From 001e663d42a2133493c11da122f2ccf313bdc2e9 Mon Sep 17 00:00:00 2001
From: Jiaming Yuan <jm.yuan@outlook.com>
Date: Thu, 15 Dec 2022 05:27:06 +0800
Subject: [PATCH] Set `enable_categorical` to True in predict. (#8592)

---
 python-package/xgboost/core.py | 18 +-----------------
 python-package/xgboost/dask.py |  6 +++---
 2 files changed, 4 insertions(+), 20 deletions(-)

diff --git a/python-package/xgboost/core.py b/python-package/xgboost/core.py
index c28f8849a..3c467e9fe 100644
--- a/python-package/xgboost/core.py
+++ b/python-package/xgboost/core.py
@@ -278,22 +278,6 @@ def _check_call(ret: int) -> None:
         raise XGBoostError(py_str(_LIB.XGBGetLastError()))
 
 
-def _has_categorical(booster: "Booster", data: DataType) -> bool:
-    """Check whether the booster and input data for prediction contain categorical data.
-
-    """
-    from .data import _is_cudf_df, _is_pandas_df
-    if _is_pandas_df(data) or _is_cudf_df(data):
-        ft = booster.feature_types
-        if ft is None:
-            enable_categorical = False
-        else:
-            enable_categorical = any(f == "c" for f in ft)
-    else:
-        enable_categorical = False
-    return enable_categorical
-
-
 def build_info() -> dict:
     """Build information of XGBoost.  The returned value format is not stable. Also, please
     note that build time dependency is not the same as runtime dependency. For instance,
@@ -2278,7 +2262,7 @@ class Booster:
             _transform_pandas_df,
         )
 
-        enable_categorical = _has_categorical(self, data)
+        enable_categorical = True
         if _is_pandas_df(data):
             data, fns, _ = _transform_pandas_df(data, enable_categorical)
             if validate_features:
diff --git a/python-package/xgboost/dask.py b/python-package/xgboost/dask.py
index cac867da0..e6fdaaf0c 100644
--- a/python-package/xgboost/dask.py
+++ b/python-package/xgboost/dask.py
@@ -72,7 +72,6 @@ from .core import (
     QuantileDMatrix,
     _deprecate_positional_args,
     _expect,
-    _has_categorical,
 )
 from .sklearn import (
     XGBClassifier,
@@ -1190,7 +1189,7 @@ def _infer_predict_output(
         kwargs = kwargs.copy()
         if kwargs.pop("predict_type") == "margin":
             kwargs["output_margin"] = True
-    m = DMatrix(test_sample)
+    m = DMatrix(test_sample, enable_categorical=True)
     # generated DMatrix doesn't have feature name, so no validation.
     test_predt = booster.predict(m, validate_features=False, **kwargs)
     n_columns = test_predt.shape[1] if len(test_predt.shape) > 1 else 1
@@ -1247,7 +1246,7 @@ async def _predict_async(
             m = DMatrix(
                 data=partition,
                 missing=missing,
-                enable_categorical=_has_categorical(booster, partition),
+                enable_categorical=True,
             )
             predt = booster.predict(
                 data=m,
@@ -1315,6 +1314,7 @@ async def _predict_async(
                 base_margin=base_margin,
                 feature_names=feature_names,
                 feature_types=feature_types,
+                enable_categorical=True,
             )
             predt = booster.predict(
                 m,