
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/feature_selection/plot_feature_selection_pipeline.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. 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

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py:


==================
Pipeline ANOVA SVM
==================

This example shows how a feature selection can be easily integrated within
a machine learning pipeline.

We also show that you can easily introspect part of the pipeline.

.. GENERATED FROM PYTHON SOURCE LINES 12-17

.. code-block:: default


    from sklearn import set_config

    set_config(display="diagram")








.. GENERATED FROM PYTHON SOURCE LINES 18-20

We will start by generating a binary classification dataset. Subsequently, we
will divide the dataset into two subsets.

.. GENERATED FROM PYTHON SOURCE LINES 20-34

.. code-block:: default


    from sklearn.datasets import make_classification
    from sklearn.model_selection import train_test_split

    X, y = make_classification(
        n_features=20,
        n_informative=3,
        n_redundant=0,
        n_classes=2,
        n_clusters_per_class=2,
        random_state=42,
    )
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)








.. GENERATED FROM PYTHON SOURCE LINES 35-47

A common mistake done with feature selection is to search a subset of
discriminative features on the full dataset instead of only using the
training set. The usage of scikit-learn :func:`~sklearn.pipeline.Pipeline`
prevents to make such mistake.

Here, we will demonstrate how to build a pipeline where the first step will
be the feature selection.

When calling `fit` on the training data, a subset of feature will be selected
and the index of these selected features will be stored. The feature selector
will subsequently reduce the number of feature and pass this subset to the
classifier which will be trained.

.. GENERATED FROM PYTHON SOURCE LINES 47-57

.. code-block:: default


    from sklearn.feature_selection import SelectKBest, f_classif
    from sklearn.pipeline import make_pipeline
    from sklearn.svm import LinearSVC

    anova_filter = SelectKBest(f_classif, k=3)
    clf = LinearSVC()
    anova_svm = make_pipeline(anova_filter, clf)
    anova_svm.fit(X_train, y_train)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 {color: black;background-color: white;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 pre{padding: 0;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-toggleable {background-color: white;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-estimator:hover {background-color: #d4ebff;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-item {z-index: 1;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-parallel-item:only-child::after {width: 0;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-6881fce6-b2ce-42a4-b147-0c7acc26e527 div.sk-text-repr-fallback {display: none;}</style><div id="sk-6881fce6-b2ce-42a4-b147-0c7acc26e527" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;selectkbest&#x27;, SelectKBest(k=3)), (&#x27;linearsvc&#x27;, LinearSVC())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="a4906400-d268-478d-914d-c8389f52525c" type="checkbox" ><label for="a4906400-d268-478d-914d-c8389f52525c" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;selectkbest&#x27;, SelectKBest(k=3)), (&#x27;linearsvc&#x27;, LinearSVC())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4a6c5491-3355-43f5-9e13-cbf53c04c550" type="checkbox" ><label for="4a6c5491-3355-43f5-9e13-cbf53c04c550" class="sk-toggleable__label sk-toggleable__label-arrow">SelectKBest</label><div class="sk-toggleable__content"><pre>SelectKBest(k=3)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="e8ddb608-ac32-4ca9-bdd9-d7ab623cedd7" type="checkbox" ><label for="e8ddb608-ac32-4ca9-bdd9-d7ab623cedd7" class="sk-toggleable__label sk-toggleable__label-arrow">LinearSVC</label><div class="sk-toggleable__content"><pre>LinearSVC()</pre></div></div></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 58-64

Once the training accomplished, we can predict on new unseen samples. In this
case, the feature selector will only select the most discriminative features
based on the information stored during training. Then, the data will be
passed to the classifier which will make the prediction.

Here, we report the final metrics via a classification report.

.. GENERATED FROM PYTHON SOURCE LINES 64-70

.. code-block:: default


    from sklearn.metrics import classification_report

    y_pred = anova_svm.predict(X_test)
    print(classification_report(y_test, y_pred))





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

 Out:

 .. code-block:: none

                  precision    recall  f1-score   support

               0       0.92      0.80      0.86        15
               1       0.75      0.90      0.82        10

        accuracy                           0.84        25
       macro avg       0.84      0.85      0.84        25
    weighted avg       0.85      0.84      0.84        25





.. GENERATED FROM PYTHON SOURCE LINES 71-74

Be aware that you can inspect a step in the pipeline. For instance, we might
be interested about the parameters of the classifier. Since we selected
three features, we expect to have three coefficients.

.. GENERATED FROM PYTHON SOURCE LINES 74-77

.. code-block:: default


    anova_svm[-1].coef_





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

 Out:

 .. code-block:: none


    array([[0.75790919, 0.27158706, 0.26109741]])



.. GENERATED FROM PYTHON SOURCE LINES 78-82

However, we do not know which features where selected from the original
dataset. We could proceed by several manner. Here, we will inverse the
transformation of these coefficients to get information about the original
space.

.. GENERATED FROM PYTHON SOURCE LINES 82-85

.. code-block:: default


    anova_svm[:-1].inverse_transform(anova_svm[-1].coef_)





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

 Out:

 .. code-block:: none


    array([[0.        , 0.        , 0.75790919, 0.        , 0.        ,
            0.        , 0.        , 0.        , 0.        , 0.27158706,
            0.        , 0.        , 0.        , 0.        , 0.        ,
            0.        , 0.        , 0.        , 0.        , 0.26109741]])



.. GENERATED FROM PYTHON SOURCE LINES 86-88

We can see that the first three features where the selected features by
the first step.


.. 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:: 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>`_
