spaCy/spacy/tests/parser/test_neural_parser.py

87 lines
1.9 KiB
Python

import pytest
from spacy.ml.component_models import Tok2Vec
from spacy.vocab import Vocab
from spacy.syntax.arc_eager import ArcEager
from spacy.syntax.nn_parser import Parser
from spacy.tokens.doc import Doc
from spacy.gold import GoldParse
@pytest.fixture
def vocab():
return Vocab()
@pytest.fixture
def arc_eager(vocab):
actions = ArcEager.get_actions(left_labels=["L"], right_labels=["R"])
return ArcEager(vocab.strings, actions)
@pytest.fixture
def tok2vec():
tok2vec = Tok2Vec(8, 100)
tok2vec.initialize()
return tok2vec
@pytest.fixture
def parser(vocab, arc_eager):
return Parser(vocab, moves=arc_eager, model=None)
@pytest.fixture
def model(arc_eager, tok2vec):
return Parser.Model(arc_eager.n_moves, token_vector_width=tok2vec.get_dim("nO"))[0]
@pytest.fixture
def doc(vocab):
return Doc(vocab, words=["a", "b", "c"])
@pytest.fixture
def gold(doc):
return GoldParse(doc, heads=[1, 1, 1], deps=["L", "ROOT", "R"])
def test_can_init_nn_parser(parser):
assert parser.model is None
def test_build_model(parser):
parser.model = Parser.Model(parser.moves.n_moves, hist_size=0)[0]
assert parser.model is not None
def test_predict_doc(parser, tok2vec, model, doc):
doc.tensor = tok2vec.predict([doc])[0]
parser.model = model
parser(doc)
def test_update_doc(parser, model, doc, gold):
parser.model = model
def optimize(key, weights, gradient):
weights -= 0.001 * gradient
return weights, gradient
parser.update((doc, gold), sgd=optimize)
@pytest.mark.xfail
def test_predict_doc_beam(parser, model, doc):
parser.model = model
parser(doc, beam_width=32, beam_density=0.001)
@pytest.mark.xfail
def test_update_doc_beam(parser, model, doc, gold):
parser.model = model
def optimize(weights, gradient, key=None):
weights -= 0.001 * gradient
parser.update_beam((doc, gold), sgd=optimize)