Cover approx tree method for categorical data tests. (#7569)

* Add tree to df tests.
* Add plotting tests.
* Add histogram tests.

tests/python-gpu/test_gpu_parse_tree.py

@@ -1,25 +1,14 @@
 import sys
-import pytest
-import xgboost as xgb
 
 sys.path.append("tests/python")
-import testing as tm
+from test_parse_tree import TestTreesToDataFrame
 
 
 def test_tree_to_df_categorical():
-    X, y = tm.make_categorical(100, 10, 31, False)
-    Xy = xgb.DMatrix(X, y, enable_categorical=True)
-    booster = xgb.train({"tree_method": "gpu_hist"}, Xy, num_boost_round=10)
-    df = booster.trees_to_dataframe()
-    for _, x in df.iterrows():
-        if x["Feature"] != "Leaf":
-            assert len(x["Category"]) == 1
+    cputest = TestTreesToDataFrame()
+    cputest.run_tree_to_df_categorical("gpu_hist")
 
 
 def test_split_value_histograms():
-    X, y = tm.make_categorical(1000, 10, 13, False)
-    reg = xgb.XGBRegressor(tree_method="gpu_hist", enable_categorical=True)
-    reg.fit(X, y)
-
-    with pytest.raises(ValueError, match="doesn't"):
-        reg.get_booster().get_split_value_histogram("3", bins=5)
+    cputest = TestTreesToDataFrame()
+    cputest.run_split_value_histograms("gpu_hist")