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