MAINT add parameters validation for SplineTransformer

[kasmith11]

Reference Issues/PRs

References #23462 as well #22722

What does this implement/fix? Explain your changes.

Adds _parameter_constraints to sklearn/preprocessing/_polynomial.py/SplineTransformer and removes SplineTransformer from PARAM_VALIDATION_ESTIMATORS_TO_IGNORE in sklearn/tests/test_common.py

Any other comments?

#23462 mentions that spotting and removing existing validation/tests is easier with codecov since it becomes unreachable code. Can this be down locally or do I need to submit a pull request?

[glemaitre]

Otherwise, the rest of the PR seems fine.

[glemaitre]

Now some parameters will be validated and you need to remove some extra unnecessary checks. I check and you should have the following diff in fit:

diff --git a/sklearn/preprocessing/_polynomial.py b/sklearn/preprocessing/_polynomial.py
index 585c21eb2d..0b651e1f2f 100644
--- a/sklearn/preprocessing/_polynomial.py
+++ b/sklearn/preprocessing/_polynomial.py
@@ -794,20 +794,8 @@ class SplineTransformer(TransformerMixin, BaseEstimator):
 
         _, n_features = X.shape
 
-        if not (isinstance(self.degree, numbers.Integral) and self.degree >= 0):
-            raise ValueError(
-                f"degree must be a non-negative integer, got {self.degree}."
-            )
-
-        if isinstance(self.knots, str) and self.knots in [
-            "uniform",
-            "quantile",
-        ]:
-            if not (isinstance(self.n_knots, numbers.Integral) and self.n_knots >= 2):
-                raise ValueError(
-                    f"n_knots must be a positive integer >= 2, got: {self.n_knots}"
-                )
 
+        if isinstance(self.knots, str):
             base_knots = self._get_base_knot_positions(
                 X, n_knots=self.n_knots, knots=self.knots, sample_weight=sample_weight
             )
@@ -820,20 +808,6 @@ class SplineTransformer(TransformerMixin, BaseEstimator):
             elif not np.all(np.diff(base_knots, axis=0) > 0):
                 raise ValueError("knots must be sorted without duplicates.")
 
-        if self.extrapolation not in (
-            "error",
-            "constant",
-            "linear",
-            "continue",
-            "periodic",
-        ):
-            raise ValueError(
-                "extrapolation must be one of 'error', "
-                "'constant', 'linear', 'continue' or 'periodic'."
-            )
-
-        if not isinstance(self.include_bias, (bool, np.bool_)):
-            raise ValueError("include_bias must be bool.")
 
         # number of knots for base interval
         n_knots = base_knots.shape[0]

[glemaitre]

Thanks LGTM.

[thomasjpfan]

LGTM