Merge branch 'workflows' of https://github.com/time-series-machine-learning/tsml-py

Author: MatthewMiddlehurst

tsml/transformations/_quantile.py

@@ -1,4 +1,4 @@
-from typing import Union, List
+from typing import List, Union
 
 import numpy as np
 from sklearn.base import TransformerMixin
@@ -19,13 +19,15 @@ def __init__(
 
         super(QuantileTransformer).__init__()
 
-    def fit(self, X: Union[np.ndarray, List[np.ndarray]], y: Union[np.ndarray, None] =None) -> object:
+    def fit(
+        self, X: Union[np.ndarray, List[np.ndarray]], y: Union[np.ndarray, None] = None
+    ) -> object:
         """Unused. Validates X."""
         self._validate_data(X=X)
         return self
 
     def transform(
-            self, X: Union[np.ndarray, List[np.ndarray]], y: Union[np.ndarray, None] =None
+        self, X: Union[np.ndarray, List[np.ndarray]], y: Union[np.ndarray, None] = None
     ) -> np.ndarray:
         """Transform input cases in X.
 
@@ -48,8 +50,8 @@ def transform(
         if num_quantiles == 1:
             return X.quantile(torch.tensor([0.5]), dim=-1).permute(1, 2, 0)
         else:
-            quantiles = X.quantile(
-                torch.linspace(0, 1, num_quantiles), dim=-1
-            ).permute(1, 2, 0)
+            quantiles = X.quantile(torch.linspace(0, 1, num_quantiles), dim=-1).permute(
+                1, 2, 0
+            )
             quantiles[..., 1::2] = quantiles[..., 1::2] - X.mean(-1, keepdims=True)
-            return quantiles
+            return quantiles

tsml/utils/numba_functions/stats.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """Numba statistic utilities."""
 
 __author__ = ["MatthewMiddlehurst"]

tsml/utils/validation.py

@@ -58,7 +58,9 @@ def check_n_jobs(n_jobs: int) -> int:
     elif n_jobs < 0:
         cpu_count = os.cpu_count()
         if cpu_count is None:
-            warnings.warn("Could not detect CPU count. Setting `n_jobs` to 1.", stacklevel=2)
+            warnings.warn(
+                "Could not detect CPU count. Setting `n_jobs` to 1.", stacklevel=2
+            )
             return 1
         else:
             return max(1, cpu_count + 1 + n_jobs)