Upgrade Linters (#21)

pyproject.toml

@@ -61,10 +61,10 @@ beartype = "^0.14.1"
 scipy = "^1.9"
 
 [tool.poetry.group.dev.dependencies]
-ruff = "^0.0.275"
-black = "^23.3.0"
-isort = "^5.12.0"
-mypy = "^1.4.1"
+ruff = "^0.2.1"
+black = "^24.1.1"
+isort = "^5.13.2"
+mypy = "^1.8.0"
 pytest = "^7.4.0"
 pytest-cov = "^4.1.0"
 datasets = "^2.13.1"
@@ -91,6 +91,12 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.ruff]
 exclude = ["__init__.py"]
-select = ["E", "F", "PL", "I", "ICN", "RET", "SIM", "NPY", "RUF"]
-ignore = ["E501", "PLR0913"]
+lint.select = ["E", "F", "PL", "I", "ICN", "RET", "SIM", "NPY", "RUF"]
+lint.ignore = ["E501", "PLR0913"]
+
+[tool.mypy]
+exclude = [ ]
+ignore_missing_imports = true
+check_untyped_defs = true
+python_version = "3.9"
 

target_permutation_importances/functional.py

@@ -178,9 +178,9 @@ def compute_permutation_importance_by_wasserstein_distance(
                 "importance"
             ].to_numpy(),
         )
-    mean_actual_importance_df[
-        "wasserstein_distance"
-    ] = mean_actual_importance_df.index.map(distances)
+    mean_actual_importance_df["wasserstein_distance"] = (
+        mean_actual_importance_df.index.map(distances)
+    )
 
     # Sort by feature name to make sure the order is the same
     mean_actual_importance_df = mean_actual_importance_df.sort_index()

target_permutation_importances/sklearn_wrapper.py

@@ -1,6 +1,7 @@
 """
 The Sklearn Class Wrappers
 """
+
 import numpy as np
 import pandas as pd
 from beartype.typing import Any, Dict, List, Union

target_permutation_importances/typing.py

@@ -21,8 +21,7 @@ class XBuilderType(Protocol):  # pragma: no cover
         return (XType): The X data
     """
 
-    def __call__(self, is_random_run: bool, run_idx: int) -> XType:
-        ...
+    def __call__(self, is_random_run: bool, run_idx: int) -> XType: ...
 
 
 @runtime_checkable
@@ -37,14 +36,12 @@ class YBuilderType(Protocol):  # pragma: no cover
         return (YType): The y data
     """
 
-    def __call__(self, is_random_run: bool, run_idx: int) -> YType:
-        ...
+    def __call__(self, is_random_run: bool, run_idx: int) -> YType: ...
 
 
 @runtime_checkable
 class ModelFitParamsBuilderType(Protocol):  # pragma: no cover
-    def __call__(self, feature_columns: Optional[List[str]]) -> dict:
-        ...
+    def __call__(self, feature_columns: Optional[List[str]]) -> dict: ...
 
 
 @runtime_checkable
@@ -60,8 +57,7 @@ class ModelBuilderType(Protocol):  # pragma: no cover
         return (Any): The newly created model
     """
 
-    def __call__(self, is_random_run: bool, run_idx: int) -> Any:
-        ...
+    def __call__(self, is_random_run: bool, run_idx: int) -> Any: ...
 
 
 @runtime_checkable
@@ -77,8 +73,7 @@ class ModelFitterType(Protocol):  # pragma: no cover
         return (Any): The fitted model
     """
 
-    def __call__(self, model: Any, X: XType, y: YType) -> Any:
-        ...
+    def __call__(self, model: Any, X: XType, y: YType) -> Any: ...
 
 
 @runtime_checkable
@@ -94,8 +89,7 @@ class ModelImportanceGetter(Protocol):  # pragma: no cover
         return (pd.DataFrame): The return DataFrame with columns ["feature", "importance"]
     """
 
-    def __call__(self, model: Any, X: XType, y: YType) -> pd.DataFrame:
-        ...
+    def __call__(self, model: Any, X: XType, y: YType) -> pd.DataFrame: ...
 
 
 @runtime_checkable
@@ -116,5 +110,4 @@ def __call__(
         self,
         actual_importance_dfs: List[pd.DataFrame],
         random_importance_dfs: List[pd.DataFrame],
-    ) -> pd.DataFrame:
-        ...
+    ) -> pd.DataFrame: ...

tests/test_functional.py

@@ -129,8 +129,6 @@ def test_compute_multi_label_classification(model_cls, imp_func, xtype):
     )
     if xtype is pd.DataFrame:
         X = pd.DataFrame(X, columns=[f"feature_{i}" for i in range(X.shape[1])])
-    else:
-        X = X
 
     model_cls_params = model_cls[1].copy()
     if "Cat" in model_cls[0].__name__:
@@ -174,8 +172,6 @@ def test_compute_multi_label_classification_with_MultiOutputClassifier(
     )
     if xtype is pd.DataFrame:
         X = pd.DataFrame(X, columns=[f"feature_{i}" for i in range(X.shape[1])])
-    else:
-        X = X
 
     if "Lasso" in model_cls[0].__name__ or "LinearSVC" in model_cls[0].__name__:
         return
@@ -247,8 +243,6 @@ def test_compute_multi_target_regression_with_MultiOutputRegressor(
     )
     if xtype is pd.DataFrame:
         X = pd.DataFrame(X, columns=[f"feature_{i}" for i in range(X.shape[1])])
-    else:
-        X = X
 
     result_df = compute(
         model_cls=MultiOutputRegressor,