def validate(model, iteration=None):
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)
llh = sum(map(crf.likelihood, iterview(train, msg='llh'))) / len(train)
with lineplot('llh') as d:
d.append(llh)
print
print 'likelihood:', llh
print
def validate(model, iteration=None):
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)
llh = sum(map(crf.likelihood, iterview(train, msg='llh'))) / len(train)
with lineplot('llh') as d:
d.append(llh)
print
print 'likelihood:', llh
print
def validate(model, iteration=None):
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 weight_sparsity(W, t=0.0001):
a = (np.abs(W) > t).sum()
b = W.size
print '%.2f (%s/%s) sparsity' % (a*100.0/b, a, b)
# f1(train, name='TRAIN')
# if test:
# f1(test, name='TEST')
# print
# weight_sparsity(model.W)
llh = sum(map(crf.likelihood, iterview(train, msg='llh'))) / len(train)
from arsenal.viz.util import lineplot
with lineplot('llh') as d:
d.append(llh)
print
print 'likelihood:', llh
print
print
