MAINT Parameters validation for sklearn.isotonic.isotonic_regression (scikit-learn#26257)

Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>

Author: jiawei-zhang-a

sklearn/isotonic.py

@@ -14,7 +14,7 @@
 from ._isotonic import _inplace_contiguous_isotonic_regression, _make_unique
 from .base import BaseEstimator, RegressorMixin, TransformerMixin, _fit_context
 from .utils import check_array, check_consistent_length
-from .utils._param_validation import Interval, StrOptions
+from .utils._param_validation import Interval, StrOptions, validate_params
 from .utils.validation import _check_sample_weight, check_is_fitted
 
 __all__ = ["check_increasing", "isotonic_regression", "IsotonicRegression"]
@@ -79,6 +79,16 @@ def check_increasing(x, y):
     return increasing_bool
 
 
+@validate_params(
+    {
+        "y": ["array-like"],
+        "sample_weight": ["array-like", None],
+        "y_min": [Interval(Real, None, None, closed="both"), None],
+        "y_max": [Interval(Real, None, None, closed="both"), None],
+        "increasing": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
 def isotonic_regression(
     y, *, sample_weight=None, y_min=None, y_max=None, increasing=True
 ):

sklearn/tests/test_public_functions.py

@@ -178,6 +178,7 @@ def _check_function_param_validation(
     "sklearn.feature_selection.r_regression",
     "sklearn.inspection.partial_dependence",
     "sklearn.inspection.permutation_importance",
+    "sklearn.isotonic.isotonic_regression",
     "sklearn.linear_model.orthogonal_mp",
     "sklearn.linear_model.ridge_regression",
     "sklearn.metrics.accuracy_score",