.. _sphx_glr_auto_examples_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



.. code-block:: python

    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

    from sklearn import linear_model, decomposition, datasets
    from sklearn.pipeline import Pipeline
    from sklearn.model_selection import GridSearchCV

    logistic = linear_model.LogisticRegression()

    pca = decomposition.PCA()
    pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])

    digits = datasets.load_digits()
    X_digits = digits.data
    y_digits = digits.target







Plot the PCA spectrum


.. code-block:: python

    pca.fit(X_digits)

    plt.figure(1, figsize=(4, 3))
    plt.clf()
    plt.axes([.2, .2, .7, .7])
    plt.plot(pca.explained_variance_, linewidth=2)
    plt.axis('tight')
    plt.xlabel('n_components')
    plt.ylabel('explained_variance_')




.. image:: /auto_examples/images/sphx_glr_plot_digits_pipe_001.png
    :align: center




Prediction


.. code-block:: python


    n_components = [20, 40, 64]
    Cs = np.logspace(-4, 4, 3)

    #Parameters of pipelines can be set using ‘__’ separated parameter names:

    estimator = GridSearchCV(pipe,
                             dict(pca__n_components=n_components,
                                  logistic__C=Cs))
    estimator.fit(X_digits, y_digits)

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



.. image:: /auto_examples/images/sphx_glr_plot_digits_pipe_002.png
    :align: center




**Total running time of the script:**
(0 minutes 9.009 seconds)



.. container:: sphx-glr-download

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


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_digits_pipe.ipynb <plot_digits_pipe.ipynb>`