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<li><a class="reference internal" href="#">Pipelining: chaining a PCA and a logistic regression</a></li>
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<section class="sphx-glr-example-title" id="pipelining-chaining-a-pca-and-a-logistic-regression">
<span id="sphx-glr-auto-examples-compose-plot-digits-pipe-py"></span><h1>Pipelining: chaining a PCA and a logistic regression<a class="headerlink" href="#pipelining-chaining-a-pca-and-a-logistic-regression" title="Permalink to this heading">¶</a></h1>
<p>The PCA does an unsupervised dimensionality reduction, while the logistic
regression does the prediction.</p>
<p>We use a GridSearchCV to set the dimensionality of the PCA</p>
<img src="../../_images/sphx_glr_plot_digits_pipe_001.png" srcset="../../_images/sphx_glr_plot_digits_pipe_001.png" alt="plot digits pipe" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best parameter (CV score=0.924):
{'logistic__C': 0.046415888336127774, 'pca__n_components': 60}
</pre></div>
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<div class="line"><br /></div>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Code source: Gaël Varoquaux</span>
<span class="c1"># Modified for documentation by Jaques Grobler</span>
<span class="c1"># License: BSD 3 clause</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">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
<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="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</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.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.preprocessing</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
<span class="c1"># Define a pipeline to search for the best combination of PCA truncation</span>
<span class="c1"># and classifier regularization.</span>
<span class="n">pca</span> <span class="o">=</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="c1"># Define a Standard Scaler to normalize inputs</span>
<span class="n">scaler</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a><span class="p">()</span>
<span class="c1"># set the tolerance to a large value to make the example faster</span>
<span class="n">logistic</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a><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">tol</span><span class="o">=</span><span class="mf">0.1</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="n">steps</span><span class="o">=</span><span class="p">[(</span><span class="s2">"scaler"</span><span class="p">,</span> <span class="n">scaler</span><span class="p">),</span> <span class="p">(</span><span class="s2">"pca"</span><span class="p">,</span> <span class="n">pca</span><span class="p">),</span> <span class="p">(</span><span class="s2">"logistic"</span><span class="p">,</span> <span class="n">logistic</span><span class="p">)])</span>
<span class="n">X_digits</span><span class="p">,</span> <span class="n">y_digits</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">datasets</span><span class="o">.</span><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="c1"># Parameters of pipelines can be set using '__' separated parameter names:</span>
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"pca__n_components"</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">45</span><span class="p">,</span> <span class="mi">60</span><span class="p">],</span>
<span class="s2">"logistic__C"</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.logspace.html#numpy.logspace" title="numpy.logspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">logspace</span></a><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
<span class="p">}</span>
<span class="n">search</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">param_grid</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">search</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_digits</span><span class="p">,</span> <span class="n">y_digits</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Best parameter (CV score=</span><span class="si">%0.3f</span><span class="s2">):"</span> <span class="o">%</span> <span class="n">search</span><span class="o">.</span><span class="n">best_score_</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">search</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
<span class="c1"># Plot the PCA spectrum</span>
<span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_digits</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax0</span><span class="p">,</span> <span class="n">ax1</span><span class="p">)</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">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><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">6</span><span class="p">))</span>
<span class="n">ax0</span><span class="o">.</span><span class="n">plot</span><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">1</span><span class="p">,</span> <span class="n">pca</span><span class="o">.</span><span class="n">n_components_</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">,</span> <span class="s2">"+"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span>
<span class="p">)</span>
<span class="n">ax0</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"PCA explained variance ratio"</span><span class="p">)</span>
<span class="n">ax0</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span>
<span class="n">search</span><span class="o">.</span><span class="n">best_estimator_</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">"pca"</span><span class="p">]</span><span class="o">.</span><span class="n">n_components</span><span class="p">,</span>
<span class="n">linestyle</span><span class="o">=</span><span class="s2">":"</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s2">"n_components chosen"</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">ax0</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">prop</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">12</span><span class="p">))</span>
<span class="c1"># For each number of components, find the best classifier results</span>
<span class="n">results</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">search</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">)</span>
<span class="n">components_col</span> <span class="o">=</span> <span class="s2">"param_pca__n_components"</span>
<span class="n">best_clfs</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">components_col</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
<span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="s2">"mean_test_score"</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">best_clfs</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="n">components_col</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"mean_test_score"</span><span class="p">,</span> <span class="n">yerr</span><span class="o">=</span><span class="s2">"std_test_score"</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span>
<span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Classification accuracy (val)"</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"n_components"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlim.html#matplotlib.pyplot.xlim" title="matplotlib.pyplot.xlim" 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">xlim</span></a><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">70</span><span class="p">)</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>
<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>
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