Support more scipy types. (#9881)

tests/python/test_dmatrix.py

@@ -112,39 +112,6 @@ class TestDMatrix:
         with pytest.raises(ValueError):
             xgb.DMatrix(data)
 
-    def test_csr(self):
-        indptr = np.array([0, 2, 3, 6])
-        indices = np.array([0, 2, 2, 0, 1, 2])
-        data = np.array([1, 2, 3, 4, 5, 6])
-        X = scipy.sparse.csr_matrix((data, indices, indptr), shape=(3, 3))
-        dtrain = xgb.DMatrix(X)
-        assert dtrain.num_row() == 3
-        assert dtrain.num_col() == 3
-
-    def test_csc(self):
-        row = np.array([0, 2, 2, 0, 1, 2])
-        col = np.array([0, 0, 1, 2, 2, 2])
-        data = np.array([1, 2, 3, 4, 5, 6])
-        X = scipy.sparse.csc_matrix((data, (row, col)), shape=(3, 3))
-        dtrain = xgb.DMatrix(X)
-        assert dtrain.num_row() == 3
-        assert dtrain.num_col() == 3
-
-        indptr = np.array([0, 3, 5])
-        data = np.array([0, 1, 2, 3, 4])
-        row_idx = np.array([0, 1, 2, 0, 2])
-        X = scipy.sparse.csc_matrix((data, row_idx, indptr), shape=(3, 2))
-        assert tm.predictor_equal(xgb.DMatrix(X.tocsr()), xgb.DMatrix(X))
-
-    def test_coo(self):
-        row = np.array([0, 2, 2, 0, 1, 2])
-        col = np.array([0, 0, 1, 2, 2, 2])
-        data = np.array([1, 2, 3, 4, 5, 6])
-        X = scipy.sparse.coo_matrix((data, (row, col)), shape=(3, 3))
-        dtrain = xgb.DMatrix(X)
-        assert dtrain.num_row() == 3
-        assert dtrain.num_col() == 3
-
     def test_np_view(self):
         # Sliced Float32 array
         y = np.array([12, 34, 56], np.float32)[::2]

tests/python/test_with_scipy.py

@@ -0,0 +1,87 @@
+import itertools
+import warnings
+from typing import Type
+
+import numpy as np
+import pytest
+import scipy.sparse
+
+import xgboost as xgb
+from xgboost import testing as tm
+
+
+@pytest.mark.filterwarnings("error")
+@pytest.mark.parametrize(
+    "DMatrixT,CSR",
+    [
+        (m, n)
+        for m, n in itertools.product(
+            (xgb.DMatrix, xgb.QuantileDMatrix),
+            (scipy.sparse.csr_matrix, scipy.sparse.csr_array),
+        )
+    ],
+)
+def test_csr(DMatrixT: Type[xgb.DMatrix], CSR: Type) -> None:
+    with warnings.catch_warnings():
+        indptr = np.array([0, 2, 3, 6])
+        indices = np.array([0, 2, 2, 0, 1, 2])
+        data = np.array([1, 2, 3, 4, 5, 6])
+        X = CSR((data, indices, indptr), shape=(3, 3))
+        dtrain = DMatrixT(X)
+        assert dtrain.num_row() == 3
+        assert dtrain.num_col() == 3
+        assert dtrain.num_nonmissing() == data.size
+
+
+@pytest.mark.filterwarnings("error")
+@pytest.mark.parametrize(
+    "DMatrixT,CSC",
+    [
+        (m, n)
+        for m, n in itertools.product(
+            (xgb.DMatrix, xgb.QuantileDMatrix),
+            (scipy.sparse.csc_matrix, scipy.sparse.csc_array),
+        )
+    ],
+)
+def test_csc(DMatrixT: Type[xgb.DMatrix], CSC: Type) -> None:
+    with warnings.catch_warnings():
+        row = np.array([0, 2, 2, 0, 1, 2])
+        col = np.array([0, 0, 1, 2, 2, 2])
+        data = np.array([1, 2, 3, 4, 5, 6])
+        X = CSC((data, (row, col)), shape=(3, 3))
+        dtrain = DMatrixT(X)
+        assert dtrain.num_row() == 3
+        assert dtrain.num_col() == 3
+        assert dtrain.num_nonmissing() == data.size
+
+        indptr = np.array([0, 3, 5])
+        data = np.array([0, 1, 2, 3, 4])
+        row_idx = np.array([0, 1, 2, 0, 2])
+        X = CSC((data, row_idx, indptr), shape=(3, 2))
+        assert tm.predictor_equal(DMatrixT(X.tocsr()), DMatrixT(X))
+
+
+@pytest.mark.filterwarnings("error")
+@pytest.mark.parametrize(
+    "DMatrixT,COO",
+    [
+        (m, n)
+        for m, n in itertools.product(
+            (xgb.DMatrix, xgb.QuantileDMatrix),
+            (scipy.sparse.coo_matrix, scipy.sparse.coo_array),
+        )
+    ],
+)
+def test_coo(DMatrixT: Type[xgb.DMatrix], COO: Type) -> None:
+    with warnings.catch_warnings():
+        row = np.array([0, 2, 2, 0, 1, 2])
+        col = np.array([0, 0, 1, 2, 2, 2])
+        data = np.array([1, 2, 3, 4, 5, 6])
+        X = COO((data, (row, col)), shape=(3, 3))
+        dtrain = DMatrixT(X)
+        assert dtrain.num_row() == 3
+        assert dtrain.num_col() == 3
+        assert dtrain.num_nonmissing() == data.size
+
+        assert tm.predictor_equal(DMatrixT(X.tocsr()), DMatrixT(X))