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


==================================
Comparing various online solvers
==================================
An example showing how different online solvers perform
on the hand-written digits dataset.

.. GENERATED FROM PYTHON SOURCE LINES 8-71



.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_sgd_comparison_001.png
   :alt: plot sgd comparison
   :srcset: /auto_examples/linear_model/images/sphx_glr_plot_sgd_comparison_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    training SGD
    training ASGD
    training Perceptron
    training Passive-Aggressive I
    training Passive-Aggressive II
    training SAG






|

.. code-block:: Python


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

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn import datasets
    from sklearn.linear_model import (
        LogisticRegression,
        PassiveAggressiveClassifier,
        Perceptron,
        SGDClassifier,
    )
    from sklearn.model_selection import train_test_split

    heldout = [0.95, 0.90, 0.75, 0.50, 0.01]
    # Number of rounds to fit and evaluate an estimator.
    rounds = 10
    X, y = datasets.load_digits(return_X_y=True)

    classifiers = [
        ("SGD", SGDClassifier(max_iter=110)),
        ("ASGD", SGDClassifier(max_iter=110, average=True)),
        ("Perceptron", Perceptron(max_iter=110)),
        (
            "Passive-Aggressive I",
            PassiveAggressiveClassifier(max_iter=110, loss="hinge", C=1.0, tol=1e-4),
        ),
        (
            "Passive-Aggressive II",
            PassiveAggressiveClassifier(
                max_iter=110, loss="squared_hinge", C=1.0, tol=1e-4
            ),
        ),
        (
            "SAG",
            LogisticRegression(max_iter=110, solver="sag", tol=1e-1, C=1.0e4 / X.shape[0]),
        ),
    ]

    xx = 1.0 - np.array(heldout)

    for name, clf in classifiers:
        print("training %s" % name)
        rng = np.random.RandomState(42)
        yy = []
        for i in heldout:
            yy_ = []
            for r in range(rounds):
                X_train, X_test, y_train, y_test = train_test_split(
                    X, y, test_size=i, random_state=rng
                )
                clf.fit(X_train, y_train)
                y_pred = clf.predict(X_test)
                yy_.append(1 - np.mean(y_pred == y_test))
            yy.append(np.mean(yy_))
        plt.plot(xx, yy, label=name)

    plt.legend(loc="upper right")
    plt.xlabel("Proportion train")
    plt.ylabel("Test Error Rate")
    plt.show()


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

   **Total running time of the script:** (0 minutes 7.976 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_sgd_comparison.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/linear_model/plot_sgd_comparison.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_sgd_comparison.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_sgd_comparison.recommendations


.. only:: html

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

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