'''Test the ability to add a label to a (potentially trained) parsing model.''' from __future__ import unicode_literals import pytest import numpy.random from thinc.neural.optimizers import Adam from thinc.neural.ops import NumpyOps from ...attrs import NORM from ...gold import GoldParse from ...vocab import Vocab from ...tokens import Doc from ...pipeline import DependencyParser numpy.random.seed(0) @pytest.fixture def vocab(): return Vocab(lex_attr_getters={NORM: lambda s: s}) @pytest.fixture def parser(vocab): parser = DependencyParser(vocab) parser.cfg['token_vector_width'] = 8 parser.cfg['hidden_width'] = 30 parser.cfg['hist_size'] = 0 parser.add_label('left') parser.begin_training([], **parser.cfg) sgd = Adam(NumpyOps(), 0.001) for i in range(10): losses = {} doc = Doc(vocab, words=['a', 'b', 'c', 'd']) gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=['left', 'ROOT', 'left', 'ROOT']) parser.update([doc], [gold], sgd=sgd, losses=losses) return parser def test_init_parser(parser): pass # TODO: This is flakey, because it depends on what the parser first learns. @pytest.mark.xfail def test_add_label(parser): doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd']) doc = parser(doc) assert doc[0].head.i == 1 assert doc[0].dep_ == 'left' assert doc[1].head.i == 1 assert doc[2].head.i == 3 assert doc[2].head.i == 3 parser.add_label('right') doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd']) doc = parser(doc) assert doc[0].head.i == 1 assert doc[0].dep_ == 'left' assert doc[1].head.i == 1 assert doc[2].head.i == 3 assert doc[2].head.i == 3 sgd = Adam(NumpyOps(), 0.001) for i in range(10): losses = {} doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd']) gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=['right', 'ROOT', 'left', 'ROOT']) parser.update([doc], [gold], sgd=sgd, losses=losses) doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd']) doc = parser(doc) assert doc[0].dep_ == 'right' assert doc[2].dep_ == 'left'