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Commit 4d2b3762 authored by Alexandre Gramfort's avatar Alexandre Gramfort
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pretifying plot_weighted_classes.py

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...@@ -3,7 +3,11 @@ ...@@ -3,7 +3,11 @@
SVM: Separating hyperplane with weighted classes SVM: Separating hyperplane with weighted classes
================================================ ================================================
Fit linear SVMs with and without class weighting.
Allows to handle problems with unbalanced classes.
""" """
print __doc__
import numpy as np import numpy as np
import pylab as pl import pylab as pl
...@@ -13,32 +17,34 @@ from scikits.learn import svm ...@@ -13,32 +17,34 @@ from scikits.learn import svm
np.random.seed(0) np.random.seed(0)
n_samples_1 = 1000 n_samples_1 = 1000
n_samples_2 = 100 n_samples_2 = 100
X = np.r_[1.5*np.random.randn(n_samples_1, 2), 0.5*np.random.randn(n_samples_2, 2) + [2, 2]] X = np.r_[1.5*np.random.randn(n_samples_1, 2),
Y = [0]*(n_samples_1) + [1]*(n_samples_2) 0.5*np.random.randn(n_samples_2, 2) + [2, 2]]
y = [0]*(n_samples_1) + [1]*(n_samples_2)
# fit the model and get the separating hyperplane # fit the model and get the separating hyperplane
clf = svm.SVC(kernel='linear') clf = svm.SVC(kernel='linear')
clf.fit(X, Y) clf.fit(X, y)
w = clf.coef_[0] w = clf.coef_[0]
a = -w[0] / w[1] a = -w[0] / w[1]
xx = np.linspace(-5, 5) xx = np.linspace(-5, 5)
yy = a*xx - (clf.intercept_[0])/w[1] yy = a * xx - clf.intercept_[0] / w[1]
# get the separating hyperplane using weighted classes # get the separating hyperplane using weighted classes
wclf = svm.SVC(kernel='linear') wclf = svm.SVC(kernel='linear')
wclf.fit(X, Y, {1: 10}) wclf.fit(X, y, class_weight={1: 10})
ww = wclf.coef_[0] ww = wclf.coef_[0]
wa = -ww[0] / ww[1] wa = -ww[0] / ww[1]
wyy = wa*xx - (wclf.intercept_[0])/ww[1] wyy = wa * xx - wclf.intercept_[0] / ww[1]
# plot separating hyperplanes and samples # plot separating hyperplanes and samples
pl.set_cmap(pl.cm.Paired) pl.set_cmap(pl.cm.Paired)
pl.plot(xx, yy, 'k-') h0 = pl.plot(xx, yy, 'k-')
pl.plot(xx, wyy, 'k--') h1 = pl.plot(xx, wyy, 'k--')
pl.scatter(X[:,0], X[:,1], c=Y) pl.scatter(X[:,0], X[:,1], c=y)
pl.legend((h0, h1), ('no weights', 'with weights'))
pl.axis('tight') pl.axis('tight')
pl.show() pl.show()
......
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