blacked

Author: svnv-svsv-jm

.github/workflows/lint.yml

@@ -8,19 +8,19 @@ on:
 
 jobs:
   linter-black:
-      name: Check code formatting with Black
-      runs-on: ubuntu-latest
-      steps:
-        - name: Checkout
-          uses: actions/checkout@v2
-        - name: Set up Python 3.8
-          uses: actions/setup-python@v2
-          with:
-            python-version: 3.8
-        - name: Install Black
-          run: pip install black[jupyter]
-        - name: Run Black
-          run: black --check .
+    name: Check code formatting with Black
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+      - name: Set up Python 3.8
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+      - name: Install Black
+        run: pip install black[jupyter]
+      - name: Run Black
+        run: black --check .
 
   imports-check-isort:
     name: Check valid import formatting with isort
@@ -53,7 +53,6 @@ jobs:
       - name: Run checks
         run: flake8
 
-
   pre-commit-hooks:
     name: Check that pre-commit hooks pass
     runs-on: ubuntu-latest

pytorch_forecasting/__init__.py

@@ -1,6 +1,7 @@
 """
 PyTorch Forecasting package for timeseries forecasting with PyTorch.
 """
+
 from pytorch_forecasting.data import (
     EncoderNormalizer,
     GroupNormalizer,

pytorch_forecasting/data/__init__.py

@@ -4,6 +4,7 @@
 Handling timeseries data is not trivial. It requires special treatment. This sub-package provides the necessary tools
 to abstracts the necessary work.
 """
+
 from pytorch_forecasting.data.encoders import (
     EncoderNormalizer,
     GroupNormalizer,

pytorch_forecasting/data/encoders.py

@@ -61,6 +61,7 @@ class SoftplusTransform(Transform):
     Transform via the mapping :math:`\text{Softplus}(x) = \log(1 + \exp(x))`.
     The implementation reverts to the linear function when :math:`x > 20`.
     """
+
     domain = constraints.real
     codomain = constraints.positive
     bijective = True
@@ -93,6 +94,7 @@ class MinusOneTransform(Transform):
     r"""
     Transform x -> x - 1.
     """
+
     domain = constraints.real
     codomain = constraints.real
     sign: int = 1
@@ -112,6 +114,7 @@ class ReLuTransform(Transform):
     r"""
     Transform x -> max(0, x).
     """
+
     domain = constraints.real
     codomain = constraints.nonnegative
     sign: int = 1
@@ -364,7 +367,7 @@ def inverse_transform(self, y: Union[torch.Tensor, np.ndarray]) -> np.ndarray:
         decoded = self.classes_vector_[y]
         return decoded
 
-    def __call__(self, data: (Dict[str, torch.Tensor])) -> torch.Tensor:
+    def __call__(self, data: Dict[str, torch.Tensor]) -> torch.Tensor:
         """
         Extract prediction from network output. Does not map back to input
         categories as this would require a numpy tensor without grad-abilities.
@@ -1189,17 +1192,23 @@ def func(*args, **kwargs):
                         results = []
                         for idx, norm in enumerate(self.normalizers):
                             new_args = [
-                                arg[idx]
-                                if isinstance(arg, (list, tuple))
-                                and not isinstance(arg, rnn.PackedSequence)
-                                and len(arg) == n
-                                else arg
+                                (
+                                    arg[idx]
+                                    if isinstance(arg, (list, tuple))
+                                    and not isinstance(arg, rnn.PackedSequence)
+                                    and len(arg) == n
+                                    else arg
+                                )
                                 for arg in args
                             ]
                             new_kwargs = {
-                                key: val[idx]
-                                if isinstance(val, list) and not isinstance(val, rnn.PackedSequence) and len(val) == n
-                                else val
+                                key: (
+                                    val[idx]
+                                    if isinstance(val, list)
+                                    and not isinstance(val, rnn.PackedSequence)
+                                    and len(val) == n
+                                    else val
+                                )
                                 for key, val in kwargs.items()
                             }
                             results.append(getattr(norm, name)(*new_args, **new_kwargs))

pytorch_forecasting/data/examples.py

@@ -1,6 +1,7 @@
 """
 Example datasets for tutorials and testing.
 """
+
 from pathlib import Path
 
 import numpy as np

pytorch_forecasting/data/samplers.py

@@ -1,6 +1,7 @@
 """
 Samplers for sampling time series from the :py:class:`~pytorch_forecasting.data.timeseries.TimeSeriesDataSet`
 """
+
 import warnings
 
 import numpy as np

pytorch_forecasting/data/timeseries.py

@@ -4,6 +4,7 @@
 Timeseries data is special and has to be processed and fed to algorithms in a special way. This module
 defines a class that is able to handle a wide variety of timeseries data problems.
 """
+
 from copy import copy as _copy, deepcopy
 from functools import lru_cache
 import inspect

pytorch_forecasting/metrics/_mqf2_utils.py

@@ -1,4 +1,5 @@
 """Classes and functions for the MQF2 metric."""
+
 from typing import List, Optional, Tuple
 
 from cpflows.flows import DeepConvexFlow, SequentialFlow

pytorch_forecasting/metrics/base_metrics.py

@@ -1,6 +1,7 @@
 """
 Base classes for metrics - only for inheritance.
 """
+
 import inspect
 from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
 import warnings
@@ -454,17 +455,23 @@ def func(*args, **kwargs):
                         results = []
                         for idx, m in enumerate(self.metrics):
                             new_args = [
-                                arg[idx]
-                                if isinstance(arg, (list, tuple))
-                                and not isinstance(arg, rnn.PackedSequence)
-                                and len(arg) == n
-                                else arg
+                                (
+                                    arg[idx]
+                                    if isinstance(arg, (list, tuple))
+                                    and not isinstance(arg, rnn.PackedSequence)
+                                    and len(arg) == n
+                                    else arg
+                                )
                                 for arg in args
                             ]
                             new_kwargs = {
-                                key: val[idx]
-                                if isinstance(val, list) and not isinstance(val, rnn.PackedSequence) and len(val) == n
-                                else val
+                                key: (
+                                    val[idx]
+                                    if isinstance(val, list)
+                                    and not isinstance(val, rnn.PackedSequence)
+                                    and len(val) == n
+                                    else val
+                                )
                                 for key, val in kwargs.items()
                             }
                             results.append(getattr(m, name)(*new_args, **new_kwargs))

pytorch_forecasting/metrics/distributions.py

@@ -1,4 +1,5 @@
 """Metrics that allow the parametric forecast of parameters of uni- and multivariate distributions."""
+
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np