spaCy/spacy/tests/parser/test_nn_beam.py

101 lines
2.1 KiB
Python

import pytest
import numpy
from spacy.vocab import Vocab
from spacy.language import Language
from spacy.pipeline import DependencyParser
from spacy.syntax.arc_eager import ArcEager
from spacy.tokens import Doc
from spacy.syntax._beam_utils import ParserBeam
from spacy.syntax.stateclass import StateClass
from spacy.gold import GoldParse
@pytest.fixture
def vocab():
return Vocab()
@pytest.fixture
def moves(vocab):
aeager = ArcEager(vocab.strings, {})
aeager.add_action(2, "nsubj")
aeager.add_action(3, "dobj")
aeager.add_action(2, "aux")
return aeager
@pytest.fixture
def docs(vocab):
return [Doc(vocab, words=["Rats", "bite", "things"])]
@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
def golds(docs):
return [GoldParse(doc) for doc in docs]
@pytest.fixture
def batch_size(docs):
return len(docs)
@pytest.fixture
def beam_width():
return 4
@pytest.fixture
def vector_size():
return 6
@pytest.fixture
def beam(moves, states, golds, beam_width):
return ParserBeam(moves, states, golds, width=beam_width, density=0.0)
@pytest.fixture
def scores(moves, batch_size, beam_width):
return [
numpy.asarray(
numpy.random.uniform(-0.1, 0.1, (batch_size, moves.n_moves)), dtype="f"
)
for _ in range(batch_size)
]
def test_create_beam(beam):
pass
def test_beam_advance(beam, scores):
beam.advance(scores)
def test_beam_advance_too_few_scores(beam, scores):
with pytest.raises(IndexError):
beam.advance(scores[:-1])
def test_beam_parse():
nlp = Language()
nlp.add_pipe(DependencyParser(nlp.vocab), name="parser")
nlp.parser.add_label("nsubj")
nlp.parser.begin_training([], token_vector_width=8, hidden_width=8)
doc = nlp.make_doc("Australia is a country")
nlp.parser(doc, beam_width=2)