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best_Hierarchical.py
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best_Hierarchical.py
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from sklearn.cluster import AgglomerativeClustering
from sklearn import metrics
from sklearn.metrics import pairwise_distances
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def best_model(X, plot_ind, eval_parm):
parm_list = []
if eval_parm == 'deep':
parm_list.append({'n_clusters': 4, \
'affinity': 'euclidian', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 4, \
'affinity': 'euclidian', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 4, \
'affinity': 'euclidian', \
'linkage': 'average'})
parm_list.append({'n_clusters': 4, \
'affinity': 'l1', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 4, \
'affinity': 'l1', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 4, \
'affinity': 'l1', \
'linkage': 'average'})
parm_list.append({'n_clusters': 4, \
'affinity': 'l2', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 4, \
'affinity': 'l2', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 4, \
'affinity': 'l2', \
'linkage': 'average'})
parm_list.append({'n_clusters': 4, \
'affinity': 'cosine', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 4, \
'affinity': 'cosine', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 4, \
'affinity': 'cosine', \
'linkage': 'average'})
parm_list.append({'n_clusters': 4, \
'affinity': 'manhattan', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 4, \
'affinity': 'manhattan', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 4, \
'affinity': 'manhattan', \
'linkage': 'average'})
parm_list.append({'n_clusters': 5, \
'affinity': 'euclidian', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 5, \
'affinity': 'euclidian', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 5, \
'affinity': 'euclidian', \
'linkage': 'average'})
parm_list.append({'n_clusters': 5, \
'affinity': 'l1', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 5, \
'affinity': 'l1', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 5, \
'affinity': 'l1', \
'linkage': 'average'})
parm_list.append({'n_clusters': 5, \
'affinity': 'l2', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 5, \
'affinity': 'l2', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 5, \
'affinity': 'l2', \
'linkage': 'average'})
parm_list.append({'n_clusters': 5, \
'affinity': 'cosine', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 5, \
'affinity': 'cosine', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 5, \
'affinity': 'cosine', \
'linkage': 'average'})
parm_list.append({'n_clusters': 5, \
'affinity': 'manhattan', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 5, \
'affinity': 'manhattan', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 5, \
'affinity': 'manhattan', \
'linkage': 'average'})
parm_list.append({'n_clusters': 6, \
'affinity': 'euclidian', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 6, \
'affinity': 'euclidian', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 6, \
'affinity': 'euclidian', \
'linkage': 'average'})
parm_list.append({'n_clusters': 6, \
'affinity': 'l1', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 6, \
'affinity': 'l1', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 6, \
'affinity': 'l1', \
'linkage': 'average'})
parm_list.append({'n_clusters': 6, \
'affinity': 'l2', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 6, \
'affinity': 'l2', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 6, \
'affinity': 'l2', \
'linkage': 'average'})
parm_list.append({'n_clusters': 6, \
'affinity': 'cosine', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 6, \
'affinity': 'cosine', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 6, \
'affinity': 'cosine', \
'linkage': 'average'})
parm_list.append({'n_clusters': 6, \
'affinity': 'manhattan', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 6, \
'affinity': 'manhattan', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 6, \
'affinity': 'manhattan', \
'linkage': 'average'})
parm_list.append({'n_clusters': 7, \
'affinity': 'euclidian', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 7, \
'affinity': 'euclidian', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 7, \
'affinity': 'euclidian', \
'linkage': 'average'})
parm_list.append({'n_clusters': 7, \
'affinity': 'l1', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 7, \
'affinity': 'l1', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 7, \
'affinity': 'l1', \
'linkage': 'average'})
parm_list.append({'n_clusters': 7, \
'affinity': 'l2', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 7, \
'affinity': 'l2', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 7, \
'affinity': 'l2', \
'linkage': 'average'})
parm_list.append({'n_clusters': 7, \
'affinity': 'cosine', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 7, \
'affinity': 'cosine', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 7, \
'affinity': 'cosine', \
'linkage': 'average'})
parm_list.append({'n_clusters': 7, \
'affinity': 'manhattan', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 7, \
'affinity': 'manhattan', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 7, \
'affinity': 'manhattan', \
'linkage': 'average'})
elif eval_parm == 'test':
parm_list.append({'n_clusters': 4, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 4, \
'affinity': 'euclidean', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 4, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 4, \
'affinity': 'cosine', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 4, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 4, \
'affinity': 'manhattan', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 5, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 5, \
'affinity': 'euclidean', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 5, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 5, \
'affinity': 'cosine', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 5, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 5, \
'affinity': 'manhattan', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 6, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 6, \
'affinity': 'euclidean', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 6, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 6, \
'affinity': 'cosine', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 6, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 6, \
'affinity': 'manhattan', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 7, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 7, \
'affinity': 'euclidean', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 7, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 7, \
'affinity': 'cosine', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 7, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 7, \
'affinity': 'manhattan', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 8, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 8, \
'affinity': 'euclidean', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 8, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 8, \
'affinity': 'cosine', \
'linkage': 'complete'})
parm_list.append({'n_clusters': 8, \
'affinity': 'euclidean', \
'linkage': 'ward'})
parm_list.append({'n_clusters': 8, \
'affinity': 'manhattan', \
'linkage': 'complete'})
s_score_list = []
ch_score_list = []
hc = AgglomerativeClustering()
for i in range(len(parm_list)):
#print (parm_list[i])
hc.set_params(**parm_list[i]).fit(X)
labels = hc.labels_
s_score_list.append \
(metrics.silhouette_score(X, labels, metric='euclidean'))
ch_score_list.append \
(metrics.calinski_harabaz_score(X, labels))
s_score = s_score_list[np.argmax(s_score_list)]
ch_score = ch_score_list[np.argmax(s_score_list)]
# for i in range(len(s_score_list)):
# print (parm_list[i], s_score_list[i], ch_score_list[i])
print (parm_list[np.argmax(s_score_list)])
hc.set_params(**parm_list[np.argmax(s_score_list)]).fit(X)
#s_score = metrics.silhouette_score(X, labels, metric='euclidean')
#ch_score = metrics.calinski_harabaz_score(X, labels)
return_parm= {'trained_model': hc, \
's_score': s_score, 'ch_score': ch_score}
return (return_parm)