Support arrow through pandas ext types. (#9612)

- Use pandas extension type for pyarrow support.
- Additional support for QDM.
- Additional support for inplace_predict.

tests/python/test_with_arrow.py

@@ -22,7 +22,7 @@ pytestmark = pytest.mark.skipif(
 dpath = "demo/data/"
 
 
-class TestArrowTable(unittest.TestCase):
+class TestArrowTable:
     def test_arrow_table(self):
         df = pd.DataFrame(
             [[0, 1, 2.0, 3.0], [1, 2, 3.0, 4.0]], columns=["a", "b", "c", "d"]
@@ -52,7 +52,8 @@ class TestArrowTable(unittest.TestCase):
         assert dm.num_row() == 4
         assert dm.num_col() == 3
 
-    def test_arrow_train(self):
+    @pytest.mark.parametrize("DMatrixT", [xgb.DMatrix, xgb.QuantileDMatrix])
+    def test_arrow_train(self, DMatrixT):
         import pandas as pd
 
         rows = 100
@@ -64,16 +65,24 @@ class TestArrowTable(unittest.TestCase):
             }
         )
         y = pd.Series(np.random.randn(rows))
+
         table = pa.Table.from_pandas(X)
-        dtrain1 = xgb.DMatrix(table)
-        dtrain1.set_label(y)
+        dtrain1 = DMatrixT(table)
+        dtrain1.set_label(pa.Table.from_pandas(pd.DataFrame(y)))
         bst1 = xgb.train({}, dtrain1, num_boost_round=10)
-        preds1 = bst1.predict(xgb.DMatrix(X))
-        dtrain2 = xgb.DMatrix(X, y)
+        preds1 = bst1.predict(DMatrixT(X))
+
+        dtrain2 = DMatrixT(X, y)
         bst2 = xgb.train({}, dtrain2, num_boost_round=10)
-        preds2 = bst2.predict(xgb.DMatrix(X))
+        preds2 = bst2.predict(DMatrixT(X))
+
         np.testing.assert_allclose(preds1, preds2)
 
+        preds3 = bst2.inplace_predict(table)
+        np.testing.assert_allclose(preds1, preds3)
+        assert bst2.feature_names == ["A", "B", "C"]
+        assert bst2.feature_types == ["int", "float", "int"]
+
     def test_arrow_survival(self):
         data = os.path.join(tm.data_dir(__file__), "veterans_lung_cancer.csv")
         table = pc.read_csv(data)