Update

.github/workflows/python-package.yml

@@ -3,6 +3,7 @@ name: RecBole tests
 on:
 - pull_request
 
+
 jobs:
   build:
 
@@ -22,12 +23,16 @@ jobs:
         python -m pip install --upgrade pip
         pip install pytest
         pip install dgl
+        pip install xgboost
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-
+        
     # Use "python -m pytest" instead of "pytest" to fix imports
     - name: Test metrics
       run: |
         python -m pytest -v tests/metrics
+    - name: Test data
+      run: |
+        python -m pytest -v tests/data
     - name: Test evaluation_setting
       run: |
         python -m pytest -v tests/evaluation_setting
@@ -39,7 +44,4 @@ jobs:
         python -m pytest -v tests/config/test_config.py
         export PYTHONPATH=.
         python tests/config/test_command_line.py --use_gpu=False --valid_metric=Recall@10 --split_ratio=[0.7,0.2,0.1] --metrics=['Recall@10'] --epochs=200 --eval_setting='LO_RS' --learning_rate=0.3
-    - name: Test evaluation_setting
-      run: |
-        python -m pytest -v tests/evaluation_setting
-
+

.gitignore

@@ -1,3 +1,7 @@
+# Saved models
+/saved*
+*.pth
+
 .vscode/
 .idea/
 *.pyc

README.md

@@ -11,13 +11,14 @@
 [![License](https://img.shields.io/badge/License-MIT-blue.svg)](./LICENSE)
 
 
-[HomePage] | [Docs] | [Datasets] | [Paper] | [Blogs]
+[HomePage] | [Docs] | [Datasets] | [Paper] | [Blogs] | [中文版]
 
 [HomePage]: https://recbole.io/
 [Docs]: https://recbole.io/docs/
 [Datasets]: https://github.com/RUCAIBox/RecDatasets
 [Paper]: https://arxiv.org/abs/2011.01731
-[Blogs]: #blogs
+[Blogs]: https://blog.csdn.net/Turinger_2000/article/details/111182852
+[中文版]: README_CN.md
 
 RecBole is developed based on Python and PyTorch for reproducing and developing recommendation algorithms in a unified,
 comprehensive and efficient framework for research purpose.
@@ -53,8 +54,9 @@ oriented to the GPU environment.
 + **Extensive and standard evaluation protocols.** We support a series of widely adopted evaluation protocols or settings
 for testing and comparing recommendation algorithms.
 
+
 ## RecBole News
-**12/10/2020**: 我们发布了RecBole小白入门系列中文博客。
+**12/10/2020**: 我们发布了[RecBole小白入门系列中文博客（持续更新中）](https://blog.csdn.net/Turinger_2000/article/details/111182852) 。
 
 **12/06/2020**: We release RecBole [v0.1.2](https://github.com/RUCAIBox/RecBole/releases/tag/v0.1.2).
 
@@ -64,11 +66,6 @@ for reference.
 
 **11/03/2020**: We release the first version of RecBole **v0.1.1**.
 
-## Blogs
-
-[RecBole小白入门系列博客（一）——快速安装和简单上手](https://blog.csdn.net/Turinger_2000/article/details/110414642)
-
-[RecBole小白入门系列博客（二）——General类模型运行流程](https://blog.csdn.net/Turinger_2000/article/details/110395198)
 
 ## Installation
 RecBole works with the following operating systems:
@@ -169,23 +166,24 @@ python run_recbole.py --model=[model_name]
 
 
 ## Time and Memory Costs
-We constructed preliminary experiments to test the time and memory cost on three different-sized datasets  (small, medium and large). For detailed information, you can click the following links.<br> 
+We constructed preliminary experiments to test the time and memory cost on three different-sized datasets 
+(small, medium and large). For detailed information, you can click the following links.
 
-* [General recommendation models](asset/time_test_result/General_recommendation.md)<br>
-* [Sequential recommendation models](asset/time_test_result/Sequential_recommendation.md)<br>
-* [Context-aware recommendation models](asset/time_test_result/Context-aware_recommendation.md)<br>
-* [Knowledge-based recommendation models](asset/time_test_result/Knowledge-based_recommendation.md)<br>
+* [General recommendation models](asset/time_test_result/General_recommendation.md)
+* [Sequential recommendation models](asset/time_test_result/Sequential_recommendation.md)
+* [Context-aware recommendation models](asset/time_test_result/Context-aware_recommendation.md)
+* [Knowledge-based recommendation models](asset/time_test_result/Knowledge-based_recommendation.md)
 
-NOTE: Our test results only gave the approximate time and memory cost of our implementations in the RecBole library (based on our machine server).  Any feedback or suggestions about the implementations and test are welcome. We will keep improving our implementations, and update these test results.
+NOTE: Our test results only gave the approximate time and memory cost of our implementations in the RecBole library
+(based on our machine server).  Any feedback or suggestions about the implementations and test are welcome. 
+We will keep improving our implementations, and update these test results.
 
