spaCy/spacy/tests/parser/test_add_label.py
Ines Montani 412dbb1f38
Remove dead and/or deprecated code (#5710)
* Remove dead and/or deprecated code

* Remove n_threads

Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-07-06 13:06:25 +02:00

101 lines
3.0 KiB
Python

import pytest
from thinc.api import Adam, fix_random_seed
from spacy.attrs import NORM
from spacy.vocab import Vocab
from spacy.gold import Example
from spacy.pipeline.defaults import default_parser, default_ner
from spacy.tokens import Doc
from spacy.pipeline import DependencyParser, EntityRecognizer
@pytest.fixture
def vocab():
return Vocab(lex_attr_getters={NORM: lambda s: s})
@pytest.fixture
def parser(vocab):
config = {
"learn_tokens": False,
"min_action_freq": 30,
"beam_width": 1,
"beam_update_prob": 1.0,
}
parser = DependencyParser(vocab, default_parser(), **config)
return parser
def test_init_parser(parser):
pass
def _train_parser(parser):
fix_random_seed(1)
parser.add_label("left")
parser.begin_training([], **parser.cfg)
sgd = Adam(0.001)
for i in range(5):
losses = {}
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
gold = {"heads": [1, 1, 3, 3], "deps": ["left", "ROOT", "left", "ROOT"]}
example = Example.from_dict(doc, gold)
parser.update([example], sgd=sgd, losses=losses)
return parser
def test_add_label(parser):
parser = _train_parser(parser)
parser.add_label("right")
sgd = Adam(0.001)
for i in range(100):
losses = {}
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
gold = {"heads": [1, 1, 3, 3], "deps": ["right", "ROOT", "left", "ROOT"]}
example = Example.from_dict(doc, gold)
parser.update([example], sgd=sgd, losses=losses)
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
doc = parser(doc)
assert doc[0].dep_ == "right"
assert doc[2].dep_ == "left"
def test_add_label_deserializes_correctly():
config = {
"learn_tokens": False,
"min_action_freq": 30,
"beam_width": 1,
"beam_update_prob": 1.0,
}
ner1 = EntityRecognizer(Vocab(), default_ner(), **config)
ner1.add_label("C")
ner1.add_label("B")
ner1.add_label("A")
ner1.begin_training([])
ner2 = EntityRecognizer(Vocab(), default_ner(), **config)
# the second model needs to be resized before we can call from_bytes
ner2.model.attrs["resize_output"](ner2.model, ner1.moves.n_moves)
ner2.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)
@pytest.mark.parametrize(
"pipe_cls,n_moves,model",
[(DependencyParser, 5, default_parser()), (EntityRecognizer, 4, default_ner())],
)
def test_add_label_get_label(pipe_cls, n_moves, model):
"""Test that added labels are returned correctly. This test was added to
test for a bug in DependencyParser.labels that'd cause it to fail when
splitting the move names.
"""
labels = ["A", "B", "C"]
pipe = pipe_cls(Vocab(), model)
for label in labels:
pipe.add_label(label)
assert len(pipe.move_names) == len(labels) * n_moves
pipe_labels = sorted(list(pipe.labels))
assert pipe_labels == labels