.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_feature_selection_plot_feature_selection_pipeline.py>`     to download the full example code or to run this example in your browser via Binder
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py:


==================
Pipeline Anova SVM
==================

Simple usage of Pipeline that runs successively a univariate
feature selection with anova and then a SVM of the selected features.

Using a sub-pipeline, the fitted coefficients can be mapped back into
the original feature space.




.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


                  precision    recall  f1-score   support

               0       0.75      0.50      0.60         6
               1       0.67      1.00      0.80         6
               2       0.67      0.80      0.73         5
               3       1.00      0.75      0.86         8

        accuracy                           0.76        25
       macro avg       0.77      0.76      0.75        25
    weighted avg       0.79      0.76      0.76        25

    [[-0.23912051  0.          0.          0.         -0.32369992  0.
       0.          0.          0.          0.          0.          0.
       0.1083669   0.          0.          0.          0.          0.
       0.          0.        ]
     [ 0.43878897  0.          0.          0.         -0.514157    0.
       0.          0.          0.          0.          0.          0.
       0.04845592  0.          0.          0.          0.          0.
       0.          0.        ]
     [-0.65382765  0.          0.          0.          0.57962287  0.
       0.          0.          0.          0.          0.          0.
      -0.04736736  0.          0.          0.          0.          0.
       0.          0.        ]
     [ 0.544033    0.          0.          0.          0.58478674  0.
       0.          0.          0.          0.          0.          0.
      -0.11344771  0.          0.          0.          0.          0.
       0.          0.        ]]






|


.. code-block:: default

    from sklearn import svm
    from sklearn.datasets import make_classification
    from sklearn.feature_selection import SelectKBest, f_regression
    from sklearn.pipeline import make_pipeline
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import classification_report

    print(__doc__)

    # import some data to play with
    X, y = make_classification(
        n_features=20, n_informative=3, n_redundant=0, n_classes=4,
        n_clusters_per_class=2)

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

    # ANOVA SVM-C
    # 1) anova filter, take 3 best ranked features
    anova_filter = SelectKBest(f_regression, k=3)
    # 2) svm
    clf = svm.LinearSVC()

    anova_svm = make_pipeline(anova_filter, clf)
    anova_svm.fit(X_train, y_train)
    y_pred = anova_svm.predict(X_test)
    print(classification_report(y_test, y_pred))

    coef = anova_svm[:-1].inverse_transform(anova_svm['linearsvc'].coef_)
    print(coef)


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.009 seconds)


.. _sphx_glr_download_auto_examples_feature_selection_plot_feature_selection_pipeline.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example


  .. container:: binder-badge

    .. image:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/feature_selection/plot_feature_selection_pipeline.ipynb
      :width: 150 px


  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_feature_selection_pipeline.py <plot_feature_selection_pipeline.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_feature_selection_pipeline.ipynb <plot_feature_selection_pipeline.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_