.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code or to run this example in your browser via Binder
.. rst-class:: sphx-glr-example-title
.. _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
:alt: plot separating hyperplane unbalanced
:class: sphx-glr-single-img
.. code-block:: default
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.datasets import make_blobs
# we create two clusters of random points
n_samples_1 = 1000
n_samples_2 = 100
centers = [[0.0, 0.0], [2.0, 2.0]]
clusters_std = [1.5, 0.5]
X, y = make_blobs(n_samples=[n_samples_1, n_samples_2],
centers=centers,
cluster_std=clusters_std,
random_state=0, shuffle=False)
# 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 the samples
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, edgecolors='k')
# 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()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.135 seconds)
.. _sphx_glr_download_auto_examples_svm_plot_separating_hyperplane_unbalanced.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/svm/plot_separating_hyperplane_unbalanced.ipynb
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_separating_hyperplane_unbalanced.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_separating_hyperplane_unbalanced.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_