--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/evaluation.py	Sat Oct 27 13:49:00 2018 -0400
@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+import glob
+from os import system
+import re
+from sklearn import metrics
+from shutil import make_archive
+import sys
+import fnmatch, os
+
+def sh(script):
+    system("bash -c '%s'" % script)
+
+fasta_dir = sys.argv[1]
+results_dir = sys.argv[2]
+dataNames = os.path.join(fasta_dir,"data.names")
+
+listOfClusters = []
+listOfHeaders = []
+headersNames = set()
+idsNames = set()
+
+
+names = os.listdir(results_dir)
+cluster_seqs_stats_files = fnmatch.filter(names, '*.cluster.all')
+with open(dataNames, "r") as names:
+    for line2 in names:
+        splits2 = line2.split()
+        fullHeader = ''
+        if len(splits2) >= 6:
+            fullHeader = splits2[5]
+            headersNames.add(fullHeader)
+            fullID = splits2[3]
+            idsNames.add(fullID)
+
+blackList = []
+numberOfClusters = 0
+for singleFile in sorted(cluster_seqs_stats_files):
+    singleFile = os.path.join(results_dir,singleFile)
+    numberOfClusters += 1
+    with open(singleFile, "r") as f:
+        for line in f:
+            splits = line.split()
+            header = ''
+            idd = ''
+            if len(splits) >= 11:
+                header = splits[10]
+                idd = splits[8]
+            clustNum = splits[2]
+            listOfHeaders.append(header)
+            listOfClusters.append(clustNum)
+            if idd in idsNames: #header in headersNames:
+                blackList.append(idd)
+
+numberOfClusters += 1  # 1 cluster for all unassigned seqs
+ignoreBlackList = False
+with open(dataNames, "r") as names:
+    for line in names:
+        splits = line.split() 
+        fullUniqeId = splits[3]
+        fullHeader = ''
+        fullID = ''
+        if len(splits) >= 6:
+            fullHeader = line.split()[5]
+            fullID = line.split()[3]
+        if ignoreBlackList or ( fullID not in blackList #fullHeader not in blackList 
+            or len(fullHeader) == 0):
+            listOfHeaders.append(fullHeader)
+            listOfClusters.append(str(numberOfClusters))
+            numberOfClusters += 1  # separate cluster for all unassigned seqs
+        # else:
+        #     print ("Skip header", fullHeader)
+
+toWrite = ""
+for i in range(len(listOfClusters)):
+    toWrite += "%s\t%s\n" % (listOfHeaders[i], listOfClusters[i]) 
+ 
+with open(os.path.join(results_dir,"fullTab.tabular"), "w") as full:
+    full.write(toWrite)
+
+
+pattern = re.compile("^RF.*$")
+
+if len(listOfHeaders) > 1: # and  pattern.match(str(listOfHeaders[0])):
+
+    completeness_score = metrics.completeness_score(listOfHeaders, listOfClusters)
+    homogeneity_score = metrics.homogeneity_score(listOfHeaders, listOfClusters)
+    adjusted_rand_score = metrics.adjusted_rand_score(listOfHeaders, listOfClusters)
+    adjusted_mutual_info_score = metrics.adjusted_mutual_info_score(listOfHeaders, listOfClusters)
+    v_measure_score = metrics.v_measure_score(listOfHeaders, listOfClusters)
+
+    toWrite = "completeness_score : {}\n".format(completeness_score) 
+    toWrite += "homogeneity_score : {}\n".format(homogeneity_score) 
+    toWrite += "adjusted_rand_score : {}\n".format(adjusted_rand_score)
+    toWrite += "adjusted_mutual_info_score : {}\n".format(adjusted_mutual_info_score)
+    toWrite += "v_measure_score : {}\n".format(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(os.path.join(results_dir,"evaluation.txt"), "w") as fOut:
+    fOut.write(toWrite)
+
+
+make_archive('RESULTS', 'zip', root_dir=results_dir)