MAINT Parameters validation for sklearn.isotonic.isotonic_regression (s…

…cikit-learn#26257)

Co-authored-by: jeremie du boisberranger <[email protected]>

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",