Support Series and Python primitives in inplace_predict and QDM (#8547)

tests/python/test_predict.py

@@ -5,7 +5,7 @@ import numpy as np
 import pandas as pd
 import pytest
 from scipy import sparse
-from xgboost.testing.data import np_dtypes
+from xgboost.testing.data import np_dtypes, pd_dtypes
 from xgboost.testing.shared import validate_leaf_output
 
 import xgboost as xgb
@@ -231,6 +231,7 @@ class TestInplacePredict:
         from_dmatrix = booster.predict(dtrain)
         np.testing.assert_allclose(from_dmatrix, from_inplace)
 
+    @pytest.mark.skipif(**tm.no_pandas())
     def test_dtypes(self) -> None:
         for orig, x in np_dtypes(self.rows, self.cols):
             predt_orig = self.booster.inplace_predict(orig)
@@ -246,3 +247,17 @@ class TestInplacePredict:
             X: np.ndarray = np.array(orig, dtype=dtype)
             with pytest.raises(ValueError):
                 self.booster.inplace_predict(X)
+
+    @pytest.mark.skipif(**tm.no_pandas())
+    def test_pd_dtypes(self) -> None:
+        from pandas.api.types import is_bool_dtype
+        for orig, x in pd_dtypes():
+            dtypes = orig.dtypes if isinstance(orig, pd.DataFrame) else [orig.dtypes]
+            if isinstance(orig, pd.DataFrame) and is_bool_dtype(dtypes[0]):
+                continue
+            y = np.arange(x.shape[0])
+            Xy = xgb.DMatrix(orig, y, enable_categorical=True)
+            booster = xgb.train({"tree_method": "hist"}, Xy, num_boost_round=1)
+            predt_orig = booster.inplace_predict(orig)
+            predt = booster.inplace_predict(x)
+            np.testing.assert_allclose(predt, predt_orig)

tests/python/test_with_pandas.py

@@ -298,22 +298,29 @@ class TestPandas:
         assert 'auc' not in cv.columns[0]
         assert 'error' in cv.columns[0]
 
-    def test_nullable_type(self) -> None:
+    @pytest.mark.parametrize("DMatrixT", [xgb.DMatrix, xgb.QuantileDMatrix])
+    def test_nullable_type(self, DMatrixT) -> None:
         from pandas.api.types import is_categorical
 
-        for DMatrixT in (xgb.DMatrix, xgb.QuantileDMatrix):
-            for orig, df in pd_dtypes():
+        for orig, df in pd_dtypes():
+            if hasattr(df.dtypes, "__iter__"):
                 enable_categorical = any(is_categorical for dtype in df.dtypes)
+            else:
+                # series
+                enable_categorical = is_categorical(df.dtype)
 
-                m_orig = DMatrixT(orig, enable_categorical=enable_categorical)
-                # extension types
-                m_etype = DMatrixT(df, enable_categorical=enable_categorical)
-                # different from pd.BooleanDtype(), None is converted to False with bool
-                if any(dtype == "bool" for dtype in orig.dtypes):
-                    assert not tm.predictor_equal(m_orig, m_etype)
-                else:
-                    assert tm.predictor_equal(m_orig, m_etype)
+            m_orig = DMatrixT(orig, enable_categorical=enable_categorical)
+            # extension types
+            m_etype = DMatrixT(df, enable_categorical=enable_categorical)
+            # different from pd.BooleanDtype(), None is converted to False with bool
+            if hasattr(orig.dtypes, "__iter__") and any(
+                dtype == "bool" for dtype in orig.dtypes
+            ):
+                assert not tm.predictor_equal(m_orig, m_etype)
+            else:
+                assert tm.predictor_equal(m_orig, m_etype)
 
+            if isinstance(df, pd.DataFrame):
                 f0 = df["f0"]
                 with pytest.raises(ValueError, match="Label contains NaN"):
                     xgb.DMatrix(df, f0, enable_categorical=enable_categorical)