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<li><a class="reference internal" href="#">Concatenating multiple feature extraction methods</a></li>
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<section class="sphx-glr-example-title" id="concatenating-multiple-feature-extraction-methods">
<span id="sphx-glr-auto-examples-compose-plot-feature-union-py"></span><h1>Concatenating multiple feature extraction methods<a class="headerlink" href="#concatenating-multiple-feature-extraction-methods" title="Permalink to this heading">¶</a></h1>
<p>In many real-world examples, there are many ways to extract features from a
dataset. Often it is beneficial to combine several methods to obtain good
performance. This example shows how to use <code class="docutils literal notranslate"><span class="pre">FeatureUnion</span></code> to combine
features obtained by PCA and univariate selection.</p>
<p>Combining features using this transformer has the benefit that it allows
cross validation and grid searches over the whole process.</p>
<p>The combination used in this example is not particularly helpful on this
dataset and is only used to illustrate the usage of FeatureUnion.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Combined space has 3 features
Fitting 5 folds for each of 18 candidates, totalling 90 fits
[CV 1/5; 1/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1
[CV 1/5; 1/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1;, score=0.933 total time= 0.0s
[CV 2/5; 1/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1
[CV 2/5; 1/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1;, score=0.933 total time= 0.0s
[CV 3/5; 1/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1
[CV 3/5; 1/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1;, score=0.867 total time= 0.0s
[CV 4/5; 1/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1
[CV 4/5; 1/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1;, score=0.933 total time= 0.0s
[CV 5/5; 1/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1
[CV 5/5; 1/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1;, score=1.000 total time= 0.0s
[CV 1/5; 2/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=1
[CV 1/5; 2/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=1;, score=0.900 total time= 0.0s
[CV 2/5; 2/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=1
[CV 2/5; 2/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=1;, score=1.000 total time= 0.0s
[CV 3/5; 2/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=1
[CV 3/5; 2/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=1;, score=0.867 total time= 0.0s
[CV 4/5; 2/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=1
[CV 4/5; 2/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=1;, score=0.933 total time= 0.0s
[CV 5/5; 2/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=1
[CV 5/5; 2/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=1;, score=1.000 total time= 0.0s
[CV 1/5; 3/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=10
[CV 1/5; 3/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=10;, score=0.933 total time= 0.0s
[CV 2/5; 3/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=10
[CV 2/5; 3/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=10;, score=1.000 total time= 0.0s
[CV 3/5; 3/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=10
[CV 3/5; 3/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=10;, score=0.900 total time= 0.0s
[CV 4/5; 3/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=10
[CV 4/5; 3/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=10;, score=0.933 total time= 0.0s
[CV 5/5; 3/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=10
[CV 5/5; 3/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=10;, score=1.000 total time= 0.0s
[CV 1/5; 4/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1
[CV 1/5; 4/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1;, score=0.933 total time= 0.0s
[CV 2/5; 4/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1
[CV 2/5; 4/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1;, score=0.967 total time= 0.0s
[CV 3/5; 4/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1
[CV 3/5; 4/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1;, score=0.933 total time= 0.0s
[CV 4/5; 4/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1
[CV 4/5; 4/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1;, score=0.933 total time= 0.0s
[CV 5/5; 4/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1
[CV 5/5; 4/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1;, score=1.000 total time= 0.0s
[CV 1/5; 5/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=1
[CV 1/5; 5/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=1;, score=0.933 total time= 0.0s
[CV 2/5; 5/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=1
[CV 2/5; 5/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=1;, score=0.967 total time= 0.0s
[CV 3/5; 5/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=1
[CV 3/5; 5/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=1;, score=0.933 total time= 0.0s
[CV 4/5; 5/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=1
[CV 4/5; 5/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=1;, score=0.933 total time= 0.0s
[CV 5/5; 5/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=1
[CV 5/5; 5/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=1;, score=1.000 total time= 0.0s
[CV 1/5; 6/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=10
[CV 1/5; 6/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=10;, score=0.967 total time= 0.0s
[CV 2/5; 6/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=10
[CV 2/5; 6/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=10;, score=0.967 total time= 0.0s
[CV 3/5; 6/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=10
[CV 3/5; 6/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=10;, score=0.933 total time= 0.0s
[CV 4/5; 6/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=10
[CV 4/5; 6/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=10;, score=0.933 total time= 0.0s
[CV 5/5; 6/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=10
[CV 5/5; 6/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=10;, score=1.000 total time= 0.0s
[CV 1/5; 7/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1
[CV 1/5; 7/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1;, score=0.933 total time= 0.0s
[CV 2/5; 7/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1
[CV 2/5; 7/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1;, score=1.000 total time= 0.0s
[CV 3/5; 7/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1
