fix: TCN model predict crash on single-sample final batch

qlib/contrib/model/pytorch_tcn_ts.py

@@ -279,7 +279,7 @@ def predict(self, dataset):
             with torch.no_grad():
                 pred = self.TCN_model(feature.float()).detach().cpu().numpy()
 
-            preds.append(pred)
+            preds.append(np.atleast_1d(pred))
 
         return pd.Series(np.concatenate(preds), index=dl_test.get_index())
 

tests/test_tcn_single_sample_batch.py

@@ -0,0 +1,39 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""Tests for TCN single-sample batch fix (Fixes #1752).
+
+When the last batch contains a single sample, the model output after
+.cpu().numpy() may be a 0-d array. np.concatenate then fails because
+it cannot concatenate 0-d arrays with 1-d arrays. Wrapping with
+np.atleast_1d ensures all arrays are at least 1-d.
+"""
+
+import numpy as np
+import pytest
+
+
+def test_concatenate_mixed_0d_and_1d():
+    """np.concatenate fails with raw 0-d + 1-d arrays, but works with atleast_1d."""
+    arr_1d = np.array([1.0, 2.0, 3.0])
+    arr_0d = np.float64(4.0)  # simulates single-sample .numpy() result
+
+    # Without fix this would raise
+    with pytest.raises((ValueError, np.exceptions.AxisError)):
+        np.concatenate([arr_1d, arr_0d])
+
+    # With atleast_1d it works
+    result = np.concatenate([np.atleast_1d(arr_1d), np.atleast_1d(arr_0d)])
+    np.testing.assert_array_equal(result, [1.0, 2.0, 3.0, 4.0])
+
+
+def test_atleast_1d_preserves_normal_arrays():
+    """atleast_1d should be a no-op for arrays that are already >= 1-d."""
+    arr = np.array([5.0, 6.0])
+    result = np.atleast_1d(arr)
+    np.testing.assert_array_equal(result, arr)
+    assert result.ndim >= 1
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])