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Auto-format code with black (#10209)

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* Auto-format code with black

* add black requirement to dev dependencies and pin to 22.x

* ignore black dependency for comparison with setup.cfg

Co-authored-by: explosion-bot <explosion-bot@users.noreply.github.com>
Co-authored-by: svlandeg <svlandeg@github.com>
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github-actions[bot] 2022-02-06 16:30:30 +01:00 committed by GitHub
parent 0668a449ba
commit 91ccacea12
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6 changed files with 7 additions and 5 deletions
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1
requirements.txt
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@ -35,3 +35,4 @@ mypy==0.910
types-dataclasses>=0.1.3; python_version < "3.7" types-dataclasses>=0.1.3; python_version < "3.7"
types-mock>=0.1.1 types-mock>=0.1.1
types-requests types-requests
black>=22.0,<23.0

2
spacy/language.py
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@ -131,7 +131,7 @@ class Language:
self, self,
vocab: Union[Vocab, bool] = True, vocab: Union[Vocab, bool] = True,
*, *,
max_length: int = 10 ** 6, max_length: int = 10**6,
meta: Dict[str, Any] = {}, meta: Dict[str, Any] = {},
create_tokenizer: Optional[Callable[["Language"], Callable[[str], Doc]]] = None, create_tokenizer: Optional[Callable[["Language"], Callable[[str], Doc]]] = None,
batch_size: int = 1000, batch_size: int = 1000,

2
spacy/ml/models/multi_task.py
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@ -85,7 +85,7 @@ def get_characters_loss(ops, docs, prediction, nr_char):
target = ops.asarray(to_categorical(target_ids, n_classes=256), dtype="f") target = ops.asarray(to_categorical(target_ids, n_classes=256), dtype="f")
target = target.reshape((-1, 256 * nr_char)) target = target.reshape((-1, 256 * nr_char))
diff = prediction - target diff = prediction - target
loss = (diff ** 2).sum() loss = (diff**2).sum()
d_target = diff / float(prediction.shape[0]) d_target = diff / float(prediction.shape[0])
return loss, d_target return loss, d_target

2
spacy/pipeline/spancat.py
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@ -377,7 +377,7 @@ class SpanCategorizer(TrainablePipe):
# If the prediction is 0.9 and it's false, the gradient will be # If the prediction is 0.9 and it's false, the gradient will be
# 0.9 (0.9 - 0.0) # 0.9 (0.9 - 0.0)
d_scores = scores - target d_scores = scores - target
loss = float((d_scores ** 2).sum()) loss = float((d_scores**2).sum())
return loss, d_scores return loss, d_scores
def initialize( def initialize(

4
spacy/pipeline/textcat.py
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@ -281,7 +281,7 @@ class TextCategorizer(TrainablePipe):
bp_scores(gradient) bp_scores(gradient)
if sgd is not None: if sgd is not None:
self.finish_update(sgd) self.finish_update(sgd)
losses[self.name] += (gradient ** 2).sum() losses[self.name] += (gradient**2).sum()
return losses return losses
def _examples_to_truth( def _examples_to_truth(
@ -315,7 +315,7 @@ class TextCategorizer(TrainablePipe):
not_missing = self.model.ops.asarray(not_missing) # type: ignore not_missing = self.model.ops.asarray(not_missing) # type: ignore
d_scores = (scores - truths) / scores.shape[0] d_scores = (scores - truths) / scores.shape[0]
d_scores *= not_missing d_scores *= not_missing
mean_square_error = (d_scores ** 2).sum(axis=1).mean() mean_square_error = (d_scores**2).sum(axis=1).mean()
return float(mean_square_error), d_scores return float(mean_square_error), d_scores
def add_label(self, label: str) -> int: def add_label(self, label: str) -> int:

1
spacy/tests/package/test_requirements.py
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@ -12,6 +12,7 @@ def test_build_dependencies():
"flake8", "flake8",
"hypothesis", "hypothesis",
"pre-commit", "pre-commit",
"black",
"mypy", "mypy",
"types-dataclasses", "types-dataclasses",
"types-mock", "types-mock",