list to List

Author: MatthewMiddlehurst

tsml/base.py

@@ -7,7 +7,7 @@
     "_clone_estimator",
 ]
 
-from typing import Tuple, Union
+from typing import List, Tuple, Union
 
 import numpy as np
 from numpy.random import RandomState
@@ -30,9 +30,9 @@ def _validate_data(
         **check_params,
     ) -> Union[
         Tuple[np.ndarray, object],
-        Tuple[list[np.ndarray], object],
+        Tuple[List[np.ndarray], object],
         np.ndarray,
-        list[np.ndarray],
+        List[np.ndarray],
     ]:
         """Validate input data and set or check the `n_features_in_` attribute.
 
@@ -109,7 +109,7 @@ def _validate_data(
 
         return out
 
-    def _check_n_features(self, X: Union[np.ndarray, list[np.ndarray]], reset: bool):
+    def _check_n_features(self, X: Union[np.ndarray, List[np.ndarray]], reset: bool):
         """Set the `n_features_in_` attribute, or check against it.
 
         Uses the `scikit-learn` 1.2.1 `_check_n_features` function as a base.
@@ -172,7 +172,7 @@ def _more_tags(self) -> dict:
     @classmethod
     def get_test_params(
         cls, parameter_set: Union[str, None] = None
-    ) -> Union[dict, list[dict]]:
+    ) -> Union[dict, List[dict]]:
         """Return unit test parameter settings for the estimator.
 
         Parameters

tsml/utils/discovery.py

@@ -9,7 +9,7 @@
 from importlib import import_module
 from operator import itemgetter
 from pathlib import Path
-from typing import Union
+from typing import List, Union
 
 from sklearn.base import (
     BaseEstimator,
@@ -25,7 +25,7 @@
 }
 
 
-def all_estimators(type_filter: Union[str, list[str]] = None):
+def all_estimators(type_filter: Union[str, List[str]] = None):
     """Get a list of all estimators from `tsml`.
 
     This function crawls the module and gets all classes that inherit

tsml/utils/testing.py

@@ -9,7 +9,7 @@
 ]
 
 from functools import partial
-from typing import Callable, Tuple, Union
+from typing import Callable, List, Tuple, Union
 
 import numpy as np
 from sklearn.base import BaseEstimator
@@ -24,7 +24,7 @@
 from tsml.utils.discovery import all_estimators
 
 
-def generate_test_estimators() -> list[BaseEstimator]:
+def generate_test_estimators() -> List[BaseEstimator]:
     """Generate a list of all estimators in tsml with test parameters.
 
     Uses estimator parameters from `get_test_params` if available.
@@ -51,7 +51,7 @@ def generate_test_estimators() -> list[BaseEstimator]:
     return estimators
 
 
-def parametrize_with_checks(estimators: list[BaseEstimator]) -> Callable:
+def parametrize_with_checks(estimators: List[BaseEstimator]) -> Callable:
     """Pytest specific decorator for parametrizing estimator checks.
 
     If the estimator is a `BaseTimeSeriesEstimator` then the `tsml` checks are used,

tsml/utils/validation.py

@@ -13,7 +13,7 @@
 import os
 import warnings
 from importlib import import_module
-from typing import Tuple, Union
+from typing import List, Tuple, Union
 
 import numpy as np
 from packaging.requirements import InvalidRequirement, Requirement
@@ -97,7 +97,7 @@ def is_clusterer(estimator: BaseEstimator) -> bool:
     return getattr(estimator, "_estimator_type", None) == "clusterer"
 
 
-def _num_features(X: Union[np.ndarray, list[np.ndarray]]) -> tuple[int]:
+def _num_features(X: Union[np.ndarray, List[np.ndarray]]) -> tuple[int]:
     """Return the number of features of a 3D numpy array or a list of 2D numpy arrays.
 
     Returns
@@ -127,7 +127,7 @@ def check_X_y(
     ensure_min_series_length: int = 2,
     estimator: Union[str, BaseEstimator, None] = None,
     y_numeric: bool = False,
-) -> Union[Tuple[np.ndarray, np.ndarray], Tuple[list[np.ndarray], np.ndarray]]:
+) -> Union[Tuple[np.ndarray, np.ndarray], Tuple[List[np.ndarray], np.ndarray]]:
     """Input validation for standard estimators.
 
     Checks X and y for consistent length, enforces X to be 3D and y 1D. By default,