.. 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:


=====================================================
تصنيف MNIST باستخدام اللوغاريتم متعدد الحدود + L1
=====================================================

هنا نقوم بضبط الانحدار اللوغاريتمي متعدد الحدود مع عقوبة L1 على مجموعة فرعية
من مهمة تصنيف أرقام MNIST. نستخدم خوارزمية SAGA لهذا
الغرض: هذه أداة حل سريعة عندما يكون عدد العينات أكبر بشكل ملحوظ
من عدد الميزات وقادرة على تحسين دقيق
وظائف الهدف غير الملساء والتي هي الحالة مع عقوبة l1. تصل دقة الاختبار
> 0.8، بينما تبقى متجهات الوزن *متفرقة* وبالتالي أكثر سهولة
*قابل للتفسير*.

ملاحظة: أن دقة هذا النموذج الخطي المعاقب عليه l1 أقل بكثير
ما يمكن الوصول إليه بواسطة نموذج خطي معاقب عليه l2 أو نموذج متعدد الطبقات
شبكة الإدراك غير الخطية على هذه المجموعة من البيانات.

.. GENERATED FROM PYTHON SOURCE LINES 19-83



.. 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: 75.87%
    Test score with L1 penalty: 0.8224
    Example run in 7.375 s






|

.. code-block:: Python


    # المؤلفون: مطوري scikit-learn
    # معرف الترخيص: 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

    # خفض للتقارب الأسرع
    t0 = time.time()
    train_samples = 5000

    # تحميل البيانات من 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)

    # زيادة التسامح للتقارب الأسرع
    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 7.462 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
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.ipynb
        :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>`_