.. _sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py:


=================================================
SVM: Separating hyperplane for unbalanced classes
=================================================

Find the optimal separating hyperplane using an SVC for classes that
are unbalanced.

We first find the separating plane with a plain SVC and then plot
(dashed) the separating hyperplane with automatically correction for
unbalanced classes.

.. currentmodule:: sklearn.linear_model

.. note::

    This example will also work by replacing ``SVC(kernel="linear")``
    with ``SGDClassifier(loss="hinge")``. Setting the ``loss`` parameter
    of the :class:`SGDClassifier` equal to ``hinge`` will yield behaviour
    such as that of a SVC with a linear kernel.

    For example try instead of the ``SVC``::

        clf = SGDClassifier(n_iter=100, alpha=0.01)





.. image:: /auto_examples/svm/images/sphx_glr_plot_separating_hyperplane_unbalanced_001.png
    :align: center





.. code-block:: python

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn import svm

    # we create clusters with 1000 and 100 points
    rng = np.random.RandomState(0)
    n_samples_1 = 1000
    n_samples_2 = 100
    X = np.r_[1.5 * rng.randn(n_samples_1, 2),
              0.5 * rng.randn(n_samples_2, 2) + [2, 2]]
    y = [0] * (n_samples_1) + [1] * (n_samples_2)

    # fit the model and get the separating hyperplane
    clf = svm.SVC(kernel='linear', C=1.0)
    clf.fit(X, y)

    # fit the model and get the separating hyperplane using weighted classes
    wclf = svm.SVC(kernel='linear', class_weight={1: 10})
    wclf.fit(X, y)

    # plot separating hyperplanes and samples
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, edgecolors='k')
    plt.legend()

    # plot the decision functions for both classifiers
    ax = plt.gca()
    xlim = ax.get_xlim()
    ylim = ax.get_ylim()

    # create grid to evaluate model
    xx = np.linspace(xlim[0], xlim[1], 30)
    yy = np.linspace(ylim[0], ylim[1], 30)
    YY, XX = np.meshgrid(yy, xx)
    xy = np.vstack([XX.ravel(), YY.ravel()]).T

    # get the separating hyperplane
    Z = clf.decision_function(xy).reshape(XX.shape)

    # plot decision boundary and margins
    a = ax.contour(XX, YY, Z, colors='k', levels=[0], alpha=0.5, linestyles=['-'])

    # get the separating hyperplane for weighted classes
    Z = wclf.decision_function(xy).reshape(XX.shape)

    # plot decision boundary and margins for weighted classes
    b = ax.contour(XX, YY, Z, colors='r', levels=[0], alpha=0.5, linestyles=['-'])

    plt.legend([a.collections[0], b.collections[0]], ["non weighted", "weighted"],
               loc="upper right")
    plt.show()

**Total running time of the script:** ( 0 minutes  0.045 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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