[MRG+1] Modify General examples for matplotlib v2 comp (scikit-learn#8413)

rishikksh20 · maskani-moh · commit 97cda09b2ee8 · 2017-11-15T12:29:19.000-05:00
* Modify plot_cv_predict.py for matplotlib v2 comp Add `edgecolors` in scatter plot for matplotlib v2. Issue: scikit-learn#8364 * Modify plot_multilabel.py for matplotlib v2 Add `edgecolors` in scatterr plot for matplotlib v2. Issue: scikit-learn#8364 * Modify plot_kernel_ridge_regression for matplotlib v2 Add `edgecolors` in scatter plot and `makeredgecolor` in plot. Issue: scikit-learn#8364 * Modify plot_kernel_approximation.py for matplotlib v2 Add `edgecolors` in scatter plot for matplotlib v2. Issue: scikit-learn#8364 * Change plot_kernel_ridge_regression for better plot Remove `markeredgecolor` for better plot. Issue:scikit-learn#8364
diff --git a/examples/plot_cv_predict.py b/examples/plot_cv_predict.py
@@ -21,7 +21,7 @@
 predicted = cross_val_predict(lr, boston.data, y, cv=10)
 
 fig, ax = plt.subplots()
-ax.scatter(y, predicted)
+ax.scatter(y, predicted, edgecolors=(0, 0, 0))
 ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)
 ax.set_xlabel('Measured')
 ax.set_ylabel('Predicted')
diff --git a/examples/plot_kernel_approximation.py b/examples/plot_kernel_approximation.py
@@ -205,7 +205,8 @@
     plt.axis('off')
 
     # Plot also the training points
-    plt.scatter(X[:, 0], X[:, 1], c=targets_train, cmap=plt.cm.Paired)
+    plt.scatter(X[:, 0], X[:, 1], c=targets_train, cmap=plt.cm.Paired,
+                edgecolors=(0, 0, 0))
 
     plt.title(titles[i])
 plt.tight_layout()
diff --git a/examples/plot_kernel_ridge_regression.py b/examples/plot_kernel_ridge_regression.py
@@ -101,8 +101,9 @@
 # look at the results
 sv_ind = svr.best_estimator_.support_
 plt.scatter(X[sv_ind], y[sv_ind], c='r', s=50, label='SVR support vectors',
-            zorder=2)
-plt.scatter(X[:100], y[:100], c='k', label='data', zorder=1)
+            zorder=2, edgecolors=(0, 0, 0))
+plt.scatter(X[:100], y[:100], c='k', label='data', zorder=1,
+            edgecolors=(0, 0, 0))
 plt.hold('on')
 plt.plot(X_plot, y_svr, c='r',
          label='SVR (fit: %.3fs, predict: %.3fs)' % (svr_fit, svr_predict))
diff --git a/examples/plot_multilabel.py b/examples/plot_multilabel.py
@@ -73,11 +73,11 @@ def plot_subfigure(X, Y, subplot, title, transform):
 
     zero_class = np.where(Y[:, 0])
     one_class = np.where(Y[:, 1])
-    plt.scatter(X[:, 0], X[:, 1], s=40, c='gray')
+    plt.scatter(X[:, 0], X[:, 1], s=40, c='gray', edgecolors=(0, 0, 0))
     plt.scatter(X[zero_class, 0], X[zero_class, 1], s=160, edgecolors='b',
-               facecolors='none', linewidths=2, label='Class 1')
+                facecolors='none', linewidths=2, label='Class 1')
     plt.scatter(X[one_class, 0], X[one_class, 1], s=80, edgecolors='orange',
-               facecolors='none', linewidths=2, label='Class 2')
+                facecolors='none', linewidths=2, label='Class 2')
 
     plot_hyperplane(classif.estimators_[0], min_x, max_x, 'k--',
                     'Boundary\nfor class 1')
