def trainMapping():
f = open("/usr/share/biotext/GeniaChallenge/xml/train.xml")
f2 = open("featurefile",'w')
parser = gifxmlParser(f)
iterator = parser.documentIterator()
mapping = buildDictionary(iterator)
writeMapping(mapping, f2)
f.close()
f2.close()
def getTP_FP_FN(g_edges, p_edges):
TP = len(g_edges.intersection(p_edges))
FP = len(p_edges)-TP
FN = len(g_edges)-TP
return TP, FP, FN
if __name__=="__main__":
optparser = instantiateOptionParser()
(options, args) = optparser.parse_args()
if len(args) != 2:
sys.stdout.write(optparser.get_usage())
print "python CandidateGenerator.py -h for options\n"
sys.exit(0)
p_file = open(args[0])
g_file = open(args[1])
p_parser = parseGifxml.gifxmlParser(p_file)
p_iterator = p_parser.documentIterator()
g_parser = parseGifxml.gifxmlParser(g_file)
g_iterator = g_parser.documentIterator()
counter = 1
oracleStatistics(p_iterator, g_iterator, options.nbest)
sys.exit(0)
for p_document, g_document in zip(p_iterator, g_iterator):
for p_child, g_child in zip(p_document, g_document):
if p_child.tag == "sentence":
assert p_child.attrib["id"]==g_child.attrib["id"]
p_entities, p_pairs = getEntitiesAndPairs(p_child)
g_entities, g_pairs = getEntitiesAndPairs(g_child)
predictions = getSimplePredictions(p_entities, p_pairs)
table, table_transpose, keys = toTable(predictions)
best = nbest.decode(table_transpose,options.nbest)
f = open("/usr/share/biotext/GeniaChallenge/xml/train.xml")
f2 = open("featurefile",'w')
parser = gifxmlParser(f)
iterator = parser.documentIterator()
mapping = buildDictionary(iterator)
writeMapping(mapping, f2)
f.close()
f2.close()
if __name__=="__main__":
f = open("/usr/share/biotext/GeniaChallenge/xml/train.xml")
f2 = open("featurefile")
mapping = readDictionaryMapping(f2)
f2.close()
f2 = open("train_inputs",'w')
parser = gifxmlParser(f)
iterator = parser.documentIterator()
for document in iterator:
for child in document:
if child.tag == "sentence":
tokenization, parse, entities = getTokenizationParseEntities(child, "split-Charniak-Lease", "split-Charniak-Lease")
token_features = getTokenFeatures(tokenization, entities)
dep_features = getDependencyFeatures(parse, token_features)
features = getFinalFeatures(token_features, dep_features)
fvalues = {}
for key in features.keys():
if key in mapping:
fvalues[int(mapping[key])] = features[key]
keys = fvalues.keys()
keys.sort()
line = "".join("%d:%f " %(x, fvalues[x]) for x in keys)+"\n"
TP = len(g_edges.intersection(p_edges))
FP = len(p_edges) - TP
FN = len(g_edges) - TP
return TP, FP, FN
if __name__ == "__main__":
optparser = instantiateOptionParser()
(options, args) = optparser.parse_args()
if len(args) != 2:
sys.stdout.write(optparser.get_usage())
print "python CandidateGenerator.py -h for options\n"
sys.exit(0)
p_file = open(args[0])
g_file = open(args[1])
p_parser = parseGifxml.gifxmlParser(p_file)
p_iterator = p_parser.documentIterator()
g_parser = parseGifxml.gifxmlParser(g_file)
g_iterator = g_parser.documentIterator()
counter = 1
oracleStatistics(p_iterator, g_iterator, options.nbest)
sys.exit(0)
for p_document, g_document in zip(p_iterator, g_iterator):
for p_child, g_child in zip(p_document, g_document):
if p_child.tag == "sentence":
assert p_child.attrib["id"] == g_child.attrib["id"]
p_entities, p_pairs = getEntitiesAndPairs(p_child)
g_entities, g_pairs = getEntitiesAndPairs(g_child)
predictions = getSimplePredictions(p_entities, p_pairs)
table, table_transpose, keys = toTable(predictions)
best = nbest.decode(table_transpose, options.nbest)