mirror of https://github.com/explosion/spaCy.git
111 lines
2.8 KiB
Python
111 lines
2.8 KiB
Python
import pytest
|
|
from thinc.api import Model
|
|
|
|
from spacy import registry
|
|
from spacy.pipeline._parser_internals.arc_eager import ArcEager
|
|
from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
|
|
from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
|
|
from spacy.pipeline.transition_parser import Parser
|
|
from spacy.tokens.doc import Doc
|
|
from spacy.training import Example
|
|
from spacy.vocab import Vocab
|
|
|
|
|
|
@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():
|
|
cfg = {"model": DEFAULT_TOK2VEC_MODEL}
|
|
tok2vec = registry.resolve(cfg, validate=True)["model"]
|
|
tok2vec.initialize()
|
|
return tok2vec
|
|
|
|
|
|
@pytest.fixture
|
|
def parser(vocab, arc_eager):
|
|
config = {
|
|
"learn_tokens": False,
|
|
"min_action_freq": 30,
|
|
"update_with_oracle_cut_size": 100,
|
|
}
|
|
cfg = {"model": DEFAULT_PARSER_MODEL}
|
|
model = registry.resolve(cfg, validate=True)["model"]
|
|
return Parser(vocab, model, moves=arc_eager, **config)
|
|
|
|
|
|
@pytest.fixture
|
|
def model(arc_eager, tok2vec, vocab):
|
|
cfg = {"model": DEFAULT_PARSER_MODEL}
|
|
model = registry.resolve(cfg, validate=True)["model"]
|
|
model.attrs["resize_output"](model, arc_eager.n_moves)
|
|
model.initialize()
|
|
return model
|
|
|
|
|
|
@pytest.fixture
|
|
def doc(vocab):
|
|
return Doc(vocab, words=["a", "b", "c"])
|
|
|
|
|
|
@pytest.fixture
|
|
def gold(doc):
|
|
return {"heads": [1, 1, 1], "deps": ["L", "ROOT", "R"]}
|
|
|
|
|
|
def test_can_init_nn_parser(parser):
|
|
assert isinstance(parser.model, Model)
|
|
|
|
|
|
def test_build_model(parser, vocab):
|
|
config = {
|
|
"learn_tokens": False,
|
|
"min_action_freq": 0,
|
|
"update_with_oracle_cut_size": 100,
|
|
}
|
|
cfg = {"model": DEFAULT_PARSER_MODEL}
|
|
model = registry.resolve(cfg, validate=True)["model"]
|
|
parser.model = Parser(vocab, model=model, moves=parser.moves, **config).model
|
|
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
|
|
|
|
example = Example.from_dict(doc, gold)
|
|
parser.update([example], sgd=optimize)
|
|
|
|
|
|
@pytest.mark.skip(reason="No longer supported")
|
|
def test_predict_doc_beam(parser, model, doc):
|
|
parser.model = model
|
|
parser(doc, beam_width=32, beam_density=0.001)
|
|
|
|
|
|
@pytest.mark.skip(reason="No longer supported")
|
|
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)
|