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Fix mypy issues

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richardpaulhudson 2022-12-09 16:52:19 +01:00
parent 7cd9a99854
commit 4b598a1b54
2 changed files with 5 additions and 5 deletions
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2
spacy/pipeline/edit_tree_lemmatizer.py
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@ -163,7 +163,7 @@ class EditTreeLemmatizer(TrainablePipe):
for i, doc_d_tree_scores in enumerate(d_tree_scores): for i, doc_d_tree_scores in enumerate(d_tree_scores):
eg_lowercasing_flags = lowercasing_flags[i] eg_lowercasing_flags = lowercasing_flags[i]
eg_d_lowercasing_flags, eg_lowercasing_loss = lowercasing_loss_func( eg_d_lowercasing_flags, eg_lowercasing_loss = lowercasing_loss_func(
eg_lowercasing_flags, self.model.ops.asarray2f(lowercasing_truths[i]) eg_lowercasing_flags, self.model.ops.asarray2i(lowercasing_truths[i])
) )
doc_d_scores = self.model.ops.xp.hstack( doc_d_scores = self.model.ops.xp.hstack(
[ [

8
spacy/tests/pipeline/test_edit_tree_lemmatizer.py
View File

@ -1,4 +1,4 @@
from typing import cast, Dict from typing import cast, Dict, Optional
import pickle import pickle
import pytest import pytest
from hypothesis import given from hypothesis import given
@ -193,7 +193,7 @@ def test_incomplete_data(lowercasing: bool):
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]))
optimizer = nlp.initialize(get_examples=lambda: train_examples) optimizer = nlp.initialize(get_examples=lambda: train_examples)
for i in range(50): for i in range(50):
losses: Dict[Floats2d, Floats2d] = {} losses: Optional[Dict[str, float]] = {}
nlp.update(train_examples, sgd=optimizer, losses=losses) nlp.update(train_examples, sgd=optimizer, losses=losses)
# test the trained model # test the trained model
@ -222,7 +222,7 @@ def test_overfitting_IO(lowercasing: bool):
optimizer = nlp.initialize(get_examples=lambda: train_examples) optimizer = nlp.initialize(get_examples=lambda: train_examples)
for i in range(50): for i in range(50):
losses: Dict[Floats2d, Floats2d] = {} losses: Optional[Dict[str, float]] = {}
nlp.update(train_examples, sgd=optimizer, losses=losses) nlp.update(train_examples, sgd=optimizer, losses=losses)
test_text = "She likes blue eggs" test_text = "She likes blue eggs"
@ -411,7 +411,7 @@ def test_lowercasing(lowercasing: bool):
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]))
optimizer = nlp.initialize(get_examples=lambda: train_examples) optimizer = nlp.initialize(get_examples=lambda: train_examples)
for _ in range(50): for _ in range(50):
losses: Dict[Floats2d, Floats2d] = {} losses: Optional[Dict[str, float]] = {}
nlp.update(train_examples, sgd=optimizer, losses=losses) nlp.update(train_examples, sgd=optimizer, losses=losses)
# test the trained model # test the trained model