ch11-clustering-kmeans-analysis.py

# Generate random data
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=150, n_features=2, centers=3, cluster_std=0.5, shuffle=True, random_state=0)

# Use scikit Kmeans for clustering
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
km = KMeans(n_clusters=3, init='k-means++', n_init=10, max_iter=300, tol=1e-04, random_state=0)
y_km = km.fit_predict(X)

import numpy as np
from matplotlib import cm
from sklearn.metrics import silhouette_samples
cluster_labels = np.unique(y_km)
n_clusters = cluster_labels.shape[0]
silhouette_vals = silhouette_samples(X, y_km, metric='euclidean')
y_ax_lower, y_ax_upper = 0, 0
yticks = []
for i, c in enumerate(cluster_labels):
    c_silhouette_vals = silhouette_vals[y_km == c]
    c_silhouette_vals.sort()
    y_ax_upper += len(c_silhouette_vals)
    color = cm.jet(i / n_clusters)
    plt.barh(range(y_ax_lower, y_ax_upper), c_silhouette_vals, height=1.0, edgecolor='none', color=color)
    yticks.append((y_ax_lower + y_ax_upper) / 2)
    y_ax_lower += len(c_silhouette_vals)

silhouette_avg = np.mean(silhouette_vals)
plt.axvline(silhouette_avg,color="red",  linestyle="--")
plt.yticks(yticks, cluster_labels + 1)
plt.ylabel('Cluster')
plt.xlabel('Silhouette coefficient')
plt.show()





#Bad clustering example
km = KMeans(n_clusters=2,init='k-means++', n_init=10, max_iter=300, tol=1e-04, random_state=0)
y_km = km.fit_predict(X)
plt.scatter(X[y_km==0,0], X[y_km==0,1],s=50, c='lightgreen',marker='s',label='cluster 1')
plt.scatter(X[y_km==1,0], X[y_km==1,1], s=50, c='orange', marker='o', label='cluster 2')
plt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:,1], s=250, marker='*', c='red', label='centroids')
plt.legend()
plt.grid()
plt.show()
cluster_labels = np.unique(y_km)
n_clusters = cluster_labels.shape[0]
silhouette_vals = silhouette_samples(X, y_km, metric='euclidean')
y_ax_lower, y_ax_upper = 0, 0
yticks = []
for i, c in enumerate(cluster_labels):
    c_silhouette_vals = silhouette_vals[y_km == c]
    c_silhouette_vals.sort()
    y_ax_upper += len(c_silhouette_vals)
    color = cm.jet(i / n_clusters)
    plt.barh(range(y_ax_lower, y_ax_upper), c_silhouette_vals, height=1.0, edgecolor='none', color=color)
    yticks.append((y_ax_lower + y_ax_upper) / 2)
    y_ax_lower += len(c_silhouette_vals)

silhouette_avg = np.mean(silhouette_vals)
plt.axvline(silhouette_avg, color="red", linestyle="--")
plt.yticks(yticks, cluster_labels + 1)
plt.ylabel('Cluster')
plt.xlabel('Silhouette coefficient')
plt.show()