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1 # -*- coding: utf-8 -*-
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2 """
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3 Created on Mon Jun 3 19:51:00 2019
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4 @author: Narger
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5 """
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6
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7 import sys
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8 import argparse
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9 import os
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10 from sklearn.datasets import make_blobs
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11 from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
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12 from sklearn.metrics import silhouette_samples, silhouette_score, davies_bouldin_score, cluster
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13 import matplotlib
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14 matplotlib.use('agg')
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15 import matplotlib.pyplot as plt
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16 import scipy.cluster.hierarchy as shc
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17 import matplotlib.cm as cm
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18 import numpy as np
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19 import pandas as pd
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20
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21 ################################# process args ###############################
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22
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23 def process_args(args):
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24 parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
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25 description = 'process some value\'s' +
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26 ' genes to create class.')
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27
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28 parser.add_argument('-ol', '--out_log',
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29 help = "Output log")
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30
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31 parser.add_argument('-in', '--input',
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32 type = str,
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33 help = 'input dataset')
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34
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35 parser.add_argument('-cy', '--cluster_type',
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36 type = str,
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37 choices = ['kmeans', 'dbscan', 'hierarchy'],
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38 default = 'kmeans',
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39 help = 'choose clustering algorythm')
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40
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41 parser.add_argument('-k1', '--k_min',
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42 type = int,
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43 default = 2,
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44 help = 'choose minimun cluster number to be generated')
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45
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46 parser.add_argument('-k2', '--k_max',
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47 type = int,
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48 default = 7,
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49 help = 'choose maximum cluster number to be generated')
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50
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51 parser.add_argument('-el', '--elbow',
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52 type = str,
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53 default = 'false',
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54 choices = ['true', 'false'],
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55 help = 'choose if you want to generate an elbow plot for kmeans')
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56
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57 parser.add_argument('-si', '--silhouette',
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58 type = str,
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59 default = 'false',
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60 choices = ['true', 'false'],
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61 help = 'choose if you want silhouette plots')
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62
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63 parser.add_argument('-td', '--tool_dir',
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64 type = str,
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65 required = True,
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66 help = 'your tool directory')
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67
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68 parser.add_argument('-ms', '--min_samples',
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69 type = float,
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70 help = 'min samples for dbscan (optional)')
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71
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72 parser.add_argument('-ep', '--eps',
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73 type = float,
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74 help = 'eps for dbscan (optional)')
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75
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76 parser.add_argument('-bc', '--best_cluster',
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77 type = str,
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78 help = 'output of best cluster tsv')
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79
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80
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81
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82 args = parser.parse_args()
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83 return args
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84
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85 ########################### warning ###########################################
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86
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87 def warning(s):
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88 args = process_args(sys.argv)
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89 with open(args.out_log, 'a') as log:
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90 log.write(s + "\n\n")
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91 print(s)
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92
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93 ########################## read dataset ######################################
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94
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95 def read_dataset(dataset):
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96 try:
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97 dataset = pd.read_csv(dataset, sep = '\t', header = 0)
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98 except pd.errors.EmptyDataError:
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99 sys.exit('Execution aborted: wrong format of dataset\n')
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100 if len(dataset.columns) < 2:
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101 sys.exit('Execution aborted: wrong format of dataset\n')
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102 return dataset
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103
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104 ############################ rewrite_input ###################################
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105
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106 def rewrite_input(dataset):
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107 #Riscrivo il dataset come dizionario di liste,
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108 #non come dizionario di dizionari
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109
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110 dataset.pop('Reactions', None)
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111
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112 for key, val in dataset.items():
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113 l = []
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114 for i in val:
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115 if i == 'None':
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116 l.append(None)
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117 else:
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118 l.append(float(i))
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119
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120 dataset[key] = l
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121
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122 return dataset
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123
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124 ############################## write to csv ##################################
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125
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126 def write_to_csv (dataset, labels, name):
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127 #labels = predict
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128 predict = [x+1 for x in labels]
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129
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130 classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
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131
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132 dest = name
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133 classe.to_csv(dest, sep = '\t', index = False,
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134 header = ['Patient_ID', 'Class'])
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135
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136 ########################### trova il massimo in lista ########################
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137 def max_index (lista):
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138 best = -1
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139 best_index = 0
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140 for i in range(len(lista)):
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141 if lista[i] > best:
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142 best = lista [i]
