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Tidy up and auto-format [ci skip]

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This commit is contained in:
Ines Montani 2020-09-13 10:55:36 +02:00
parent a5633b205f
commit febb99916d
8 changed files with 19 additions and 12 deletions
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3
spacy/cli/debug_model.py
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@ -5,7 +5,8 @@ from thinc.api import require_gpu, fix_random_seed, set_dropout_rate, Adam
from thinc.api import Model, data_validation from thinc.api import Model, data_validation
import typer import typer
from ._util import Arg, Opt, debug_cli, show_validation_error, parse_config_overrides, string_to_list from ._util import Arg, Opt, debug_cli, show_validation_error
from ._util import parse_config_overrides, string_to_list
from .. import util from .. import util

2
spacy/cli/init_model.py
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@ -277,7 +277,7 @@ def read_vectors(msg: Printer, vectors_loc: Path, truncate_vectors: int):
def ensure_shape(lines): def ensure_shape(lines):
"""Ensure that the first line of the data is the vectors shape. """Ensure that the first line of the data is the vectors shape.
If it's not, we read in the data and output the shape as the first result, If it's not, we read in the data and output the shape as the first result,
so that the reader doesn't have to deal with the problem. so that the reader doesn't have to deal with the problem.
""" """

3
spacy/language.py
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@ -244,7 +244,8 @@ class Language:
self._config["nlp"]["disabled"] = list(self.disabled) self._config["nlp"]["disabled"] = list(self.disabled)
self._config["components"] = pipeline self._config["components"] = pipeline
if not self._config["training"].get("score_weights"): if not self._config["training"].get("score_weights"):
self._config["training"]["score_weights"] = combine_score_weights(score_weights) combined_score_weights = combine_score_weights(score_weights)
self._config["training"]["score_weights"] = combined_score_weights
if not srsly.is_json_serializable(self._config): if not srsly.is_json_serializable(self._config):
raise ValueError(Errors.E961.format(config=self._config)) raise ValueError(Errors.E961.format(config=self._config))
return self._config return self._config

5
spacy/tests/doc/test_add_entities.py
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@ -9,7 +9,10 @@ from spacy.pipeline.ner import DEFAULT_NER_MODEL
def _ner_example(ner): def _ner_example(ner):
doc = Doc(ner.vocab, words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"]) doc = Doc(
ner.vocab,
words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"],
)
gold = {"entities": [(0, 3, "PERSON"), (19, 25, "LOC")]} gold = {"entities": [(0, 3, "PERSON"), (19, 25, "LOC")]}
return Example.from_dict(doc, gold) return Example.from_dict(doc, gold)

8
spacy/tests/matcher/test_dependency_matcher.py
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@ -78,7 +78,7 @@ def patterns(en_vocab):
"REL_OP": ">", "REL_OP": ">",
"RIGHT_ID": "fox", "RIGHT_ID": "fox",
"RIGHT_ATTRS": {"ORTH": "fox"}, "RIGHT_ATTRS": {"ORTH": "fox"},
} },
] ]
pattern5 = [ pattern5 = [
@ -233,9 +233,7 @@ def test_dependency_matcher_callback(en_vocab, doc):
assert matches == matches2 assert matches == matches2
@pytest.mark.parametrize( @pytest.mark.parametrize("op,num_matches", [(".", 8), (".*", 20), (";", 8), (";*", 20)])
"op,num_matches", [(".", 8), (".*", 20), (";", 8), (";*", 20),]
)
def test_dependency_matcher_precedence_ops(en_vocab, op, num_matches): def test_dependency_matcher_precedence_ops(en_vocab, op, num_matches):
# two sentences to test that all matches are within the same sentence # two sentences to test that all matches are within the same sentence
doc = get_doc( doc = get_doc(
@ -248,7 +246,7 @@ def test_dependency_matcher_precedence_ops(en_vocab, op, num_matches):
for text in ["a", "b", "c", "d", "e"]: for text in ["a", "b", "c", "d", "e"]:
pattern = [ pattern = [
{"RIGHT_ID": "1", "RIGHT_ATTRS": {"ORTH": text}}, {"RIGHT_ID": "1", "RIGHT_ATTRS": {"ORTH": text}},
{"LEFT_ID": "1", "REL_OP": op, "RIGHT_ID": "2", "RIGHT_ATTRS": {},}, {"LEFT_ID": "1", "REL_OP": op, "RIGHT_ID": "2", "RIGHT_ATTRS": {}},
] ]
matcher = DependencyMatcher(en_vocab) matcher = DependencyMatcher(en_vocab)
matcher.add("A", [pattern]) matcher.add("A", [pattern])

5
spacy/tests/parser/test_add_label.py
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@ -54,7 +54,10 @@ def _parser_example(parser):
def _ner_example(ner): def _ner_example(ner):
doc = Doc(ner.vocab, words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"]) doc = Doc(
ner.vocab,
words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"],
)
gold = {"entities": [(0, 3, "PERSON"), (19, 25, "LOC")]} gold = {"entities": [(0, 3, "PERSON"), (19, 25, "LOC")]}
return Example.from_dict(doc, gold) return Example.from_dict(doc, gold)

3
spacy/tests/pipeline/test_senter.py
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@ -30,9 +30,10 @@ TRAIN_DATA = [
), ),
] ]
def test_begin_training_examples(): def test_begin_training_examples():
nlp = Language() nlp = Language()
senter = nlp.add_pipe("senter") nlp.add_pipe("senter")
train_examples = [] train_examples = []
for t in TRAIN_DATA: for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1])) train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))

2
spacy/tests/pipeline/test_textcat.py
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@ -89,7 +89,7 @@ def test_no_label():
def test_implicit_label(): def test_implicit_label():
nlp = Language() nlp = Language()
textcat = nlp.add_pipe("textcat") nlp.add_pipe("textcat")
train_examples = [] train_examples = []
for t in TRAIN_DATA: for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1])) train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))