DOC Refactor plot_kmeans_digits.py to use DecisionBoundaryDisplay (scikit-learn#34515)

Author: lisaleemcb

examples/cluster/plot_kmeans_digits.py

@@ -155,32 +155,22 @@ def bench_k_means(kmeans, name, data, labels):
 # 2-dimensional space and plot the data and the clusters in this new space.
 import matplotlib.pyplot as plt
 
+from sklearn.inspection import DecisionBoundaryDisplay
+
 reduced_data = PCA(n_components=2).fit_transform(data)
-kmeans = KMeans(init="k-means++", n_clusters=n_digits, n_init=4)
+kmeans = KMeans(init="k-means++", n_clusters=n_digits, n_init=4, random_state=42)
 kmeans.fit(reduced_data)
 
-# Step size of the mesh. Decrease to increase the quality of the VQ.
-h = 0.02  # point in the mesh [x_min, x_max]x[y_min, y_max].
-
-# Plot the decision boundary. For that, we will assign a color to each
-x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
-y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
-xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
-
-# Obtain labels for each point in mesh. Use last trained model.
-Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
-
-# Put the result into a color plot
-Z = Z.reshape(xx.shape)
-plt.figure(1)
-plt.clf()
-plt.imshow(
-    Z,
-    interpolation="nearest",
-    extent=(xx.min(), xx.max(), yy.min(), yy.max()),
-    cmap=plt.cm.Paired,
-    aspect="auto",
-    origin="lower",
+plt.figure()
+
+DecisionBoundaryDisplay.from_estimator(
+    kmeans,
+    reduced_data,
+    ax=plt.gca(),
+    response_method="predict",
+    alpha=0.8,
+    plot_method="pcolormesh",
+    grid_resolution=1000,
 )
 
 plt.plot(reduced_data[:, 0], reduced_data[:, 1], "k.", markersize=2)
@@ -199,8 +189,6 @@ def bench_k_means(kmeans, name, data, labels):
     "K-means clustering on the digits dataset (PCA-reduced data)\n"
     "Centroids are marked with white cross"
 )
-plt.xlim(x_min, x_max)
-plt.ylim(y_min, y_max)
 plt.xticks(())
 plt.yticks(())
 plt.show()