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143 best_index = i
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144
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145 return best_index
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146
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147 ################################ kmeans #####################################
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148
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149 def kmeans (k_min, k_max, dataset, elbow, silhouette, best_cluster):
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150 if not os.path.exists('clustering'):
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151 os.makedirs('clustering')
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152
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153
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154 if elbow == 'true':
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155 elbow = True
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156 else:
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157 elbow = False
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158
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159 if silhouette == 'true':
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160 silhouette = True
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161 else:
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162 silhouette = False
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163
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164 range_n_clusters = [i for i in range(k_min, k_max+1)]
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165 distortions = []
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166 scores = []
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167 all_labels = []
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168
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169 clusterer = KMeans(n_clusters=1, random_state=10)
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170 distortions.append(clusterer.fit(dataset).inertia_)
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171
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172
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173 for n_clusters in range_n_clusters:
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174 clusterer = KMeans(n_clusters=n_clusters, random_state=10)
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175 cluster_labels = clusterer.fit_predict(dataset)
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176
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177 all_labels.append(cluster_labels)
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178 if n_clusters == 1:
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179 silhouette_avg = 0
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180 else:
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181 silhouette_avg = silhouette_score(dataset, cluster_labels)
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182 scores.append(silhouette_avg)
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183 distortions.append(clusterer.fit(dataset).inertia_)
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184
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185 best = max_index(scores) + k_min
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186
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187 for i in range(len(all_labels)):
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188 prefix = ''
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189 if (i + k_min == best):
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190 prefix = '_BEST'
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191
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192 write_to_csv(dataset, all_labels[i], 'clustering/kmeans_with_' + str(i + k_min) + prefix + '_clusters.tsv')
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193
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194
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195 if (prefix == '_BEST'):
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196 labels = all_labels[i]
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197 predict = [x+1 for x in labels]
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198 classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
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199 classe.to_csv(best_cluster, sep = '\t', index = False, header = ['Patient_ID', 'Class'])
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200
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201
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202
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203
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204 if silhouette:
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205 silihouette_draw(dataset, all_labels[i], i + k_min, 'clustering/silhouette_with_' + str(i + k_min) + prefix + '_clusters.png')
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206
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207
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208 if elbow:
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209 elbow_plot(distortions, k_min,k_max)
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210
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211
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212
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213
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214
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215 ############################## elbow_plot ####################################
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216
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217 def elbow_plot (distortions, k_min, k_max):
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218 plt.figure(0)
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219 x = list(range(k_min, k_max + 1))
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220 x.insert(0, 1)
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221 plt.plot(x, distortions, marker = 'o')
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222 plt.xlabel('Number of clusters (k)')
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223 plt.ylabel('Distortion')
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224 s = 'clustering/elbow_plot.png'
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225 fig = plt.gcf()
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226 fig.set_size_inches(18.5, 10.5, forward = True)
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227 fig.savefig(s, dpi=100)
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228
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229
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230 ############################## silhouette plot ###############################
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231 def silihouette_draw(dataset, labels, n_clusters, path):
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232 if n_clusters == 1:
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233 return None
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234
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235 silhouette_avg = silhouette_score(dataset, labels)
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236 warning("For n_clusters = " + str(n_clusters) +
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237 " The average silhouette_score is: " + str(silhouette_avg))
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238
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239 plt.close('all')
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240 # Create a subplot with 1 row and 2 columns
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241 fig, (ax1) = plt.subplots(1, 1)
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242
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243 fig.set_size_inches(18, 7)
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244
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245 # The 1st subplot is the silhouette plot
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246 # The silhouette coefficient can range from -1, 1 but in this example all
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247 # lie within [-0.1, 1]
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248 ax1.set_xlim([-1, 1])
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249 # The (n_clusters+1)*10 is for inserting blank space between silhouette
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250 # plots of individual clusters, to demarcate them clearly.
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251 ax1.set_ylim([0, len(dataset) + (n_clusters + 1) * 10])
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252
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253 # Compute the silhouette scores for each sample
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254 sample_silhouette_values = silhouette_samples(dataset, labels)
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255
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256 y_lower = 10
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257 for i in range(n_clusters):
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258 # Aggregate the silhouette scores for samples belonging to
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259 # cluster i, and sort them
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260 ith_cluster_silhouette_values = \
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261 sample_silhouette_values[labels == i]
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262
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263 ith_cluster_silhouette_values.sort()
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264
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265 size_cluster_i = ith_cluster_silhouette_values.shape[0]
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266 y_upper = y_lower + size_cluster_i
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267
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268 color = cm.nipy_spectral(float(i) / n_clusters)
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269 ax1.fill_betweenx(np.arange(y_lower, y_upper),
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270 0, ith_cluster_silhouette_values,
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271 facecolor=color, edgecolor=color, alpha=0.7)
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272
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273 # Label the silhouette plots with their cluster numbers at the middle
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274 ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
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275
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276 # Compute the new y_lower for next plot
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277 y_lower = y_upper + 10 # 10 for the 0 samples
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278
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279 ax1.set_title("The silhouette plot for the various clusters.")
