Mercurial > repos > bimib > marea

comparison Marea/marea_cluster.py @ 13:e96f3b85e5a0 draft

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author bimib
date Wed, 13 Feb 2019 05:42:20 -0500
parents 3d77287caf22
children 1a0c8c2780f2
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12:3d77287caf22 13:e96f3b85e5a0
1
2 from __future__ import division 1 from __future__ import division
3 import os 2 import os
4 import sys 3 import sys
5 import pandas as pd 4 import pandas as pd
6 import collections 5 import collections
7 import pickle as pk 6 import pickle as pk
8 import argparse 7 import argparse
9 from sklearn.cluster import KMeans 8 from sklearn.cluster import KMeans
9 import matplotlib
10 matplotlib.use('GTKAgg')
10 import matplotlib.pyplot as plt 11 import matplotlib.pyplot as plt
11 12
12 ########################## argparse ########################################### 13 ########################## argparse ###########################################
13 14
14 def process_args(args): 15 def process_args(args):
538 ################################# clustering ################################## 539 ################################# clustering ##################################
539 540
540 def f_cluster(resolve_rules): 541 def f_cluster(resolve_rules):
541 os.makedirs('cluster_out') 542 os.makedirs('cluster_out')
542 args = process_args(sys.argv) 543 args = process_args(sys.argv)
544 k_min = args.k_min
545 k_max = args.k_max
546 if k_min > k_max:
547 warning('k range boundaries inverted.\n')
548 tmp = k_min
549 k_min = k_max
550 k_max = tmp
551 else:
552 warning('k range correct.\n')
543 cluster_data = pd.DataFrame.from_dict(resolve_rules, orient = 'index') 553 cluster_data = pd.DataFrame.from_dict(resolve_rules, orient = 'index')
544 for i in cluster_data.columns: 554 for i in cluster_data.columns:
545 tmp = cluster_data[i][0] 555 tmp = cluster_data[i][0]
546 if tmp == None: 556 if tmp == None:
547 cluster_data = cluster_data.drop(columns=[i]) 557 cluster_data = cluster_data.drop(columns=[i])
548 distorsion = [] 558 distorsion = []
549 for i in range(args.k_min, args.k_max+1): 559 for i in range(k_min, k_max+1):
550 tmp_kmeans = KMeans(n_clusters = i, 560 tmp_kmeans = KMeans(n_clusters = i,
551 n_init = 100, 561 n_init = 100,
552 max_iter = 300, 562 max_iter = 300,
553 random_state = 0).fit(cluster_data) 563 random_state = 0).fit(cluster_data)
554 distorsion.append(tmp_kmeans.inertia_) 564 distorsion.append(tmp_kmeans.inertia_)
557 classe = (pd.DataFrame(list(zip(cluster_data.index, predict)))).astype(str) 567 classe = (pd.DataFrame(list(zip(cluster_data.index, predict)))).astype(str)
558 dest = 'cluster_out/K=' + str(i) + '_' + args.name+'.tsv' 568 dest = 'cluster_out/K=' + str(i) + '_' + args.name+'.tsv'
559 classe.to_csv(dest, sep = '\t', index = False, 569 classe.to_csv(dest, sep = '\t', index = False,
560 header = ['Patient_ID', 'Class']) 570 header = ['Patient_ID', 'Class'])
561 plt.figure(0) 571 plt.figure(0)
562 plt.plot(range(args.k_min, args.k_max+1), distorsion, marker = 'o') 572 plt.plot(range(k_min, k_max+1), distorsion, marker = 'o')
563 plt.xlabel('Number of cluster') 573 plt.xlabel('Number of cluster')
564 plt.ylabel('Distorsion') 574 plt.ylabel('Distorsion')
565 plt.savefig(args.elbow, dpi = 240, format = 'pdf') 575 plt.savefig(args.elbow, dpi = 240, format = 'pdf')
566 if args.cond_hier == 'yes': 576 if args.cond_hier == 'yes':
567 import scipy.cluster.hierarchy as hier 577 import scipy.cluster.hierarchy as hier
574 584
575 ################################# main ######################################## 585 ################################# main ########################################
576 586
577 def main(): 587 def main():
578 args = process_args(sys.argv) 588 args = process_args(sys.argv)
579 if args.k_min > args.k_max:
580 sys.exit('Execution aborted: max cluster > min cluster')
581 if args.rules_selector == 'HMRcore': 589 if args.rules_selector == 'HMRcore':
582 recon = pk.load(open(args.tool_dir + '/local/HMRcore_rules.p', 'rb')) 590 recon = pk.load(open(args.tool_dir + '/local/HMRcore_rules.p', 'rb'))
583 elif args.rules_selector == 'Recon': 591 elif args.rules_selector == 'Recon':
584 recon = pk.load(open(args.tool_dir + '/local/Recon_rules.p', 'rb')) 592 recon = pk.load(open(args.tool_dir + '/local/Recon_rules.p', 'rb'))
585 elif args.rules_selector == 'Custom': 593 elif args.rules_selector == 'Custom':