class CoraCitations(Dataset):
def __init__(self, filename):
self.Y = Alphabet()
data = list(
fromSGML(filename, linegrouper="<NEW.*?>", bioencoding=False))
np.random.shuffle(data)
super(CoraCitations, self).__init__(train=data[len(data) // 5:],
dev=data[:len(data) // 5],
test=[])
self.train = self.make_instances('train', Instance)
self.dev = self.make_instances('dev', Instance)
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)
1: 'hypernym',
2: 'hyponym',
}
A = Alphabet()
A.map([x.strip().split()[1] for i, x in enumerate(file('res/bowman_wordnet_longer_shuffled_synset_relations.map')) if i > 2])
tst = BWD_dataset('test').data
trn = BWD_dataset('train').data
trn_x = trn[0]
trn_y = trn[1]
seen = set(trn_x.flatten()) | set(trn_y.flatten())
X,Y,_ = tst
X = list(A.lookup_many(X.flatten()))
Y = list(A.lookup_many(Y.flatten()))
#D = np.array([X,Y,L.flatten()]).T
model_file = 'res/experiments/BWD-projection-Softmax_best.pkl'
#model_file = '/home/timv/Downloads/BWD-projection-identity_sub_glue-Softmax.pkl'
model_func = make_model_func(cPickle.load(open(model_file, 'rb')))
(x_left, x_right, y_true) = tst
y_true = y_true.flatten()
y_dist = model_func((x_left, x_right))
y_hat = y_dist.argmax(axis=1)
print 'before unseen filter acc: %g' % (y_hat == y_true).mean()
A = Alphabet()
A.map([
x.strip().split()[1] for i, x in enumerate(
file('res/bowman_wordnet_longer_shuffled_synset_relations.map'))
if i > 2
])
tst = BWD_dataset('test').data
trn = BWD_dataset('train').data
trn_x = trn[0]
trn_y = trn[1]
seen = set(trn_x.flatten()) | set(trn_y.flatten())
X, Y, _ = tst
X = list(A.lookup_many(X.flatten()))
Y = list(A.lookup_many(Y.flatten()))
#D = np.array([X,Y,L.flatten()]).T
model_file = 'res/experiments/BWD-projection-Softmax_best.pkl'
#model_file = '/home/timv/Downloads/BWD-projection-identity_sub_glue-Softmax.pkl'
model_func = make_model_func(cPickle.load(open(model_file, 'rb')))
(x_left, x_right, y_true) = tst
y_true = y_true.flatten()
y_dist = model_func((x_left, x_right))
y_hat = y_dist.argmax(axis=1)
print 'before unseen filter acc: %g' % (y_hat == y_true).mean()
unseen = 0