[CV 3/5; 7/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1;, score=0.867 total time= 0.0s
[CV 4/5; 7/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1
[CV 4/5; 7/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1;, score=0.933 total time= 0.0s
[CV 5/5; 7/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1
[CV 5/5; 7/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1;, score=1.000 total time= 0.0s
[CV 1/5; 8/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=1
[CV 1/5; 8/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=1;, score=0.967 total time= 0.0s
[CV 2/5; 8/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=1
[CV 2/5; 8/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=1;, score=1.000 total time= 0.0s
[CV 3/5; 8/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=1
[CV 3/5; 8/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=1;, score=0.933 total time= 0.0s
[CV 4/5; 8/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=1
[CV 4/5; 8/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=1;, score=0.933 total time= 0.0s
[CV 5/5; 8/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=1
[CV 5/5; 8/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=1;, score=1.000 total time= 0.0s
[CV 1/5; 9/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=10
[CV 1/5; 9/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=10;, score=0.967 total time= 0.0s
[CV 2/5; 9/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=10
[CV 2/5; 9/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=10;, score=0.967 total time= 0.0s
[CV 3/5; 9/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=10
[CV 3/5; 9/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=10;, score=0.900 total time= 0.0s
[CV 4/5; 9/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=10
[CV 4/5; 9/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=10;, score=0.933 total time= 0.0s
[CV 5/5; 9/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=10
[CV 5/5; 9/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=10;, score=1.000 total time= 0.0s
[CV 1/5; 10/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1
[CV 1/5; 10/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1;, score=0.967 total time= 0.0s
[CV 2/5; 10/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1
[CV 2/5; 10/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1;, score=1.000 total time= 0.0s
[CV 3/5; 10/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1
[CV 3/5; 10/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1;, score=0.933 total time= 0.0s
[CV 4/5; 10/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1
[CV 4/5; 10/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1;, score=0.933 total time= 0.0s
[CV 5/5; 10/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1
[CV 5/5; 10/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1;, score=1.000 total time= 0.0s
[CV 1/5; 11/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=1
[CV 1/5; 11/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=1;, score=0.967 total time= 0.0s
[CV 2/5; 11/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=1
[CV 2/5; 11/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=1;, score=1.000 total time= 0.0s
[CV 3/5; 11/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=1
[CV 3/5; 11/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=1;, score=0.933 total time= 0.0s
[CV 4/5; 11/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=1
[CV 4/5; 11/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=1;, score=0.967 total time= 0.0s
[CV 5/5; 11/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=1
[CV 5/5; 11/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=1;, score=1.000 total time= 0.0s
[CV 1/5; 12/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=10
[CV 1/5; 12/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=10;, score=0.967 total time= 0.0s
[CV 2/5; 12/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=10
[CV 2/5; 12/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=10;, score=1.000 total time= 0.0s
[CV 3/5; 12/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=10
[CV 3/5; 12/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=10;, score=0.900 total time= 0.0s
[CV 4/5; 12/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=10
[CV 4/5; 12/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=10;, score=0.933 total time= 0.0s
[CV 5/5; 12/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=10
[CV 5/5; 12/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=10;, score=1.000 total time= 0.0s
[CV 1/5; 13/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1
[CV 1/5; 13/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1;, score=0.967 total time= 0.0s
[CV 2/5; 13/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1
[CV 2/5; 13/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1;, score=1.000 total time= 0.0s
[CV 3/5; 13/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1
[CV 3/5; 13/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1;, score=0.933 total time= 0.0s
[CV 4/5; 13/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1
[CV 4/5; 13/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1;, score=0.967 total time= 0.0s
[CV 5/5; 13/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1
[CV 5/5; 13/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1;, score=1.000 total time= 0.0s
[CV 1/5; 14/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=1
[CV 1/5; 14/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=1;, score=0.967 total time= 0.0s
[CV 2/5; 14/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=1
[CV 2/5; 14/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=1;, score=1.000 total time= 0.0s
[CV 3/5; 14/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=1
[CV 3/5; 14/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=1;, score=0.933 total time= 0.0s
[CV 4/5; 14/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=1
[CV 4/5; 14/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=1;, score=0.967 total time= 0.0s
[CV 5/5; 14/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=1
[CV 5/5; 14/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=1;, score=1.000 total time= 0.0s
[CV 1/5; 15/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=10
[CV 1/5; 15/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=10;, score=1.000 total time= 0.0s
[CV 2/5; 15/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=10
[CV 2/5; 15/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=10;, score=1.000 total time= 0.0s
[CV 3/5; 15/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=10
[CV 3/5; 15/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=10;, score=0.933 total time= 0.0s
[CV 4/5; 15/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=10
[CV 4/5; 15/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=10;, score=0.967 total time= 0.0s
[CV 5/5; 15/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=10
[CV 5/5; 15/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=10;, score=1.000 total time= 0.0s
[CV 1/5; 16/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1
[CV 1/5; 16/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1;, score=0.967 total time= 0.0s
[CV 2/5; 16/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1
[CV 2/5; 16/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1;, score=1.000 total time= 0.0s
[CV 3/5; 16/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1
[CV 3/5; 16/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1;, score=0.933 total time= 0.0s
[CV 4/5; 16/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1
[CV 4/5; 16/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1;, score=0.967 total time= 0.0s
[CV 5/5; 16/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1
[CV 5/5; 16/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1;, score=1.000 total time= 0.0s
[CV 1/5; 17/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=1
[CV 1/5; 17/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=1;, score=0.967 total time= 0.0s
[CV 2/5; 17/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=1
[CV 2/5; 17/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=1;, score=1.000 total time= 0.0s
[CV 3/5; 17/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=1
[CV 3/5; 17/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=1;, score=0.967 total time= 0.0s
[CV 4/5; 17/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=1
[CV 4/5; 17/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=1;, score=0.967 total time= 0.0s
[CV 5/5; 17/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=1
[CV 5/5; 17/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=1;, score=1.000 total time= 0.0s
[CV 1/5; 18/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=10
[CV 1/5; 18/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=10;, score=1.000 total time= 0.0s
[CV 2/5; 18/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=10
[CV 2/5; 18/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=10;, score=1.000 total time= 0.0s
[CV 3/5; 18/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=10
[CV 3/5; 18/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=10;, score=0.900 total time= 0.0s
[CV 4/5; 18/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=10
[CV 4/5; 18/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=10;, score=0.967 total time= 0.0s
[CV 5/5; 18/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=10
[CV 5/5; 18/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=10;, score=1.000 total time= 0.0s
Pipeline(steps=[('features',
FeatureUnion(transformer_list=[('pca', PCA(n_components=3)),
('univ_select',
SelectKBest(k=1))])),
('svm', SVC(C=10, kernel='linear'))])
</pre></div>
</div>
<div class="line-block">
<div class="line"><br /></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Andreas Mueller <[email protected]></span>
<span class="c1">#</span>
<span class="c1"># License: BSD 3 clause</span>
<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="p">,</span> <a href="../../modules/generated/sklearn.pipeline.FeatureUnion.html#sklearn.pipeline.FeatureUnion" title="sklearn.pipeline.FeatureUnion" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">FeatureUnion</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.svm</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_iris</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="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="n">iris</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_iris</span></a><span class="p">()</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span>
<span class="c1"># This dataset is way too high-dimensional. Better do PCA:</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="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="c1"># Maybe some original features were good, too?</span>
<span class="n">selection</span> <span class="o">=</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><span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># Build estimator from PCA and Univariate selection:</span>
<span class="n">combined_features</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.pipeline.FeatureUnion.html#sklearn.pipeline.FeatureUnion" title="sklearn.pipeline.FeatureUnion" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">FeatureUnion</span></a><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">"univ_select"</span><span class="p">,</span> <span class="n">selection</span><span class="p">)])</span>
<span class="c1"># Use combined features to transform dataset:</span>
<span class="n">X_features</span> <span class="o">=</span> <span class="n">combined_features</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><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Combined space has"</span><span class="p">,</span> <span class="n">X_features</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">"features"</span><span class="p">)</span>
<span class="n">svm</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"linear"</span><span class="p">)</span>
<span class="c1"># Do grid search over k, n_components and C:</span>
<span class="n">pipeline</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="s2">"features"</span><span class="p">,</span> <span class="n">combined_features</span><span class="p">),</span> <span class="p">(</span><span class="s2">"svm"</span><span class="p">,</span> <span class="n">svm</span><span class="p">)])</span>
<span class="n">param_grid</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
<span class="n">features__pca__n_components</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
<span class="n">features__univ_select__k</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="n">svm__C</span><span class="o">=</span><span class="p">[</span><span class="mf">0.1</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="p">)</span>
<span class="n">grid_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">pipeline</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">verbose</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">grid_search</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>
<span class="nb">print</span><span class="p">(</span><span class="n">grid_search</span><span class="o">.</span><span class="n">best_estimator_</span><span class="p">)</span>
</pre></div>
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