comparison trunk/experiments/scripts/cnbh-syllables/results_plotting/gen_results.py @ 381:e6f006238034

- Python results plotting (finally).
- Proper results reporting script.
- Test on ALL talkers. The results script then generates a summary based on all the various subsets.
- Fixed chown users (hopefully sudos to be deleted entirely soon)
- More...

+#!/usr/bin/env python
+# encoding: utf-8
+"""
+gen_results.py
+
+Created by Thomas Walters on 2010-09-12.
+"""
+
+import sys
+import getopt
+import re
+
+
+help_message = '''
+Generate a file containing all the results for a run of the
+syllable recognition experiment. Expected input is a 'misclassificaions'
+file of the type generated by run_test_instance.sh, along with the locations
+of files containing the training talkers and all the talkers that the system
+was tested on.
+
+Arguments:
+-i --input_file
+-t --train_talkers
+-s --test_talkers
+-o --output_filename
+-c --value_count
+-p --spoke_pattern
+'''
+
+class Usage(Exception):
+  def __init__(self, msg):
+    self.msg = msg
+
+
+def main(argv=None):
+  if argv is None:
+    argv = sys.argv
+  try:
+    try:
+      opts, args = getopt.getopt(argv[1:], "hi:t:s:o:c:p:v",
+                                 ["help", "input_file=", "train_talkers=",
+                                  "test_talkers=", "output_filename=",
+                                  "value_count=", "spoke_pattern="])
+    except getopt.error, msg:
+      raise Usage(msg)
+  
+    # defaults
+    input_file = "misclassified_syllables_iteration_15"
+    train_talkers = "training_talkers"
+    test_talkers = "testing_talkers"
+    output_filename = "results.txt"
+    total_value_count = 185
+    spoke_pattern_file = "spoke_pattern.txt"
+    
+    # option processing
+    for option, value in opts:
+      if option == "-v":
+        verbose = True
+      if option in ("-h", "--help"):
+        raise Usage(help_message)
+      if option in ("-i", "--input_file"):
+        input_file = value
+      if option in ("-t", "--train_talkers"):
+        train_talkers = value
+      if option in ("-s", "--test_talkers"):
+        test_talkers = value
+      if option in ("-c", "--value_count"):
+        total_value_count = int(value)
+      if option in ("-p", "--spoke_pattern_file"):
+        spoke_pattern_file = value
+  
+  except Usage, err:
+    print >> sys.stderr, sys.argv[0].split("/")[-1] + ": " + str(err.msg)
+    print >> sys.stderr, "\t for help use --help"
+    return 2
+    
+  results = dict()
+  f = open(input_file, 'r')
+  for line in f:
+    values = line.strip().split(' ')
+    results[values[1]]=100*(1-float(values[0])/total_value_count)
+ 	
+  f = open(test_talkers, 'r')
+  test_talkers_list = f.readlines()
+  f.close()
+
+  f = open(train_talkers, 'r')
+  train_talkers_list = f.readlines()
+  f.close()
+
+  spoke_pattern = []
+  f = open(spoke_pattern_file, 'r')
+  for line in f:
+	spoke_pattern.append(line.strip().split(' '))
+
+  all_talkers_list = []
+  all_talkers_list.extend(train_talkers_list)
+  all_talkers_list.extend(test_talkers_list)
+
+  # Here I make the rather rash assumption that the model was tested on all talkers
+  # this should be true if the training and testing was done using my scripts.
+  for t in all_talkers_list:
+	results.setdefault(t.strip(), 100.0)
+
+  total_score = 0.0
+  element_count = 0
+  for t in train_talkers_list:
+	total_score += results[t.strip()]
+	element_count += 1
+  score = total_score / element_count
+  print ("# Score on training talkers: %.1f%%" % score)
+
+  total_score = 0.0
+  element_count = 0
+  for t in all_talkers_list:
+	total_score += results[t.strip()]
+	element_count += 1
+  score = total_score / element_count
+  print ("# Score on all talkers: %.1f%%" % score)
+
+  total_score = 0.0
+  element_count = 0
+  for t in test_talkers_list:
+	total_score += results[t.strip()]
+	element_count += 1
+  score = total_score / element_count
+  print ("# Score on test talkers: %.1f%%" % score)
+
+  score = results[spoke_pattern[0][0]]
+  print ("# Score on central talker: %.1f" % score)
+
+  for s in xrange(1,9):
+	print ("# Results for spoke %d" % s)
+	for p in xrange(0, 7):
+	  score = results[spoke_pattern[s][p]]
+	  m = re.match('(.*)p(.*)s', spoke_pattern[s][p])
+	  gpr = m.group(1)
+	  vtl=m.group(2)
+	  print ("%s %s %s" % (gpr, vtl, score))
+
+if __name__ == "__main__":
+  sys.exit(main())