Support list and tuple for QDM. (#8542)

python-package/xgboost/data.py

@@ -187,8 +187,7 @@ def _from_numpy_array(
     feature_types: Optional[FeatureTypes],
 ) -> DispatchedDataBackendReturnType:
     """Initialize data from a 2-D numpy matrix."""
-    if len(data.shape) != 2:
-        raise ValueError("Expecting 2 dimensional numpy.ndarray, got: ", data.shape)
+    _check_data_shape(data)
     data, _ = _ensure_np_dtype(data, data.dtype)
     handle = ctypes.c_void_p()
     _check_call(
@@ -1199,6 +1198,8 @@ def _proxy_transform(
         return data, None, feature_names, feature_types
     if _is_dlpack(data):
         return _transform_dlpack(data), None, feature_names, feature_types
+    if _is_list(data) or _is_tuple(data):
+        data = np.array(data)
     if _is_numpy_array(data):
         data, _ = _ensure_np_dtype(data, data.dtype)
         return data, None, feature_names, feature_types
@@ -1245,6 +1246,7 @@ def dispatch_proxy_set_data(
         raise err
 
     if _is_numpy_array(data):
+        _check_data_shape(data)
         proxy._set_data_from_array(data)  # pylint: disable=W0212
         return
     if _is_scipy_csr(data):

tests/python/test_quantile_dmatrix.py

@@ -19,6 +19,7 @@ import xgboost as xgb
 
 class TestQuantileDMatrix:
     def test_basic(self) -> None:
+        """Checks for np array, list, tuple."""
         n_samples = 234
         n_features = 8
 
@@ -41,6 +42,18 @@ class TestQuantileDMatrix:
         assert Xy.num_row() == n_samples
         assert Xy.num_col() == n_features
 
+        n_samples = 64
+        data = []
+        for f in range(n_samples):
+            row = [f] * n_features
+            data.append(row)
+        assert np.array(data).shape == (n_samples, n_features)
+        Xy = xgb.QuantileDMatrix(data, max_bin=256)
+        assert Xy.num_row() == n_samples
+        assert Xy.num_col() == n_features
+        r = np.arange(1.0, n_samples)
+        np.testing.assert_allclose(Xy.get_data().toarray()[1:, 0], r)
+
     @pytest.mark.parametrize("sparsity", [0.0, 0.1, 0.8, 0.9])
     def test_with_iterator(self, sparsity: float) -> None:
         n_samples_per_batch = 317
@@ -242,6 +255,7 @@ class TestQuantileDMatrix:
         )
 
     def test_dtypes(self) -> None:
+        """Checks for both np array and pd DataFrame."""
         n_samples = 128
         n_features = 16
         for orig, x in np_dtypes(n_samples, n_features):