lint: freeze & run Black version 24.02 (awslabs#3131)

*Issue #, if available:*
set a single black version to ensure reproducibility
UPDATE: seem that the latest Black would need to be applied

*Description of changes:*
freeze Black version, but the better way is in awslabs#3111


By submitting this pull request, I confirm that you can use, modify,
copy, and redistribute this contribution, under the terms of your
choice.


**Please tag this pr with at least one of these labels to make our
release process faster:** BREAKING, new feature, bug fix, other change,
dev setup

cc: @jaheba @kashif @lostella

.github/workflows/style_type_checks.yml

@@ -9,22 +9,16 @@ jobs:
  steps:
  - uses: actions/checkout@v3
  - uses: extractions/setup-just@v1
- env:
- GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- - name: Set up Python 3.8
- uses: actions/setup-python@v4
- with:
- python-version: '3.8'
+ - uses: actions/setup-python@v4
  - name: Install dependencies
  run: |
  pip install .
- pip install click black mypy
- pip install types-python-dateutil
- pip install types-waitress
- pip install types-PyYAML
- - name: Style and type checks
- run: |
- just black
- just mypy
+ # todo: install also `black[jupyter]`
+ pip install click "black==24.02" "mypy==1.8.0" \
+ types-python-dateutil types-waitress types-PyYAML
+ - name: Style check
+ run: just black
+ - name: Type check
+ run: just mypy
  - name: Check license headers
  run: just license

Justfile

@@ -34,7 +34,7 @@ release:
  python setup.py sdist
 
 black:
- black --check src test examples
+ black --check --color src test examples
 
 mypy:
  python setup.py type_check

examples/anomaly_detection.py

@@ -14,6 +14,7 @@
 """
 This example shows how to do anomaly detection with DeepAR.
 The model is first trained and then time-points with the largest negative log-likelihood are plotted.
+
 """
 import numpy as np
 from itertools import islice

examples/warm_start.py

@@ -13,6 +13,7 @@
 
 """
 This example show how to intialize the network with parameters from a model that was previously trained.
+
 """
 
 from gluonts.dataset.repository import get_dataset, dataset_recipes

pyproject.toml

@@ -1,4 +1,5 @@
 [tool.black]
+target-version = ['py38']
 line-length = 79
 
 [tool.pytest.ini_options]

src/gluonts/ext/rotbaum/_model.py

@@ -142,7 +142,7 @@ def fit(
  # XGBoost, but True if one uses lightgbm.
  model_is_already_trained: bool = False, # True if there is no need to
  # train self.model
- **kwargs
+ **kwargs,
  ):
  """
  Fits self.model and partitions R^n into cells.

src/gluonts/ext/rotbaum/_preprocess.py

@@ -49,7 +49,7 @@ def __init__(
  max_n_datapts: int = 400000,
  seed: Optional[int] = None,
  num_samples: Optional[int] = None,
- **kwargs
+ **kwargs,
  ):
  """
  Parameters
@@ -296,7 +296,7 @@ def __init__(
  one_hot_encode: bool = True,
  subtract_mean: bool = True,
  count_nans: bool = False,
- **kwargs
+ **kwargs,
  ):
  if one_hot_encode:
  assert cardinality != "ignore" or (
@@ -313,7 +313,7 @@ def __init__(
  stratify_targets=stratify_targets,
  n_ignore_last=n_ignore_last,
  num_samples=num_samples,
- **kwargs
+ **kwargs,
  )
 
  self.use_feat_static_real = use_feat_static_real

src/gluonts/model/evaluation.py

@@ -104,7 +104,7 @@ def evaluate_forecasts_raw(
  batch_size: int = 100,
  mask_invalid_label: bool = True,
  allow_nan_forecast: bool = False,
- seasonality: Optional[int] = None
+ seasonality: Optional[int] = None,
 ) -> dict:
  """
  Evaluate ``forecasts`` by comparing them with ``test_data``, according
@@ -189,7 +189,7 @@ def evaluate_forecasts(
  batch_size: int = 100,
  mask_invalid_label: bool = True,
  allow_nan_forecast: bool = False,
- seasonality: Optional[int] = None
+ seasonality: Optional[int] = None,
 ) -> pd.DataFrame:
  """
  Evaluate ``forecasts`` by comparing them with ``test_data``, according
@@ -243,7 +243,7 @@ def evaluate_model(
  batch_size: int = 100,
  mask_invalid_label: bool = True,
  allow_nan_forecast: bool = False,
- seasonality: Optional[int] = None
+ seasonality: Optional[int] = None,
 ) -> pd.DataFrame:
  """
  Evaluate ``model`` when applied to ``test_data``, according

src/gluonts/model/forecast_generator.py

@@ -94,7 +94,7 @@ def __call__(
  input_names: List[str],
  output_transform: Optional[OutputTransform],
  num_samples: Optional[int],
- **kwargs
+ **kwargs,
  ) -> Iterator[Forecast]:
  raise NotImplementedError()
 
@@ -111,7 +111,7 @@ def __call__(
  input_names: List[str],
  output_transform: Optional[OutputTransform],
  num_samples: Optional[int],
- **kwargs
+ **kwargs,
  ) -> Iterator[Forecast]:
  for batch in inference_data_loader:
  inputs = select(input_names, batch, ignore_missing=True)
@@ -155,7 +155,7 @@ def __call__(
  input_names: List[str],
  output_transform: Optional[OutputTransform],
  num_samples: Optional[int],
- **kwargs
+ **kwargs,
  ) -> Iterator[Forecast]:
  for batch in inference_data_loader:
  inputs = select(input_names, batch, ignore_missing=True)
@@ -205,7 +205,7 @@ def __call__(
  input_names: List[str],
  output_transform: Optional[OutputTransform],
  num_samples: Optional[int],
- **kwargs
+ **kwargs,
  ) -> Iterator[Forecast]:
  for batch in inference_data_loader:
  inputs = select(input_names, batch, ignore_missing=True)

src/gluonts/mx/block/regularization.py

@@ -49,7 +49,7 @@ def __init__(
  weight: Optional[float] = None,
  batch_axis: int = 1,
  time_axis: int = 0,
- **kwargs
+ **kwargs,
  ):
  super().__init__(weight, batch_axis, **kwargs)
  self._alpha = alpha
@@ -121,7 +121,7 @@ def __init__(
  weight: Optional[float] = None,
  batch_axis: int = 1,
  time_axis: int = 0,
- **kwargs
+ **kwargs,
  ):
  super().__init__(weight, batch_axis, **kwargs)
  self._beta = beta

src/gluonts/mx/block/scaler.py

@@ -121,7 +121,7 @@ def __init__(
  minimum_scale: float = 1e-10,
  default_scale: Optional[float] = None,
  *args,
- **kwargs
+ **kwargs,
  ):
  super().__init__(*args, **kwargs)
  self.minimum_scale = minimum_scale

src/gluonts/mx/block/sndense.py

@@ -51,7 +51,7 @@ def __init__(
  dtype="float32",
  num_power_iter: int = 1,
  ctx: Optional[mx.Context] = None,
- **kwargs
+ **kwargs,
  ):
  super().__init__(**kwargs)
  self._coeff = coeff

src/gluonts/mx/distribution/iresnet.py

@@ -65,7 +65,7 @@ def __init__(
  coeff: float = 0.9,
  use_caching: bool = True,
  *args,
- **kwargs
+ **kwargs,
  ):
  super().__init__(*args, **kwargs)
  assert len(event_shape) == 1

src/gluonts/mx/distribution/lds.py

@@ -65,7 +65,7 @@ def _safe_split(x, num_outputs, axis, squeeze_axis, *args, **kwargs):
  num_outputs=num_outputs,
  squeeze_axis=squeeze_axis,
  *args,
- **kwargs
+ **kwargs,
  )
  return [x.squeeze(axis=axis)] if squeeze_axis else [x]
 

src/gluonts/nursery/SCott/dataset_tools/algo_clustering.py

@@ -232,14 +232,14 @@ def KMeans_m5_dataset(
  {
  "target": ts_slice,
  "start": unsplit_start,
- } # , 'feat_static_cat': train_entry['feat_static_cat']}
- )
+ }
+ ) # , 'feat_static_cat': train_entry['feat_static_cat']}
  whole_data.append(
  {
  "target": ts_slice,
  "start": unsplit_start,
- } # , 'feat_static_cat': train_entry['feat_static_cat']}
- )
+ }
+ ) # , 'feat_static_cat': train_entry['feat_static_cat']}
  sample_id += 1
  print(len(whole_data))
  ret["group_ratio"] = [len(i) / len(whole_data) for i in dataset_group]

