[dask] prediction with categorical data.

python-package/xgboost/core.py

@@ -192,6 +192,22 @@ def _check_call(ret: int) -> None:
         raise XGBoostError(py_str(_LIB.XGBGetLastError()))
 
 
+def _has_categorical(booster: "Booster", data: Any) -> bool:
+    """Check whether the booster and input data for prediction contain categorical data.
+
+    """
+    from .data import _is_pandas_df, _is_cudf_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,
@@ -2046,17 +2062,9 @@ def inplace_predict(
                     f"got {data.shape[1]}"
                 )
 
-        from .data import _is_pandas_df, _transform_pandas_df
+        from .data import _is_pandas_df, _transform_pandas_df, _is_cudf_df
         from .data import _array_interface
-        if (
-            _is_pandas_df(data)
-            or lazy_isinstance(data, "cudf.core.dataframe", "DataFrame")
-        ):
-            ft = self.feature_types
-            if ft is None:
-                enable_categorical = False
-            else:
-                enable_categorical = any(f == "c" for f in ft)
+        enable_categorical = _has_categorical(self, data)
         if _is_pandas_df(data):
             data, _, _ = _transform_pandas_df(data, enable_categorical)
 
@@ -2111,7 +2119,7 @@ def inplace_predict(
                 )
             )
             return _prediction_output(shape, dims, preds, True)
-        if lazy_isinstance(data, "cudf.core.dataframe", "DataFrame"):
+        if _is_cudf_df(data):
             from .data import _cudf_array_interfaces, _transform_cudf_df
             data, cat_codes, _, _ = _transform_cudf_df(
                 data, None, None, enable_categorical

python-package/xgboost/dask.py

@@ -56,7 +56,7 @@
 
 from .core import DMatrix, DeviceQuantileDMatrix, Booster, _expect, DataIter
 from .core import Objective, Metric
-from .core import _deprecate_positional_args
+from .core import _deprecate_positional_args, _has_categorical
 from .data import FeatNamesT
 from .training import train as worker_train
 from .tracker import RabitTracker, get_host_ip
@@ -1241,7 +1241,11 @@ def mapped_predict(
         booster: Booster, partition: Any, is_df: bool, columns: List[int], _: Any
     ) -> Any:
         with config.config_context(**global_config):
-            m = DMatrix(data=partition, missing=missing)
+            m = DMatrix(
+                data=partition,
+                missing=missing,
+                enable_categorical=_has_categorical(booster, partition)
+            )
             predt = booster.predict(
                 data=m,
                 output_margin=output_margin,

python-package/xgboost/data.py

@@ -466,12 +466,8 @@ def _from_dt_df(
     return handle, feature_names, feature_types
 
 
-def _is_cudf_df(data):
-    try:
-        import cudf
-    except ImportError:
-        return False
-    return hasattr(cudf, 'DataFrame') and isinstance(data, cudf.DataFrame)
+def _is_cudf_df(data) -> bool:
+    return lazy_isinstance(data, "cudf.core.dataframe", "DataFrame")
 
 
 def _cudf_array_interfaces(data, cat_codes: list) -> bytes:

tests/python/test_with_dask.py

@@ -288,10 +288,23 @@ def check_model_output(model: xgb.dask.Booster) -> None:
     reg.fit(X, y, eval_set=[(X, y)])
     assert tm.non_increasing(reg.evals_result()["validation_0"]["rmse"])
 
+    booster = reg.get_booster()
+    predt = xgb.dask.predict(client, booster, X).compute().values
+    inpredt = xgb.dask.inplace_predict(client, booster, X).compute().values
+
+    if hasattr(predt, "get"):
+        predt = predt.get()
+    if hasattr(inpredt, "get"):
+        inpredt = inpredt.get()
+
+    np.testing.assert_allclose(predt, inpredt)
+
+
 def test_categorical(client: "Client") -> None:
     X, y = make_categorical(client, 10000, 30, 13)
     X_onehot, _ = make_categorical(client, 10000, 30, 13, True)
     run_categorical(client, "approx", X, X_onehot, y)
+    run_categorical(client, "hist", X, X_onehot, y)
 
 
 def test_dask_predict_shape_infer(client: "Client") -> None: