DOC improved example plot in plot_lda_qda.py (#12942)

examples/classification/plot_lda_qda.py

@@ -72,19 +72,15 @@ def plot_data(lda, X, y, y_pred, fig_index):
     X0_tp, X0_fp = X0[tp0], X0[~tp0]
     X1_tp, X1_fp = X1[tp1], X1[~tp1]
 
-    alpha = 0.5
-
     # class 0: dots
-    plt.plot(X0_tp[:, 0], X0_tp[:, 1], 'o', alpha=alpha,
-             color='red', markeredgecolor='k')
-    plt.plot(X0_fp[:, 0], X0_fp[:, 1], '*', alpha=alpha,
-             color='#990000', markeredgecolor='k')  # dark red
+    plt.scatter(X0_tp[:, 0], X0_tp[:, 1], marker='.', color='red')
+    plt.scatter(X0_fp[:, 0], X0_fp[:, 1], marker='x',
+                s=20, color='#990000')  # dark red
 
     # class 1: dots
-    plt.plot(X1_tp[:, 0], X1_tp[:, 1], 'o', alpha=alpha,
-             color='blue', markeredgecolor='k')
-    plt.plot(X1_fp[:, 0], X1_fp[:, 1], '*', alpha=alpha,
-             color='#000099', markeredgecolor='k')  # dark blue
+    plt.scatter(X1_tp[:, 0], X1_tp[:, 1], marker='.', color='blue')
+    plt.scatter(X1_fp[:, 0], X1_fp[:, 1], marker='x',
+                s=20, color='#000099')  # dark blue
 
     # class 0 and 1 : areas
     nx, ny = 200, 100
@@ -95,14 +91,14 @@ def plot_data(lda, X, y, y_pred, fig_index):
     Z = lda.predict_proba(np.c_[xx.ravel(), yy.ravel()])
     Z = Z[:, 1].reshape(xx.shape)
     plt.pcolormesh(xx, yy, Z, cmap='red_blue_classes',
-                   norm=colors.Normalize(0., 1.))
-    plt.contour(xx, yy, Z, [0.5], linewidths=2., colors='k')
+                   norm=colors.Normalize(0., 1.), zorder=0)
+    plt.contour(xx, yy, Z, [0.5], linewidths=2., colors='white')
 
     # means
     plt.plot(lda.means_[0][0], lda.means_[0][1],
-             'o', color='black', markersize=10, markeredgecolor='k')
+             '*', color='yellow', markersize=15, markeredgecolor='grey')
     plt.plot(lda.means_[1][0], lda.means_[1][1],
-             'o', color='black', markersize=10, markeredgecolor='k')
+             '*', color='yellow', markersize=15, markeredgecolor='grey')
 
     return splot
 
@@ -115,10 +111,9 @@ def plot_ellipse(splot, mean, cov, color):
     # filled Gaussian at 2 standard deviation
     ell = mpl.patches.Ellipse(mean, 2 * v[0] ** 0.5, 2 * v[1] ** 0.5,
                               180 + angle, facecolor=color,
-                              edgecolor='yellow',
-                              linewidth=2, zorder=2)
+                              edgecolor='black', linewidth=2)
     ell.set_clip_box(splot.bbox)
-    ell.set_alpha(0.5)
+    ell.set_alpha(0.2)
     splot.add_artist(ell)
     splot.set_xticks(())
     splot.set_yticks(())
@@ -133,6 +128,8 @@ def plot_qda_cov(qda, splot):
     plot_ellipse(splot, qda.means_[0], qda.covariance_[0], 'red')
     plot_ellipse(splot, qda.means_[1], qda.covariance_[1], 'blue')
 
+
+plt.figure(figsize=(10, 8), facecolor='white')
 for i, (X, y) in enumerate([dataset_fixed_cov(), dataset_cov()]):
     # Linear Discriminant Analysis
     lda = LinearDiscriminantAnalysis(solver="svd", store_covariance=True)
@@ -147,6 +144,7 @@ def plot_qda_cov(qda, splot):
     splot = plot_data(qda, X, y, y_pred, fig_index=2 * i + 2)
     plot_qda_cov(qda, splot)
     plt.axis('tight')
-plt.suptitle('Linear Discriminant Analysis vs Quadratic Discriminant'
-             'Analysis')
+plt.suptitle('Linear Discriminant Analysis vs Quadratic Discriminant Analysis',
+             y=1.02, fontsize=15)
+plt.tight_layout()
 plt.show()