From 5220007240c78713d07e6590230dc311327a306e Mon Sep 17 00:00:00 2001
From: Saulo Martiello Mastelini <mastelini@usp.br>
Date: Sat, 16 Nov 2024 14:19:31 -0300
Subject: [PATCH] format

---
 river/ensemble/streaming_random_patches.py      | 16 ++++++++++++----
 river/forest/adaptive_random_forest.py          |  9 +++++++--
 river/rules/amrules.py                          |  5 ++++-
 river/tree/hoeffding_adaptive_tree_regressor.py |  4 +++-
 4 files changed, 26 insertions(+), 8 deletions(-)

diff --git a/river/ensemble/streaming_random_patches.py b/river/ensemble/streaming_random_patches.py
index 0cd0dd61f9..415b30be10 100644
--- a/river/ensemble/streaming_random_patches.py
+++ b/river/ensemble/streaming_random_patches.py
@@ -161,7 +161,9 @@ def _generate_subspaces(self, features: list):
                     # Generate n_models subspaces from all possible
                     # feature combinations of size k
                     self._subspaces = []
-                    for i, combination in enumerate(itertools.cycle(itertools.combinations(features, k))):
+                    for i, combination in enumerate(
+                        itertools.cycle(itertools.combinations(features, k))
+                    ):
                         if i == self.n_models:
                             break
                         self._subspaces.append(list(combination))
@@ -171,7 +173,8 @@ def _generate_subspaces(self, features: list):
                 # subspaces without worrying about repetitions.
                 else:
                     self._subspaces = [
-                        random_subspace(all_features=features, k=k, rng=self._rng) for _ in range(self.n_models)
+                        random_subspace(all_features=features, k=k, rng=self._rng)
+                        for _ in range(self.n_models)
                     ]
             else:
                 # k == 0 or k > n_features (subspace size is larger than the
@@ -180,8 +183,13 @@ def _generate_subspaces(self, features: list):
 
     def _init_ensemble(self, features: list):
         self._generate_subspaces(features=features)
-        subspace_indexes = list(range(self.n_models))  # For matching subspaces with ensemble members
-        if self.training_method == self._TRAIN_RANDOM_PATCHES or self.training_method == self._TRAIN_RANDOM_SUBSPACES:
+        subspace_indexes = list(
+            range(self.n_models)
+        )  # For matching subspaces with ensemble members
+        if (
+            self.training_method == self._TRAIN_RANDOM_PATCHES
+            or self.training_method == self._TRAIN_RANDOM_SUBSPACES
+        ):
             # Shuffle indexes
             self._rng.shuffle(subspace_indexes)
 
diff --git a/river/forest/adaptive_random_forest.py b/river/forest/adaptive_random_forest.py
index ea5a2811df..791e614d88 100644
--- a/river/forest/adaptive_random_forest.py
+++ b/river/forest/adaptive_random_forest.py
@@ -162,7 +162,8 @@ def learn_one(self, x: dict, y: base.typing.Target, **kwargs):
                 y_true=y,
                 y_pred=(
                     model.predict_proba_one(x)
-                    if isinstance(self.metric, metrics.base.ClassificationMetric) and not self.metric.requires_labels
+                    if isinstance(self.metric, metrics.base.ClassificationMetric)
+                    and not self.metric.requires_labels
                     else y_pred
                 ),
             )
@@ -188,7 +189,11 @@ def learn_one(self, x: dict, y: base.typing.Target, **kwargs):
                         self._warning_tracker[i] += 1
 
                 if not self._drift_detection_disabled:
-                    drift_input = drift_input if drift_input is not None else self._drift_detector_input(i, y, y_pred)
+                    drift_input = (
+                        drift_input
+                        if drift_input is not None
+                        else self._drift_detector_input(i, y, y_pred)
+                    )
                     self._drift_detectors[i].update(drift_input)
 
                     if self._drift_detectors[i].drift_detected:
diff --git a/river/rules/amrules.py b/river/rules/amrules.py
index ac3c44312a..245f71c1f4 100644
--- a/river/rules/amrules.py
+++ b/river/rules/amrules.py
@@ -389,7 +389,10 @@ def learn_one(self, x: dict, y: base.typing.RegTarget, w: int = 1):
             self._default_rule.learn_one(x, y, w)
 
             expanded = False
-            if self._default_rule.total_weight - self._default_rule.last_expansion_attempt_at >= self.n_min:
+            if (
+                self._default_rule.total_weight - self._default_rule.last_expansion_attempt_at
+                >= self.n_min
+            ):
                 updated_rule, expanded = self._default_rule.expand(self.delta, self.tau)
 
             if expanded:
diff --git a/river/tree/hoeffding_adaptive_tree_regressor.py b/river/tree/hoeffding_adaptive_tree_regressor.py
index 8087208ff0..6c609cc8f4 100644
--- a/river/tree/hoeffding_adaptive_tree_regressor.py
+++ b/river/tree/hoeffding_adaptive_tree_regressor.py
@@ -304,7 +304,9 @@ def _new_leaf(self, initial_stats=None, parent=None, is_active=True):
 
             return new_adaptive
 
-    def _branch_selector(self, numerical_feature=True, multiway_split=False) -> type[AdaBranchRegressor]:
+    def _branch_selector(
+        self, numerical_feature=True, multiway_split=False
+    ) -> type[AdaBranchRegressor]:
         """Create a new split node."""
         if numerical_feature:
             if not multiway_split: