.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/linear_model/plot_sparse_logistic_regression_mnist.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_linear_model_plot_sparse_logistic_regression_mnist.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_linear_model_plot_sparse_logistic_regression_mnist.py:


=====================================================
MNIST classification using multinomial logistic + L1
=====================================================

Here we fit a multinomial logistic regression with L1 penalty on a subset of
the MNIST digits classification task. We use the SAGA algorithm for this
purpose: this a solver that is fast when the number of samples is significantly
larger than the number of features and is able to finely optimize non-smooth
objective functions which is the case with the l1-penalty. Test accuracy
reaches > 0.8, while weight vectors remains *sparse* and therefore more easily
*interpretable*.

Note that this accuracy of this l1-penalized linear model is significantly
below what can be reached by an l2-penalized linear model or a non-linear
multi-layer perceptron model on this dataset.

.. GENERATED FROM PYTHON SOURCE LINES 19-84



.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_sparse_logistic_regression_mnist_001.png
   :alt: Classification vector for...
   :srcset: /auto_examples/linear_model/images/sphx_glr_plot_sparse_logistic_regression_mnist_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Sparsity with L1 penalty: 74.57%
    Test score with L1 penalty: 0.8253
    Example run in 7.602 s






|

.. code-block:: Python


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

    import time

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import fetch_openml
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.utils import check_random_state

    # Turn down for faster convergence
    t0 = time.time()
    train_samples = 5000

    # Load data from https://www.openml.org/d/554
    X, y = fetch_openml("mnist_784", version=1, return_X_y=True, as_frame=False)

    random_state = check_random_state(0)
    permutation = random_state.permutation(X.shape[0])
    X = X[permutation]
    y = y[permutation]
    X = X.reshape((X.shape[0], -1))

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, train_size=train_samples, test_size=10000
    )

    scaler = StandardScaler()
    X_train = scaler.fit_transform(X_train)
    X_test = scaler.transform(X_test)

    # Turn up tolerance for faster convergence
    clf = LogisticRegression(C=50.0 / train_samples, penalty="l1", solver="saga", tol=0.1)
    clf.fit(X_train, y_train)
    sparsity = np.mean(clf.coef_ == 0) * 100
    score = clf.score(X_test, y_test)
    # print('Best C % .4f' % clf.C_)
    print("Sparsity with L1 penalty: %.2f%%" % sparsity)
    print("Test score with L1 penalty: %.4f" % score)

    coef = clf.coef_.copy()
    plt.figure(figsize=(10, 5))
    scale = np.abs(coef).max()
    for i in range(10):
        l1_plot = plt.subplot(2, 5, i + 1)
        l1_plot.imshow(
            coef[i].reshape(28, 28),
            interpolation="nearest",
            cmap=plt.cm.RdBu,
            vmin=-scale,
            vmax=scale,
        )
        l1_plot.set_xticks(())
        l1_plot.set_yticks(())
        l1_plot.set_xlabel("Class %i" % i)
    plt.suptitle("Classification vector for...")

    run_time = time.time() - t0
    print("Example run in %.3f s" % run_time)
    plt.show()


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

   **Total running time of the script:** (0 minutes 7.662 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
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        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

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

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

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

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

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

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

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


.. include:: plot_sparse_logistic_regression_mnist.recommendations


.. only:: html

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

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