Refactor: use postponed evaluation of type annotations

tests/importance_tests/fanova_tests/test_tree.py

@@ -1,7 +1,6 @@
+from __future__ import annotations
+
 import math
-from typing import Dict
-from typing import List
-from typing import Tuple
 from unittest.mock import Mock
 
 import numpy as np
@@ -28,7 +27,7 @@ def tree() -> _FanovaTree:
 
 
 @pytest.fixture
-def expected_tree_statistics() -> List[Dict[str, List]]:
+def expected_tree_statistics() -> list[dict[str, list]]:
     # Statistics the each node in the tree.
     return [
         {"values": [0.1, 0.2, 0.5], "weights": [0.75, 0.25, 1.0]},
@@ -39,7 +38,7 @@ def expected_tree_statistics() -> List[Dict[str, List]]:
     ]
 
 
-def test_tree_variance(tree: _FanovaTree, expected_tree_statistics: List[Dict[str, List]]) -> None:
+def test_tree_variance(tree: _FanovaTree, expected_tree_statistics: list[dict[str, list]]) -> None:
     # The root node at node index `0` holds the values and weights for all nodes in the tree.
     expected_statistics = expected_tree_statistics[0]
     expected_values = expected_statistics["values"]
@@ -87,9 +86,9 @@ def test_tree_variance(tree: _FanovaTree, expected_tree_statistics: List[Dict[st
 )
 def test_tree_get_marginal_variance(
     tree: _FanovaTree,
-    features: List[int],
-    expected: List[Tuple[List[Size], List[Tuple[NodeIndex, Cardinality]]]],
-    expected_tree_statistics: List[Dict[str, List]],
+    features: list[int],
+    expected: list[tuple[list[Size], list[tuple[NodeIndex, Cardinality]]]],
+    expected_tree_statistics: list[dict[str, list]],
 ) -> None:
     variance = tree.get_marginal_variance(np.array(features))
 
@@ -145,9 +144,9 @@ def test_tree_get_marginal_variance(
 )
 def test_tree_get_marginalized_statistics(
     tree: _FanovaTree,
-    feature_vector: List[float],
-    expected: List[Tuple[NodeIndex, Cardinality]],
-    expected_tree_statistics: List[Dict[str, List]],
+    feature_vector: list[float],
+    expected: list[tuple[NodeIndex, Cardinality]],
+    expected_tree_statistics: list[dict[str, list]],
 ) -> None:
     value, weight = tree._get_marginalized_statistics(np.array(feature_vector))
 
@@ -167,7 +166,7 @@ def test_tree_get_marginalized_statistics(
 
 
 def test_tree_statistics(
-    tree: _FanovaTree, expected_tree_statistics: List[Dict[str, List]]
+    tree: _FanovaTree, expected_tree_statistics: list[dict[str, list]]
 ) -> None:
     statistics = tree._statistics
 
@@ -184,13 +183,13 @@ def test_tree_statistics(
 
 @pytest.mark.parametrize("node_index,expected", [(0, [0.5]), (1, [0.25, 0.75]), (2, [0.75, 1.75])])
 def test_tree_split_midpoints(
-    tree: _FanovaTree, node_index: NodeIndex, expected: List[float]
+    tree: _FanovaTree, node_index: NodeIndex, expected: list[float]
 ) -> None:
     np.testing.assert_equal(tree._split_midpoints[node_index], expected)
 
 
 @pytest.mark.parametrize("node_index,expected", [(0, [1.0]), (1, [0.5, 0.5]), (2, [1.5, 0.5])])
-def test_tree_split_sizes(tree: _FanovaTree, node_index: NodeIndex, expected: List[float]) -> None:
+def test_tree_split_sizes(tree: _FanovaTree, node_index: NodeIndex, expected: list[float]) -> None:
     np.testing.assert_equal(tree._split_sizes[node_index], expected)
 
 
@@ -205,7 +204,7 @@ def test_tree_split_sizes(tree: _FanovaTree, node_index: NodeIndex, expected: Li
     ],
 )
 def test_tree_subtree_active_features(
-    tree: _FanovaTree, node_index: NodeIndex, expected: List[bool]
+    tree: _FanovaTree, node_index: NodeIndex, expected: list[bool]
 ) -> None:
     active_features: np.ndarray = tree._subtree_active_features[node_index] == expected
     assert active_features.all()