ruff

river/datasets/synth/logical.py

@@ -67,7 +67,10 @@ def __iter__(self):
         X, Y = self._make_logical(n_tiles=self.n_tiles, shuffle=self.shuffle)
 
         for xi, yi in itertools.zip_longest(X, Y if hasattr(Y, "__iter__") else []):
-            yield dict(zip(self.feature_names, xi.tolist())), dict(zip(self.target_names, yi.tolist()))
+            yield (
+                dict(zip(self.feature_names, xi.tolist())),
+                dict(zip(self.target_names, yi.tolist())),
+            )
 
     def _make_logical(self, n_tiles: int = 1, shuffle: bool = True):
         """Make toy dataset"""

river/linear_model/bayesian_lin_reg.py

@@ -211,11 +211,7 @@ def predict_one(self, x, with_dist=False):
         """
 
         # Bishop equation 3.58
-        y_pred_mean = (
-            0.0
-            if not len(self._m)
-            else utils.math.dot(self._m, x).item()
-        )
+        y_pred_mean = 0.0 if not len(self._m) else utils.math.dot(self._m, x).item()
         if not with_dist:
             return y_pred_mean
 

river/preprocessing/scale.py

@@ -214,7 +214,9 @@ def learn_many(self, X: pd.DataFrame):
 
             self.means[col] = (a * old_mean + b * new_mean).item()
             if self.with_std:
-                self.vars[col] = (a * old_var + b * new_var + a * b * (old_mean - new_mean) ** 2).item()
+                self.vars[col] = (
+                    a * old_var + b * new_var + a * b * (old_mean - new_mean) ** 2
+                ).item()
             self.counts[col] += new_count.item()
 
     def transform_many(self, X: pd.DataFrame):