mirror of https://github.com/explosion/spaCy.git
144 lines
3.5 KiB
Python
144 lines
3.5 KiB
Python
import hypothesis
|
|
import hypothesis.strategies
|
|
import numpy
|
|
import pytest
|
|
from thinc.tests.strategies import ndarrays_of_shape
|
|
|
|
from spacy.language import Language
|
|
from spacy.pipeline._parser_internals._beam_utils import BeamBatch
|
|
from spacy.pipeline._parser_internals.arc_eager import ArcEager
|
|
from spacy.pipeline._parser_internals.stateclass import StateClass
|
|
from spacy.tokens import Doc
|
|
from spacy.training import Example
|
|
from spacy.vocab import Vocab
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def vocab():
|
|
return Vocab()
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def moves(vocab):
|
|
aeager = ArcEager(vocab.strings, {})
|
|
aeager.add_action(0, "")
|
|
aeager.add_action(1, "")
|
|
aeager.add_action(2, "nsubj")
|
|
aeager.add_action(2, "punct")
|
|
aeager.add_action(2, "aux")
|
|
aeager.add_action(2, "nsubjpass")
|
|
aeager.add_action(3, "dobj")
|
|
aeager.add_action(2, "aux")
|
|
aeager.add_action(4, "ROOT")
|
|
return aeager
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def docs(vocab):
|
|
return [
|
|
Doc(
|
|
vocab,
|
|
words=["Rats", "bite", "things"],
|
|
heads=[1, 1, 1],
|
|
deps=["nsubj", "ROOT", "dobj"],
|
|
sent_starts=[True, False, False],
|
|
)
|
|
]
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def examples(docs):
|
|
return [Example(doc, doc.copy()) for doc in docs]
|
|
|
|
|
|
@pytest.fixture
|
|
def states(docs):
|
|
return [StateClass(doc) for doc in docs]
|
|
|
|
|
|
@pytest.fixture
|
|
def tokvecs(docs, vector_size):
|
|
output = []
|
|
for doc in docs:
|
|
vec = numpy.random.uniform(-0.1, 0.1, (len(doc), vector_size))
|
|
output.append(numpy.asarray(vec))
|
|
return output
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def batch_size(docs):
|
|
return len(docs)
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def beam_width():
|
|
return 4
|
|
|
|
|
|
@pytest.fixture(params=[0.0, 0.5, 1.0])
|
|
def beam_density(request):
|
|
return request.param
|
|
|
|
|
|
@pytest.fixture
|
|
def vector_size():
|
|
return 6
|
|
|
|
|
|
@pytest.fixture
|
|
def beam(moves, examples, beam_width):
|
|
states, golds, _ = moves.init_gold_batch(examples)
|
|
return BeamBatch(moves, states, golds, width=beam_width, density=0.0)
|
|
|
|
|
|
@pytest.fixture
|
|
def scores(moves, batch_size, beam_width):
|
|
return numpy.asarray(
|
|
numpy.concatenate(
|
|
[
|
|
numpy.random.uniform(-0.1, 0.1, (beam_width, moves.n_moves))
|
|
for _ in range(batch_size)
|
|
]
|
|
),
|
|
dtype="float32",
|
|
)
|
|
|
|
|
|
def test_create_beam(beam):
|
|
pass
|
|
|
|
|
|
def test_beam_advance(beam, scores):
|
|
beam.advance(scores)
|
|
|
|
|
|
def test_beam_advance_too_few_scores(beam, scores):
|
|
n_state = sum(len(beam) for beam in beam)
|
|
scores = scores[:n_state]
|
|
with pytest.raises(IndexError):
|
|
beam.advance(scores[:-1])
|
|
|
|
|
|
def test_beam_parse(examples, beam_width):
|
|
nlp = Language()
|
|
parser = nlp.add_pipe("beam_parser")
|
|
parser.cfg["beam_width"] = beam_width
|
|
parser.add_label("nsubj")
|
|
parser.initialize(lambda: examples)
|
|
doc = nlp.make_doc("Australia is a country")
|
|
parser(doc)
|
|
|
|
|
|
@hypothesis.given(hyp=hypothesis.strategies.data())
|
|
def test_beam_density(moves, examples, beam_width, hyp):
|
|
beam_density = float(hyp.draw(hypothesis.strategies.floats(0.0, 1.0, width=32)))
|
|
states, golds, _ = moves.init_gold_batch(examples)
|
|
beam = BeamBatch(moves, states, golds, width=beam_width, density=beam_density)
|
|
n_state = sum(len(beam) for beam in beam)
|
|
scores = hyp.draw(ndarrays_of_shape((n_state, moves.n_moves)))
|
|
beam.advance(scores)
|
|
for b in beam:
|
|
beam_probs = b.probs
|
|
assert b.min_density == beam_density
|
|
assert beam_probs[-1] >= beam_probs[0] * beam_density
|