[python] add keep_training_booster (#673)

* add keep_training_booster

* use model string

* reset handle; free dataset

python-package/lightgbm/basic.py

@@ -1245,7 +1245,7 @@ class Booster(object):
             self.__num_class = out_num_class.value
             self.pandas_categorical = _load_pandas_categorical(model_file)
         elif 'model_str' in params:
-            self.__load_model_from_string(params['model_str'])
+            self._load_model_from_string(params['model_str'])
         else:
             raise TypeError('Need at least one training dataset or model file to create booster instance')
 
@@ -1257,7 +1257,7 @@ class Booster(object):
         return self.__deepcopy__(None)
 
     def __deepcopy__(self, _):
-        model_str = self.__save_model_to_string()
+        model_str = self._save_model_to_string()
         booster = Booster({'model_str': model_str})
         booster.pandas_categorical = self.pandas_categorical
         return booster
@@ -1268,7 +1268,7 @@ class Booster(object):
         this.pop('train_set', None)
         this.pop('valid_sets', None)
         if handle is not None:
-            this["handle"] = self.__save_model_to_string()
+            this["handle"] = self._save_model_to_string()
         return this
 
     def __setstate__(self, state):
@@ -1286,6 +1286,7 @@ class Booster(object):
     def free_dataset(self):
         self.__dict__.pop('train_set', None)
         self.__dict__.pop('valid_sets', None)
+        self.__num_dataset = 0
 
     def set_train_data_name(self, name):
         self.__train_data_name = name
@@ -1505,7 +1506,7 @@ class Booster(object):
             c_str(filename)))
         _save_pandas_categorical(filename, self.pandas_categorical)
 
-    def __load_model_from_string(self, model_str):
+    def _load_model_from_string(self, model_str):
         """[Private] Load model from string"""
         out_num_iterations = ctypes.c_int(0)
         _safe_call(_LIB.LGBM_BoosterLoadModelFromString(
@@ -1518,7 +1519,7 @@ class Booster(object):
             ctypes.byref(out_num_class)))
         self.__num_class = out_num_class.value
 
-    def __save_model_to_string(self, num_iteration=-1):
+    def _save_model_to_string(self, num_iteration=-1):
         """[Private] Save model to string"""
         if num_iteration <= 0:
             num_iteration = self.best_iteration

python-package/lightgbm/engine.py

@@ -19,7 +19,8 @@ def train(params, train_set, num_boost_round=100,
           fobj=None, feval=None, init_model=None,
           feature_name='auto', categorical_feature='auto',
           early_stopping_rounds=None, evals_result=None,
-          verbose_eval=True, learning_rates=None, callbacks=None):
+          verbose_eval=True, learning_rates=None,
+          keep_training_booster=False, callbacks=None):
     """
     Train with given parameters.
 
@@ -80,6 +81,10 @@ def train(params, train_set, num_boost_round=100,
         in terms of current number of round (e.g. yields learning rate decay)
         - list l: learning_rate = l[current_round]
         - function f: learning_rate = f(current_round)
+    keep_training_booster : boolean
+        Whether the return booster will be used to keep training.
+        If false, will convert into _InnerPredictor before return.
+        You can still use _InnerPredictor as init_model for future continue training.
     callbacks : list of callback functions
         List of callback functions that are applied at each iteration.
         See Callbacks in Python-API.md for more information.
@@ -200,6 +205,9 @@ def train(params, train_set, num_boost_round=100,
     booster.best_score = collections.defaultdict(dict)
     for dataset_name, eval_name, score, _ in evaluation_result_list:
         booster.best_score[dataset_name][eval_name] = score
+    if not keep_training_booster:
+        booster._load_model_from_string(booster._save_model_to_string())
+        booster.free_dataset()
     return booster