def test_training(pairwise):
'''Tests that training does something.'''
from numpy.testing import assert_approx_equal
content_objs, labels = interesting_training_data()
indiced = mod_pairwise.labels_to_indexed_coref_values(content_objs, labels)
names, model, vec = pairwise.train(content_objs, indiced)
assert names == vec.get_feature_names()
assert_approx_equal(model.coef_[0][0], 0.61903921)
assert model.classes_.tolist() == [-1, 1]
def test_training(pairwise):
'''Tests that training does something.'''
from numpy.testing import assert_approx_equal
content_objs, labels = interesting_training_data()
indiced = mod_pairwise.labels_to_indexed_coref_values(content_objs, labels)
names, model, vec = pairwise.train(content_objs, indiced)
assert names == vec.get_feature_names()
assert_approx_equal(model.coef_[0][0], 0.61903921)
assert model.classes_.tolist() == [-1, 1]
def test_label_indexing():
'''Make sure labels get converted to indices correctly.'''
content_objs = [
('a', counter_fc({})),
('b', counter_fc({})),
('c', counter_fc({})),
]
labels = [
pos_label('a', 'c'),
pos_label('c', 'b'),
neg_label('b', 'a'),
]
indiced = mod_pairwise.labels_to_indexed_coref_values(content_objs, labels)
assert indiced == [
# ({-1, 1}, index1, index2)
(1, 0, 2),
(1, 1, 2),
(-1, 0, 1),
]
def test_label_indexing():
'''Make sure labels get converted to indices correctly.'''
content_objs = [
('a', counter_fc({})),
('b', counter_fc({})),
('c', counter_fc({})),
]
labels = [
pos_label('a', 'c'),
pos_label('c', 'b'),
neg_label('b', 'a'),
]
indiced = mod_pairwise.labels_to_indexed_coref_values(content_objs, labels)
assert indiced == [
# ({-1, 1}, index1, index2)
(1, 0, 2),
(1, 1, 2),
(-1, 0, 1),
]
