Support pandas nullable types.

python-package/xgboost/data.py

@@ -220,6 +220,11 @@ def _is_modin_df(data):
     'float32': 'float',
     'float64': 'float',
     'bool': 'i',
+    # nullable types
+    "Int16": "int",
+    "Int32": "int",
+    "Int64": "int",
+    "boolean": "i",
 }
 
 
@@ -242,6 +247,7 @@ def _invalid_dataframe_dtype(data: Any) -> None:
     raise ValueError(msg)
 
 
+# pylint: disable=too-many-locals
 def _transform_pandas_df(
     data: DataFrame,
     enable_categorical: bool,
@@ -251,11 +257,26 @@ def _transform_pandas_df(
     meta_type: Optional[str] = None,
 ) -> Tuple[np.ndarray, FeatureNames, Optional[List[str]]]:
     import pandas as pd
-    from pandas.api.types import is_sparse, is_categorical_dtype
+    from pandas.api.types import (
+        is_sparse,
+        is_categorical_dtype,
+        is_integer_dtype,
+        is_bool_dtype,
+    )
+
+    nullable_alias = {"Int16", "Int32", "Int64"}
+
+    # dtype: pd.core.arrays.numeric.NumericDtype
+    def is_nullable_dtype(dtype: Any) -> bool:
+        is_int = is_integer_dtype(dtype) and dtype.name in nullable_alias
+        # np.bool has alias `bool`, while pd.BooleanDtype has `boolean`.
+        is_bool = is_bool_dtype(dtype) and dtype.name == "boolean"
+        return is_int or is_bool
 
     if not all(
         dtype.name in _pandas_dtype_mapper
         or is_sparse(dtype)
+        or is_nullable_dtype(dtype)
         or (is_categorical_dtype(dtype) and enable_categorical)
         for dtype in data.dtypes
     ):
@@ -284,7 +305,9 @@ def _transform_pandas_df(
     # handle category codes.
     transformed = pd.DataFrame()
     # Avoid transformation due to: PerformanceWarning: DataFrame is highly fragmented
-    if enable_categorical and any(is_categorical_dtype(dtype) for dtype in data.dtypes):
+    if (
+        enable_categorical and any(is_categorical_dtype(dtype) for dtype in data.dtypes)
+    ) or any(is_nullable_dtype(dtype) for dtype in data.dtypes):
         for i, dtype in enumerate(data.dtypes):
             if is_categorical_dtype(dtype):
                 # pandas uses -1 as default missing value for categorical data
@@ -293,6 +316,9 @@ def _transform_pandas_df(
                     .cat.codes.astype(np.float32)
                     .replace(-1.0, np.NaN)
                 )
+            elif is_nullable_dtype(dtype):
+                # Converts integer <NA> to float NaN
+                transformed[data.columns[i]] = data[data.columns[i]].astype(np.float32)
             else:
                 transformed[data.columns[i]] = data[data.columns[i]]
     else:

tests/python/test_with_pandas.py

@@ -1,4 +1,5 @@
-# -*- coding: utf-8 -*-
+import os
+import tempfile
 import numpy as np
 import xgboost as xgb
 import testing as tm
@@ -293,3 +294,39 @@ def test_cv_as_pandas(self):
         assert isinstance(params, list)
         assert 'auc' not in cv.columns[0]
         assert 'error' in cv.columns[0]
+
+    def test_nullable_type(self):
+        y = np.random.default_rng(0).random(4)
+
+        def to_bytes(Xy: xgb.DMatrix) -> bytes:
+            with tempfile.TemporaryDirectory() as tmpdir:
+                path = os.path.join(tmpdir, "Xy.dmatrix")
+                Xy.save_binary(path)
+                with open(path, "rb") as fd:
+                    result = fd.read()
+            return result
+
+        def test_int(dtype) -> bytes:
+            arr = pd.DataFrame(
+                {"f0": [1, 2, None, 3], "f1": [4, 3, None, 1]}, dtype=dtype
+            )
+            Xy = xgb.DMatrix(arr, y)
+            Xy.feature_types = None
+            return to_bytes(Xy)
+
+        b0 = test_int(np.float32)
+        b1 = test_int(pd.Int16Dtype())
+        assert b0 == b1
+
+        def test_bool(dtype) -> bytes:
+            arr = pd.DataFrame(
+                {"f0": [True, False, None, True], "f1": [False, True, None, True]},
+                dtype=dtype,
+            )
+            Xy = xgb.DMatrix(arr, y)
+            Xy.feature_types = None
+            return to_bytes(Xy)
+
+        b0 = test_bool(pd.BooleanDtype())
+        b1 = test_bool(np.bool)
+        assert b0 != b1         # None is converted to False with np.bool