.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_linear_model_plot_sgd_weighted_samples.py>` to download the full example code
.. 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.



.. image:: /auto_examples/linear_model/images/sphx_glr_plot_sgd_weighted_samples_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    /home/circleci/project/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.
      FutureWarning)
    /home/circleci/project/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.
      FutureWarning)





|


.. code-block:: default

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt
    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))
    plt.figure()
    plt.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 = plt.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 = plt.contour(xx, yy, Z, levels=[0], linestyles=['dashed'])

    plt.legend([no_weights.collections[0], samples_weights.collections[0]],
               ["no weights", "with weights"], loc="lower left")

    plt.xticks(())
    plt.yticks(())
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.199 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_sgd_weighted_samples.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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