Merge branch 'main' into pr/1699

pytorch_forecasting/data/encoders.py

@@ -896,7 +896,7 @@ def swap_parameters(norm):
             self.missing_ = self.norm_.median().to_dict()
 
         if (
-            (self.scale_by_group and any([(self.norm_[group]["scale"] < 1e-7).any() for group in self.groups]))
+            (self.scale_by_group and any((self.norm_[group]["scale"] < 1e-7).any() for group in self.groups))
             or (not self.scale_by_group and isinstance(self.norm_["scale"], float) and self.norm_["scale"] < 1e-7)
             or (
                 not self.scale_by_group
@@ -1186,7 +1186,7 @@ def __getattr__(self, name: str):
         try:
             return super().__getattr__(name)
         except AttributeError as e:
-            attribute_exists = all([hasattr(norm, name) for norm in self.normalizers])
+            attribute_exists = all(hasattr(norm, name) for norm in self.normalizers)
             if attribute_exists:
                 # check if to return callable or not and return function if yes
                 if callable(getattr(self.normalizers[0], name)):

pytorch_forecasting/metrics/base_metrics.py

@@ -443,7 +443,7 @@ def __getattr__(self, name: str):
         try:
             return super().__getattr__(name)
         except AttributeError as e:
-            attribute_exists = all([hasattr(metric, name) for metric in self.metrics])
+            attribute_exists = all(hasattr(metric, name) for metric in self.metrics)
             if attribute_exists:
                 # check if to return callable or not and return function if yes
                 if callable(getattr(self.metrics[0], name)):

pytorch_forecasting/models/base_model.py

@@ -95,7 +95,7 @@ def _torch_cat_na(x: List[torch.Tensor]) -> torch.Tensor:
 
     # check if remaining dimensions are all equal
     if x[0].ndim > 2:
-        remaining_dimensions_equal = all([all([xi.size(i) == x[0].size(i) for xi in x]) for i in range(2, x[0].ndim)])
+        remaining_dimensions_equal = all(all(xi.size(i) == x[0].size(i) for xi in x) for i in range(2, x[0].ndim))
     else:
         remaining_dimensions_equal = True
 
@@ -796,7 +796,7 @@ def step(
                 [self.hparams.x_reals.index(name) for name in self.hparams.monotone_constaints.keys()]
             )
             monotonicity = torch.tensor(
-                [val for val in self.hparams.monotone_constaints.values()], dtype=gradient.dtype, device=gradient.device
+                list(self.hparams.monotone_constaints.values()), dtype=gradient.dtype, device=gradient.device
             )
             # add additionl loss if gradient points in wrong direction
             gradient = gradient[..., indices] * monotonicity[None, None]
@@ -1225,7 +1225,7 @@ def configure_optimizers(self):
                 )
             from pytorch_optimizer import Ranger21
 
-            if any([isinstance(c, LearningRateFinder) for c in self.trainer.callbacks]):
+            if any(isinstance(c, LearningRateFinder) for c in self.trainer.callbacks):
                 # if finding learning rate, switch off warm up and cool down
                 optimizer_params.setdefault("num_warm_up_iterations", 0)
                 optimizer_params.setdefault("num_warm_down_iterations", 0)

pytorch_forecasting/models/deepar/__init__.py

@@ -208,7 +208,7 @@ def from_dataset(
         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])
+            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
         if isinstance(new_kwargs.get("loss", None), MultivariateDistributionLoss):
             assert (
@@ -230,7 +230,7 @@ def construct_input_vector(
         # create input vector
         if len(self.categoricals) > 0:
             embeddings = self.embeddings(x_cat)
-            flat_embeddings = torch.cat([emb for emb in embeddings.values()], dim=-1)
+            flat_embeddings = torch.cat(list(embeddings.values()), dim=-1)
             input_vector = flat_embeddings
 
         if len(self.reals) > 0:

pytorch_forecasting/models/rnn/__init__.py

@@ -179,7 +179,7 @@ def from_dataset(
         new_kwargs.update(cls.deduce_default_output_parameters(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])
+            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
         return super().from_dataset(
             dataset, allowed_encoder_known_variable_names=allowed_encoder_known_variable_names, **new_kwargs
@@ -197,7 +197,7 @@ def construct_input_vector(
         # create input vector
         if len(self.categoricals) > 0:
             embeddings = self.embeddings(x_cat)
-            flat_embeddings = torch.cat([emb for emb in embeddings.values()], dim=-1)
+            flat_embeddings = torch.cat(list(embeddings.values()), dim=-1)
             input_vector = flat_embeddings
 
         if len(self.reals) > 0: