[ENH] extend sktime ForecastingOptCV with broadcasting options and returned parameters

examples/integrations/README.md

@@ -99,6 +99,14 @@ y_pred = tuned_naive.predict()
 # 4. obtaining best parameters and best forecaster
 best_params = tuned_naive.best_params_
 best_forecaster = tuned_naive.best_forecaster_
+
+# Broadcasting options & diagnostics
+
+``ForecastingOptCV`` mirrors ``ForecastingGridSearchCV`` by exposing
+``tune_by_instance`` and ``tune_by_variable`` flags for automatic sktime
+broadcasting over panel or multivariate data. After ``fit`` completes you also
+gain access to ``best_score_``, ``cv_results_`` (per-fold backtesting results),
+``n_splits_``, ``scorer_``, and ``refit_time_`` for downstream inspection.
 ```
 
 ## Integration with sktime - time series classification

src/hyperactive/integrations/sktime/_forecasting.py

@@ -1,9 +1,13 @@
 # copyright: hyperactive developers, MIT License (see LICENSE file)
 
+import time
+
 import numpy as np
 from skbase.utils.dependencies import _check_soft_dependencies
 
-if _check_soft_dependencies("sktime", severity="none"):
+_HAS_SKTIME = _check_soft_dependencies("sktime", severity="none")
+
+if _HAS_SKTIME:
     from sktime.forecasting.base._delegate import _DelegatedForecaster
 else:
     from skbase.base import BaseEstimator as _DelegatedForecaster
@@ -151,6 +155,15 @@ class ForecastingOptCV(_DelegatedForecaster):
             - "logger_name": str, default="ray"; name of the logger to use.
             - "mute_warnings": bool, default=False; if True, suppresses warnings
 
+    tune_by_instance : bool, optional (default=False)
+        Whether to tune parameters separately for each time series instance when
+        panel or hierarchical data is passed. Mirrors ``ForecastingGridSearchCV``
+        semantics by delegating broadcasting to sktime's vectorization logic.
+    tune_by_variable : bool, optional (default=False)
+        Whether to tune parameters per variable for strictly multivariate series.
+        When enabled, only univariate targets are accepted and internal
+        broadcasting is handled by sktime.
+
     Example
     -------
     Any available tuning engine from hyperactive can be used, for example:
@@ -215,6 +228,8 @@ def __init__(
         cv_X=None,
         backend=None,
         backend_params=None,
+        tune_by_instance=False,
+        tune_by_variable=False,
     ):
         self.forecaster = forecaster
         self.optimizer = optimizer
@@ -227,8 +242,20 @@ def __init__(
         self.cv_X = cv_X
         self.backend = backend
         self.backend_params = backend_params
+        self.tune_by_instance = tune_by_instance
+        self.tune_by_variable = tune_by_variable
         super().__init__()
 
+        if _HAS_SKTIME:
+            self._set_delegated_tags(delegate=self.forecaster)
+            tags_to_clone = ["y_inner_mtype", "X_inner_mtype"]
+            self.clone_tags(self.forecaster, tags_to_clone)
+            self._extend_to_all_scitypes("y_inner_mtype")
+            self._extend_to_all_scitypes("X_inner_mtype")
+
+            if self.tune_by_variable:
+                self.set_tags(**{"scitype:y": "univariate"})
+
     def _fit(self, y, X, fh):
         """Fit to training data.
 
@@ -250,6 +277,16 @@ def _fit(self, y, X, fh):
         forecaster = self.forecaster.clone()
 
         scoring = check_scoring(self.scoring, obj=self)
+        self.scorer_ = scoring
+        get_n_splits = getattr(self.cv, "get_n_splits", None)
+        if callable(get_n_splits):
+            try:
+                self.n_splits_ = get_n_splits(y)
+            except TypeError:
+                # fallback for splitters that expect no args
+                self.n_splits_ = get_n_splits()
+        else:
+            self.n_splits_ = None
         # scoring_name = f"test_{scoring.name}"
 
         experiment = SktimeForecastingExperiment(
@@ -270,14 +307,53 @@ def _fit(self, y, X, fh):
         best_params = optimizer.solve()
 
         self.best_params_ = best_params
+        self.best_index_ = getattr(optimizer, "best_index_", None)
+        raw_best_score, best_metadata = experiment.evaluate(best_params)
+        self.best_score_ = float(raw_best_score)
+        results_table = best_metadata.get("results") if best_metadata else None
+        if results_table is not None:
+            try:
+                self.cv_results_ = results_table.copy()
+            except AttributeError:
+                self.cv_results_ = results_table
+        else:
+            self.cv_results_ = None
         self.best_forecaster_ = forecaster.set_params(**best_params)
 
         # Refit model with best parameters.
         if self.refit:
+            refit_start = time.perf_counter()
             self.best_forecaster_.fit(y=y, X=X, fh=fh)
+            self.refit_time_ = time.perf_counter() - refit_start
+        else:
+            self.refit_time_ = 0.0
 
         return self
 
+    def _extend_to_all_scitypes(self, tagname):
+        """Ensure mtypes for all scitypes are present in tag ``tagname``."""
+        from sktime.datatypes import mtype_to_scitype
+
+        tagval = self.get_tag(tagname)
+        if not isinstance(tagval, list):
+            tagval = [tagval]
+        scitypes = mtype_to_scitype(tagval, return_unique=True)
+
+        if "Series" not in scitypes:
+            tagval = tagval + ["pd.DataFrame"]
+        elif "pd.Series" in tagval and "pd.DataFrame" not in tagval:
+            tagval = ["pd.DataFrame"] + tagval
+
+        if "Panel" not in scitypes:
+            tagval = tagval + ["pd-multiindex"]
+        if "Hierarchical" not in scitypes:
+            tagval = tagval + ["pd_multiindex_hier"]
+
+        if self.tune_by_instance:
+            tagval = [x for x in tagval if mtype_to_scitype(x) == "Series"]
+
+        self.set_tags(**{tagname: tagval})
+
     def _predict(self, fh, X):
         """Forecast time series at future horizon.
 

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

@@ -1,11 +1,18 @@
 """Integration tests for sktime tuners."""
 # copyright: hyperactive developers, MIT License (see LICENSE file)
 
+import numpy as np
 import pytest
 from skbase.utils.dependencies import _check_soft_dependencies
 
 if _check_soft_dependencies("sktime", severity="none"):
+    from sktime.datasets import load_airline
+    from sktime.forecasting.naive import NaiveForecaster
+    from sktime.performance_metrics.forecasting import MeanAbsolutePercentageError
+    from sktime.split import ExpandingWindowSplitter
+
     from hyperactive.integrations.sktime import ForecastingOptCV, TSCOptCV
+    from hyperactive.opt import GridSearchSk
 
     EST_TO_TEST = [ForecastingOptCV, TSCOptCV]
 else:
@@ -20,3 +27,53 @@ def test_sktime_estimator(estimator):
     check_estimator(estimator, raise_exceptions=True)
     # The above line collects all API conformance tests in sktime and runs them.
     # It will raise an error if the estimator is not API conformant.
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies("sktime", severity="none"),
+    reason="sktime not installed",
+)
+def test_forecasting_opt_cv_sets_attributes():
+    """ForecastingOptCV exposes useful attributes after fitting."""
+    fh = [1, 2]
+    y = load_airline().iloc[:36]
+    cv = ExpandingWindowSplitter(initial_window=24, step_length=6, fh=fh)
+    optimizer = GridSearchSk(param_grid={"strategy": ["last", "mean"]})
+
+    tuner = ForecastingOptCV(
+        forecaster=NaiveForecaster(),
+        optimizer=optimizer,
+        cv=cv,
+        scoring=MeanAbsolutePercentageError(symmetric=True),
+        backend="None",
+    )
+
+    tuner.fit(y=y, fh=fh)
+
+    assert tuner.scorer_.name == "MeanAbsolutePercentageError"
+    assert tuner.n_splits_ == cv.get_n_splits(y)
+    assert tuner.refit_time_ >= 0
+
+    metric_col = "test_" + tuner.scorer_.name
+    assert metric_col in tuner.cv_results_.columns
+    assert np.isclose(tuner.best_score_, tuner.cv_results_[metric_col].mean())
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies("sktime", severity="none"),
+    reason="sktime not installed",
+)
+def test_forecasting_opt_cv_tune_by_flags():
+    """Tune-by flags should adjust estimator tags."""
+    tuner = ForecastingOptCV(
+        forecaster=NaiveForecaster(),
+        optimizer=GridSearchSk(param_grid={"strategy": ["last"]}),
+        cv=ExpandingWindowSplitter(initial_window=5, step_length=1, fh=[1]),
+        tune_by_instance=True,
+        tune_by_variable=True,
+    )
+
+    assert tuner.get_tag("scitype:y") == "univariate"
+    y_mtypes = tuner.get_tag("y_inner_mtype")
+    assert "pd-multiindex" not in y_mtypes
+    assert "pd_multiindex_hier" not in y_mtypes