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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_selection_Selectors-Pipelines.py>`
        to download the full example code.

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

.. _sphx_glr_examples_selection_Selectors-Pipelines.py:


Using scikit-matter selectors with scikit-learn pipelines
=========================================================

.. GENERATED FROM PYTHON SOURCE LINES 11-23

.. code-block:: Python


    import numpy as np
    from matplotlib import pyplot as plt
    from sklearn.datasets import load_diabetes
    from sklearn.linear_model import RidgeCV
    from sklearn.model_selection import train_test_split
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler

    from skmatter.feature_selection import CUR, FPS









.. GENERATED FROM PYTHON SOURCE LINES 24-28

Simple integration of scikit-matter selectors
---------------------------------------------

This example shows how to use FPS to subselect features before training a RidgeCV.

.. GENERATED FROM PYTHON SOURCE LINES 29-50

.. code-block:: Python



    scaler = StandardScaler()
    selector = FPS(n_to_select=4)
    ridge = RidgeCV(cv=2, alphas=np.logspace(-8, 2, 10))

    X, y = load_diabetes(return_X_y=True)

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    pipe = Pipeline([("scaler", scaler), ("selector", selector), ("ridge", ridge)])
    pipe.fit(X_train.copy(), y_train.copy())

    plt.scatter(y_test, pipe.predict(X_test))
    plt.gca().set_aspect("equal")
    plt.plot(plt.xlim(), plt.xlim(), "r--")
    plt.xlabel("True Values")
    plt.ylabel("Predicted Values")
    plt.show()





.. image-sg:: /examples/selection/images/sphx_glr_Selectors-Pipelines_001.png
   :alt: Selectors Pipelines
   :srcset: /examples/selection/images/sphx_glr_Selectors-Pipelines_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 51-56

Stacking selectors one after another
------------------------------------

This example shows how to use an FPS, then CUR selector
to subselect features before training a RidgeCV.

.. GENERATED FROM PYTHON SOURCE LINES 57-79

.. code-block:: Python



    scaler = StandardScaler()
    fps = FPS(n_to_select=8)
    cur = CUR(n_to_select=4)
    ridge = RidgeCV(cv=2, alphas=np.logspace(-8, 2, 10))

    X, y = load_diabetes(return_X_y=True)

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    pipe = Pipeline(
        [("scaler", scaler), ("selector1", fps), ("selector2", cur), ("ridge", ridge)]
    )
    pipe.fit(X_train.copy(), y_train.copy())

    plt.scatter(y_test, pipe.predict(X_test))
    plt.gca().set_aspect("equal")
    plt.plot(plt.xlim(), plt.xlim(), "r--")
    plt.xlabel("True Values")
    plt.ylabel("Predicted Values")
    plt.show()



.. image-sg:: /examples/selection/images/sphx_glr_Selectors-Pipelines_002.png
   :alt: Selectors Pipelines
   :srcset: /examples/selection/images/sphx_glr_Selectors-Pipelines_002.png
   :class: sphx-glr-single-img






.. _sphx_glr_download_examples_selection_Selectors-Pipelines.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: Selectors-Pipelines.ipynb <Selectors-Pipelines.ipynb>`

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

      :download:`Download Python source code: Selectors-Pipelines.py <Selectors-Pipelines.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: Selectors-Pipelines.zip <Selectors-Pipelines.zip>`


.. only:: html

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

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