From 34d3720fc064b43aa3c18008bffc8cd3126842a4 Mon Sep 17 00:00:00 2001 From: thomashacker Date: Fri, 31 Mar 2023 12:00:14 +0200 Subject: [PATCH] Fix black and mypy issues --- spacy/pipeline/textcat.py | 4 ++-- spacy/training/batchers.py | 4 ++-- spacy/training/loop.py | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/spacy/pipeline/textcat.py b/spacy/pipeline/textcat.py index ceac76b85..7c58ee2b7 100644 --- a/spacy/pipeline/textcat.py +++ b/spacy/pipeline/textcat.py @@ -295,7 +295,7 @@ class TextCategorizer(TrainablePipe): """ if losses is None: losses = {} - losses.setdefault(self.name+"_rehearse", 0.0) + losses.setdefault(self.name + "_rehearse", 0.0) if self._rehearsal_model is None: return losses validate_examples(examples, "TextCategorizer.rehearse") @@ -311,7 +311,7 @@ class TextCategorizer(TrainablePipe): bp_scores(gradient) if sgd is not None: self.finish_update(sgd) - losses[self.name+"_rehearse"] += (gradient**2).sum() + losses[self.name + "_rehearse"] += (gradient**2).sum() return losses def _examples_to_truth( diff --git a/spacy/training/batchers.py b/spacy/training/batchers.py index d9aa04e32..9a2d56872 100644 --- a/spacy/training/batchers.py +++ b/spacy/training/batchers.py @@ -2,7 +2,7 @@ from typing import Union, Iterable, Sequence, TypeVar, List, Callable, Iterator from typing import Optional, Any from functools import partial import itertools -from thinc.schedules import Schedule +from thinc.schedules import Schedule #type:ignore[attr-defined] from ..util import registry, minibatch @@ -221,7 +221,7 @@ def _batch_by_length( if not batch: batch.append(i) elif length * (len(batch) + 1) <= max_words: - batch.append(i) + batch.append(i) else: batches.append(batch) batch = [i] diff --git a/spacy/training/loop.py b/spacy/training/loop.py index 3b9d13a4f..da9ffb9bd 100644 --- a/spacy/training/loop.py +++ b/spacy/training/loop.py @@ -241,7 +241,7 @@ def train_while_improving( score, other_scores = evaluate() else: score, other_scores = evaluate() - optimizer.last_score = score + optimizer.last_score = score #type:ignore[attr-defined] results.append((score, step)) is_best_checkpoint = score == max(results)[0] else: