Merge pull request #1829 from Sherry-XLL/master

FIX: fix issue #1825 and outdated parameters in LightGBM

recbole/data/dataset/kg_dataset.py

@@ -390,7 +390,7 @@ def _add_auxiliary_relation(self):
             reverse_kg_data = {
                 self.head_entity_field: original_tids,
                 self.relation_field: reverse_rels,
-                self.head_entity_field: original_hids,
+                self.tail_entity_field: original_hids,
             }
             reverse_kg_feat = pd.DataFrame(reverse_kg_data)
             self.kg_feat = pd.concat([self.kg_feat, reverse_kg_feat])

recbole/properties/model/lightgbm.yaml

@@ -1,10 +1,8 @@
 # Dataset
 convert_token_to_onehot: False  # (bool) Whether to convert token type features into one-hot form.
 token_num_threhold: 10000       # (int) The threshold of one-hot conversion.
-lgb_silent: False               # (bool) Whether to print messages during construction.
 
 # Train
-lgb_model: ~                    # (file name of stored lgb model or 'Booster' instance)
 lgb_params:                     # (dict) Booster params.
     boosting: gbdt
     num_leaves: 90  
@@ -15,7 +13,4 @@ lgb_params:                     # (dict) Booster params.
     lambda_l1: 0.1
     metric: ['auc', 'binary_logloss']
     force_row_wise: True
-lgb_learning_rates: ~           # (list, callable or None) List of learning rates or a customized function.
 lgb_num_boost_round: 300        # (int) Number of boosting iterations.
-lgb_early_stopping_rounds: ~    # (int or None) Activates early stopping.
-lgb_verbose_eval: 100           # (bool or int) Requires at least one validation data to print evaluation metrics.

recbole/trainer/trainer.py

@@ -1150,18 +1150,11 @@ def __init__(self, config, model):
         super(LightGBMTrainer, self).__init__(config, model)
 
         self.lgb = __import__("lightgbm")
-        self.boost_model = config["lgb_model"]
-        self.silent = config["lgb_silent"]
 
         # train params
         self.params = config["lgb_params"]
         self.num_boost_round = config["lgb_num_boost_round"]
         self.evals = ()
-        self.early_stopping_rounds = config["lgb_early_stopping_rounds"]
-        self.evals_result = {}
-        self.verbose_eval = config["lgb_verbose_eval"]
-        self.learning_rates = config["lgb_learning_rates"]
-        self.callbacks = None
         self.deval_data = self.deval_label = None
         self.eval_pred = self.eval_true = None
 
@@ -1174,7 +1167,7 @@ def _interaction_to_lib_datatype(self, dataloader):
             dataset(lgb.Dataset): Data in the form of 'lgb.Dataset'.
         """
         data, label = self._interaction_to_sparse(dataloader)
-        return self.lgb.Dataset(data=data, label=label, silent=self.silent)
+        return self.lgb.Dataset(data=data, label=label)
 
     def _train_at_once(self, train_data, valid_data):
         r"""
@@ -1187,16 +1180,7 @@ def _train_at_once(self, train_data, valid_data):
         self.dvalid = self._interaction_to_lib_datatype(valid_data)
         self.evals = [self.dtrain, self.dvalid]
         self.model = self.lgb.train(
-            self.params,
-            self.dtrain,
-            self.num_boost_round,
-            self.evals,
-            early_stopping_rounds=self.early_stopping_rounds,
-            evals_result=self.evals_result,
-            verbose_eval=self.verbose_eval,
-            learning_rates=self.learning_rates,
-            init_model=self.boost_model,
-            callbacks=self.callbacks,
+            self.params, self.dtrain, self.num_boost_round, self.evals
         )
 
         self.model.save_model(self.temp_file)