Merge pull request #1 from RUCAIBox/master

a

.github/ISSUE_TEMPLATE/bug_report_CN.md

@@ -0,0 +1,33 @@
+---
+name: Bug 报告
+about: 提交一份 bug 报告，帮助 RecBole 变得更好
+title: "[\U0001F41BBUG] 用一句话描述您的问题。"
+labels: bug
+assignees: ''
+
+---
+
+**描述这个 bug**
+对 bug 作一个清晰简明的描述。
+
+**如何复现**
+复现这个 bug 的步骤：
+1. 您引入的额外 yaml 文件
+2. 您的代码
+3. 您的运行脚本
+
+**预期**
+对您的预期作清晰简明的描述。
+
+**屏幕截图**
+添加屏幕截图以帮助解释您的问题。（可选）
+
+**链接**
+添加能够复现 bug 的代码链接，如 Colab 或者其他在线 Jupyter 平台。（可选）
+
+**实验环境（请补全下列信息）：**
+ - 操作系统: [如 Linux, macOS 或 Windows]
+- RecBole 版本 [如 0.1.0]
+ - Python 版本 [如 3.79]
+- PyTorch 版本 [如 1.60]
+- cudatoolkit 版本 [如 9.2, none]

.github/ISSUE_TEMPLATE/feature_request_CN.md

@@ -0,0 +1,20 @@
+---
+name: 请求添加新功能
+about: 提出一个关于本项目新功能/新特性的建议
+title: "[\U0001F4A1SUG] 一句话描述您希望新增的功能或特性"
+labels: enhancement
+assignees: ''
+
+---
+
+**您希望添加的功能是否与某个问题相关？**
+关于这个问题的简洁清晰的描述，例如，当 [...] 时，我总是很沮丧。
+
+**描述您希望的解决方案**
+关于解决方案的简洁清晰的描述。
+
+**描述您考虑的替代方案**
+关于您考虑的，能实现这个功能的其他替代方案的简洁清晰的描述。
+
+**其他**
+您可以添加其他任何的资料、链接或者屏幕截图，以帮助我们理解这个新功能。

.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

@@ -1,7 +1,12 @@
+# Saved models
+/saved*
+*.pth
+
 .vscode/
 .idea/
 *.pyc
 *.log
 saved/
 *.lprof
 *.egg-info/
+docs/build/

README.md

@@ -11,23 +11,25 @@
 [![License](https://img.shields.io/badge/License-MIT-blue.svg)](./LICENSE)
 
 
-[HomePage] | [Docs] | [Datasets] | [Paper]
+[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]: 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.
-Our library includes 53 recommendation algorithms, covering four major categories:
+Our library includes 65 recommendation algorithms, covering four major categories:
 
 + General Recommendation
 + Sequential Recommendation
 + Context-aware Recommendation
 + Knowledge-based Recommendation
 
-We design a unified and flexible data file format, and provide the support for 27 benchmark recommendation datasets.
+We design a unified and flexible data file format, and provide the support for 28 benchmark recommendation datasets.
 A user can apply the provided script to process the original data copy, or simply download the processed datasets
 by our team.
 
@@ -43,16 +45,23 @@ by our team.
 + **General and extensible data structure.** We design general and extensible data structures to unify the formatting and
 usage of various recommendation datasets.
 
-+ **Comprehensive benchmark models and datasets.** We implement 53 commonly used recommendation algorithms, and provide
-the formatted copies of 27 recommendation datasets.
++ **Comprehensive benchmark models and datasets.** We implement 65 commonly used recommendation algorithms, and provide
+the formatted copies of 28 recommendation datasets.
 
 + **Efficient GPU-accelerated execution.** We optimize the efficiency of our library with a number of improved techniques
 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
+**01/15/2021**: We release RecBole [v0.2.0](https://github.com/RUCAIBox/RecBole/releases/tag/v0.2.0).
+
+**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).
+
 **11/29/2020**: We constructed preliminary experiments to test the time and memory cost on three
 different-sized datasets and provided the [test result](https://github.com/RUCAIBox/RecBole#time-and-memory-costs)
 for reference.
@@ -159,22 +168,25 @@ 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 |
 |-----------|--------|-------------------------|
+| v0.2.0    | 01/15/2021 |  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).
@@ -183,6 +195,9 @@ We welcome all contributions from bug fixes to new features and extensions.
 
 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), [@deklanw](https://github.com/deklanw) 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):