Mercurial > repos > bimib > marea

comparison Marea/marea_cluster.py @ 28:e6831924df01 draft

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small fixes (elbow plot and output managment)
author bimib
date Mon, 14 Oct 2019 05:01:08 -0400
parents 9992eba50cfb
children 944e15aa970a
comparison
equal deleted inserted replaced
27:8c480c977a12 28:e6831924df01
76 help = 'min samples for dbscan (optional)') 76 help = 'min samples for dbscan (optional)')
77 77
78 parser.add_argument('-ep', '--eps', 78 parser.add_argument('-ep', '--eps',
79 type = int, 79 type = int,
80 help = 'eps for dbscan (optional)') 80 help = 'eps for dbscan (optional)')
81
82 parser.add_argument('-bc', '--best_cluster',
83 type = str,
84 help = 'output of best cluster tsv')
85
81 86
82 87
83 args = parser.parse_args() 88 args = parser.parse_args()
84 return args 89 return args
85 90
129 classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str) 134 classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
130 135
131 dest = name 136 dest = name
132 classe.to_csv(dest, sep = '\t', index = False, 137 classe.to_csv(dest, sep = '\t', index = False,
133 header = ['Patient_ID', 'Class']) 138 header = ['Patient_ID', 'Class'])
134 139
135
136 #list_labels = labels
137 #list_values = dataset
138
139 #list_values = list_values.tolist()
140 #d = {'Label' : list_labels, 'Value' : list_values}
141
142 #df = pd.DataFrame(d, columns=['Value','Label'])
143
144 #dest = name + '.tsv'
145 #df.to_csv(dest, sep = '\t', index = False,
146 # header = ['Value', 'Label'])
147
148 ########################### trova il massimo in lista ######################## 140 ########################### trova il massimo in lista ########################
149 def max_index (lista): 141 def max_index (lista):
150 best = -1 142 best = -1
151 best_index = 0 143 best_index = 0
152 for i in range(len(lista)): 144 for i in range(len(lista)):
156 148
157 return best_index 149 return best_index
158 150
159 ################################ kmeans ##################################### 151 ################################ kmeans #####################################
160 152
161 def kmeans (k_min, k_max, dataset, elbow, silhouette, davies): 153 def kmeans (k_min, k_max, dataset, elbow, silhouette, davies, best_cluster):
162 if not os.path.exists('clustering'): 154 if not os.path.exists('clustering'):
163 os.makedirs('clustering') 155 os.makedirs('clustering')
164 156
165 157
166 if elbow == 'true': 158 if elbow == 'true':
187 for n_clusters in range_n_clusters: 179 for n_clusters in range_n_clusters:
188 clusterer = KMeans(n_clusters=n_clusters, random_state=10) 180 clusterer = KMeans(n_clusters=n_clusters, random_state=10)
189 cluster_labels = clusterer.fit_predict(dataset) 181 cluster_labels = clusterer.fit_predict(dataset)
190 182
191 all_labels.append(cluster_labels) 183 all_labels.append(cluster_labels)
192 silhouette_avg = silhouette_score(dataset, cluster_labels) 184 if n_clusters == 1:
185 silhouette_avg = 0
186 else:
187 silhouette_avg = silhouette_score(dataset, cluster_labels)
193 scores.append(silhouette_avg) 188 scores.append(silhouette_avg)
194 distortions.append(clusterer.fit(dataset).inertia_) 189 distortions.append(clusterer.fit(dataset).inertia_)
195 190
196 best = max_index(scores) + k_min 191 best = max_index(scores) + k_min
197 192
199 prefix = '' 194 prefix = ''
200 if (i + k_min == best): 195 if (i + k_min == best):
201 prefix = '_BEST' 196 prefix = '_BEST'
202 197
203 write_to_csv(dataset, all_labels[i], 'clustering/kmeans_with_' + str(i + k_min) + prefix + '_clusters.tsv') 198 write_to_csv(dataset, all_labels[i], 'clustering/kmeans_with_' + str(i + k_min) + prefix + '_clusters.tsv')
199
200
201 if (prefix == '_BEST'):
202 labels = all_labels[i]
203 predict = [x+1 for x in labels]
204 classe = (pd.DataFrame(list(zip(dataset.index, predict)))).astype(str)
205 classe.to_csv(best_cluster, sep = '\t', index = False, header = ['Patient_ID', 'Class'])
206
204 207
205 if davies: 208 if davies:
206 with np.errstate(divide='ignore', invalid='ignore'): 209 with np.errstate(divide='ignore', invalid='ignore'):
207 davies_bouldin = davies_bouldin_score(dataset, all_labels[i]) 210 davies_bouldin = davies_bouldin_score(dataset, all_labels[i])
208 warning("\nFor n_clusters = " + str(i + k_min) + 211 warning("\nFor n_clusters = " + str(i + k_min) +
233 fig.savefig(s, dpi=100) 236 fig.savefig(s, dpi=100)
234 237
235 238
236 ############################## silhouette plot ############################### 239 ############################## silhouette plot ###############################
237 def silihouette_draw(dataset, labels, n_clusters, path): 240 def silihouette_draw(dataset, labels, n_clusters, path):
241 if n_clusters == 1:
242 return None
243
238 silhouette_avg = silhouette_score(dataset, labels) 244 silhouette_avg = silhouette_score(dataset, labels)
239 warning("For n_clusters = " + str(n_clusters) + 245 warning("For n_clusters = " + str(n_clusters) +
240 " The average silhouette_score is: " + str(silhouette_avg)) 246 " The average silhouette_score is: " + str(silhouette_avg))
241 247
242 plt.close('all') 248 plt.close('all')
373 if tmp == None: 379 if tmp == None:
374 X = X.drop(columns=[i]) 380 X = X.drop(columns=[i])
375 381
376 382
377 if args.cluster_type == 'kmeans': 383 if args.cluster_type == 'kmeans':
378 kmeans(args.k_min, args.k_max, X, args.elbow, args.silhouette, args.davies) 384 kmeans(args.k_min, args.k_max, X, args.elbow, args.silhouette, args.davies, args.best_cluster)
379 385
380 if args.cluster_type == 'dbscan': 386 if args.cluster_type == 'dbscan':
381 dbscan(X, args.eps, args.min_samples) 387 dbscan(X, args.eps, args.min_samples)
382 388
383 if args.cluster_type == 'hierarchy': 389 if args.cluster_type == 'hierarchy':