[MRG+1] DOC Simplifying margin plotting in SVM examples (scikit-learn…

…#8501) (scikit-learn#8875) * Simplifying margin plotting in SVM examples (scikit-learn#8501) * updated to use contour levels on decision function * separating unbalanced class now uses a red line to show the change in the decision boundary when the classes are weighted * corrected the target variable from Y to y * DOC Updates to SVM examples * Fixing flake8 issues * Altered make_blobs to move clusters to corners and be more compact * Reverted changes converting Y to y * Fixes for flake8 errors
dmohns · Aug 7, 2017 · a0b247b · a0b247b
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diff --git a/examples/svm/plot_separating_hyperplane.py b/examples/svm/plot_separating_hyperplane.py
@@ -12,37 +12,33 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn import svm
+from sklearn.datasets import make_blobs
+
 
 # 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_blobs(n_samples=40, centers=2, random_state=12, cluster_std=0.35)
 
 # 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.axis('tight')
-plt.show()
+clf.fit(X, y)
+
+plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
+
+# plot the decision function
+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
+Z = clf.decision_function(xy).reshape(XX.shape)
+
+# plot decision boundary and margins
+ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,
+           linestyles=['--', '-', '--'])
+# plot support vectors
+ax.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100,
+           linewidth=1, facecolors='none')
diff --git a/examples/svm/plot_separating_hyperplane_unbalanced.py b/examples/svm/plot_separating_hyperplane_unbalanced.py
@@ -29,7 +29,6 @@
 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)
@@ -43,25 +42,36 @@
 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
+# fit the model and 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, edgecolors='k')
 plt.legend()
 
-plt.axis('tight')
-plt.show()
+# 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")