Formatting

Author: e10e3

river/bandit/evaluate.py

@@ -139,7 +139,7 @@ def evaluate(
                 arm = policy_.pull(range(env_.action_space.n))  # type: ignore[attr-defined, arg-type]
                 observation, reward, terminated, truncated, info = env_.step(arm)
                 policy_.update(arm, reward)
-                reward_stat_.update(reward) # type: ignore[arg-type]
+                reward_stat_.update(reward)  # type: ignore[arg-type]
 
                 yield {
                     "episode": episode,

river/base/base.py

@@ -73,7 +73,9 @@ def _get_params(self) -> dict[str, typing.Any]:
 
         return params
 
-    def clone(self, new_params: dict[str, typing.Any] | None = None, include_attributes: bool = False) -> typing_extensions.Self:
+    def clone(
+        self, new_params: dict[str, typing.Any] | None = None, include_attributes: bool = False
+    ) -> typing_extensions.Self:
         """Return a fresh estimator with the same parameters.
 
         The clone has the same parameters but has not been updated with any data.
@@ -371,7 +373,7 @@ def _raw_memory_usage(self) -> int:
                 buffer.extend([k for k in obj.keys()])
                 buffer.extend([v for v in obj.values()])
             elif hasattr(obj, "__dict__"):  # Save object contents
-                contents= vars(obj)
+                contents = vars(obj)
                 size += sys.getsizeof(contents)
                 buffer.extend([k for k in contents.keys()])
                 buffer.extend([v for v in contents.values()])
@@ -398,7 +400,12 @@ def _memory_usage(self) -> str:
         return utils.pretty.humanize_bytes(self._raw_memory_usage)
 
 
-def _log_method_calls(self: typing.Any, name: str, class_condition: typing.Callable[[typing.Any], bool], method_condition: typing.Callable[[typing.Any], bool]) -> typing.Any:
+def _log_method_calls(
+    self: typing.Any,
+    name: str,
+    class_condition: typing.Callable[[typing.Any], bool],
+    method_condition: typing.Callable[[typing.Any], bool],
+) -> typing.Any:
     method = object.__getattribute__(self, name)
     if (
         not name.startswith("_")

river/base/classifier.py

@@ -28,7 +28,9 @@ def learn_one(self, x: dict[base.typing.FeatureName, Any], y: base.typing.ClfTar
 
         """
 
-    def predict_proba_one(self, x: dict[base.typing.FeatureName, Any], **kwargs: Any) -> dict[base.typing.ClfTarget, float]:
+    def predict_proba_one(
+        self, x: dict[base.typing.FeatureName, Any], **kwargs: Any
+    ) -> dict[base.typing.ClfTarget, float]:
         """Predict the probability of each label for a dictionary of features `x`.
 
         Parameters
@@ -48,7 +50,9 @@ def predict_proba_one(self, x: dict[base.typing.FeatureName, Any], **kwargs: Any
         # that a classifier does not support predict_proba_one.
         raise NotImplementedError
 
-    def predict_one(self, x: dict[base.typing.FeatureName, Any], **kwargs: Any) -> base.typing.ClfTarget | None:
+    def predict_one(
+        self, x: dict[base.typing.FeatureName, Any], **kwargs: Any
+    ) -> base.typing.ClfTarget | None:
         """Predict the label of a set of features `x`.
 
         Parameters

river/base/estimator.py

@@ -1,14 +1,14 @@
 from __future__ import annotations
 
 import abc
-from typing import Any, TYPE_CHECKING
 from collections.abc import Iterator
-
-from . import base
+from typing import TYPE_CHECKING, Any
 
 if TYPE_CHECKING:
     from river import compose
 
+from . import base
+
 
 class Estimator(base.Base, abc.ABC):
     """An estimator."""

river/base/multi_output.py

@@ -23,7 +23,9 @@ def learn_one(self, x: dict[FeatureName, typing.Any], y: dict[FeatureName, bool]
 
         """
 
-    def predict_proba_one(self, x: dict[FeatureName, typing.Any], **kwargs: typing.Any) -> dict[FeatureName, dict[bool, float]]:
+    def predict_proba_one(
+        self, x: dict[FeatureName, typing.Any], **kwargs: typing.Any
+    ) -> dict[FeatureName, dict[bool, float]]:
         """Predict the probability of each label appearing given dictionary of features `x`.
 
