mirror of
https://github.com/explosion/spaCy.git
synced 2025-01-31 19:54:10 +03:00
c0f4a1e43b
* verbose and tag_map options * adding init_tok2vec option and only changing the tok2vec that is specified * adding omit_extra_lookups and verifying textcat config * wip * pretrain bugfix * add replace and resume options * train_textcat fix * raw text functionality * improve UX when KeyError or when input data can't be parsed * avoid unnecessary access to goldparse in TextCat pipe * save performance information in nlp.meta * add noise_level to config * move nn_parser's defaults to config file * multitask in config - doesn't work yet * scorer offering both F and AUC options, need to be specified in config * add textcat verification code from old train script * small fixes to config files * clean up * set default config for ner/parser to allow create_pipe to work as before * two more test fixes * small fixes * cleanup * fix NER pickling + additional unit test * create_pipe as before
92 lines
2.1 KiB
Python
92 lines
2.1 KiB
Python
import pytest
|
|
from spacy.pipeline.defaults import default_parser, default_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
|
|
from thinc.api import 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():
|
|
tok2vec = default_tok2vec()
|
|
tok2vec.initialize()
|
|
return tok2vec
|
|
|
|
|
|
@pytest.fixture
|
|
def parser(vocab, arc_eager):
|
|
config = {"learn_tokens": False, "min_action_freq": 30, "beam_width": 1, "beam_update_prob": 1.0}
|
|
return Parser(vocab, model=default_parser(), moves=arc_eager, **config)
|
|
|
|
|
|
@pytest.fixture
|
|
def model(arc_eager, tok2vec, vocab):
|
|
model = default_parser()
|
|
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 GoldParse(doc, 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):
|
|
parser.model = Parser(vocab, model=default_parser(), moves=parser.moves).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
|
|
|
|
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)
|