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Add xfail test for #3433. Improve test for add label.

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This commit is contained in:
Matthew Honnibal 2019-03-23 12:35:29 +01:00
parent 6b6e9b638e
commit 444a3abfe5
1 changed files with 30 additions and 16 deletions
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46
spacy/tests/parser/test_add_label.py
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@ -8,7 +8,8 @@ from spacy.attrs import NORM
from spacy.gold import GoldParse
from spacy.vocab import Vocab
from spacy.tokens import Doc
from spacy.pipeline import DependencyParser
from spacy.pipeline import DependencyParser, EntityRecognizer
from spacy.util import fix_random_seed
@pytest.fixture
@ -19,18 +20,6 @@ def vocab():
@pytest.fixture
def parser(vocab):
parser = DependencyParser(vocab)
parser.cfg["token_vector_width"] = 8
parser.cfg["hidden_width"] = 30
parser.cfg["hist_size"] = 0
parser.add_label("left")
parser.begin_training([], **parser.cfg)
sgd = Adam(NumpyOps(), 0.001)
for i in range(10):
losses = {}
doc = Doc(vocab, words=["a", "b", "c", "d"])
gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=["left", "ROOT", "left", "ROOT"])
parser.update([doc], [gold], sgd=sgd, losses=losses)
return parser
@ -38,10 +27,22 @@ def test_init_parser(parser):
pass
# TODO: This is flakey, because it depends on what the parser first learns.
# TODO: This now seems to be implicated in segfaults. Not sure what's up!
@pytest.mark.skip
def _train_parser(parser):
fix_random_seed(1)
parser.add_label("left")
parser.begin_training([], **parser.cfg)
sgd = Adam(NumpyOps(), 0.001)
for i in range(10):
losses = {}
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=["left", "ROOT", "left", "ROOT"])
parser.update([doc], [gold], sgd=sgd, losses=losses)
return parser
def test_add_label(parser):
parser = _train_parser(parser)
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
doc = parser(doc)
assert doc[0].head.i == 1
@ -69,3 +70,16 @@ def test_add_label(parser):
doc = parser(doc)
assert doc[0].dep_ == "right"
assert doc[2].dep_ == "left"
@pytest.mark.xfail
def test_add_label_deserializes_correctly():
ner1 = EntityRecognizer(Vocab())
ner1.add_label("C")
ner1.add_label("B")
ner1.add_label("A")
ner1.begin_training([])
ner2 = EntityRecognizer(Vocab()).from_bytes(ner1.to_bytes())
assert ner1.moves.n_moves == ner2.moves.n_moves
for i in range(ner1.moves.n_moves):
assert ner1.moves.get_class_name(i) == ner2.moves.get_class_name(i)