feat: add shuffle parameter to train_test_split

src/litdata/utilities/train_test_split.py

@@ -12,7 +12,7 @@
 
 
 def train_test_split(
-    streaming_dataset: StreamingDataset, splits: list[float], seed: int = 42
+    streaming_dataset: StreamingDataset, splits: list[float], seed: int = 42, shuffle: bool = True
 ) -> list[StreamingDataset]:
     """Splits a StreamingDataset into multiple subsets for training, testing, and validation.
 
@@ -24,6 +24,7 @@ def train_test_split(
         splits: A list of floats representing the proportion of data to be allocated to each split
                              (e.g., [0.8, 0.1, 0.1] for 80% training, 10% testing, and 10% validation).
         seed: An integer used to seed the random number generator for reproducibility.
+        shuffle: A boolean indicating whether to shuffle the data before splitting.
 
     Returns:
         List[StreamingDataset]: A list of StreamingDataset instances, where each element represents a split of the
@@ -71,9 +72,10 @@ def train_test_split(
 
     dataset_length = sum([my_roi[1] - my_roi[0] for my_roi in dummy_subsampled_roi])
 
-    subsampled_chunks, dummy_subsampled_roi = shuffle_lists_together(
-        subsampled_chunks, dummy_subsampled_roi, np.random.RandomState([seed])
-    )
+    if shuffle:
+        subsampled_chunks, dummy_subsampled_roi = shuffle_lists_together(
+            subsampled_chunks, dummy_subsampled_roi, np.random.RandomState([seed])
+        )
 
     item_count_list = [int(dataset_length * split) for split in splits]
 

tests/utilities/test_train_test_split.py

@@ -108,3 +108,34 @@ def test_train_test_split_with_streaming_dataloader(tmpdir, compression):
             for curr_idx in _dl:
                 assert curr_idx not in visited_indices
                 visited_indices.add(curr_idx)
+
+
+@pytest.mark.parametrize(
+    "compression",
+    [
+        pytest.param(None),
+        pytest.param("zstd", marks=pytest.mark.skipif(condition=not _ZSTD_AVAILABLE, reason="Requires: ['zstd']")),
+    ],
+)
+def test_train_test_split_with_shuffle_parameter(tmpdir, compression):
+    cache = Cache(str(tmpdir), chunk_size=10, compression=compression)
+    for i in range(100):
+        cache[i] = i
+    cache.done()
+    cache.merge()
+
+    my_streaming_dataset = StreamingDataset(input_dir=str(tmpdir))
+
+    train_shuffled, test_shuffled = train_test_split(my_streaming_dataset, splits=[0.8, 0.2], shuffle=True)
+    train_no_shuffle, test_no_shuffle = train_test_split(my_streaming_dataset, splits=[0.8, 0.2], shuffle=False)
+
+    assert len(train_shuffled) == 80
+    assert len(train_no_shuffle) == 80
+    assert len(test_shuffled) == 20
+    assert len(test_no_shuffle) == 20
+
+    shuffled_combined = train_shuffled.subsampled_files + test_shuffled.subsampled_files
+    no_shuffle_combined = train_no_shuffle.subsampled_files + test_no_shuffle.subsampled_files
+    assert shuffled_combined != no_shuffle_combined
+
+    assert no_shuffle_combined == my_streaming_dataset.subsampled_files