Updated util_knn.py to new version of scikit

ml1/util_knn.py

@@ -2,6 +2,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.colors import ListedColormap
 from sklearn import neighbors, datasets
+import seaborn as sns
 from sklearn.neighbors import KNeighborsClassifier
 
 # Taken from http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html
@@ -19,9 +20,9 @@ def plot_classification_iris():
     h = .02  # step size in the mesh
     n_neighbors = 15
 
-    # Create color maps
-    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
-    cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
+  # Create color maps
+    cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])
+    cmap_bold = ['darkorange', 'c', 'darkblue']
 
     for weights in ['uniform', 'distance']:
         # we create an instance of Neighbours Classifier and fit the data.
@@ -29,7 +30,7 @@ def plot_classification_iris():
         clf.fit(X, y)
 
         # Plot the decision boundary. For that, we will assign a color to each
-        # point in the mesh [x_min, m_max]x[y_min, y_max].
+        # point in the mesh [x_min, x_max]x[y_min, y_max].
         x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
         y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
         xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
@@ -38,14 +39,17 @@ def plot_classification_iris():
 
         # Put the result into a color plot
         Z = Z.reshape(xx.shape)
-        plt.figure()
-        plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
+        plt.figure(figsize=(8, 6))
+        plt.contourf(xx, yy, Z, cmap=cmap_light)
 
         # Plot also the training points
-        plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
+        sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=iris.target_names[y],
+                        palette=cmap_bold, alpha=1.0, edgecolor="black")
         plt.xlim(xx.min(), xx.max())
         plt.ylim(yy.min(), yy.max())
         plt.title("3-Class classification (k = %i, weights = '%s')"
-                % (n_neighbors, weights))
+                  % (n_neighbors, weights))
+        plt.xlabel(iris.feature_names[0])
+        plt.ylabel(iris.feature_names[1])
 
-    plt.show()
+plt.show()