Feature pre commit yaml (#145)

* added pre-commit yaml from PL

* added yapf and flake8 to pre-commit config

* manifest

* names

* formatting

Co-authored-by: Jirka Borovec <jirka.borovec@seznam.cz>

.pre-commit-config.yaml

@@ -0,0 +1,50 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+default_language_version:
+ python: python3.8
+
+repos:
+ - repo: https://github.com/pre-commit/pre-commit-hooks
+ rev: v2.3.0
+ hooks:
+ - id: trailing-whitespace
+ - id: end-of-file-fixer
+
+ - repo: local
+ hooks:
+ - id: isort
+ name: imports
+ entry: isort
+ args: [--settings-path, ./pyproject.toml]
+ language: system
+ types: [python]
+
+ - id: yapf
+ name: formatting
+ entry: yapf
+ args: [ --parallel ]
+ language: system
+ types: [python]
+
+ - id: flake8
+ name: PEP8
+ entry: flake8
+ language: system
+ types: [python]
+
+ - repo: https://github.com/pre-commit/mirrors-mypy
+ rev: v0.790
+ hooks:
+ - id: mypy

MANIFEST.in

@@ -29,6 +29,7 @@ recursive-exclude requirements *.txt
 
 # Exclude build configs
 exclude *.yml
+exclude *.yaml
 exclude Makefile
 
 prune .git

tests/functional/test_retrieval.py

@@ -73,11 +73,11 @@ def test_metrics_output_values_with_k(sklearn_metric, torch_metric, size, k):
  assert torch.allclose(sk.float(), tm.float())
 
 
-@pytest.mark.parametrize(['torch_metric'], [
+@pytest.mark.parametrize(['torch_metric'], (
  [retrieval_average_precision],
  [retrieval_reciprocal_rank],
- [retrieval_precision]
-])
+ [retrieval_precision],
+))
 def test_input_dtypes(torch_metric) -> None:
  """ Check wrong input dtypes are managed correctly. """
  device = 'cuda' if torch.cuda.is_available() else 'cpu'
@@ -104,11 +104,11 @@ def test_input_dtypes(torch_metric) -> None:
  assert torch.allclose(torch_metric(preds=preds, target=target), torch.tensor(0.0))
 
 
-@pytest.mark.parametrize(['torch_metric'], [
+@pytest.mark.parametrize(['torch_metric'], (
  [retrieval_average_precision],
  [retrieval_reciprocal_rank],
- [retrieval_precision]
-])
+ [retrieval_precision],
+))
 def test_input_shapes(torch_metric) -> None:
  """ Check wrong input shapes are managed correctly. """
  device = 'cuda' if torch.cuda.is_available() else 'cpu'
@@ -129,9 +129,7 @@ def test_input_shapes(torch_metric) -> None:
 
 
 # test metrics using top K parameter
-@pytest.mark.parametrize(['torch_metric'], [
- [retrieval_precision]
-])
+@pytest.mark.parametrize(['torch_metric'], ([retrieval_precision], ))
 @pytest.mark.parametrize('k', [-1, 1.0])
 def test_input_params(torch_metric, k) -> None:
  """ Check wrong input shapes are managed correctly. """

tests/retrieval/helpers.py

@@ -40,7 +40,7 @@ def _test_retrieval_against_sklearn(
  size: int,
  n_documents: int,
  query_without_relevant_docs_options: str,
- **kwargs
+ **kwargs,
 ) -> None:
  """ Compare PL metrics to standard version. """
  metric = torch_metric(query_without_relevant_docs=query_without_relevant_docs_options, **kwargs)

tests/retrieval/test_precision.py

@@ -31,12 +31,7 @@ def _precision_at_k(target: np.array, preds: np.array, k: int = None):
 def test_results(size, n_documents, query_without_relevant_docs_options, k):
  """ Test metrics are computed correctly. """
  _test_retrieval_against_sklearn(
- _precision_at_k,
- RetrievalPrecision,
- size,
- n_documents,
- query_without_relevant_docs_options,
- k=k
+ _precision_at_k, RetrievalPrecision, size, n_documents, query_without_relevant_docs_options, k=k
  )
 
 

torchmetrics/wrappers/bootstrapping.py

@@ -22,7 +22,10 @@
 from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_7
 
 
-def _bootstrap_sampler(size: int, sampling_strategy: str = 'poisson') -> Tensor:
+def _bootstrap_sampler(
+ size: int,
+ sampling_strategy: str = 'poisson',
+) -> Tensor:
  """ Resample a tensor along its first dimension with replacement
  Args:
  size: number of samples