 
 ## RecBole Major Releases
 | Releases  | Date   | Features |
 |-----------|--------|-------------------------|
-| v0.1.2    | 12/06/2020 |  Basic RecBole |
 | v0.1.1    | 11/03/2020 |  Basic RecBole |
 
-
 ## Contributing
 
 Please let us know if you encounter a bug or have any suggestions by [filing an issue](https://github.com/RUCAIBox/RecBole/issues).
@@ -196,7 +194,7 @@ We expect all contributions discussed in the issue tracker and going through PRs
 
 We thank the insightful suggestions from [@tszumowski](https://github.com/tszumowski), [@rowedenny](https://github.com/rowedenny) et.al.
 
-We thank the nice contributions through PRs from [@rowedenny](https://github.com/rowedenny) et.al.
+We thank the nice contributions through PRs from [@rowedenny](https://github.com/rowedenny)，[@deklanw](https://github.com/deklanw) et.al.
 
 ## Cite
 If you find RecBole useful for your research or development, please cite the following [paper](https://arxiv.org/abs/2011.01731):

README_CN.md

@@ -0,0 +1,210 @@
+![RecBole Logo](asset/logo.png)
+
+--------------------------------------------------------------------------------
+
+# RecBole (伯乐)
+
+*“世有伯乐，然后有千里马。千里马常有，而伯乐不常有。”——韩愈《马说》*
+
+[![PyPi Latest Release](https://img.shields.io/pypi/v/recbole)](https://pypi.org/project/recbole/)
+[![Conda Latest Release](https://anaconda.org/aibox/recbole/badges/version.svg)](https://anaconda.org/aibox/recbole)
+[![License](https://img.shields.io/badge/License-MIT-blue.svg)](./LICENSE)
+
+
+[中文主页] | [文档] | [数据集] | [论文] | [博客] | [English Version]
+
+[中文主页]: https://recbole.io/cn
+[文档]: https://recbole.io/docs/
+[数据集]: https://github.com/RUCAIBox/RecDatasets
+[论文]: https://arxiv.org/abs/2011.01731
+[博客]: https://blog.csdn.net/Turinger_2000/article/details/111182852
+[English Version]: README.md
+
+
+RecBole 是一个基于 PyTorch 实现的，面向研究者的，易于开发与复现的，统一、全面、高效的推荐系统代码库。
+我们实现了53个推荐系统模型，包含常见的推荐系统类别，如:
+
++ General Recommendation
++ Sequential Recommendation
++ Context-aware Recommendation
++ Knowledge-based Recommendation
+
+
+我们约定了一个统一、易用的数据文件格式，并已支持 27 个 benchmark dataset。
+用户可以选择使用我们的数据集预处理脚本，或直接下载已被处理好的数据集文件。
+
+
+<p align="center">
+  <img src="asset/framework.png" alt="RecBole v0.1 架构" width="600">
+  <br>
+  <b>图片</b>: RecBole 总体架构
+</p>
+
+
+## 特色
++ **通用和可扩展的数据结构** 我们设计了通用和可扩展的数据结构来支持各种推荐数据集统一化格式和使用。
+
++ **全面的基准模型和数据集** 我们实现了53个常用的推荐算法，并提供了27个推荐数据集的格式化副本。
+
++ **高效的 GPU 加速实现** 我们针对GPU环境使用了一系列的优化技术来提升代码库的效率。
+
++ **大规模的标准评测** 我们支持一系列被广泛认可的评估方式来测试和比较不同的推荐算法。
+
+
+## RecBole 新闻
+**12/10/2020**: 我们发布了[RecBole小白入门系列中文博客（持续更新中）](https://blog.csdn.net/Turinger_2000/article/details/111182852) 。
+
+**12/06/2020**: 我们发布了 RecBole [v0.1.2](https://github.com/RUCAIBox/RecBole/releases/tag/v0.1.2).
+
+**11/29/2020**: 我们在三个不同大小的数据集上进行了时间和内存开销的初步测试，
+并提供了 [测试结果](https://github.com/RUCAIBox/RecBole#time-and-memory-costs) 以供参考。
+
+**11/03/2020**: 我们发布了第一版 RecBole **v0.1.1**.
+
+
+## 安装
+RecBole可以在以下几种系统上运行:
+
+* Linux
+* Windows 10
+* macOS X
+
+RecBole需要在python 3.6或更高的环境下运行。
+
+RecBole要求torch版本在1.6.0及以上，如果你想在GPU上运行RecBole，请确保你的CUDA版本或CUDAToolkit版本在9.2及以上。
+这需要你的NVIDIA驱动版本为396.26或以上（在linux系统上）或者为397.44或以上（在Windows10系统上）。
+
+
+### 从Conda安装
+
+```bash
+conda install -c aibox recbole
+```
+
+### 从pip安装
+
+```bash
+pip install recbole
+```
+
+### 从源文件安装
+```bash
+git clone https://github.com/RUCAIBox/RecBole.git && cd RecBole
+pip install -e . --verbose
+```
+
+## 快速上手
+如果你从GitHub下载了RecBole的源码，你可以使用提供的脚本进行简单的使用：
+
+```bash
+python run_recbole.py
+```
+
+这个例子将会在ml-100k这个数据集上进行BPR模型的训练和测试。
+