         Parameters
@@ -40,7 +42,9 @@ def predict_proba_one(self, x: dict[FeatureName, typing.Any], **kwargs: typing.A
         # In case the multi-label classifier does not support probabilities
         raise NotImplementedError
 
-    def predict_one(self, x: dict[FeatureName, typing.Any], **kwargs: typing.Any) -> dict[FeatureName, bool]:
+    def predict_one(
+        self, x: dict[FeatureName, typing.Any], **kwargs: typing.Any
+    ) -> dict[FeatureName, bool]:
         """Predict the labels of a set of features `x`.
 
         Parameters
@@ -69,7 +73,12 @@ class MultiTargetRegressor(Estimator, abc.ABC):
     """Multi-target regressor."""
 
     @abc.abstractmethod
-    def learn_one(self, x: dict[FeatureName, typing.Any], y: dict[FeatureName, RegTarget], **kwargs: typing.Any) -> None:
+    def learn_one(
+        self,
+        x: dict[FeatureName, typing.Any],
+        y: dict[FeatureName, RegTarget],
+        **kwargs: typing.Any,
+    ) -> None:
         """Fits to a set of features `x` and a real-valued target `y`.
 
         Parameters

river/base/transformer.py

@@ -8,6 +8,7 @@
 
 if typing.TYPE_CHECKING:
     import pandas as pd
+
     from river import compose
 
 
@@ -24,20 +25,34 @@ def __radd__(self, other: BaseTransformer) -> compose.TransformerUnion:
 
         return compose.TransformerUnion(other, self)
 
-    def __mul__(self, other: BaseTransformer | compose.Pipeline | base.typing.FeatureName | list[base.typing.FeatureName]) -> compose.Grouper | compose.TransformerProduct:
+    def __mul__(
+        self,
+        other: BaseTransformer
+        | compose.Pipeline
+        | base.typing.FeatureName
+        | list[base.typing.FeatureName],
+    ) -> compose.Grouper | compose.TransformerProduct:
         from river import compose
 
         if isinstance(other, BaseTransformer) or isinstance(other, compose.Pipeline):
             return compose.TransformerProduct(self, other)
 
-        return compose.Grouper(transformer=self, by=other) # type: ignore[arg-type]
+        return compose.Grouper(transformer=self, by=other)
 
-    def __rmul__(self, other: BaseTransformer | compose.Pipeline | base.typing.FeatureName | list[base.typing.FeatureName]) -> compose.Grouper | compose.TransformerProduct:
+    def __rmul__(
+        self,
+        other: BaseTransformer
+        | compose.Pipeline
+        | base.typing.FeatureName
+        | list[base.typing.FeatureName],
+    ) -> compose.Grouper | compose.TransformerProduct:
         """Creates a Grouper."""
         return self * other
 
     @abc.abstractmethod
-    def transform_one(self, x: dict[base.typing.FeatureName, Any]) -> dict[base.typing.FeatureName, Any]:
+    def transform_one(
+        self, x: dict[base.typing.FeatureName, Any]
+    ) -> dict[base.typing.FeatureName, Any]:
         """Transform a set of features `x`.
 
         Parameters

river/base/viz.py

@@ -1,12 +1,12 @@
 from __future__ import annotations
 
-# This import is not cyclic because 'viz' is not exported by 'base'
-from river import base, compose
-
 import inspect
 import textwrap
 from xml.etree import ElementTree as ET
 
+# This import is not cyclic because 'viz' is not exported by 'base'
+from river import base, compose
+
 
 def to_html(obj: base.Estimator) -> ET.Element:
     if isinstance(obj, compose.Pipeline):

river/forest/adaptive_random_forest.py

@@ -663,7 +663,9 @@ def _mutable_attributes(self):
     def _multiclass(self):
         return True
 
-    def predict_proba_one(self, x: dict, **kwargs: typing.Any) -> dict[base.typing.ClfTarget, float]:
+    def predict_proba_one(
+        self, x: dict, **kwargs: typing.Any
+    ) -> dict[base.typing.ClfTarget, float]:
         y_pred: typing.Counter = collections.Counter()
 
         if len(self) == 0:

river/stream/iter_sql.py

@@ -102,4 +102,4 @@ def iter_sql(
     for row in result_proxy:
         x = dict(row._mapping.items())
         y = x.pop(target_name)
-        yield x, y # type: ignore[misc]
+        yield x, y  # type: ignore[misc]

river/time_series/snarimax.py

@@ -4,7 +4,7 @@
 import itertools
 import math
 
-from river import base, linear_model, preprocessing, time_series, compose
+from river import base, compose, linear_model, preprocessing, time_series
 
 __all__ = ["SNARIMAX"]