Handle categorical split in model histogram and dataframe. (#7065)

* Error on get_split_value_histogram when feature is categorical
* Add a category column to output dataframe

tests/python-gpu/test_gpu_parse_tree.py

@@ -0,0 +1,25 @@
+import sys
+import pytest
+import xgboost as xgb
+
+sys.path.append("tests/python")
+import testing as tm
+
+
+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
+
+
+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)