silhouette.py

import numpy as np
import matplotlib.cm as cm

from sklearn.metrics import silhouette_samples, silhouette_score
from sklearn.cluster import KMeans

from matplotlib import pyplot as plt



#range_n_clusters = [2, 4,  6,8]
def silhouette(X_stan,range_n_clusters = [2, 4,  6,8],columns=[0,1],visual=True):
    for n_clusters in range_n_clusters:
        # Create a subplot with 1 row and 2 columns
        #if visual:
        fig, (ax1, ax2) = plt.subplots(1, 2)
        #fig, ax1 = plt.subplots(1, 1)
        fig.set_size_inches(18, 7)
        # The 1st subplot is the silhouette plot
        # The silhouette coefficient can range from -1, 1 but in this example all
        # lie within [-0.1, 1]
        ax1.set_xlim([-0.1, 1])
        # The (n_clusters+1)*10 is for inserting blank space between silhouette
        # plots of individual clusters, to demarcate them clearly.
        ax1.set_ylim([0, len(X_stan) + (n_clusters + 1) * 10])

        # Initialize the clusterer with n_clusters value and a random generator
        # seed of 10 for reproducibility.
        clusterer = KMeans(n_clusters=n_clusters, random_state=10)
        cluster_labels = clusterer.fit_predict(X_stan)

        # The silhouette_score gives the average value for all the samples.
        # This gives a perspective into the density and separation of the formed
        # clusters
        silhouette_avg = silhouette_score(X_stan, cluster_labels)
        print("For n_clusters =", n_clusters,
            "The average silhouette_score is :", silhouette_avg)

        # Compute the silhouette scores for each sample
        sample_silhouette_values = silhouette_samples(X_stan, cluster_labels)

        y_lower = 10
        for i in range(n_clusters):
            # Aggregate the silhouette scores for samples belonging to
            # cluster i, and sort them
            ith_cluster_silhouette_values = \
                sample_silhouette_values[cluster_labels == i]

            ith_cluster_silhouette_values.sort()

            size_cluster_i = ith_cluster_silhouette_values.shape[0]
            y_upper = y_lower + size_cluster_i

            color = cm.nipy_spectral(float(i) / n_clusters)
            ax1.fill_betweenx(np.arange(y_lower, y_upper),
                            0, ith_cluster_silhouette_values,
                            facecolor=color, edgecolor=color, alpha=0.7)

            # Label the silhouette plots with their cluster numbers at the middle
            ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))

            # Compute the new y_lower for next plot
            y_lower = y_upper + 10  # 10 for the 0 samples

        ax1.set_title("The silhouette plot for the various clusters.")
        ax1.set_xlabel("The silhouette coefficient values")
        ax1.set_ylabel("Cluster label")

        # The vertical line for average silhouette score of all the values
        ax1.axvline(x=silhouette_avg, color="red", linestyle="--")

        ax1.set_yticks([])  # Clear the yaxis labels / ticks
        ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])

        # 2nd Plot showing the actual clusters formed
        if visual:
            colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)

            ax2.scatter(X_stan[:, columns[0]], X_stan[:, columns[1]], marker='.', s=30, lw=0, alpha=0.7,
                        c=colors, edgecolor='k')

            # Labeling the clusters
            centers = clusterer.cluster_centers_
            # Draw white circles at cluster centers
            ax2.scatter(centers[:, columns[0]], centers[:, columns[1]], marker='o',
                        c="white", alpha=1, s=200, edgecolor='k')

            for i, c in enumerate(centers):
                ax2.scatter(c[columns[0]], c[columns[1]], marker='$%d$' % i, alpha=1,
                            s=50, edgecolor='k')

            ax2.set_title("The visualization of the clustered data.")
            ax2.set_xlabel("Feature space for the 1st feature")
            ax2.set_ylabel("Feature space for the 2nd feature")

        plt.suptitle(("Silhouette analysis for KMeans clustering on sample data "
                    "with n_clusters = %d" % n_clusters),
                    fontsize=14, fontweight='bold')

    plt.show()