mirror of
https://github.com/explosion/spaCy.git
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86 lines
2.4 KiB
Python
86 lines
2.4 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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import pytest
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from thinc.neural.optimizers import Adam
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from thinc.neural.ops import NumpyOps
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from spacy.attrs import NORM
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from spacy.gold import GoldParse
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from spacy.vocab import Vocab
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from spacy.tokens import Doc
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from spacy.pipeline import DependencyParser, EntityRecognizer
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from spacy.util import fix_random_seed
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@pytest.fixture
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def vocab():
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return Vocab(lex_attr_getters={NORM: lambda s: s})
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@pytest.fixture
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def parser(vocab):
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parser = DependencyParser(vocab)
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return parser
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def test_init_parser(parser):
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pass
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def _train_parser(parser):
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fix_random_seed(1)
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parser.add_label("left")
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parser.begin_training([], **parser.cfg)
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sgd = Adam(NumpyOps(), 0.001)
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for i in range(10):
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losses = {}
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doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
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gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=["left", "ROOT", "left", "ROOT"])
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parser.update([doc], [gold], sgd=sgd, losses=losses)
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return parser
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def test_add_label(parser):
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parser = _train_parser(parser)
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doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
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doc = parser(doc)
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assert doc[0].head.i == 1
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assert doc[0].dep_ == "left"
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assert doc[1].head.i == 1
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assert doc[2].head.i == 3
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assert doc[2].head.i == 3
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parser.add_label("right")
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doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
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doc = parser(doc)
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assert doc[0].head.i == 1
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assert doc[0].dep_ == "left"
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assert doc[1].head.i == 1
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assert doc[2].head.i == 3
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assert doc[2].head.i == 3
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sgd = Adam(NumpyOps(), 0.001)
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for i in range(10):
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losses = {}
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doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
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gold = GoldParse(
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doc, heads=[1, 1, 3, 3], deps=["right", "ROOT", "left", "ROOT"]
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)
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parser.update([doc], [gold], sgd=sgd, losses=losses)
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doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
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doc = parser(doc)
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assert doc[0].dep_ == "right"
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assert doc[2].dep_ == "left"
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@pytest.mark.xfail
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def test_add_label_deserializes_correctly():
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ner1 = EntityRecognizer(Vocab())
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ner1.add_label("C")
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ner1.add_label("B")
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ner1.add_label("A")
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ner1.begin_training([])
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ner2 = EntityRecognizer(Vocab()).from_bytes(ner1.to_bytes())
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assert ner1.moves.n_moves == ner2.moves.n_moves
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for i in range(ner1.moves.n_moves):
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assert ner1.moves.get_class_name(i) == ner2.moves.get_class_name(i)
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