Fix deterministic group_shuffle_split (#1839)

* order sets

* suggestion

* add unit test

* fix

* updated test

* fix

* indices from file

* test util update

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tests/datamodules/test_utils.py

@@ -30,8 +30,11 @@ def test_dataset_split() -> None:
 
 
 def test_group_shuffle_split() -> None:
-    alphabet = np.array(list("abcdefghijklmnopqrstuvwxyz"))
-    groups = np.random.randint(0, 26, size=(1000))
+    train_indices = [0, 2, 5, 6, 7, 8, 9, 10, 11, 13, 14]
+    test_indices = [1, 3, 4, 12]
+    np.random.seed(0)
+    alphabet = np.array(list("abc"))
+    groups = np.random.randint(0, 3, size=(15))
     groups = alphabet[groups]
 
     with pytest.raises(ValueError, match="You must specify `train_size` *"):
@@ -43,16 +46,21 @@ def test_group_shuffle_split() -> None:
         match=re.escape("`train_size` and `test_size` must be in the range (0,1)."),
     ):
         group_shuffle_split(groups, train_size=-0.2, test_size=1.2)
-    with pytest.raises(ValueError, match="26 groups were found, however the current *"):
+    with pytest.raises(ValueError, match="3 groups were found, however the current *"):
         group_shuffle_split(groups, train_size=None, test_size=0.999)
 
-    train_indices, test_indices = group_shuffle_split(
-        groups, train_size=None, test_size=0.2
-    )
-    assert len(set(train_indices) & set(test_indices)) == 0
-    assert len(set(groups[train_indices])) == 21
-    train_indices, test_indices = group_shuffle_split(
-        groups, train_size=0.8, test_size=None
-    )
-    assert len(set(train_indices) & set(test_indices)) == 0
-    assert len(set(groups[train_indices])) == 21
+    test_cases = [(None, 0.2, 42), (0.8, None, 42)]
+
+    for train_size, test_size, random_state in test_cases:
+        train_indices1, test_indices1 = group_shuffle_split(
+            groups,
+            train_size=train_size,
+            test_size=test_size,
+            random_state=random_state,
+        )
+        # Check that the results are the same as expected
+        assert np.array_equal(train_indices, train_indices1)
+        assert np.array_equal(test_indices, test_indices1)
+
+        assert len(set(train_indices1) & set(test_indices1)) == 0
+        assert len(set(groups[train_indices1])) == 2

torchgeo/datamodules/utils.py

@@ -185,7 +185,7 @@ def group_shuffle_split(
     if train_size <= 0 or train_size >= 1 or test_size <= 0 or test_size >= 1:
         raise ValueError("`train_size` and `test_size` must be in the range (0,1).")
 
-    group_vals = set(groups)
+    group_vals = sorted(set(groups))
     n_groups = len(group_vals)
     n_test_groups = round(n_groups * test_size)
     n_train_groups = n_groups - n_test_groups
@@ -198,7 +198,7 @@ def group_shuffle_split(
 
     generator = np.random.default_rng(seed=random_state)
     train_group_vals = set(
-        generator.choice(list(group_vals), size=n_train_groups, replace=False)
+        generator.choice(group_vals, size=n_train_groups, replace=False)
     )
 
     train_idxs = []