feat: parallel intra strided-rolling

tsflex/features/function_wrapper.py

@@ -66,6 +66,7 @@ def __init__(
         output_names: Optional[Union[List[str], str]] = None,
         input_type: Optional[Union[np.array, pd.Series]] = np.array,
         vectorized: bool = False,
+        parallel: bool = False,
         **kwargs,
     ):
         """Create FuncWrapper instance."""
@@ -85,8 +86,10 @@ def __init__(
         assert not (
             vectorized & (input_type is not np.array)
         ), "The input_type must be np.array if vectorized is True!"
+        assert not (vectorized & parallel), "vectorized and parallel cannot be both True!"
         self.input_type = input_type
         self.vectorized = vectorized
+        self.parallel = parallel
 
         self._freeze()
 

tsflex/features/segmenter/strided_rolling.py

@@ -18,6 +18,7 @@
 from abc import ABC, abstractmethod
 from collections import namedtuple
 from typing import List, Optional, Tuple, TypeVar, Union
+from multiprocess import Pool
 
 import numpy as np
 import pandas as pd
@@ -444,6 +445,24 @@ def apply_func(self, func: FuncWrapper) -> pd.DataFrame:
             # when combining into an array
             out = out.T if out_type is tuple else out
 
+        elif func.parallel:
+            # Parallel function execution
+            with Pool() as pool:
+                out = np.array(
+                    list(
+                        pool.imap(
+                            func,
+                            *[
+                                [
+                                    sc.values[sc.start_indexes[idx] : sc.end_indexes[idx]]
+                                    for idx in range(len(self.index))
+                                ]
+                                for sc in self.series_containers
+                            ],
+                        )
+                    )
+                )
+
         else:
             # Sequential function execution (default)
             out = np.array(