.. 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_weighted_samples.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_weighted_samples.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_weighted_samples.py:


=====================
SGD: Weighted samples
=====================

Plot decision function of a weighted dataset, where the size of points
is proportional to its weight.

.. GENERATED FROM PYTHON SOURCE LINES 10-61



.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_sgd_weighted_samples_001.png
   :alt: plot sgd weighted samples
   :srcset: /auto_examples/linear_model/images/sphx_glr_plot_sgd_weighted_samples_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn import linear_model

    # we create 20 points
    np.random.seed(0)
    X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]
    y = [1] * 10 + [-1] * 10
    sample_weight = 100 * np.abs(np.random.randn(20))
    # and assign a bigger weight to the last 10 samples
    sample_weight[:10] *= 10

    # plot the weighted data points
    xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))
    fig, ax = plt.subplots()
    ax.scatter(
        X[:, 0],
        X[:, 1],
        c=y,
        s=sample_weight,
        alpha=0.9,
        cmap=plt.cm.bone,
        edgecolor="black",
    )

    # fit the unweighted model
    clf = linear_model.SGDClassifier(alpha=0.01, max_iter=100)
    clf.fit(X, y)
    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    no_weights = ax.contour(xx, yy, Z, levels=[0], linestyles=["solid"])

    # fit the weighted model
    clf = linear_model.SGDClassifier(alpha=0.01, max_iter=100)
    clf.fit(X, y, sample_weight=sample_weight)
    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    samples_weights = ax.contour(xx, yy, Z, levels=[0], linestyles=["dashed"])

    no_weights_handles, _ = no_weights.legend_elements()
    weights_handles, _ = samples_weights.legend_elements()
    ax.legend(
        [no_weights_handles[0], weights_handles[0]],
        ["no weights", "with weights"],
        loc="lower left",
    )

    ax.set(xticks=(), yticks=())
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.075 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_sgd_weighted_samples.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/1.4.X?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_sgd_weighted_samples.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

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

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

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

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

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


.. include:: plot_sgd_weighted_samples.recommendations


.. only:: html

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

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