Fix documentation typo

docs/source/index.rst

@@ -11,7 +11,7 @@ Introduction
 RecBole is a unified, comprehensive and efficient framework developed based on PyTorch.
 It aims to help the researchers to reproduce and develop recommendation models.
 
-In the lastest release, our library includes 77 recommendation algorithms `[Model List]`_, covering four major categories:
+In the lastest release, our library includes 81 recommendation algorithms `[Model List]`_, covering four major categories:
 
 - General Recommendation
 - Sequential Recommendation
@@ -29,7 +29,7 @@ Features:
 - General and extensible data structure
     We deign general and extensible data structures to unify the formatting and usage of various recommendation datasets.
 - Comprehensive benchmark models and datasets
-    We implement 77 commonly used recommendation algorithms, and provide the formatted copies of 28 recommendation datasets.
+    We implement 81 commonly used recommendation algorithms, and provide the formatted copies of 28 recommendation datasets.
 - Efficient GPU-accelerated execution
     We design many tailored strategies in the GPU environment to enhance the efficiency of our library.
 - Extensive and standard evaluation protocols

docs/source/user_guide/model_intro.rst

@@ -1,6 +1,6 @@
 Model Introduction
 =====================
-We implement 77 recommendation models covering general recommendation, sequential recommendation,
+We implement 81 recommendation models covering general recommendation, sequential recommendation,
 context-aware recommendation and knowledge-based recommendation. A brief introduction to these models are as follows:
 
 

recbole/model/exlib_recommender/__init__.py

@@ -1,2 +1,2 @@
-from recbole.model.exlib_recommender.lightgbm import lightgbm
-from recbole.model.exlib_recommender.xgboost import xgboost
+from recbole.model.exlib_recommender.lightgbm import LightGBM
+from recbole.model.exlib_recommender.xgboost import XGBoost

recbole/model/exlib_recommender/lightgbm.py

@@ -12,8 +12,8 @@
 from recbole.utils import ModelType, InputType
 
 
-class lightgbm(lgb.Booster):
-    r"""lightgbm is inherited from lgb.Booster"""
+class LightGBM(lgb.Booster):
+    r"""LightGBM is inherited from lgb.Booster"""
     type = ModelType.DECISIONTREE
     input_type = InputType.POINTWISE
 

recbole/model/exlib_recommender/xgboost.py

@@ -12,8 +12,8 @@
 from recbole.utils import ModelType, InputType
 
 
-class xgboost(xgb.Booster):
-    r"""xgboost is inherited from xgb.Booster"""
+class XGBoost(xgb.Booster):
+    r"""XGBoost is inherited from xgb.Booster"""
     type = ModelType.DECISIONTREE
     input_type = InputType.POINTWISE
 

recbole/model/general_recommender/nceplrec.py

@@ -8,7 +8,7 @@
 NCE-PLRec
 ######################################
 Reference:
-    Ga Wu, et al. "Noise Contrastive Estimation for One-Class Collaborative Filtering" in Sigir 2019.
+    Ga Wu, et al. "Noise Contrastive Estimation for One-Class Collaborative Filtering" in SIGIR 2019.
 Reference code:
     https://github.com/wuga214/NCE_Projected_LRec
 """

recbole/model/sequential_recommender/npe.py

@@ -9,7 +9,7 @@
 ################################################
 
 Reference:
-    ThaiBinh Nguyen, et al. "NPE: Neural Personalized Embedding for Collaborative Filtering" in ijcai2018
+    ThaiBinh Nguyen, et al. "NPE: Neural Personalized Embedding for Collaborative Filtering" in IJCAI 2018.
 
 Reference code:
     https://github.com/wubinzzu/NeuRec

recbole/trainer/trainer.py

@@ -1065,11 +1065,11 @@ def _train_at_once(self, train_data, valid_data):
         raise NotImplementedError
 
 
-class xgboostTrainer(DecisionTreeTrainer):
-    """xgboostTrainer is designed for XGBOOST."""
+class XGBoostTrainer(DecisionTreeTrainer):
+    """XGBoostTrainer is designed for XGBOOST."""
 
     def __init__(self, config, model):
-        super(xgboostTrainer, self).__init__(config, model)
+        super(XGBoostTrainer, self).__init__(config, model)
 
         self.xgb = __import__("xgboost")
         self.boost_model = config["xgb_model"]
@@ -1144,11 +1144,11 @@ def evaluate(
         return result
 
 
-class lightgbmTrainer(DecisionTreeTrainer):
-    """lightgbmTrainer is designed for lightgbm."""
+class LightGBMTrainer(DecisionTreeTrainer):
+    """LightGBMTrainer is designed for LightGBM."""
 
     def __init__(self, config, model):
-        super(lightgbmTrainer, self).__init__(config, model)
+        super(LightGBMTrainer, self).__init__(config, model)
 
         self.lgb = __import__("lightgbm")
         self.boost_model = config["lgb_model"]

tests/model/test_model_auto.py

@@ -362,7 +362,7 @@ def test_dcn(self):
 
     def test_xgboost(self):
         config_dict = {
-            "model": "xgboost",
+            "model": "XGBoost",
             "threshold": {"rating": 4},
             "xgb_params": {
                 "booster": "gbtree",
@@ -375,7 +375,7 @@ def test_xgboost(self):
 
     def test_lightgbm(self):
         config_dict = {
-            "model": "lightgbm",
+            "model": "LightGBM",
             "threshold": {"rating": 4},
             "lgb_params": {
                 "boosting": "gbdt",