Add weightage parameters for more customized blending

kaggle_vote.py

@@ -1,16 +1,30 @@
 from collections import defaultdict, Counter
 from glob import glob
 import sys
+import re
 
 glob_files = sys.argv[1]
 loc_outfile = sys.argv[2]
+weights_strategy = "uniform"
+if len(sys.argv) == 4:
+  weights_strategy = sys.argv[3]
 
 def kaggle_bag(glob_files, loc_outfile, method="average", weights="uniform"):
+  pattern = re.compile("^_[w|W](\d*)_[.]*")
   if method == "average":
     scores = defaultdict(list)
   with open(loc_outfile,"wb") as outfile:
+    #weight_liss may be usefull using a different method
+    weight_list = [1]*len(glob(glob_files))
     for i, glob_file in enumerate( glob(glob_files) ):
       print "parsing:", glob_file
+      if weights == "weighted":
+         weight = pattern.match(glob_file)
+         if weight:
+            print "Using weight: ",int(weight.group(1))
+            weight_list[i] = weight_list[i]*int(weight.group(1))
+         else:
+            print "Using weight: 1"
       # sort glob_file by first column, ignoring the first line
       lines = open(glob_file).readlines()
       lines = [lines[0]] + sorted(lines[1:])
@@ -19,9 +33,10 @@ def kaggle_bag(glob_files, loc_outfile, method="average", weights="uniform"):
           outfile.write(line)
         if e > 0:
           row = line.strip().split(",")
-          scores[(e,row[0])].append(row[1])
+          for l in range(1,weight_list[i]+1):
+            scores[(e,row[0])].append(row[1])
     for j,k in sorted(scores):
       outfile.write("%s,%s\n"%(k,Counter(scores[(j,k)]).most_common(1)[0][0]))
     print("wrote to %s"%loc_outfile)
 
-kaggle_bag(glob_files, loc_outfile)
+kaggle_bag(glob_files, loc_outfile, weights=weights_strategy)