transformers refactor: summarizer and base class

sktime/tests/test_all_estimators.py

@@ -348,6 +348,7 @@ def test_fit_returns_self(estimator_instance):
 def test_raises_not_fitted_error(estimator_instance):
     """Check that we raise appropriate error for unfitted estimators."""
     estimator = estimator_instance
+
     # call methods without prior fitting and check that they raise our
     # NotFittedError
     for method in NON_STATE_CHANGING_METHODS:

sktime/transformations/base.py

@@ -318,11 +318,8 @@ def transform(self, X, y=None, Z=None):
         """
         X = _handle_alias(X, Z)
 
-        # check whether is fitted, unless fit-in-transform is true
-        if self.get_tag("fit-in-transform"):
-            self.fit(X=X, y=y, Z=Z)
-        else:
-            self.check_is_fitted()
+        # check whether is fitted
+        self.check_is_fitted()
 
         # input checks and minor coercions on X, y
         ###########################################
@@ -400,16 +397,14 @@ def transform(self, X, y=None, Z=None):
                 )
             X = convert_to(X, to_type="df-list", as_scitype="Panel")
 
-            if self.get_tag("fit-in-transform"):
-                Xt = [clone(self).transform(Xi) for Xi in X]
+            # these are the transformers-per-instanced, fitted in fit
+            transformers = self.transformers_
+            if len(transformers) != len(X):
+                raise RuntimeError(
+                    "found different number of instances in transform than in fit"
+                )
             else:
-                transformers = self.transformers_
-                if len(transformers) != len(X):
-                    raise RuntimeError(
-                        "found different number of instances in transform than in fit"
-                    )
-                else:
-                    Xt = [transformers[i].transform(X[i]) for i in range(len(X))]
+                Xt = [transformers[i].transform(X[i]) for i in range(len(X))]
             # now we have a list of transformed instances
 
             # if the output is Series, Xt is a Panel and we convert back
@@ -420,7 +415,7 @@ def transform(self, X, y=None, Z=None):
             # we concatenate those and overwrite the index with that of X
             elif output_scitype == "Primitives":
                 Xt = pd.concat(Xt)
-                Xt.index = X.index
+                Xt = Xt.reset_index(drop=True)
             return Xt
 
         # convert X/y to supported inner type, if necessary
@@ -476,6 +471,9 @@ def transform(self, X, y=None, Z=None):
             )
         elif output_scitype == "Primitives":
             # we "abuse" the Series converter to ensure df output
+            # & reset index to have integers for instances
+            if isinstance(Xt, (pd.DataFrame, pd.Series)):
+                Xt = Xt.reset_index(drop=True)
             Xt = convert_to(
                 Xt,
                 to_type="pd.DataFrame",

sktime/transformations/series/summarize.py

@@ -10,7 +10,6 @@
 
 from sktime.transformations.base import _SeriesToPrimitivesTransformer
 from sktime.utils._maint import deprecated
-from sktime.utils.validation.series import check_series
 
 ALLOWED_SUM_FUNCS = [
     "mean",
@@ -133,6 +132,7 @@ class SummaryTransformer(_SeriesToPrimitivesTransformer):
 
     _tags = {
         "fit-in-transform": True,
+        "X_inner_mtype": ["pd.DataFrame", "pd.Series"],
     }
 
     def __init__(
@@ -176,25 +176,6 @@ def _transform(self, Z, X=None):
 
         return summary_value.T
 
-    def transform(self, Z, X=None):
-        """Transform series.
-
-        Parameters
-        ----------
-        Z : pd.Series or pd.DataFrame
-            The series to transform.
-
-        Returns
-        -------
-        summary_value : pd.DataFrame
-            DataFrame where series are instances (rows) and calculated summary
-            values are treated as features (columns).
-        """
-        self.check_is_fitted()
-        Z = check_series(Z)
-        summary_value = self._transform(Z, X=X)
-        return summary_value
-
 
 class MeanTransformer(SummaryTransformer):
     """Calculate mean value of a time series.