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280 ax1.set_xlabel("The silhouette coefficient values")
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281 ax1.set_ylabel("Cluster label")
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282
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283 # The vertical line for average silhouette score of all the values
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284 ax1.axvline(x=silhouette_avg, color="red", linestyle="--")
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285
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286 ax1.set_yticks([]) # Clear the yaxis labels / ticks
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287 ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
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288
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289
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290 plt.suptitle(("Silhouette analysis for clustering on sample data "
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291 "with n_clusters = " + str(n_clusters) + "\nAverage silhouette_score = " + str(silhouette_avg)), fontsize=12, fontweight='bold')
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292
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293
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294 plt.savefig(path, bbox_inches='tight')
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295
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296 ######################## dbscan ##############################################
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297
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298 def dbscan(dataset, eps, min_samples, best_cluster):
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299 if not os.path.exists('clustering'):
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300 os.makedirs('clustering')
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301
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302 if eps is not None:
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303 clusterer = DBSCAN(eps = eps, min_samples = min_samples)
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304 else:
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305 clusterer = DBSCAN()
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306
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307 clustering = clusterer.fit(dataset)
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308
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309 core_samples_mask = np.zeros_like(clustering.labels_, dtype=bool)
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310 core_samples_mask[clustering.core_sample_indices_] = True
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311 labels = clustering.labels_
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312
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313 # Number of clusters in labels, ignoring noise if present.
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314 n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
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315
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316
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317 labels = labels
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318 predict = [x+1 for x in labels]
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319 classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
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320 classe.to_csv(best_cluster, sep = '\t', index = False, header = ['Patient_ID', 'Class'])
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321
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322
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323 ########################## hierachical #######################################
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324
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325 def hierachical_agglomerative(dataset, k_min, k_max, best_cluster, silhouette):
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326
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327 if not os.path.exists('clustering'):
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328 os.makedirs('clustering')
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329
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330 plt.figure(figsize=(10, 7))
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331 plt.title("Classes Dendogram")
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332 shc.dendrogram(shc.linkage(dataset, method='ward'), labels=dataset.index.values.tolist())
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333 fig = plt.gcf()
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334 fig.savefig('clustering/dendogram.png', dpi=200)
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335
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336 range_n_clusters = [i for i in range(k_min, k_max+1)]
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337
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338 scores = []
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339 labels = []
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340
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341 for n_clusters in range_n_clusters:
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342 cluster = AgglomerativeClustering(n_clusters=n_clusters, affinity='euclidean', linkage='ward')
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343 cluster.fit_predict(dataset)
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344 cluster_labels = cluster.labels_
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345 labels.append(cluster_labels)
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346 write_to_csv(dataset, cluster_labels, 'clustering/hierarchical_with_' + str(n_clusters) + '_clusters.tsv')
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347
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348 best = max_index(scores) + k_min
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349
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350 for i in range(len(labels)):
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351 prefix = ''
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352 if (i + k_min == best):
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353 prefix = '_BEST'
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354 if silhouette == 'true':
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355 silihouette_draw(dataset, labels[i], i + k_min, 'clustering/silhouette_with_' + str(i + k_min) + prefix + '_clusters.png')
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356
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357 for i in range(len(labels)):
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358 if (i + k_min == best):
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359 labels = labels[i]
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360 predict = [x+1 for x in labels]
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361 classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
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362 classe.to_csv(best_cluster, sep = '\t', index = False, header = ['Patient_ID', 'Class'])
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363
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364
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365 ############################# main ###########################################
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366
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367
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368 def main():
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369 if not os.path.exists('clustering'):
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370 os.makedirs('clustering')
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371
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372 args = process_args(sys.argv)
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373
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374 #Data read
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375
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376 X = read_dataset(args.input)
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377 X = pd.DataFrame.to_dict(X, orient='list')
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378 X = rewrite_input(X)
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379 X = pd.DataFrame.from_dict(X, orient = 'index')
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380
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381 for i in X.columns:
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382 tmp = X[i][0]
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383 if tmp == None:
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384 X = X.drop(columns=[i])
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385
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386
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387 if args.cluster_type == 'kmeans':
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388 kmeans(args.k_min, args.k_max, X, args.elbow, args.silhouette, args.best_cluster)
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389
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390 if args.cluster_type == 'dbscan':
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391 dbscan(X, args.eps, args.min_samples, args.best_cluster)
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392
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393 if args.cluster_type == 'hierarchy':
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394 hierachical_agglomerative(X, args.k_min, args.k_max, args.best_cluster, args.silhouette)
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395
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396 ##############################################################################
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397
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398 if __name__ == "__main__":
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399 main()
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