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_plotting.py

@@ -1,40 +1,17 @@
 import sys
-import xgboost as xgb
 import pytest
-import json
 
 sys.path.append("tests/python")
 import testing as tm
-
-try:
-    import matplotlib
-
-    matplotlib.use("Agg")
-    from matplotlib.axes import Axes
-    from graphviz import Source
-except ImportError:
-    pass
+import test_plotting as tp
 
 
 pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_matplotlib(), tm.no_graphviz()))
 
 
 class TestPlotting:
+    cputest = tp.TestPlotting()
+
     @pytest.mark.skipif(**tm.no_pandas())
     def test_categorical(self):
-        X, y = tm.make_categorical(1000, 31, 19, onehot=False)
-        reg = xgb.XGBRegressor(
-            enable_categorical=True, n_estimators=10, tree_method="gpu_hist"
-        )
-        reg.fit(X, y)
-        trees = reg.get_booster().get_dump(dump_format="json")
-        for tree in trees:
-            j_tree = json.loads(tree)
-            assert "leaf" in j_tree.keys() or isinstance(
-                j_tree["split_condition"], list
-            )
-
-        graph = xgb.to_graphviz(reg, num_trees=len(j_tree) - 1)
-        assert isinstance(graph, Source)
-        ax = xgb.plot_tree(reg, num_trees=len(j_tree) - 1)
-        assert isinstance(ax, Axes)
+        self.cputest.run_categorical("gpu_hist")