[MNT] Replace Black with Ruff formatting and update configuration

.pre-commit-config.yaml

@@ -13,10 +13,7 @@ repos:
     hooks:
       - id: ruff
         args: [--fix]
-  - repo: https://github.com/psf/black
-    rev: 24.8.0
-    hooks:
-      - id: black
+      - id: ruff-format
   - repo: https://github.com/nbQA-dev/nbQA
     rev: 1.8.7
     hooks:

pyproject.toml

@@ -142,6 +142,13 @@ exclude = [
 ]
 target-version = "py39"
 
+[tool.ruff.format]
+# Enable formatting
+quote-style = "double"
+indent-style = "space"
+skip-magic-trailing-comma = false
+line-ending = "auto"
+
 [tool.ruff.lint]
 select = ["E", "F", "W", "C4", "S"]
 extend-select = [
@@ -171,30 +178,6 @@ force-sort-within-sections = true
   "E501", # Line too long being fixed in #1746 To be removed after merging
 ]
 
-[tool.black]
-line-length = 88
-include = '\.pyi?$'
-exclude = '''
-(
-  /(
-      \.eggs         # exclude a few common directories in the
-    | \.git          # root of the project
-    | \.hg
-    | \.mypy_cache
-    | \.tox
-    | \.venv
-    | _build
-    | buck-out
-    | build
-    | dist
-  )/
-  | docs/build/
-  | node_modules/
-  | venve/
-  | .venv/
-)
-'''
-
 [tool.nbqa.mutate]
 ruff = 1
 black = 1

pytorch_forecasting/data/data_module.py

@@ -108,7 +108,6 @@ def __init__(
         num_workers: int = 0,
         train_val_test_split: tuple = (0.7, 0.15, 0.15),
     ):
-
         self.time_series_dataset = time_series_dataset
         self.max_encoder_length = max_encoder_length
         self.min_encoder_length = min_encoder_length

pytorch_forecasting/data/timeseries/_timeseries.py

@@ -2198,27 +2198,23 @@ def __getitem__(self, idx: int) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
             # select subset of sequence of new sequence
             if new_encoder_length + new_decoder_length < len(target[0]):
                 data_cat = data_cat[
-                    encoder_length
-                    - new_encoder_length : encoder_length
+                    encoder_length - new_encoder_length : encoder_length
                     + new_decoder_length
                 ]
                 data_cont = data_cont[
-                    encoder_length
-                    - new_encoder_length : encoder_length
+                    encoder_length - new_encoder_length : encoder_length
                     + new_decoder_length
                 ]
                 target = [
                     t[
-                        encoder_length
-                        - new_encoder_length : encoder_length
+                        encoder_length - new_encoder_length : encoder_length
                         + new_decoder_length
                     ]
                     for t in target
                 ]
                 if weight is not None:
                     weight = weight[
-                        encoder_length
-                        - new_encoder_length : encoder_length
+                        encoder_length - new_encoder_length : encoder_length
                         + new_decoder_length
                     ]
                 encoder_length = new_encoder_length

pytorch_forecasting/data/timeseries/_timeseries_v2.py

@@ -90,7 +90,6 @@ def __init__(
         unknown: Optional[list[Union[str, list[str]]]] = None,
         static: Optional[list[Union[str, list[str]]]] = None,
     ):
-
         self.data = data
         self.data_future = data_future
         self.time = time

pytorch_forecasting/metrics/base_metrics.py

@@ -1123,7 +1123,9 @@ def sample(self, y_pred, n_samples: int) -> torch.Tensor:
             torch.Tensor: tensor with samples  (shape batch_size x n_timesteps x n_samples)
         """  # noqa: E501
         dist = self.map_x_to_distribution(y_pred)
-        samples = dist.sample((n_samples,)).permute(
+        samples = dist.sample(
+            (n_samples,)
+        ).permute(
             2, 1, 0
         )  # returned as (n_samples, n_timesteps, batch_size), so reshape to (batch_size, n_timesteps, n_samples) # noqa: E501
         return samples

pytorch_forecasting/models/base/_base_object.py

@@ -8,7 +8,6 @@
 
 
 class _BaseObject(_SkbaseBaseObject):
-
     pass
 
 

pytorch_forecasting/models/deepar/_deepar.py

@@ -222,13 +222,18 @@ def from_dataset(
                 MultiLoss([NormalDistributionLoss()] * len(dataset.target_names)),
             )
         new_kwargs.update(kwargs)
-        assert not isinstance(dataset.target_normalizer, NaNLabelEncoder) and (
-            not isinstance(dataset.target_normalizer, MultiNormalizer)
-            or all(
-                not isinstance(normalizer, NaNLabelEncoder)
-                for normalizer in dataset.target_normalizer
+        assert (
+            not isinstance(dataset.target_normalizer, NaNLabelEncoder)
+            and (
+                not isinstance(dataset.target_normalizer, MultiNormalizer)
+                or all(
+                    not isinstance(normalizer, NaNLabelEncoder)
+                    for normalizer in dataset.target_normalizer
+                )
             )
-        ), "target(s) should be continuous - categorical targets are not supported"  # todo: remove this restriction # noqa: E501
+        ), (
+            "target(s) should be continuous - categorical targets are not supported"
+        )  # todo: remove this restriction # noqa: E501
         if isinstance(new_kwargs.get("loss", None), MultivariateDistributionLoss):
             assert (
                 dataset.min_prediction_length == dataset.max_prediction_length

pytorch_forecasting/models/rnn/_rnn.py

@@ -204,13 +204,18 @@ def from_dataset(
                 dataset=dataset, kwargs=kwargs, default_loss=MAE()
             )
         )
-        assert not isinstance(dataset.target_normalizer, NaNLabelEncoder) and (
-            not isinstance(dataset.target_normalizer, MultiNormalizer)
-            or all(
-                not isinstance(normalizer, NaNLabelEncoder)
-                for normalizer in dataset.target_normalizer
+        assert (
+            not isinstance(dataset.target_normalizer, NaNLabelEncoder)
+            and (
+                not isinstance(dataset.target_normalizer, MultiNormalizer)
+                or all(
+                    not isinstance(normalizer, NaNLabelEncoder)
+                    for normalizer in dataset.target_normalizer
+                )
             )
-        ), "target(s) should be continuous - categorical targets are not supported"  # todo: remove this restriction # noqa: E501
+        ), (
+            "target(s) should be continuous - categorical targets are not supported"
+        )  # todo: remove this restriction # noqa: E501
         return super().from_dataset(
             dataset,
             allowed_encoder_known_variable_names=allowed_encoder_known_variable_names,

pytorch_forecasting/models/temporal_fusion_transformer/_tft_v2.py

@@ -179,7 +179,6 @@ def forward(self, x: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
                 static_context = self.static_context_linear(static_input)
                 static_context = static_context.view(batch_size, self.hidden_size)
             else:
-
                 static_input = torch.cat([static_cont, static_cat], dim=2).to(
                     dtype=self.static_context_linear.weight.dtype
                 )