.chi2" title="View documentation for sklearn.feature_selection.chi2"><span class="n">chi2</span></a><span class="p">)],</span>
<span class="s1">'reduce_dim__k'</span><span class="p">:</span> <span class="n">N_FEATURES_OPTIONS</span><span class="p">,</span>
<span class="s1">'classify__C'</span><span class="p">:</span> <span class="n">C_OPTIONS</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="s1">'PCA'</span><span class="p">,</span> <span class="s1">'NMF'</span><span class="p">,</span> <span class="s1">'KBest(chi2)'</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="View documentation for sklearn.model_selection.GridSearchCV"><span class="n">GridSearchCV</span></a><span class="p">(</span><span class="n">pipe</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">param_grid</span><span class="p">)</span>
<span class="n">digits</span> <span class="o">=</span> <a href="../modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="View documentation for sklearn.datasets.load_digits"><span class="n">load_digits</span></a><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">digits</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
<span class="n">mean_scores</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html#numpy.array" title="View documentation for numpy.array"><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="s1">'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="n">bar_offsets</span> <span class="o">=</span> <span class="p">(</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html#numpy.arange" title="View documentation for numpy.arange"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><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="o">*</span>
<span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">reducer_labels</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="o">.</span><span class="mi">5</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="View documentation for matplotlib.pyplot.figure"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
<span class="n">COLORS</span> <span class="o">=</span> <span class="s1">'bgrcmyk'</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">reducer_scores</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">reducer_labels</span><span class="p">,</span> <span class="n">mean_scores</span><span class="p">)):</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.bar.html#matplotlib.pyplot.bar" title="View documentation for matplotlib.pyplot.bar"><span class="n">plt</span><span class="o">.</span><span class="n">bar</span></a><span class="p">(</span><span class="n">bar_offsets</span> <span class="o">+</span> <span class="n">i</span><span class="p">,</span> <span class="n">reducer_scores</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">COLORS</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="View documentation for matplotlib.pyplot.title"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="s2">"Comparing feature reduction techniques"</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="View documentation for matplotlib.pyplot.xlabel"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s1">'Reduced number of features'</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.xticks.html#matplotlib.pyplot.xticks" title="View documentation for matplotlib.pyplot.xticks"><span class="n">plt</span><span class="o">.</span><span class="n">xticks</span></a><span class="p">(</span><span class="n">bar_offsets</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">reducer_labels</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">N_FEATURES_OPTIONS</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel" title="View documentation for matplotlib.pyplot.ylabel"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s1">'Digit classification accuracy'</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.ylim.html#matplotlib.pyplot.ylim" title="View documentation for matplotlib.pyplot.ylim"><span class="n">plt</span><span class="o">.</span><span class="n">ylim</span></a><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="View documentation for matplotlib.pyplot.legend"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<img alt="../_images/sphx_glr_plot_compare_reduction_001.png" class="sphx-glr-single-img" src="../_images/sphx_glr_plot_compare_reduction_001.png" />
</div>
<div class="section" id="caching-transformers-within-a-pipeline">
<h2>Caching transformers within a <code class="docutils literal"><span class="pre">Pipeline</span></code><a class="headerlink" href="#caching-transformers-within-a-pipeline" title="Permalink to this headline">¶</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"><span class="pre">GridSearchCV</span></code> triggers
such situations. Therefore, we use the argument <code class="docutils literal"><span class="pre">memory</span></code> to enable caching.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">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"><span class="pre">memory</span></code> constructor parameter when the fitting
of a transformer is costly.</p>
</div>
</div></blockquote>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tempfile</span> <span class="kn">import</span> <span class="n">mkdtemp</span>
<span class="kn">from</span> <span class="nn">shutil</span> <span class="kn">import</span> <span class="n">rmtree</span>
<span class="kn">from</span> <span class="nn">sklearn.externals.joblib</span> <span class="kn">import</span> <span class="n">Memory</span>
<span class="c1"># Create a temporary folder to store the transformers of the pipeline</span>
<span class="n">cachedir</span> <span class="o">=</span> <span class="n">mkdtemp</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="n">cachedir</span><span class="o">=</span><span class="n">cachedir</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="View documentation for sklearn.pipeline.Pipeline"><span class="n">Pipeline</span></a><span class="p">([(</span><span class="s1">'reduce_dim'</span><span class="p">,</span> <a href="../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="View documentation for sklearn.decomposition.PCA"><span class="n">PCA</span></a><span class="p">()),</span>
<span class="p">(</span><span class="s1">'classify'</span><span class="p">,</span> <a href="../modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="View documentation for sklearn.svm.LinearSVC"><span class="n">LinearSVC</span></a><span class="p">())],</span>
<span class="n">memory</span><span class="o">=</span><span class="n">memory</span><span class="p">)</span>
<span class="c1"># This time, a cached pipeline will be used within the grid search</span>
<span class="n">grid</span> <span class="o">=</span> <a href="../modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="View documentation for sklearn.model_selection.GridSearchCV"><span class="n">GridSearchCV</span></a><span class="p">(</span><span class="n">cached_pipe</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">param_grid</span><span class="p">)</span>
<span class="n">digits</span> <span class="o">=</span> <a href="../modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="View documentation for sklearn.datasets.load_digits"><span class="n">load_digits</span></a><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">digits</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
<span class="c1"># Delete the temporary cache before exiting</span>
<span class="n">rmtree</span><span class="p">(</span><span class="n">cachedir</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none"><div class="highlight"><pre><span></span>________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=2, random_state=None,
svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=2, random_state=None,
svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=2, random_state=None,
svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 4]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=4, random_state=None,
svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=4, random_state=None,
svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=4, random_state=None,
svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 4]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=8, random_state=None,
svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=8, random_state=None,
svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=8, random_state=None,
svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 4]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
n_components=2, random_state=None, shuffle=False, solver='cd',
tol=0.0001, verbose=0),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
n_components=2, random_state=None, shuffle=False, solver='cd',
tol=0.0001, verbose=0),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
n_components=2, random_state=None, shuffle=False, solver='cd',
tol=0.0001, verbose=0),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 4]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
n_components=4, random_state=None, shuffle=False, solver='cd',
tol=0.0001, verbose=0),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
n_components=4, random_state=None, shuffle=False, solver='cd',
tol=0.0001, verbose=0),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
n_components=4, random_state=None, shuffle=False, solver='cd',
tol=0.0001, verbose=0),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 4]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
n_components=8, random_state=None, shuffle=False, solver='cd',
tol=0.0001, verbose=0),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
n_components=8, random_state=None, shuffle=False, solver='cd',
tol=0.0001, verbose=0),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,
n_components=8, random_state=None, shuffle=False, solver='cd',
tol=0.0001, verbose=0),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 4]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/d8a616f804252c078fc32f6bdb670f55
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/68c922d96ccd9b4e93bb7f4682f8faf7
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/41d8b755b00d9e867b8f55c7feb78ea4
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/9d59f224d9fa5b9bc645992ac4c160de
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/02cebad06b67ef76644aa7a94bcca5bd
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/6854cef0730032efc0b9dbc97314d044
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/916ee3ea85217ef76741c607965f78f9
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/3adb9fbae081a46a97a9bce3ee78773e
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/7a90e56b3dfc3001209b11a80ac20d0d
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/05bd6cc397982f0635d2e93961daa0e9
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/1d408b114cf2ab23aec37d5beb9464a4
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/44b7b13de52d338a14032b03f1f77b60
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/3adb9fbae081a46a97a9bce3ee78773e
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/7a90e56b3dfc3001209b11a80ac20d0d
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/9759ad9708357c64d8d40f817f86b57c
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/f7118c67e71075c5208c3cec738aa761
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/554e6d687f4b8379573b55ce0876e6d0
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/a99286cc3a762d4604f45f5886080e23
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x7fa556750f28>), array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x7fa556750f28>), array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=2, score_func=<function chi2 at 0x7fa556750f28>), array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 4]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=4, score_func=<function chi2 at 0x7fa556750f28>), array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=4, score_func=<function chi2 at 0x7fa556750f28>), array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=4, score_func=<function chi2 at 0x7fa556750f28>), array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 4]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=8, score_func=<function chi2 at 0x7fa556750f28>), array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=8, score_func=<function chi2 at 0x7fa556750f28>), array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(SelectKBest(k=8, score_func=<function chi2 at 0x7fa556750f28>), array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 4]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/a1f421076765dbdfe3b91a792d7622b7
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/a9f9602be0de32a37575531214e3dc62
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/56a54583eee027f70e6cd9c9ed62b3f5
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/dc5d8487baf2f525a443dc33fa7f8304
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/b6ae9585140589feefbb94bb69c86535
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/2420278922bb5a91989e40bc39644832
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/dbf5e5b853cbafebff42185bea46333a
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/a1f421076765dbdfe3b91a792d7622b7
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/a9f9602be0de32a37575531214e3dc62
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/56a54583eee027f70e6cd9c9ed62b3f5
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/dc5d8487baf2f525a443dc33fa7f8304
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/b6ae9585140589feefbb94bb69c86535
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/2420278922bb5a91989e40bc39644832
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/dbf5e5b853cbafebff42185bea46333a
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/0250b9c05b1793d4b3c4b808f95b7d09
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/2a21b7625d0cf8c8824fc2ce9ce5ea8a
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/a1f421076765dbdfe3b91a792d7622b7
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/a9f9602be0de32a37575531214e3dc62
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/56a54583eee027f70e6cd9c9ed62b3f5
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/dc5d8487baf2f525a443dc33fa7f8304
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/b6ae9585140589feefbb94bb69c86535
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/2420278922bb5a91989e40bc39644832
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/dbf5e5b853cbafebff42185bea46333a
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/0250b9c05b1793d4b3c4b808f95b7d09
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory] 0.0s, 0.0min: Loading _fit_transform_one from /tmp/tmpojweh2l_/joblib/sklearn/pipeline/_fit_transform_one/2a21b7625d0cf8c8824fc2ce9ce5ea8a
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(PCA(copy=True, iterated_power=7, n_components=8, random_state=None,
svd_solver='auto', tol=0.0, whiten=False),
array([[0., ..., 0.],
...,
[0., ..., 0.]]), array([0, ..., 8]), None)
________________________________________________fit_transform_one - 0.0s, 0.0min
</pre></div>
</div>
<p>The <code class="docutils literal"><span class="pre">PCA</span></code> fitting is only computed at the evaluation of the first
configuration of the <code class="docutils literal"><span class="pre">C</span></code> parameter of the <code class="docutils literal"><span class="pre">LinearSVC</span></code> classifier. The
other configurations of <code class="docutils literal"><span class="pre">C</span></code> will trigger the loading of the cached <code class="docutils literal"><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"><span class="pre">memory</span></code> is highly beneficial when fitting
a transformer is costly.</p>
<p><strong>Total running time of the script:</strong> ( 0 minutes 50.261 seconds)</p>
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