spaCy/spacy/tests/parser/test_neural_parser.py
Sofie Van Landeghem 8e7557656f
Renaming gold & annotation_setter (#6042)
* version bump to 3.0.0a16

* rename "gold" folder to "training"

* rename 'annotation_setter' to 'set_extra_annotations'

* formatting
2020-09-09 10:31:03 +02:00

111 lines
2.8 KiB
Python

import pytest
from spacy import registry
from spacy.training import Example
from spacy.vocab import Vocab
from spacy.pipeline._parser_internals.arc_eager import ArcEager
from spacy.pipeline.transition_parser import Parser
from spacy.tokens.doc import Doc
from thinc.api import Model
from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
@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.make_from_config(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.make_from_config(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.make_from_config(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.make_from_config(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)