diff --git a/docs/source/index.rst b/docs/source/index.rst index 6f503428d..0a75c5e31 100644 --- a/docs/source/index.rst +++ b/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 diff --git a/docs/source/user_guide/model_intro.rst b/docs/source/user_guide/model_intro.rst index cbc88a25d..088146116 100644 --- a/docs/source/user_guide/model_intro.rst +++ b/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: diff --git a/recbole/model/exlib_recommender/__init__.py b/recbole/model/exlib_recommender/__init__.py index f11fa9977..30c071f55 100644 --- a/recbole/model/exlib_recommender/__init__.py +++ b/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 diff --git a/recbole/model/exlib_recommender/lightgbm.py b/recbole/model/exlib_recommender/lightgbm.py index c64f64d8c..1bc64e6f3 100644 --- a/recbole/model/exlib_recommender/lightgbm.py +++ b/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 diff --git a/recbole/model/exlib_recommender/xgboost.py b/recbole/model/exlib_recommender/xgboost.py index 4981a666c..8bdda60e4 100644 --- a/recbole/model/exlib_recommender/xgboost.py +++ b/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 diff --git a/recbole/model/general_recommender/nceplrec.py b/recbole/model/general_recommender/nceplrec.py index 62b88b62f..d262da3eb 100644 --- a/recbole/model/general_recommender/nceplrec.py +++ b/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 """ diff --git a/recbole/model/sequential_recommender/npe.py b/recbole/model/sequential_recommender/npe.py index 0fc9cde3e..fbf4a5242 100644 --- a/recbole/model/sequential_recommender/npe.py +++ b/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 diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 0d71bf0d1..f885a722a 100644 --- a/recbole/trainer/trainer.py +++ b/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"] diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 198a54663..622113987 100644 --- a/tests/model/test_model_auto.py +++ b/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",