Support categorical split in tree model dump. (#7036)

tests/python-gpu/test_gpu_plotting.py

@@ -0,0 +1,40 @@
+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
+
+
+pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_matplotlib(), tm.no_graphviz()))
+
+
+class 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)

tests/python-gpu/test_gpu_updaters.py

@@ -71,7 +71,6 @@ class TestGPUUpdaters:
     @settings(deadline=None)
     @pytest.mark.skipif(**tm.no_pandas())
     def test_categorical(self, rows, cols, rounds, cats):
-        pytest.xfail(reason='TestGPUUpdaters::test_categorical is flaky')
         self.run_categorical_basic(rows, cols, rounds, cats)
 
     def test_categorical_32_cat(self):

tests/python-gpu/test_gpu_with_sklearn.py

@@ -55,7 +55,6 @@ def test_categorical():
         tree_method="gpu_hist",
         use_label_encoder=False,
         enable_categorical=True,
-        predictor="gpu_predictor",
         n_estimators=10,
     )
     X = pd.DataFrame(X.todense()).astype("category")