DOC Updates to SVM examples

VathsalaAchar · VathsalaAchar · commit d8d9358eb95e · 2017-06-07T13:12:17.000+01:00
* Fixing flake8 issues

* Altered make_blobs to move clusters to corners and be more compact

* Reverted changes converting Y to y
diff --git a/examples/svm/plot_custom_kernel.py b/examples/svm/plot_custom_kernel.py
@@ -17,26 +17,26 @@
 iris = datasets.load_iris()
 X = iris.data[:, :2]  # we only take the first two features. We could
                       # avoid this ugly slicing by using a two-dim dataset
-y = iris.target
+Y = iris.target
 
 
-def my_kernel(X, y):
+def my_kernel(X, Y):
     """
     We create a custom kernel:
 
                  (2  0)
-    k(X, y) = X  (    ) y.T
+    k(X, Y) = X  (    ) Y.T
                  (0  1)
     """
     M = np.array([[2, 0], [0, 1.0]])
-    return np.dot(np.dot(X, M), y.T)
+    return np.dot(np.dot(X, M), Y.T)
 
 
 h = .02  # step size in the mesh
 
 # we create an instance of SVM and fit out data.
 clf = svm.SVC(kernel=my_kernel)
-clf.fit(X, y)
+clf.fit(X, Y)
 
 # Plot the decision boundary. For that, we will assign a color to each
 # point in the mesh [x_min, x_max]x[y_min, y_max].
@@ -50,7 +50,7 @@ def my_kernel(X, y):
 plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
 
 # Plot also the training points
-plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, edgecolors='k')
+plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolors='k')
 plt.title('3-Class classification using Support Vector Machine with custom'
           ' kernel')
 plt.axis('tight')
diff --git a/examples/svm/plot_separating_hyperplane.py b/examples/svm/plot_separating_hyperplane.py
@@ -16,7 +16,7 @@
 
 
 # we create 40 separable points
-X, y = make_blobs(n_samples=40, centers=2, random_state=7)
+X, y = make_blobs(n_samples=40, centers=2, random_state=12, cluster_std=0.35)
 
 # fit the model
 clf = svm.SVC(kernel='linear')
@@ -37,10 +37,6 @@
 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=['--', '-', '--'])
+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');
+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
@@ -30,7 +30,6 @@
 import matplotlib.pyplot as plt
 from sklearn import svm
 
-
 # we create 40 separable points
 rng = np.random.RandomState(0)
 n_samples_1 = 1000
@@ -66,18 +65,12 @@
 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=['-'])
+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=['-'])
+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.legend([a.collections[0], b.collections[0]], ["non weighted", "weighted"], loc="upper right")
diff --git a/examples/svm/plot_svm_kernels.py b/examples/svm/plot_svm_kernels.py
@@ -41,23 +41,23 @@
           (.2, -2.3),
           (0, -2.7),
           (1.3, 2.1)].T
-y = [0] * 8 + [1] * 8
+Y = [0] * 8 + [1] * 8
 
 # figure number
 fignum = 1
 
 # fit the model
 for kernel in ('linear', 'poly', 'rbf'):
     clf = svm.SVC(kernel=kernel, gamma=2)
-    clf.fit(X, y)
+    clf.fit(X, Y)
 
     # plot the line, the points, and the nearest vectors to the plane
     plt.figure(fignum, figsize=(4, 3))
     plt.clf()
 
     plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80,
                 facecolors='none', zorder=10, edgecolors='k')
-    plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired,
+    plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired,
                 edgecolors='k')
 
     plt.axis('tight')
diff --git a/examples/svm/plot_svm_margin.py b/examples/svm/plot_svm_margin.py
@@ -29,7 +29,7 @@
 # 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
+Y = [0] * 20 + [1] * 20
 
 # figure number
 fignum = 1
@@ -38,7 +38,7 @@
 for name, penalty in (('unreg', 1), ('reg', 0.05)):
 
     clf = svm.SVC(kernel='linear', C=penalty)
-    clf.fit(X, y)
+    clf.fit(X, Y)
 
     # get the separating hyperplane
     w = clf.coef_[0]
@@ -63,7 +63,7 @@
 
     plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80,
                 facecolors='none', zorder=10, edgecolors='k')
-    plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired,
+    plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired,
                 edgecolors='k')
 
     plt.axis('tight')
diff --git a/examples/svm/plot_svm_nonlinear.py b/examples/svm/plot_svm_nonlinear.py
@@ -19,11 +19,11 @@
                      np.linspace(-3, 3, 500))
 np.random.seed(0)
 X = np.random.randn(300, 2)
-y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
+Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
 
 # fit the model
 clf = svm.NuSVC()
-clf.fit(X, y)
+clf.fit(X, Y)
 
 # plot the decision function for each datapoint on the grid
 Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
@@ -34,7 +34,7 @@
            origin='lower', cmap=plt.cm.PuOr_r)
 contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2,
                        linetypes='--')
-plt.scatter(X[:, 0], X[:, 1], s=30, c=y, cmap=plt.cm.Paired,
+plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired,
             edgecolors='k')
 plt.xticks(())
 plt.yticks(())
