auto-update pre-commit hooks

.pre-commit-config.yaml

@@ -6,7 +6,7 @@ ci:
 
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.5.0
+  rev: v4.6.0
   hooks:
   - id: check-yaml
   - id: fix-encoding-pragma
@@ -18,7 +18,7 @@ repos:
     args: ['--maxkb=500']
 
 - repo: https://github.com/psf/black
-  rev: 23.12.1
+  rev: 24.4.2
   hooks:
   - id: black
 
@@ -38,13 +38,13 @@ repos:
     - --profile=black
 
 - repo: https://github.com/asottile/pyupgrade
-  rev: v3.15.0
+  rev: v3.16.0
   hooks:
     - id: pyupgrade
       args: [--py38-plus]
 
 - repo: https://github.com/PyCQA/autoflake
-  rev: v2.2.1
+  rev: v2.3.1
   hooks:
     - id: autoflake
       args:
@@ -71,6 +71,6 @@ repos:
   - id: rst-inline-touching-normal
 
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: 'v1.8.0'  # Use the sha / tag you want to point at
+    rev: 'v1.10.1'  # Use the sha / tag you want to point at
     hooks:
     -   id: mypy

src/sparselm/model/__init__.py

@@ -1,4 +1,5 @@
 """Classes implementing generalized linear regression Regressors."""
+
 from ._adaptive_lasso import (
     AdaptiveGroupLasso,
     AdaptiveLasso,

src/sparselm/model/_adaptive_lasso.py

@@ -113,8 +113,9 @@ def __init__(
         max_iter: int = 3,
         eps: float = 1e-6,
         tol: float = 1e-10,
-        update_function: Callable[[NDArray[np.floating], float], ArrayLike]
-        | None = None,
+        update_function: (
+            Callable[[NDArray[np.floating], float], ArrayLike] | None
+        ) = None,
         fit_intercept: bool = False,
         copy_X: bool = True,
         warm_start: bool = True,
@@ -310,8 +311,9 @@ def __init__(
         max_iter: int = 3,
         eps: float = 1e-6,
         tol: float = 1e-10,
-        update_function: Callable[[NDArray[np.floating], float], ArrayLike]
-        | None = None,
+        update_function: (
+            Callable[[NDArray[np.floating], float], ArrayLike] | None
+        ) = None,
         standardize: bool = False,
         fit_intercept: bool = False,
         copy_X: bool = True,
@@ -457,8 +459,9 @@ def __init__(
         max_iter: int = 3,
         eps: float = 1e-6,
         tol: float = 1e-10,
-        update_function: Callable[[NDArray[np.floating], float], ArrayLike]
-        | None = None,
+        update_function: (
+            Callable[[NDArray[np.floating], float], ArrayLike] | None
+        ) = None,
         standardize: bool = False,
         fit_intercept: bool = False,
         copy_X: bool = True,
@@ -606,8 +609,9 @@ def __init__(
         max_iter: int = 3,
         eps: float = 1e-6,
         tol: float = 1e-10,
-        update_function: Callable[[NDArray[np.floating], float], ArrayLike]
-        | None = None,
+        update_function: (
+            Callable[[NDArray[np.floating], float], ArrayLike] | None
+        ) = None,
         standardize: bool = False,
         fit_intercept: bool = False,
         copy_X: bool = True,
@@ -808,8 +812,9 @@ def __init__(
         max_iter: int = 3,
         eps: float = 1e-6,
         tol: float = 1e-10,
-        update_function: Callable[[NDArray[np.floating], float], ArrayLike]
-        | None = None,
+        update_function: (
+            Callable[[NDArray[np.floating], float], ArrayLike] | None
+        ) = None,
         standardize: bool = False,
         fit_intercept: bool = False,
         copy_X: bool = True,

src/sparselm/model/_miqp/__init__.py

@@ -1,6 +1,5 @@
 """MIQP based regression Regressors."""
 
-
 from ._best_subset import BestSubsetSelection, RidgedBestSubsetSelection
 from ._regularized_l0 import L1L0, L2L0, RegularizedL0
 

tests/test_stepwise.py

@@ -1,4 +1,5 @@
 """Test composite estimator class."""
+
 import numpy as np
 import numpy.testing as npt
 import pytest