🔍 review

predict-idlab · Jan 5, 2024 · 7e2d5c6 · 7e2d5c6
1 parent 9526a32
commit 7e2d5c6
Showing 1 changed file with 11 additions and 9 deletions.
diff --git a/tsflex/features/feature_collection.py b/tsflex/features/feature_collection.py
@@ -277,8 +277,10 @@ def _executor_stroll(idx: int) -> pd.DataFrame:
         """Executor function for the StridedRolling.apply_func method.
 
         Strided rolling feature calculation occurs when either;
-        - a `window` and `stride` argument is stored in the `FeatureDescriptor` object
-          (or the `stride` argument is passed to the `calculate` method)
+        - a `window` and `stride` argument are stored in the `FeatureDescriptor` object
+        - the `window` is stored in the `FeatureDescriptor` object and the `stride`
+          argument is passed to the `calculate` method, potentially overriding the
+          `stride`
         - segment indices are passed to the `calculate` method
         - a `group_by_consecutive` argument is passed to the `calculate` method (since
           we calculate the segment indices for the consecutive groups)
@@ -339,9 +341,11 @@ def f(x: pd.DataFrame):
 
         # Aggregate function output in a dictionary
         output_names = [
-            "|".join(cols) + "__" + o + "__w=manual" for o in function.output_names
+            StridedRolling.construct_output_index(cols, feat_name, win_str="manual")
+            for feat_name in function.output_names
         ]
         feat_out = _process_func_output(out, group_ids, output_names, str(function))
+
         # Log the function execution time
         _log_func_execution(
             t_start, function, tuple(cols), "manual", "manual", output_names
@@ -353,18 +357,16 @@ def _group_feat_generator(
         self,
         grouped_df: pd.api.typing.DataFrameGroupBy,
     ) -> Callable[[int], Tuple[pd.api.typing.DataFrameGroupBy, FuncWrapper,],]:
-        keys_wins_strides = list(self._feature_desc_dict.keys())
-        lengths = np.cumsum(
-            [len(self._feature_desc_dict[k]) for k in keys_wins_strides]
-        )
+        keys_wins = list(self._feature_desc_dict.keys())
+        lengths = np.cumsum([len(self._feature_desc_dict[k]) for k in keys_wins])
 
         def get_group_function(
             idx,
         ) -> Tuple[pd.api.typing.DataFrameGroupBy, FuncWrapper,]:
             key_idx = np.searchsorted(lengths, idx, "right")  # right bc idx starts at 0
-            key, win = keys_wins_strides[key_idx]
+            key, win = keys_wins[key_idx]
 
-            feature = self._feature_desc_dict[keys_wins_strides[key_idx]][
+            feature = self._feature_desc_dict[keys_wins[key_idx]][
                 idx - lengths[key_idx]
             ]
             function: FuncWrapper = feature.function