[BUG] Fix dynamic make_forecasting_scorer for newer sklearn metri…

…cs (#5717)

Fixes #5715.

The issue with `sklearn` metrics is coming from their decorator which no
longer allows inspection of admissible arguments via `python` native
`sigature`.

Question for reviewers: is there an `sklearn` native way to inspect
permissible signature, without reading private attriutes?

sktime/performance_metrics/forecasting/_classes.py

@@ -573,6 +573,10 @@ def _evaluate(self, y_true, y_pred, **kwargs):
         else:
             func = self.func
 
+        return self._evaluate_func(func=func, y_true=y_true, y_pred=y_pred, **params)
+
+    def _evaluate_func(self, func, y_true, y_pred, **params):
+        """Call func with kwargs subset to func parameters."""
         # import here for now to avoid interaction with getmembers in tests
         # todo: clean up ancient getmembers in test_metrics_classes
         from functools import partial
@@ -584,6 +588,15 @@ def _evaluate(self, y_true, y_pred, **kwargs):
             func_params = func_params.intersection(params.keys())
             params = {key: params[key] for key in func_params}
 
+        # deal with sklearn specific parameter constraints
+        # as these are a decorator, they obfuscate python native inspection
+        # via signature, so have to be dealt with separately
+        if hasattr(func, "_skl_parameter_constraints"):
+            constr = func._skl_parameter_constraints
+            if isinstance(constr, dict):
+                constr_params = set(constr.keys()).intersection(params.keys())
+                params = {key: params[key] for key in constr_params}
+
         res = func(y_true=y_true, y_pred=y_pred, **params)
         return res
 
@@ -609,6 +622,16 @@ def __init__(
 
         self.set_tags(**{"lower_is_better": lower_is_better})
 
+    def _evaluate(self, y_true, y_pred, **kwargs):
+        """Evaluate the desired metric on given inputs."""
+        # this dict should contain all parameters
+        params = kwargs
+        params.update({"multioutput": self.multioutput, "multilevel": self.multilevel})
+
+        func = self.func
+
+        return self._evaluate_func(func=func, y_true=y_true, y_pred=y_pred, **params)
+
     @classmethod
     def get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.

sktime/performance_metrics/forecasting/tests/test_metrics.py

@@ -129,3 +129,18 @@ def test_make_scorer():
     scorer = make_forecasting_scorer(rmsle, name="RMSLE")
 
     scorer.evaluate(pd.Series([1, 2, 3]), pd.Series([1, 2, 4]))
+
+
+def test_make_scorer_sklearn():
+    """Test make_forecasting_scorer and the failure case in #5715.
+
+    Naive adaptation fails on newer sklearn versions due to
+    decoration with sklearn's custom input constraint wrapper.
+    """
+    from sklearn.metrics import mean_absolute_error
+
+    from sktime.performance_metrics.forecasting import make_forecasting_scorer
+
+    scorer = make_forecasting_scorer(mean_absolute_error, name="RMSLE")
+
+    scorer.evaluate(pd.Series([1, 2, 3]), pd.Series([1, 2, 4]))