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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_compose_plot_digits_pipe.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_compose_plot_digits_pipe.py:


=========================================================
Pipelining: chaining a PCA and a logistic regression
=========================================================

The PCA does an unsupervised dimensionality reduction, while the logistic
regression does the prediction.

We use a GridSearchCV to set the dimensionality of the PCA

.. GENERATED FROM PYTHON SOURCE LINES 15-80



.. image:: /auto_examples/compose/images/sphx_glr_plot_digits_pipe_001.png
    :alt: plot digits pipe
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    Best parameter (CV score=0.920):
    {'logistic__C': 0.046415888336127774, 'pca__n_components': 45}






|

.. code-block:: default

    print(__doc__)


    # Code source: Gaël Varoquaux
    # Modified for documentation by Jaques Grobler
    # License: BSD 3 clause


    import numpy as np
    import matplotlib.pyplot as plt
    import pandas as pd

    from sklearn import datasets
    from sklearn.decomposition import PCA
    from sklearn.linear_model import LogisticRegression
    from sklearn.pipeline import Pipeline
    from sklearn.model_selection import GridSearchCV


    # Define a pipeline to search for the best combination of PCA truncation
    # and classifier regularization.
    pca = PCA()
    # set the tolerance to a large value to make the example faster
    logistic = LogisticRegression(max_iter=10000, tol=0.1)
    pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])

    X_digits, y_digits = datasets.load_digits(return_X_y=True)

    # Parameters of pipelines can be set using ‘__’ separated parameter names:
    param_grid = {
        'pca__n_components': [5, 15, 30, 45, 64],
        'logistic__C': np.logspace(-4, 4, 4),
    }
    search = GridSearchCV(pipe, param_grid, n_jobs=-1)
    search.fit(X_digits, y_digits)
    print("Best parameter (CV score=%0.3f):" % search.best_score_)
    print(search.best_params_)

    # Plot the PCA spectrum
    pca.fit(X_digits)

    fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(6, 6))
    ax0.plot(np.arange(1, pca.n_components_ + 1),
             pca.explained_variance_ratio_, '+', linewidth=2)
    ax0.set_ylabel('PCA explained variance ratio')

    ax0.axvline(search.best_estimator_.named_steps['pca'].n_components,
                linestyle=':', label='n_components chosen')
    ax0.legend(prop=dict(size=12))

    # For each number of components, find the best classifier results
    results = pd.DataFrame(search.cv_results_)
    components_col = 'param_pca__n_components'
    best_clfs = results.groupby(components_col).apply(
        lambda g: g.nlargest(1, 'mean_test_score'))

    best_clfs.plot(x=components_col, y='mean_test_score', yerr='std_test_score',
                   legend=False, ax=ax1)
    ax1.set_ylabel('Classification accuracy (val)')
    ax1.set_xlabel('n_components')

    plt.xlim(-1, 70)

    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  39.695 seconds)


.. _sphx_glr_download_auto_examples_compose_plot_digits_pipe.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: images/binder_badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/compose/plot_digits_pipe.ipynb
      :alt: Launch binder
      :width: 150 px


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

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



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

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


.. only:: html

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

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