[python] add parameter object_hook to method dump_model

python-package/lightgbm/basic.py

@@ -3342,7 +3342,7 @@ def model_to_string(self, num_iteration=None, start_iteration=0, importance_type
         ret += _dump_pandas_categorical(self.pandas_categorical)
         return ret
 
-    def dump_model(self, num_iteration=None, start_iteration=0, importance_type='split'):
+    def dump_model(self, num_iteration=None, start_iteration=0, importance_type='split', object_hook=None):
         """Dump Booster to JSON format.
 
         Parameters
@@ -3357,6 +3357,15 @@ def dump_model(self, num_iteration=None, start_iteration=0, importance_type='spl
             What type of feature importance should be dumped.
             If "split", result contains numbers of times the feature is used in a model.
             If "gain", result contains total gains of splits which use the feature.
+        object_hook : callable or None, optional (default=None)
+            If not None, ``object_hook`` is a function called while parsing the json
+            string returned by the C API. It may be used to alter the json, to store
+            specific values while building the json structure. It avoids
+            walking through the structure again. It saves a significant amount
+            of time if the number of trees is huge.
+            Signature is ``def object_hook(node: dict) -> dict``.
+            None is equivalent to ``lambda node: node``.
+            See documentation of ``json.loads()`` for further details.
 
         Returns
         -------
@@ -3391,7 +3400,7 @@ def dump_model(self, num_iteration=None, start_iteration=0, importance_type='spl
                 ctypes.c_int64(actual_len),
                 ctypes.byref(tmp_out_len),
                 ptr_string_buffer))
-        ret = json.loads(string_buffer.value.decode('utf-8'))
+        ret = json.loads(string_buffer.value.decode('utf-8'), object_hook=object_hook)
         ret['pandas_categorical'] = json.loads(json.dumps(self.pandas_categorical,
                                                           default=json_default_with_numpy))
         return ret

tests/python_package_test/test_engine.py

@@ -2846,3 +2846,23 @@ def test_dump_model():
     assert "leaf_const" in dumped_model_str
     assert "leaf_value" in dumped_model_str
     assert "leaf_count" in dumped_model_str
+
+
+def test_dump_model_hook():
+
+    def hook(obj):
+        if 'leaf_value' in obj:
+            obj['LV'] = obj['leaf_value']
+            del obj['leaf_value']
+        return obj
+
+    X, y = load_breast_cancer(return_X_y=True)
+    train_data = lgb.Dataset(X, label=y)
+    params = {
+        "objective": "binary",
+        "verbose": -1
+    }
+    bst = lgb.train(params, train_data, num_boost_round=5)
+    dumped_model_str = str(bst.dump_model(5, 0, object_hook=hook))
+    assert "leaf_value" not in dumped_model_str
+    assert "LV" in dumped_model_str