.. _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
    #from sklearn.linear_model import SGDClassifier

    # we create 40 separable 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)

    w = clf.coef_[0]
    a = -w[0] / w[1]
    xx = np.linspace(-5, 5)
    yy = a * xx - clf.intercept_[0] / w[1]


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

    ww = wclf.coef_[0]
    wa = -ww[0] / ww[1]
    wyy = wa * xx - wclf.intercept_[0] / ww[1]

    # plot separating hyperplanes and samples
    h0 = plt.plot(xx, yy, 'k-', label='no weights')
    h1 = plt.plot(xx, wyy, 'k--', label='with weights')
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
    plt.legend()

    plt.axis('tight')
    plt.show()

**Total running time of the script:**
(0 minutes 0.077 seconds)



.. container:: sphx-glr-download

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


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_separating_hyperplane_unbalanced.ipynb <plot_separating_hyperplane_unbalanced.ipynb>`