Reduce warnings and flakiness in tests. (#10659)

- Fix warnings in tests.
- Try to reduce the flakiness of dask test.

python-package/xgboost/testing/__init__.py

@@ -37,6 +37,7 @@ from scipy import sparse
 import xgboost as xgb
 from xgboost import RabitTracker
 from xgboost.core import ArrayLike
+from xgboost.data import is_pd_cat_dtype
 from xgboost.sklearn import SklObjective
 from xgboost.testing.data import (
     get_california_housing,
@@ -403,7 +404,6 @@ def make_categorical(
     X, y
     """
     import pandas as pd
-    from pandas.api.types import is_categorical_dtype
 
     rng = np.random.RandomState(1994)
 
@@ -431,8 +431,8 @@ def make_categorical(
                 low=0, high=n_samples - 1, size=int(n_samples * sparsity)
             )
             df.iloc[index, i] = np.nan
-            if is_categorical_dtype(df.dtypes[i]):
-                assert n_categories == np.unique(df.dtypes[i].categories).size
+            if is_pd_cat_dtype(df.dtypes.iloc[i]):
+                assert n_categories == np.unique(df.dtypes.iloc[i].categories).size
 
     if onehot:
         df = pd.get_dummies(df)

python-package/xgboost/testing/updater.py

@@ -8,6 +8,7 @@ import numpy as np
 
 import xgboost as xgb
 import xgboost.testing as tm
+from xgboost.data import is_pd_cat_dtype
 
 
 def get_basescore(model: xgb.XGBModel) -> float:
@@ -166,8 +167,6 @@ def check_cut(
     n_entries: int, indptr: np.ndarray, data: np.ndarray, dtypes: Any
 ) -> None:
     """Check the cut values."""
-    from pandas.api.types import is_categorical_dtype
-
     assert data.shape[0] == indptr[-1]
     assert data.shape[0] == n_entries
 
@@ -177,18 +176,18 @@ def check_cut(
         end = int(indptr[i])
         for j in range(beg + 1, end):
             assert data[j] > data[j - 1]
-            if is_categorical_dtype(dtypes[i - 1]):
+            if is_pd_cat_dtype(dtypes.iloc[i - 1]):
                 assert data[j] == data[j - 1] + 1
 
 
 def check_get_quantile_cut_device(tree_method: str, use_cupy: bool) -> None:
     """Check with optional cupy."""
-    from pandas.api.types import is_categorical_dtype
+    import pandas as pd
 
     n_samples = 1024
     n_features = 14
     max_bin = 16
-    dtypes = [np.float32] * n_features
+    dtypes = pd.Series([np.float32] * n_features)
 
     # numerical
     X, y, w = tm.make_regression(n_samples, n_features, use_cupy=use_cupy)
@@ -237,7 +236,7 @@ def check_get_quantile_cut_device(tree_method: str, use_cupy: bool) -> None:
     X, y = tm.make_categorical(
         n_samples, n_features, n_categories, False, sparsity=0.8, cat_ratio=0.5
     )
-    n_cat_features = len([0 for dtype in X.dtypes if is_categorical_dtype(dtype)])
+    n_cat_features = len([0 for dtype in X.dtypes if is_pd_cat_dtype(dtype)])
     n_num_features = n_features - n_cat_features
     n_entries = n_categories * n_cat_features + (max_bin + 1) * n_num_features
     # - qdm