mirror of
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126 lines
3.8 KiB
Python
126 lines
3.8 KiB
Python
import pytest
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from thinc.api import Adam, fix_random_seed
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from spacy import registry
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from spacy.attrs import NORM
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from spacy.vocab import Vocab
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from spacy.training import Example
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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.pipeline.ner import DEFAULT_NER_MODEL
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from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
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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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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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"update_with_oracle_cut_size": 100,
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}
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cfg = {"model": DEFAULT_PARSER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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parser = DependencyParser(vocab, model, **config)
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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(lambda: [_parser_example(parser)], **parser.cfg)
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sgd = Adam(0.001)
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for i in range(5):
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losses = {}
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doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
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gold = {"heads": [1, 1, 3, 3], "deps": ["left", "ROOT", "left", "ROOT"]}
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example = Example.from_dict(doc, gold)
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parser.update([example], sgd=sgd, losses=losses)
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return parser
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def _parser_example(parser):
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doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
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gold = {"heads": [1, 1, 3, 3], "deps": ["right", "ROOT", "left", "ROOT"]}
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return Example.from_dict(doc, gold)
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def _ner_example(ner):
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doc = Doc(
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ner.vocab,
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words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"],
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)
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gold = {"entities": [(0, 3, "PERSON"), (19, 25, "LOC")]}
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return Example.from_dict(doc, gold)
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def test_add_label(parser):
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parser = _train_parser(parser)
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parser.add_label("right")
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sgd = Adam(0.001)
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for i in range(100):
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losses = {}
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parser.update([_parser_example(parser)], 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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def test_add_label_deserializes_correctly():
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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"update_with_oracle_cut_size": 100,
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}
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cfg = {"model": DEFAULT_NER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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ner1 = EntityRecognizer(Vocab(), model, **config)
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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(lambda: [_ner_example(ner1)])
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ner2 = EntityRecognizer(Vocab(), model, **config)
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# the second model needs to be resized before we can call from_bytes
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ner2.model.attrs["resize_output"](ner2.model, ner1.moves.n_moves)
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ner2.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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@pytest.mark.parametrize(
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"pipe_cls,n_moves,model_config",
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[
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(DependencyParser, 5, DEFAULT_PARSER_MODEL),
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(EntityRecognizer, 4, DEFAULT_NER_MODEL),
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],
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)
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def test_add_label_get_label(pipe_cls, n_moves, model_config):
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"""Test that added labels are returned correctly. This test was added to
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test for a bug in DependencyParser.labels that'd cause it to fail when
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splitting the move names.
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"""
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labels = ["A", "B", "C"]
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model = registry.resolve({"model": model_config}, validate=True)["model"]
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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"update_with_oracle_cut_size": 100,
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}
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pipe = pipe_cls(Vocab(), model, **config)
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for label in labels:
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pipe.add_label(label)
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assert len(pipe.move_names) == len(labels) * n_moves
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pipe_labels = sorted(list(pipe.labels))
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assert pipe_labels == labels
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