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), ]