src/gluonts/nursery/daf/tslib/nn/attention/selfattn.py

@@ -309,7 +309,7 @@ def forward(
  value: Tensor,
  shape: Tensor,
  *,
- mask: Optional[BoolTensor] = None
+ mask: Optional[BoolTensor] = None,
  ) -> Tensor:
  q, k, v = self._compute_qkv(value, shape)
  score = self._compute_attn_score(q, k, mask)

src/gluonts/nursery/robust-mts-attack/pts/modules/gaussian_diffusion.py

@@ -312,7 +312,7 @@ def log_prob(self, x, cond, *args, **kwargs):
  cond.reshape(B * T, 1, -1),
  time,
  *args,
- **kwargs
+ **kwargs,
  )
 
  return loss

src/gluonts/nursery/temporal_hierarchical_forecasting/model/cop_deepar/gnn.py

@@ -23,7 +23,7 @@ def __init__(
  num_layers: int,
  adj_matrix: Tensor,
  use_mlp: bool = True,
- **kwargs
+ **kwargs,
  ):
  super().__init__(**kwargs)
 

src/gluonts/nursery/tsbench/src/cli/analysis/scripts/ensemble_recommender.py

@@ -100,7 +100,7 @@ def main(
  surrogate[surrogate["name"]]
  if surrogate["name"] in surrogate
  else {}
- )
+ ),
  )
 
  # Then, we can create the recommender

src/gluonts/nursery/tsbench/src/cli/analysis/scripts/recommender.py

@@ -123,7 +123,7 @@ def main(
  surrogate[surrogate["name"]]
  if surrogate["name"] in surrogate
  else {}
- )
+ ),
  )
  elif recommender == "optimal":
  recommender_args["tracker"] = tracker

src/gluonts/nursery/tsbench/src/cli/analysis/scripts/surrogate.py

@@ -92,7 +92,7 @@ def main(
  input_flags=inputs,
  output_normalization=outputs["normalization"],
  impute_simulatable=outputs["imputation"],
- **(_config[surrogate] if surrogate in _config else {})
+ **(_config[surrogate] if surrogate in _config else {}),
  )
 
  # And evaluate it

src/gluonts/torch/model/i_transformer/estimator.py

@@ -232,7 +232,7 @@ def create_training_data_loader(
  data: Dataset,
  module: ITransformerLightningModule,
  shuffle_buffer_length: Optional[int] = None,
- **kwargs
+ **kwargs,
  ) -> Iterable:
  data = Cyclic(data).stream()
  instances = self._create_instance_splitter(module, "training").apply(

src/gluonts/torch/model/lag_tst/estimator.py

@@ -225,7 +225,7 @@ def create_training_data_loader(
  data: Dataset,
  module: LagTSTLightningModule,
  shuffle_buffer_length: Optional[int] = None,
- **kwargs
+ **kwargs,
  ) -> Iterable:
  data = Cyclic(data).stream()
  instances = self._create_instance_splitter(module, "training").apply(

src/gluonts/torch/model/patch_tst/estimator.py

@@ -229,7 +229,7 @@ def create_training_data_loader(
  data: Dataset,
  module: PatchTSTLightningModule,
  shuffle_buffer_length: Optional[int] = None,
- **kwargs
+ **kwargs,
  ) -> Iterable:
  data = Cyclic(data).stream()
  instances = self._create_instance_splitter(module, "training").apply(

src/gluonts/torch/model/wavenet/estimator.py

@@ -348,7 +348,7 @@ def create_training_data_loader(
  data: Dataset,
  module: WaveNetLightningModule,
  shuffle_buffer_length: Optional[int] = None,
- **kwargs
+ **kwargs,
  ) -> Iterable:
  data = Cyclic(data).stream()
  instances = self._create_instance_splitter("training").apply(

test/nursery/sagemaker_sdk/test_entry_point_scripts.py

@@ -91,7 +91,7 @@ def test_train_script(dataset_name, custom_dataset):
  estimator = estimator_cls.from_hyperparameters(
  prediction_length=dataset.metadata.prediction_length,
  freq=dataset.metadata.freq,
- **hyperparameters
+ **hyperparameters,
  )
  serialized = serde.dump_json(estimator)
  with open(temp_dir_path / "estimator.json", "w") as estimator_file: