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https://github.com/explosion/spaCy.git
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144 lines
3.5 KiB
Python
144 lines
3.5 KiB
Python
import hypothesis
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import hypothesis.strategies
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import numpy
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import pytest
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from thinc.tests.strategies import ndarrays_of_shape
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from spacy.language import Language
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from spacy.pipeline._parser_internals._beam_utils import BeamBatch
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from spacy.pipeline._parser_internals.arc_eager import ArcEager
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from spacy.pipeline._parser_internals.stateclass import StateClass
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from spacy.tokens import Doc
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from spacy.training import Example
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from spacy.vocab import Vocab
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@pytest.fixture(scope="module")
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def vocab():
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return Vocab()
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@pytest.fixture(scope="module")
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def moves(vocab):
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aeager = ArcEager(vocab.strings, {})
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aeager.add_action(0, "")
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aeager.add_action(1, "")
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aeager.add_action(2, "nsubj")
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aeager.add_action(2, "punct")
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aeager.add_action(2, "aux")
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aeager.add_action(2, "nsubjpass")
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aeager.add_action(3, "dobj")
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aeager.add_action(2, "aux")
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aeager.add_action(4, "ROOT")
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return aeager
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@pytest.fixture(scope="module")
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def docs(vocab):
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return [
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Doc(
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vocab,
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words=["Rats", "bite", "things"],
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heads=[1, 1, 1],
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deps=["nsubj", "ROOT", "dobj"],
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sent_starts=[True, False, False],
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)
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]
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@pytest.fixture(scope="module")
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def examples(docs):
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return [Example(doc, doc.copy()) for doc in docs]
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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(scope="module")
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def batch_size(docs):
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return len(docs)
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@pytest.fixture(scope="module")
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def beam_width():
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return 4
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@pytest.fixture(params=[0.0, 0.5, 1.0])
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def beam_density(request):
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return request.param
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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, examples, beam_width):
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states, golds, _ = moves.init_gold_batch(examples)
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return BeamBatch(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 numpy.asarray(
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numpy.concatenate(
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[
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numpy.random.uniform(-0.1, 0.1, (beam_width, moves.n_moves))
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for _ in range(batch_size)
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]
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),
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dtype="float32",
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)
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def test_create_beam(beam):
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pass
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def test_beam_advance(beam, scores):
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beam.advance(scores)
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def test_beam_advance_too_few_scores(beam, scores):
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n_state = sum(len(beam) for beam in beam)
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scores = scores[:n_state]
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with pytest.raises(IndexError):
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beam.advance(scores[:-1])
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def test_beam_parse(examples, beam_width):
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nlp = Language()
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parser = nlp.add_pipe("beam_parser")
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parser.cfg["beam_width"] = beam_width
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parser.add_label("nsubj")
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parser.initialize(lambda: examples)
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doc = nlp.make_doc("Australia is a country")
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parser(doc)
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@hypothesis.given(hyp=hypothesis.strategies.data())
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def test_beam_density(moves, examples, beam_width, hyp):
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beam_density = float(hyp.draw(hypothesis.strategies.floats(0.0, 1.0, width=32)))
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states, golds, _ = moves.init_gold_batch(examples)
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beam = BeamBatch(moves, states, golds, width=beam_width, density=beam_density)
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n_state = sum(len(beam) for beam in beam)
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scores = hyp.draw(ndarrays_of_shape((n_state, moves.n_moves)))
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beam.advance(scores)
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for b in beam:
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beam_probs = b.probs
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assert b.min_density == beam_density
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assert beam_probs[-1] >= beam_probs[0] * beam_density
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