+一般来说，这个例子将花费不到一分钟的时间，我们会得到一些类似下面的输出：
+
+```
+INFO ml-100k
+The number of users: 944
+Average actions of users: 106.04453870625663
+The number of items: 1683
+Average actions of items: 59.45303210463734
+The number of inters: 100000
+The sparsity of the dataset: 93.70575143257098%
+
+INFO Evaluation Settings:
+Group by user_id
+Ordering: {'strategy': 'shuffle'}
+Splitting: {'strategy': 'by_ratio', 'ratios': [0.8, 0.1, 0.1]}
+Negative Sampling: {'strategy': 'full', 'distribution': 'uniform'}
+
+INFO BPRMF(
+    (user_embedding): Embedding(944, 64)
+    (item_embedding): Embedding(1683, 64)
+    (loss): BPRLoss()
+)
+Trainable parameters: 168128
+
+INFO epoch 0 training [time: 0.27s, train loss: 27.7231]
+INFO epoch 0 evaluating [time: 0.12s, valid_score: 0.021900]
+INFO valid result:
+recall@10: 0.0073  mrr@10: 0.0219  ndcg@10: 0.0093  hit@10: 0.0795  precision@10: 0.0088
+
+...
+
+INFO epoch 63 training [time: 0.19s, train loss: 4.7660]
+INFO epoch 63 evaluating [time: 0.08s, valid_score: 0.394500]
+INFO valid result:
+recall@10: 0.2156  mrr@10: 0.3945  ndcg@10: 0.2332  hit@10: 0.7593  precision@10: 0.1591
+
+INFO Finished training, best eval result in epoch 52
+INFO Loading model structure and parameters from saved/***.pth
+INFO best valid result:
+recall@10: 0.2169  mrr@10: 0.4005  ndcg@10: 0.235  hit@10: 0.7582  precision@10: 0.1598
+INFO test result:
+recall@10: 0.2368  mrr@10: 0.4519  ndcg@10: 0.2768  hit@10: 0.7614  precision@10: 0.1901
+```
+
+如果你要改参数，例如 ``learning_rate``, ``embedding_size``, 只需根据您的需求增加额外的参数，例如：
+
+```bash
+python run_recbole.py --learning_rate=0.0001 --embedding_size=128
+```
+
+如果你想改变运行模型，只需要在执行脚本时添加额外的设置参数即可：
+
+```bash
+python run_recbole.py --model=[model_name]
+```
+
+
+## 时间和内存开销
+我们构建了初步的实验来测试三个不同大小的数据集（小、中、大）的时间和内存开销。
+有关详细信息，请单击以下链接。
+
+* [General recommendation models](asset/time_test_result/General_recommendation.md)<br>
+* [Sequential recommendation models](asset/time_test_result/Sequential_recommendation.md)<br>
+* [Context-aware recommendation models](asset/time_test_result/Context-aware_recommendation.md)<br>
+* [Knowledge-based recommendation models](asset/time_test_result/Knowledge-based_recommendation.md)<br>
+
+NOTE: 我们的测试结果只给出了RecBole库中实现模型的大致时间和内存开销（基于我们的机器服务器）。
+我们欢迎任何关于测试、实现的建议。我们将继续改进我们的实现，并更新这些测试结果。
+
+
+## RecBole 重要发布
+| Releases  | Date   | Features |
+|-----------|--------|-------------------------|
+| v0.1.1    | 11/03/2020 |  Basic RecBole |
+
+
+## 贡献
+
+如果您遇到错误或有任何建议，请通过 [Issue](https://github.com/RUCAIBox/RecBole/issues) 进行反馈
+
+我们欢迎关于修复错误、添加新特性的任何贡献。
+
+如果想贡献代码，请先在issue中提出问题，然后再提PR。
+
+我们对[@tszumowski](https://github.com/tszumowski), [@rowedenny](https://github.com/rowedenny) 等用户提出的建议表示感谢。
+
+我们也对[@rowedenny](https://github.com/rowedenny), [@deklanw](https://github.com/deklanw) 等用户做出的贡献表示感谢。
+
+
+## 引用
+如果你觉得RecBole对你的科研工作有帮助，请引用我们的[论文](https://arxiv.org/abs/2011.01731):
+
+```
+@article{recbole,
+    title={RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms},
+    author={Wayne Xin Zhao and Shanlei Mu and Yupeng Hou and Zihan Lin and Kaiyuan Li and Yushuo Chen and Yujie Lu and Hui Wang and Changxin Tian and Xingyu Pan and Yingqian Min and Zhichao Feng and Xinyan Fan and Xu Chen and Pengfei Wang and Wendi Ji and Yaliang Li and Xiaoling Wang and Ji-Rong Wen},
+    year={2020},
+    journal={arXiv preprint arXiv:2011.01731}
+}
+```
+
+## 项目团队
+RecBole由 [中国人民大学, 北京邮电大学, 华东师范大学](https://www.recbole.io/about.html) 的同学和老师进行开发和维护。 
+
+## 免责声明
+RecBole 基于 [MIT License](./LICENSE) 进行开发，本项目的所有数据和代码只能被用于学术目的。