Mercurial > repos > rnateam > graphclust_postprocessing
view evaluation.py @ 8:9a69a6098b71 draft
planemo upload for repository https://github.com/eteriSokhoyan/galaxytools/tree/branchForIterations/tools/GraphClust/CollectResults commit 5a545fc913b3a07e1452e67f66b2ca593660abdf
author | rnateam |
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date | Mon, 27 Feb 2017 12:02:09 -0500 |
parents | 869a6e807d76 |
children | b5f49453af8c |
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import glob from os import system import re from sklearn import metrics from shutil import make_archive def sh(script): system("bash -c '%s'" % script) dataNames = "FASTA/data.names" listOfClusters = [] listOfClasses = [] cluster_seqs_stats_path = "RESULTS/*.cluster.all" cluster_seqs_stats_files = glob.glob(cluster_seqs_stats_path) blackList = [] numberOfClusters = 0 for singleFile in sorted(cluster_seqs_stats_files): numberOfClusters += 1 with open(singleFile, "r") as f: for line in f.readlines(): uniqueId = line.split()[8] clustNum = line.split()[2] rnaClass, sep, tail = uniqueId.partition("_") listOfClasses.append(rnaClass) listOfClusters.append(clustNum) with open(dataNames, "r") as names: for line in names.readlines(): fullUniqeId = line.split()[3] rnaClass, sep, tail = fullUniqeId.partition("_") if fullUniqeId == uniqueId: blackList.append(uniqueId) numberOfClusters += 1 # 1 cluster for all unassigned seqs with open(dataNames, "r") as names: for line in names.readlines(): fullUniqeId = line.split()[3] rnaClass, sep, tail = fullUniqeId.partition("_") rnaClass, sep, tail = fullUniqeId.partition("_") if fullUniqeId not in blackList: listOfClasses.append(rnaClass) listOfClusters.append(str(numberOfClusters)) numberOfClusters += 1 # separate cluster for all unassigned seqs toWrite = "" for i in range(len(listOfClusters)): toWrite += listOfClasses[i] + "\t" + listOfClusters[i] + '\n' with open("RESULTS/fullTab.tabular", "w") as full: full.write(toWrite) pattern = re.compile("^RF.*$") if len(listOfClasses) > 0 and pattern.match(str(listOfClasses[0])): completeness_score = metrics.completeness_score(listOfClasses, listOfClusters) homogeneity_score = metrics.homogeneity_score(listOfClasses, listOfClusters) adjusted_rand_score = metrics.adjusted_rand_score(listOfClasses, listOfClusters) adjusted_mutual_info_score = metrics.adjusted_mutual_info_score(listOfClasses, listOfClusters) v_measure_score = metrics.v_measure_score(listOfClasses, listOfClusters) toWrite = "completeness_score : " + str(completeness_score) + "\n" + "homogeneity_score : " + str(homogeneity_score) + "\n" + "adjusted_rand_score : " +str(adjusted_rand_score) + "\n" + "adjusted_mutual_info_score : " + str(adjusted_mutual_info_score)+ "\n" + "v_measure_score : " + str(v_measure_score) else: toWrite = "completeness_score : NA \nhomogeneity_score : NA \nadjusted_rand_score : NA \nadjusted_mutual_info_score : NA \nv_measure_score : NA" with open("RESULTS/evaluation.txt", "w") as fOut: fOut.write(toWrite) make_archive('RESULTS', 'zip', root_dir='RESULTS')