analyse de silhouette sur GaussianMixture

Question

Je fais une analyse de silhouette en utilisant GaussianMixture. J'ai essayé de modifier un code similaire écrit sur le site de scikit mais obtenir une erreur bizarre: -

--> 82 centers = clusterer.cluster_centers_ 83 # Draw white circles at cluster centers 84 ax2.scatter(centers[:, 0], centers[:, 1], marker='o',

AttributeError: 'GaussianMixture' object has no attribute 'cluster_centers_'

from sklearn.metrics import silhouette_samples, silhouette_score 

import matplotlib.pyplot as plt 
import matplotlib.cm as cm 
import numpy as np 

print(__doc__) 

X=reduced_data.values 
range_n_clusters = [2, 3, 4, 5, 6] 

for n_clusters in range_n_clusters: 
    # Create a subplot with 1 row and 2 columns 
    fig, (ax1, ax2) = plt.subplots(1, 2) 
    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) + (n_clusters + 1) * 10]) 

    # Initialize the clusterer with n_clusters value and a random generator 
    # seed of 10 for reproducibility. 
    clusterer = mixture.GaussianMixture(n_components=n_clusters, covariance_type='full') 
    clusterer.fit(X) 
    cluster_labels=clusterer.predict(X) 
    cluster_labels.shape 
    #clusterer = KMeans(n_clusters=n_clusters, random_state=10) 
    #cluster_labels = clusterer.fit_predict(X) 

    # 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, 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, 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.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 
    colors = cm.spectral(cluster_labels.astype(float)/n_clusters) 
    ax2.scatter(X[:, 0], X[:, 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[:, 0], centers[:, 1], marker='o', 
       c="white", alpha=1, s=200, edgecolor='k') 

    for i, c in enumerate(centers): 
     ax2.scatter(c[0], c[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() 

silhouette analysis using k-means clustering

Answer 1

# Labeling the clusters 
    centers = clusterer.means_