Fix mypy errors

Mark the parameters with a default value of None as optionals.

river/anomaly/lof.py

@@ -253,7 +253,7 @@ class LocalOutlierFactor(anomaly.base.AnomalyDetector):
     def __init__(
         self,
         n_neighbors: int = 10,
-        distance_func: DistanceFunc = None,
+        distance_func: DistanceFunc | None = None,
     ):
         self.n_neighbors = n_neighbors
         self.x_list: list = []

river/anomaly/sad.py

@@ -59,7 +59,7 @@ class StandardAbsoluteDeviation(anomaly.base.SupervisedAnomalyDetector):
 
     """
 
-    def __init__(self, sub_stat: stats.base.Univariate = None):
+    def __init__(self, sub_stat: stats.base.Univariate | None = None):
         self.variance = stats.Var()
         self.sub_stat = sub_stat or stats.Mean()
 

river/bandit/base.py

@@ -162,7 +162,7 @@ class ContextualPolicy(Policy):
     def _pull(self, arm_ids: list[ArmID], context: dict) -> ArmID:  # type: ignore[override]
         ...
 
-    def pull(self, arm_ids: list[ArmID], context: dict = None) -> ArmID:
+    def pull(self, arm_ids: list[ArmID], context: dict | None = None) -> ArmID:
         """Pull arm(s).
 
         This method is a generator that yields the arm(s) that should be pulled. During the burn-in

river/bandit/evaluate.py

@@ -167,7 +167,7 @@ def evaluate(
 def evaluate_offline(
     policy: bandit.base.Policy,
     history: History | bandit.datasets.BanditDataset,
-    reward_stat: stats.base.Univariate = None,
+    reward_stat: stats.base.Univariate | None = None,
 ) -> tuple[stats.base.Univariate, int]:
     """Evaluate a policy on historical logs using replay.
 

river/bandit/thompson.py

@@ -73,7 +73,7 @@ class ThompsonSampling(bandit.base.Policy):
 
     def __init__(
         self,
-        reward_obj: proba.base.Distribution = None,
+        reward_obj: proba.base.Distribution | None = None,
         burn_in=0,
         seed: int | None = None,
     ):

river/bandit/ucb.py

@@ -75,7 +75,7 @@ def __init__(
         reward_obj=None,
         reward_scaler=None,
         burn_in=0,
-        seed: int = None,
+        seed: int | None = None,
     ):
         super().__init__(
             reward_obj=reward_obj, reward_scaler=reward_scaler, burn_in=burn_in

river/drift/binary/fhddm.py

@@ -66,7 +66,7 @@ class FHDDM(base.BinaryDriftAndWarningDetector):
 
     """
     def __init__(
-        self, sliding_window_size: int = 100, confidence_level: float = 0.000001, short_window_size: int = None
+        self, sliding_window_size: int = 100, confidence_level: float = 0.000001, short_window_size: int | None = None
     ):
 
         super().__init__()

river/forest/aggregated_mondrian_forest.py

@@ -275,7 +275,7 @@ def __init__(
         n_estimators: int = 10,
         step: float = 1.0,
         use_aggregation: bool = True,
-        seed: int = None,
+        seed: int | None = None,
     ):
         super().__init__(
             n_estimators=n_estimators,

river/linear_model/bayesian_lin_reg.py

@@ -109,7 +109,7 @@ class BayesianLinearRegression(base.Regressor):
 
     """
 
-    def __init__(self, alpha=1, beta=1, smoothing: float = None):
+    def __init__(self, alpha=1, beta=1, smoothing: float | None = None):
         self.alpha = alpha
         self.beta = beta
         self.smoothing = smoothing

river/neighbors/ann/swinn.py

@@ -93,11 +93,11 @@ def __init__(
         dist_func: DistanceFunc | FunctionWrapper | None = None,
         maxlen: int = 1000,
         warm_up: int = 500,
-        max_candidates: int = None,
+        max_candidates: int | None = None,
         delta: float = 0.0001,
         prune_prob: float = 0.0,
         n_iters: int = 10,
-        seed: int = None,
+        seed: int | None = None,
     ):
         self.graph_k = graph_k
         if dist_func is None:
@@ -203,7 +203,7 @@ def _safe_node_removal(self, nid: int):
 
         self._refine(affected)
 
-    def _refine(self, nodes: list[int] = None):
+    def _refine(self, nodes: list[int] | None = None):
         """Update the nearest neighbor graph to improve the edge distances.
 
         Parameters
@@ -343,7 +343,7 @@ def _linear_scan(self, item, k):
 
         return None
 
-    def _search(self, item, k, epsilon: float = 0.1, seed: Vertex = None, exclude: set[int] | None = None) -> tuple[list, list]:
+    def _search(self, item, k, epsilon: float = 0.1, seed: Vertex | None = None, exclude: set[int] | None = None) -> tuple[list, list]:
         # Limiter for the distance bound
         distance_scale = 1 + epsilon
         # Distance threshold for early stops

river/tree/mondrian/mondrian_tree_regressor.py

@@ -42,7 +42,7 @@ def __init__(
         step: float = 0.1,
         use_aggregation: bool = True,
         iteration: int = 0,
-        seed: int = None,
+        seed: int | None = None,
     ):
         super().__init__(
             step=step,