Fix minor silent issues in TransformedTargetForecaster (#845)

* Fix minor silent issues

* Apply inverse transform to pred_int

* Fix pydocstyle

Co-authored-by: Walter <walmar2@emea.corpdir.net>

sktime/forecasting/compose/_pipeline.py

@@ -19,7 +19,7 @@ class _Pipeline(
     _HeterogenousMetaEstimator,
 ):
     def _check_steps(self):
-        """Check Steps
+        """Check Steps.
 
         Parameters
         ----------
@@ -70,23 +70,45 @@ def _iter_transformers(self, reverse=False):
             yield idx, name, transformer
 
     def __len__(self):
-        """
-        Returns the length of the Pipeline
-        """
+        """Return the length of the Pipeline."""
         return len(self.steps)
 
+    def _get_inverse_transform(self, y):
+        """Iterate over transformers.
+
+        Inverse transform y (used for y_pred and pred_int)
+
+        Parameters
+        ----------
+        y : pd.Series
+
+        Returns
+        -------
+        y : pd.Series
+            Inverse transformed y
+        """
+        for _, _, transformer in self._iter_transformers(reverse=True):
+            # skip sktime transformers where inverse transform
+            # is not wanted ur meaningful (e.g. Imputer, HampelFilter)
+            skip_trafo = transformer.get_tag("skip-inverse-transform", False)
+            if not skip_trafo:
+                y = transformer.inverse_transform(y)
+        return y
+
     @property
     def named_steps(self):
-        """Map the steps to a dictionary"""
+        """Map the steps to a dictionary."""
         return dict(self.steps)
 
     def get_params(self, deep=True):
         """Get parameters for this estimator.
+
         Parameters
         ----------
         deep : boolean, optional
             If True, will return the parameters for this estimator and
             contained subobjects that are estimators.
+
         Returns
         -------
         params : mapping of string to any
@@ -96,7 +118,9 @@ def get_params(self, deep=True):
 
     def set_params(self, **kwargs):
         """Set the parameters of this estimator.
+
         Valid parameter keys can be listed with ``get_params()``.
+
         Returns
         -------
         self
@@ -106,12 +130,12 @@ def set_params(self, **kwargs):
 
 
 class ForecastingPipeline(_Pipeline):
-    """
-    Pipeline for forecasting with exogenous data to apply transformers
-    to the exogenous serieses. The forecaster can also be a
-    TransformedTargetForecaster containing transformers to
-    transform y. ForecastingPipeline is only applying the given transformers
-    to X.
+    """Meta-estimator for forecasting with exogenous data.
+
+    ForecastingPipeline is apply transformers to the exogenous serieses.
+    The given forecaster as last step can also be a TransformedTargetForecaster
+    containing transformers to transform y. ForecastingPipeline is only applying
+    the given transformers to X.
 
     Parameters
     ----------
@@ -207,16 +231,17 @@ def _predict(self, fh=None, X=None, return_pred_int=False, alpha=DEFAULT_ALPHA):
             Prediction intervals
         """
         forecaster = self.steps_[-1][1]
+
         # If X is not given, just passthrough the data without transformation
         if self._X is not None:
             # transform X before doing prediction
             for _, _, transformer in self._iter_transformers():
                 X = transformer.transform(X)
-        y_pred = forecaster.predict(fh, X, return_pred_int=return_pred_int, alpha=alpha)
-        return y_pred
+
+        return forecaster.predict(fh, X, return_pred_int=return_pred_int, alpha=alpha)
 
     def _update(self, y, X=None, update_params=True):
-        """Update fitted parameters
+        """Update fitted parameters.
 
         Parameters
         ----------
@@ -259,9 +284,11 @@ def _update(self, y, X=None, update_params=True):
 
 
 class TransformedTargetForecaster(_Pipeline, _SeriesToSeriesTransformer):
-    """
-    Meta-estimator for forecasting transformed time series.
-    Pipeline functionality to apply transformers to the target series.
+    """Meta-estimator for forecasting transformed time series.
+
+    Pipeline functionality to apply transformers to the target series. The
+    X data is not transformed. If you want to transform X, please use the
+    ForecastingPipeline.
 
     Parameters
     ----------
@@ -317,7 +344,7 @@ def _fit(self, y, X=None, fh=None):
         # transform
         for step_idx, name, transformer in self._iter_transformers():
             t = clone(transformer)
-            y = t.fit_transform(y)
+            y = t.fit_transform(y, X)
             self.steps_[step_idx] = (name, t)
 
         # fit forecaster
@@ -348,18 +375,26 @@ def _predict(self, fh=None, X=None, return_pred_int=False, alpha=DEFAULT_ALPHA):
             Prediction intervals
         """
         forecaster = self.steps_[-1][1]
-        y_pred = forecaster.predict(fh, X, return_pred_int=return_pred_int, alpha=alpha)
-
-        for _, _, transformer in self._iter_transformers(reverse=True):
-            # skip sktime transformers where inverse transform
-            # is not wanted ur meaningful (e.g. Imputer, HampelFilter)
-            skip_trafo = transformer.get_tag("skip-inverse-transform", False)
-            if not skip_trafo:
-                y_pred = transformer.inverse_transform(y_pred)
-        return y_pred
+        if return_pred_int:
+            y_pred, pred_int = forecaster.predict(
+                fh, X, return_pred_int=return_pred_int, alpha=alpha
+            )
+            # inverse transform pred_int
+            pred_int["lower"] = self._get_inverse_transform(pred_int["lower"])
+            pred_int["upper"] = self._get_inverse_transform(pred_int["upper"])
+        else:
+            y_pred = forecaster.predict(
+                fh, X, return_pred_int=return_pred_int, alpha=alpha
+            )
+        # inverse transform y_pred
+        y_pred = self._get_inverse_transform(y_pred)
+        if return_pred_int:
+            return y_pred, pred_int
+        else:
+            return y_pred
 
     def _update(self, y, X=None, update_params=True):
-        """Update fitted parameters
+        """Update fitted parameters.
 
         Parameters
         ----------
@@ -371,7 +406,6 @@ def _update(self, y, X=None, update_params=True):
         -------
         self : an instance of self
         """
-
         for step_idx, name, transformer in self._iter_transformers():
             if hasattr(transformer, "update"):
                 transformer.update(y, X, update_params=update_params)
@@ -383,13 +417,38 @@ def _update(self, y, X=None, update_params=True):
         return self
 
     def transform(self, Z, X=None):
+        """Transform data.
+
+        Returns a transformed version of Z.
+
+        Parameters
+        ----------
+        Z : pd.Series, pd.DataFrame
+
+        Returns
+        -------
+        Z : pd.Series, pd.DataFrame
+            Transformed time series(es).
+        """
         self.check_is_fitted()
         zt = check_series(Z, enforce_univariate=True)
         for _, _, transformer in self._iter_transformers():
             zt = transformer.transform(zt, X)
         return zt
 
     def inverse_transform(self, Z, X=None):
+        """Reverse transformation on input series `Z`.
+
+        Parameters
+        ----------
+        Z : pd.Series or pd.DataFrame
+            A time series to reverse the transformation on.
+
+        Returns
+        -------
+        Z_inv : pd.Series or pd.DataFrame
+            The reconstructed timeseries after the transformation has been reversed.
+        """
         self.check_is_fitted()
         zt = check_series(Z, enforce_univariate=True)
         for _, _, transformer in self._iter_transformers(reverse=True):