101 lines
2.6 KiB
Python
101 lines
2.6 KiB
Python
from scipy.io import arff
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import numpy as np
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from sklearn.cluster import KMeans
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from sklearn.datasets import make_blobs
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import matplotlib.pyplot as plt
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from sklearn import metrics
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from sklearn.cluster import AgglomerativeClustering
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from sklearn.cluster import DBSCAN
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import hdbscan
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n_clusters = 2
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data_final = []
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x_list = []
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y_list = []
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z_list = []
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silhouette = []
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calinski = []
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davies = []
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data = np.loadtxt('h.data')
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for (x, y, z) in data :
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x_list.append(x)
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y_list.append(y)
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z_list.append(z)
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data_final.append([x,y,z])
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#get the values of the different coefficients for different min_samples values from 2 to 20
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for n in range(2, 20):
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clustering = hdbscan.HDBSCAN(min_samples=n)
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colors = clustering.fit_predict(data_final)
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silh = metrics.silhouette_score(data_final, colors, metric='euclidean')
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silhouette.append(silh)
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dbsc = metrics.davies_bouldin_score(data_final, colors)
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davies.append(dbsc)
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caha = metrics.calinski_harabasz_score(data_final, colors)
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calinski.append(caha)
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"""plt.plot(range(2,20), silhouette, marker='o', label='Silhouette')
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plt.xlim(2,20)
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plt.xlabel('Nb minimum de voisins')
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plt.plot(range(2,20), davies, marker='o', label='Davies')
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plt.xlim(2,20)
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plt.xlabel('Nb minimum de voisins')"""
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plt.plot(range(2,20), calinski, marker='o')
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plt.xlim(2,20)
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plt.xlabel('Nb minimum de voisins')
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plt.ylabel('Calinski coeff')
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plt.legend()
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plt.show()
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#silhouettte coefficient
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#get the index of the best result
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m = max(silhouette)
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indice = [i for i, j in enumerate(silhouette) if j == m][0] +2
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print("Silhouette : ", indice)
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plt.subplot(3,1,1)
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#display the best obtained result
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clustering = hdbscan.HDBSCAN(min_samples=indice)
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colors = clustering.fit_predict(data_final)
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plt.axes(projection='3d').scatter3D(x_list, y_list, z_list, c=colors)
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plt.show()
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#davies bouldin metrics
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#get the index of the best result
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m = min(davies)
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indice = [i for i, j in enumerate(davies) if j == m][0] +2
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print("Davies Bouldin : ", indice)
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plt.subplot(3,1,2)
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#display the best obtained result with davies bouldin metrics
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clustering = hdbscan.HDBSCAN(min_samples=indice)
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colors = clustering.fit_predict(data_final)
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plt.axes(projection='3d').scatter3D(x_list, y_list, z_list, c=colors)
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plt.show()
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#calinski metrics
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#get the index of the best result
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m = max(calinski)
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indice = [i for i, j in enumerate(calinski) if j == m][0] +2
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print("Calinski Harabasz : ", indice)
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plt.subplot(3,1,3)
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#display the best obtained result
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clustering = hdbscan.HDBSCAN(min_samples=indice)
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colors = clustering.fit_predict(data_final)
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plt.axes(projection='3d').scatter3D(x_list, y_list, z_list, c=colors)
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plt.show()
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