.. 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

        :ref:`Go to the end <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 JupyterLite or 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 12-86



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


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

 .. code-block:: none

    Best parameter (CV score=0.874):
    {'logistic__C': np.float64(21.54434690031882), 'pca__n_components': 60}






|

.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause

    import matplotlib.pyplot as plt
    import numpy as np
    import polars as pl

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

    # Define a pipeline to search for the best combination of PCA truncation
    # and classifier regularization.
    pca = PCA()
    # Define a Standard Scaler to normalize inputs
    scaler = StandardScaler()

    # set the tolerance to a large value to make the example faster
    logistic = LogisticRegression(max_iter=10000, tol=0.1)
    pipe = Pipeline(steps=[("scaler", scaler), ("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, 60],
        "logistic__C": np.logspace(-4, 4, 4),
    }
    search = GridSearchCV(pipe, param_grid, n_jobs=2)
    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
    components_col = "param_pca__n_components"
    is_max_test_score = pl.col("mean_test_score") == pl.col("mean_test_score").max()
    best_clfs = (
        pl.LazyFrame(search.cv_results_)
        .filter(is_max_test_score.over(components_col))
        .unique(components_col)
        .sort(components_col)
        .collect()
    )
    ax1.errorbar(
        best_clfs[components_col],
        best_clfs["mean_test_score"],
        yerr=best_clfs["std_test_score"],
    )
    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 1.159 seconds)


.. _sphx_glr_download_auto_examples_compose_plot_digits_pipe.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.6.X?urlpath=lab/tree/notebooks/auto_examples/compose/plot_digits_pipe.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/compose/plot_digits_pipe.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

    .. 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-zip

      :download:`Download zipped: plot_digits_pipe.zip <plot_digits_pipe.zip>`


.. include:: plot_digits_pipe.recommendations


.. only:: html

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

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