def main(args):
# classDir = "CLASSIFIER_"
classFile = "*.tml"
weight = "1"
convexifying = 0.5
weights = ["1", "2", "3", "4", "5", "6", "7",
"8"] # '1' = same weight across classifiers
# '2' = F1 score of each classifier
# '3' = Precision score of each classifier
# '4' = Recall score of each classifier
# '5' = new F1 score of each classifier
# '6' = new Precision score of each classifier
# '7' = new Recall score of each classifier
# '8' = a convex combination of Precision and Recall, with convexifying coefficient
weightFormula = "1"
formulas = ["1", "2", "3", "4", "5", "6", "7", "8",
"9"] # '1' = proportion of classifiers that detected arc
# '2' = proportion of ALL classifiers
# '3' = weights normalised to 1 and link excluded if below 0.5 threshold
# '4' = sum of logs of probabilities
# '5' = product of probabilities
# '6' = loss function
opt = '1'
for arg in args:
if arg[:4] == "dir=":
classDir = arg[4:]
if arg[:5] == "file=":
classFile = arg[5:]
if arg[:7] == "weight=":
weight = arg[7:]
if not weight in weights:
print "Weight must be in range 1 to 8"
return False
if arg[:8] == "formula=":
weightFormula = arg[8:]
if not weightFormula in formulas:
print "Weight formula must be in range 1 to 9"
return False
if arg[:4] == "opt=":
opt = arg[4:]
if arg[:13] == "convexifying=":
convexifying = float(arg[13:])
#print "Received a convexifying coefficient of", convexifying
# list of classifiers - sub-directories starting with CLASSIFIER_
# classifiers = [] # initialise list of classifiers
classifierList = args[
0] # Change input parameter if you want to use different directories
clList = []
for cl in classifierList:
if os.path.isdir(cl):
clList.append(cl)
if len(clList) == 0:
print "No directories in list ", classifierList
return False
# Files for each classifier must be placed in folders starting with "CLASSIFIER_" (default) or folder name specified in parameter
filelist = glob.glob(clList[0] + "/" +
classFile) # Lists the files in the first directory
if len(filelist) == 0:
print "No files in directory ", clList[0]
return False
try:
for filename in filelist: # for each file in the first directory
finalClassifier = Classifier(
"FINAL", True
) # TLINKS and probability of relTypes across ALL classifiers goes here
finalClassifier.setWeightFormula(
weight, weightFormula,
convexifying) # determine which weighting formula to use
for classifier in clList: # for each classifier
clName = Classifier(
classifier, False
) # Classifier object instantiated for each classifier
cFilename = filename.replace(
clList[0], classifier
) # file path is replaced by classifier path name
if os.path.isfile(
cFilename
): # parse the file for events, timex3, makeinstance and tlinks
# print "Calling parseTml ", cFilename, classifier
clName.parseTml(cFilename)
else:
print "File ", cFilename, "not found"
v = finalClassifier.arcProbability(
) # Dictionary of probabilities across multiple classifiers
# print v
if opt == '1':
result = optimizer.main(
v) # optimise to maximise probability of reltypes
else:
result = finalClassifier.maxProbability(
v) # get relType with maximum probability
finalClassifier.mapResults(
result) # create new tml in folder RESULTS
except Exception as X:
print X
return False
return True
weight, weightFormula) # determine which weighting formula to use
for classifier in clList: # for each classifier
clName = Classifier(
classifier,
False) # Classifier object instantiated for each classifier
cFilename = filename.replace(
clList[0],
classifier) # file path is replaced by classifier path name
if os.path.isfile(
cFilename
): # parse the file for events, timex3, makeinstance and tlinks
print "Calling parseTml ", cFilename, classifier
clName.parseTml(cFilename)
else:
print "File ", cFilename, "not found"
print Classifier.globalArcs
v = finalClassifier.arcProbability(
) # Dictionary of probabilities across multiple classifiers
# print v
if opt == '1':
result = optimizer.main(
v) # optimise to maximise probability of reltypes
else:
result = finalClassifier.maxProbability(
v) # get relType with maximum probability
finalClassifier.mapResults(result) # create new tml in folder RESULTS
except Exception as X:
print X
