[ENH] Refactoring and Generalizing DelegatedForecaster

sktime/base/_base.py

@@ -60,6 +60,7 @@ class name: BaseEstimator
 from sklearn.ensemble._base import _set_random_states
 
 from sktime.exceptions import NotFittedError
+from sktime.utils.estimator_checks import delegate_if_needed
 
 
 class BaseObject(_BaseEstimator):
@@ -68,6 +69,14 @@ class BaseObject(_BaseEstimator):
     Extends scikit-learn's BaseEstimator to include sktime interface for tags.
     """
 
+    _delegate_name = None
+
+    def _get_delegate(self):
+        est = self
+        while est._delegate_name:
+            est = getattr(est, est._delegate_name)
+        return est
+
     def __init__(self):
         self._tags_dynamic = dict()
         super(BaseObject, self).__init__()
@@ -634,10 +643,12 @@ def __init__(self):
         super(BaseEstimator, self).__init__()
 
     @property
+    @delegate_if_needed
     def is_fitted(self):
         """Whether `fit` has been called."""
         return self._is_fitted
 
+    @delegate_if_needed
     def check_is_fitted(self):
         """Check if the estimator has been fitted.
 

sktime/forecasting/base/_base.py

@@ -55,6 +55,7 @@ class name: BaseForecaster
 )
 from sktime.forecasting.base import ForecastingHorizon
 from sktime.utils.datetime import _shift
+from sktime.utils.estimator_checks import delegate_if_needed
 from sktime.utils.validation._dependencies import _check_dl_dependencies
 from sktime.utils.validation.forecasting import check_alpha, check_cv, check_fh, check_X
 from sktime.utils.validation.series import check_equal_time_index
@@ -191,6 +192,7 @@ def __or__(self, other):
         else:
             return NotImplemented
 
+    @delegate_if_needed
     def fit(self, y, X=None, fh=None):
         """Fit forecaster to training data.
 
@@ -271,6 +273,7 @@ def fit(self, y, X=None, fh=None):
 
         return self
 
+    @delegate_if_needed
     def predict(
         self,
         fh=None,
@@ -331,6 +334,7 @@ def predict(
 
         return y_out
 
+    @delegate_if_needed
     def fit_predict(self, y, X=None, fh=None):
         """Fit and forecast time series at future horizon.
 
@@ -408,6 +412,7 @@ def fit_predict(self, y, X=None, fh=None):
         #  input conversions are skipped since we are using X_inner
         return self.predict(fh=fh, X=X_inner)
 
+    @delegate_if_needed
     def predict_quantiles(self, fh=None, X=None, alpha=None):
         """Compute/return quantile forecasts.
 
@@ -479,6 +484,7 @@ def predict_quantiles(self, fh=None, X=None, alpha=None):
 
         return quantiles
 
+    @delegate_if_needed
     def predict_interval(
         self,
         fh=None,
@@ -556,6 +562,7 @@ def predict_interval(
 
         return pred_int
 
+    @delegate_if_needed
     def predict_var(self, fh=None, X=None, cov=False):
         """Compute/return variance forecasts.
 
@@ -626,6 +633,7 @@ def predict_var(self, fh=None, X=None, cov=False):
 
         return pred_var
 
+    @delegate_if_needed
     def predict_proba(self, fh=None, X=None, marginal=True):
         """Compute/return fully probabilistic forecasts.
 
@@ -691,6 +699,7 @@ def predict_proba(self, fh=None, X=None, marginal=True):
 
         return pred_dist
 
+    @delegate_if_needed
     def update(self, y, X=None, update_params=True):
         """Update cutoff value and, optionally, fitted parameters.
 
@@ -765,6 +774,7 @@ def update(self, y, X=None, update_params=True):
 
         return self
 
+    @delegate_if_needed
     def update_predict(
         self,
         y,
@@ -851,6 +861,7 @@ def update_predict(
             reset_forecaster=reset_forecaster,
         )
 
+    @delegate_if_needed
     def update_predict_single(
         self,
         y=None,
@@ -939,6 +950,7 @@ def update_predict_single(
             update_params=update_params,
         )
 
+    @delegate_if_needed
     def predict_residuals(self, y=None, X=None):
         """Return residuals of time series forecasts.
 
@@ -1013,6 +1025,7 @@ def predict_residuals(self, y=None, X=None):
 
         return y_res
 
+    @delegate_if_needed
     def score(self, y, X=None, fh=None):
         """Scores forecast against ground truth, using MAPE.
 
@@ -1377,6 +1390,7 @@ def _get_y_pred(self, y_in_sample, y_out_sample):
         return y_pred
 
     @property
+    @delegate_if_needed
     def cutoff(self):
         """Cut-off = "present time" state of forecaster.
 
@@ -1417,6 +1431,13 @@ def _set_cutoff_from_y(self, y):
         self._cutoff = cutoff_idx
 
     @property
+    @delegate_if_needed
+    def y(self):
+        """Return y that was passed."""
+        return self._y
+
+    @property
+    @delegate_if_needed
     def fh(self):
         """Forecasting horizon that was passed."""
         # raise error if some method tries to accessed it before it has been set