10000 [MRG] Modify svm/plot_separating_hyperplane.py example for matplotlib v2 by rishikksh20 · Pull Request #8369 · scikit-learn/scikit-learn · GitHub
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[MRG] Modify svm/plot_separating_hyperplane.py example for matplotlib v2 #8369

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[MRG] Modify svm/plot_separating_hyperplane.py example for matplotlib v2 #8369

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0f149ce
Modify SVM example for matplotlib v2
rishikksh20 Feb 15, 2017
5cf29fe
Modify file to remove flake8 error from travis build
rishikksh20 Feb 16, 2017
73b7442
Modify plot for better visual appearance
rishikksh20 Mar 4, 2017
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50 changes: 26 additions & 24 deletions examples/svm/plot_separating_hyperplane.py
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Expand Up @@ -12,37 +12,39 @@
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm

from sklearn.datasets import make_classification
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You should probably not change the data unless you have a very good reason to.

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@lesteve under the Issue #8364 , @amueller point out it good to use make_classification for data generation.

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Ah OK I missed that, thanks.

8000

# we create 40 separable points
np.random.seed(0)
X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
Y = [0] * 20 + [1] * 20

X, Y = make_classification(n_features=2, n_redundant=0, n_informative=1,
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You should use y here. The scikit-learn convention is that variable names starting with a capital letter should be reserved for 2d arrays.

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Can you use a random_state argument otherwise the plot will change each time you run the example.

n_clusters_per_class=1)
# fit the model
clf = svm.SVC(kernel='linear')
clf.fit(X, Y)

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

# plot the parallels to the separating hyperplane that pass through the
# support vectors
b = clf.support_vectors_[0]
yy_down = a * xx + (b[1] - a * b[0])
b = clf.support_vectors_[-1]
yy_up = a * xx + (b[1] - a * b[0])

# plot the line, the points, and the nearest vectors to the plane
plt.plot(xx, yy, 'k-')
plt.plot(xx, yy_down, 'k--')
plt.plot(xx, yy_up, 'k--')

plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],
s=80, facecolors='none')
plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
plt.scatter(X[:, 0], X[:, 1], c=Y, s=50, cmap='autumn', edgecolors='k')
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Do not change something unless there is a very good reason to. In this case, I would keep the plt.cm.Paired colormap.


ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()

# create grid to evaluate model
x = np.linspace(xlim[0], xlim[1], 30)
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We should really have a function for this but that's another issue #6338

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That's not merge yet.

y = np.linspace(ylim[0], ylim[1], 30)
Y, X = np.meshgrid(y, x)
xy = np.vstack([X.ravel(), Y.ravel()]).T
P = clf.decision_function(xy).reshape(X.shape)

# plot decision boundary and margins
ax.contour(X, Y, P, colors='k',
levels=[-1, 0, 1], alpha=0.5,
linestyles=['--', '-', '--'])

ax.scatter(clf.support_vectors_[:, 0],
clf.support_vectors_[:, 1], edgecolors='k',
s=200, linewidth=1, facecolors='none')
ax.set_xlim(xlim)
ax.set_ylim(ylim)

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