From 9dc2d19cfe880d6baf9aba3929a8aabae845c53b Mon Sep 17 00:00:00 2001
From: Saulo Martiello Mastelini <mastelini@usp.br>
Date: Fri, 31 May 2024 10:54:07 -0300
Subject: [PATCH] format

---
 river/model_selection/sspt.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/river/model_selection/sspt.py b/river/model_selection/sspt.py
index e164cb1168..c800c38beb 100644
--- a/river/model_selection/sspt.py
+++ b/river/model_selection/sspt.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import collections
 import copy
 import math
@@ -118,7 +120,7 @@ def __init__(
         self,
         estimator: base.Estimator,
         metric: metrics.base.Metric,
-        params_range: typing.Dict[str, typing.Tuple],
+        params_range: dict[str, tuple],
         drift_input: typing.Callable[[float, float], float],
         grace_period: int = 500,
         drift_detector: base.DriftDetector = drift.ADWIN(),
@@ -145,7 +147,7 @@ def __init__(
         self._simplex = self._create_simplex(estimator)
 
         # Models expanded from the simplex
-        self._expanded: typing.Optional[typing.Dict] = None
+        self._expanded: dict | None = None
 
         # Convergence criterion
         self._old_centroid = None
@@ -155,7 +157,7 @@ def __init__(
         if isinstance(border, compose.Pipeline):
             border = border[-1]
 
-        if isinstance(border, (base.Classifier, base.Regressor)):
+        if isinstance(border, base.Classifier | base.Regressor):
             self._scorer_name = "predict_one"
         elif isinstance(border, anomaly.base.AnomalyDetector):
             self._scorer_name = "score_one"
@@ -189,7 +191,7 @@ def __flatten(self, prefix, scaled_hps, hp_data, est_data):
 
     def _traverse_hps(
         self, operation: str, hp_data: dict, est_1, *, func=None, est_2=None, hp_prefix=None, scaled_hps=None
-    ) -> typing.Optional[typing.Union[dict, numbers.Number]]:
+    ) -> dict | numbers.Number | None:
         """Traverse the hyperparameters of the estimator/pipeline and perform an operation.
 
         Parameters
@@ -291,7 +293,7 @@ def _random_config(self):
             est_1=self.estimator._get_params()
         )
 
-    def _create_simplex(self, model) -> typing.List:
+    def _create_simplex(self, model) -> list:
         # The simplex is divided in:
         # * 0: the best model
         # * 1: the 'good' model
@@ -343,7 +345,7 @@ def _gen_new_estimator(self, e1, e2, func):
 
         return new
 
-    def _nelder_mead_expansion(self) -> typing.Dict:
+    def _nelder_mead_expansion(self) -> dict:
         """Create expanded models given the simplex models."""
         expanded = {}
         # Midpoint between 'best' and 'good'
@@ -455,7 +457,7 @@ def _models_converged(self) -> bool:
         )
         self._old_centroid = new_centroid
         ndim = len(scaled_params_b)
-        r_sphere = max_dist * math.sqrt((ndim / (2 * (ndim + 1))))
+        r_sphere = max_dist * math.sqrt(ndim / (2 * (ndim + 1)))
 
         if r_sphere < self.convergence_sphere or centroid_distance == 0:
             return True