[BUG] fix TSCOptCV integration for metric function input (#190)

fixes an unreported bug in the `TSCOptCV` integration, for metric
function input

Author: fkiraly

src/hyperactive/experiment/integrations/_skl_metrics.py

@@ -71,6 +71,9 @@ def _coerce_to_scorer(scoring, estimator):
             scorer = scoring  # passthrough scorer signature
         else:
             scorer = make_scorer(scoring)
+    elif isinstance(estimator, str):
+        metric = _default_metric_for(estimator)
+        scorer = make_scorer(metric)
     else:
         # string (scorer name)
         scorer = check_scoring(estimator, scoring=scoring)

src/hyperactive/integrations/sktime/_classification.py

@@ -232,19 +232,11 @@ class labels for fitting
         -------
         self : Reference to self.
         """
-        from sklearn.dummy import DummyClassifier
-        from sklearn.metrics import check_scoring
-
         estimator = self.estimator.clone()
 
-        # use dummy classifier from sklearn to get default coercion behaviour
-        # for classificatoin metrics
-        scoring = check_scoring(DummyClassifier(), self.scoring)
-        # scoring_name = f"test_{scoring.name}"
-
         experiment = SktimeClassificationExperiment(
             estimator=estimator,
-            scoring=scoring,
+            scoring=self.scoring,
             cv=self.cv,
             X=X,
             y=y,
@@ -316,6 +308,7 @@ def get_test_params(cls, parameter_set="default"):
         """
         from sklearn.metrics import accuracy_score
         from sklearn.model_selection import KFold
+        from sktime.classification.distance_based import KNeighborsTimeSeriesClassifier
         from sktime.classification.dummy import DummyClassifier
 
         from hyperactive.opt.gfo import HillClimbing
@@ -337,10 +330,10 @@ def get_test_params(cls, parameter_set="default"):
             "scoring": accuracy_score,
         }
         params_hillclimb = {
-            "estimator": DummyClassifier(strategy="stratified"),
+            "estimator": KNeighborsTimeSeriesClassifier(),
             "cv": KFold(n_splits=2, shuffle=False),
             "optimizer": HillClimbing(
-                search_space={"strategy": ["most_frequent", "stratified"]},
+                search_space={"n_neighbors": [1, 2, 4]},
                 n_iter=10,
                 n_neighbours=5,
             ),

src/hyperactive/integrations/sktime/tests/test_sktime_estimators.py

@@ -5,9 +5,9 @@
 from skbase.utils.dependencies import _check_soft_dependencies
 
 if _check_soft_dependencies("sktime", severity="none"):
-    from hyperactive.integrations.sktime import ForecastingOptCV
+    from hyperactive.integrations.sktime import ForecastingOptCV, TSCOptCV
 
-    EST_TO_TEST = [ForecastingOptCV]
+    EST_TO_TEST = [ForecastingOptCV, TSCOptCV]
 else:
     EST_TO_TEST = []