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88 lines
1.7 KiB
Python
88 lines
1.7 KiB
Python
from __future__ import unicode_literals
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import pytest
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import numpy
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from thinc.api import layerize
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from ...vocab import Vocab
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from ...syntax.arc_eager import ArcEager
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from ...tokens import Doc
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from ...gold import GoldParse
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from ...syntax._beam_utils import ParserBeam, update_beam
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from ...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 golds(docs):
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return [GoldParse(doc) for doc in docs]
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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)),
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dtype='f')
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for _ in range(batch_size)]
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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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with pytest.raises(IndexError):
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beam.advance(scores[:-1])
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