Implement tree model dump with code generator. (#4602)

* Implement tree model dump with a code generator.

* Split up generators.
* Implement graphviz generator.
* Use pattern matching.

* [Breaking] Return a Source in `to_graphviz` instead of Digraph in Python package.


Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu>

tests/python/test_plotting.py

@@ -10,7 +10,7 @@ try:
     import matplotlib
     matplotlib.use('Agg')
     from matplotlib.axes import Axes
-    from graphviz import Digraph
+    from graphviz import Source
 except ImportError:
     pass
 
@@ -57,7 +57,7 @@ class TestPlotting(unittest.TestCase):
         assert ax.patches[3].get_facecolor() == (0, 0, 1.0, 1.0)  # blue
 
         g = xgb.to_graphviz(bst2, num_trees=0)
-        assert isinstance(g, Digraph)
+        assert isinstance(g, Source)
 
         ax = xgb.plot_tree(bst2, num_trees=0)
         assert isinstance(ax, Axes)

tests/python/test_shap.py

@@ -87,7 +87,6 @@ class TestSHAP(unittest.TestCase):
             r_exp = r"([0-9]+):\[f([0-9]+)<([0-9\.e-]+)\] yes=([0-9]+),no=([0-9]+).*cover=([0-9e\.]+)"
             r_exp_leaf = r"([0-9]+):leaf=([0-9\.e-]+),cover=([0-9e\.]+)"
             for tree in model.get_dump(with_stats=True):
-
                 lines = list(tree.splitlines())
                 trees.append([None for i in range(len(lines))])
                 for line in lines:

tests/python/test_with_sklearn.py

@@ -352,7 +352,7 @@ def test_sklearn_plotting():
     matplotlib.use('Agg')
 
     from matplotlib.axes import Axes
-    from graphviz import Digraph
+    from graphviz import Source
 
     ax = xgb.plot_importance(classifier)
     assert isinstance(ax, Axes)
@@ -362,7 +362,7 @@ def test_sklearn_plotting():
     assert len(ax.patches) == 4
 
     g = xgb.to_graphviz(classifier, num_trees=0)
-    assert isinstance(g, Digraph)
+    assert isinstance(g, Source)
 
     ax = xgb.plot_tree(classifier, num_trees=0)
     assert isinstance(ax, Axes)
@@ -641,7 +641,8 @@ def test_XGBClassifier_resume():
 
         X, Y = load_breast_cancer(return_X_y=True)
 
-        model1 = xgb.XGBClassifier(learning_rate=0.3, seed=0, n_estimators=8)
+        model1 = xgb.XGBClassifier(
+            learning_rate=0.3, random_state=0, n_estimators=8)
         model1.fit(X, Y)
 
         pred1 = model1.predict(X)
@@ -649,7 +650,8 @@ def test_XGBClassifier_resume():
 
         # file name of stored xgb model
         model1.save_model(model1_path)
-        model2 = xgb.XGBClassifier(learning_rate=0.3, seed=0, n_estimators=8)
+        model2 = xgb.XGBClassifier(
+            learning_rate=0.3, random_state=0, n_estimators=8)
         model2.fit(X, Y, xgb_model=model1_path)
 
         pred2 = model2.predict(X)
@@ -660,7 +662,8 @@ def test_XGBClassifier_resume():
 
         # file name of 'Booster' instance Xgb model
         model1.get_booster().save_model(model1_booster_path)
-        model2 = xgb.XGBClassifier(learning_rate=0.3, seed=0, n_estimators=8)
+        model2 = xgb.XGBClassifier(
+            learning_rate=0.3, random_state=0, n_estimators=8)
         model2.fit(X, Y, xgb_model=model1_booster_path)
 
         pred2 = model2.predict(X)