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101 lines
2.2 KiB
Python
101 lines
2.2 KiB
Python
import pytest
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import numpy
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from spacy.vocab import Vocab
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from spacy.language import Language
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from spacy.pipeline.defaults import default_parser
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from spacy.pipeline import DependencyParser
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from spacy.syntax.arc_eager import ArcEager
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from spacy.tokens import Doc
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from spacy.syntax.stateclass import StateClass
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@pytest.fixture
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def vocab():
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return Vocab()
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@pytest.fixture
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def moves(vocab):
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aeager = ArcEager(vocab.strings, {})
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aeager.add_action(2, "nsubj")
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aeager.add_action(3, "dobj")
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aeager.add_action(2, "aux")
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return aeager
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@pytest.fixture
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def docs(vocab):
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return [Doc(vocab, words=["Rats", "bite", "things"])]
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@pytest.fixture
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def states(docs):
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return [StateClass(doc) for doc in docs]
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@pytest.fixture
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def tokvecs(docs, vector_size):
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output = []
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for doc in docs:
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vec = numpy.random.uniform(-0.1, 0.1, (len(doc), vector_size))
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output.append(numpy.asarray(vec))
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return output
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@pytest.fixture
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def batch_size(docs):
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return len(docs)
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@pytest.fixture
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def beam_width():
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return 4
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@pytest.fixture
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def vector_size():
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return 6
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@pytest.fixture
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def beam(moves, states, golds, beam_width):
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return ParserBeam(moves, states, golds, width=beam_width, density=0.0)
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@pytest.fixture
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def scores(moves, batch_size, beam_width):
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return [
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numpy.asarray(
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numpy.random.uniform(-0.1, 0.1, (batch_size, moves.n_moves)), dtype="f"
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)
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for _ in range(batch_size)
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]
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# All tests below are skipped after removing Beam stuff during the Example/GoldParse refactor
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@pytest.mark.skip
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def test_create_beam(beam):
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pass
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@pytest.mark.skip
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def test_beam_advance(beam, scores):
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beam.advance(scores)
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@pytest.mark.skip
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def test_beam_advance_too_few_scores(beam, scores):
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with pytest.raises(IndexError):
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beam.advance(scores[:-1])
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@pytest.mark.skip
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def test_beam_parse():
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nlp = Language()
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config = {"learn_tokens": False, "min_action_freq": 30, "beam_width": 1, "beam_update_prob": 1.0}
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nlp.add_pipe(DependencyParser(nlp.vocab, default_parser(), **config), name="parser")
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nlp.parser.add_label("nsubj")
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nlp.parser.begin_training([], token_vector_width=8, hidden_width=8)
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doc = nlp.make_doc("Australia is a country")
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nlp.parser(doc, beam_width=2)
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