def f1(data, name):
print
print 'Phrase-based F1:', name
f1 = F1()
for i, x in enumerate(iterview(data)):
predict = extract_contiguous(model(x))
truth = extract_contiguous(x.truth)
# (i,begin,end) uniquely identifies the span
for (label, begins, ends) in truth:
f1.add_relevant(label, (i, begins, ends))
for (label, begins, ends) in predict:
f1.add_retrieved(label, (i, begins, ends))
print
return f1.scores(verbose=True)
def evaluate(self, predict, data, name, verbosity=1):
if not data:
return
if verbosity:
print()
print('Phrase-based F1:', name)
f1 = F1()
for i, x in enumerate(iterview(data, msg='Eval %s' % name)):
pred = extract_contiguous(predict(x))
gold = extract_contiguous(self.Y.lookup_many(x.tags))
# (i,begin,end) uniquely identifies the span
for (label, begins, ends) in gold:
f1.add_relevant(label, (i, begins, ends))
for (label, begins, ends) in pred:
f1.add_retrieved(label, (i, begins, ends))
if verbosity:
print()
return f1.scores(verbose=verbosity >= 1)
def predict(self,tokenlist):
""" Takes a list of Tokens and returns the prediction on the data. """
for i, x in enumerate(iterview([tokenlist])):
if x:
predict = extract_contiguous(self.model(x))
else:
return None
return predict