https://github.com/tongjilibo/rec4torch
推荐系统的pytorch算法实现
https://github.com/tongjilibo/rec4torch
ctr deepfm dien widedeep
Last synced: 5 months ago
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推荐系统的pytorch算法实现
- Host: GitHub
- URL: https://github.com/tongjilibo/rec4torch
- Owner: Tongjilibo
- License: mit
- Created: 2022-09-28T03:17:07.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-02-04T09:30:09.000Z (over 1 year ago)
- Last Synced: 2024-11-27T17:51:08.283Z (6 months ago)
- Topics: ctr, deepfm, dien, widedeep
- Language: Python
- Homepage:
- Size: 194 KB
- Stars: 66
- Watchers: 3
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# rec4torch
推荐系统的pytorch算法实现
[](https://github.com/Tongjilibo/rec4torch/blob/master/LICENSE)
[](https://pypi.org/project/rec4torch/)
[](https://pypistats.org/packages/rec4torch)
[](https://github.com/Tongjilibo/rec4torch)
[](https://github.com/Tongjilibo/rec4torch/issues)
[](https://github.com/Tongjilibo/rec4torch/issues)
## 1. 下载安装
安装稳定版
```shell
pip install rec4torch
```
安装最新版
```shell
pip install git+https://www.github.com/Tongjilibo/rec4torch.git
```
## 2. 功能
- **核心功能**：基于pytorch实现各类推荐算法(DeepFM, WideDeep, DCN, DIN, DIEN)
- **主要区别**：相对于deep-ctr, 去除对tensorflow和keras的依赖，去除重复过embedding的操作，原生支持multiclass
- **训练过程**：
```text
2022-10-28 23:16:10 - Start Training
2022-10-28 23:16:10 - Epoch: 1/5
5000/5000 [==============================] - 13s 3ms/step - loss: 0.1351 - acc: 0.9601
Evaluate: 100%|██████████████████████████████████████████████████| 2500/2500 [00:03<00:00, 798.09it/s]
test_acc: 0.98045. best_test_acc: 0.98045
2022-10-28 23:16:27 - Epoch: 2/5
5000/5000 [==============================] - 13s 3ms/step - loss: 0.0465 - acc: 0.9862
Evaluate: 100%|██████████████████████████████████████████████████| 2500/2500 [00:03<00:00, 635.78it/s]
test_acc: 0.98280. best_test_acc: 0.98280
2022-10-28 23:16:44 - Epoch: 3/5
5000/5000 [==============================] - 15s 3ms/step - loss: 0.0284 - acc: 0.9915
Evaluate: 100%|██████████████████████████████████████████████████| 2500/2500 [00:03<00:00, 673.60it/s]
test_acc: 0.98365. best_test_acc: 0.98365
2022-10-28 23:17:03 - Epoch: 4/5
5000/5000 [==============================] - 15s 3ms/step - loss: 0.0179 - acc: 0.9948
Evaluate: 100%|██████████████████████████████████████████████████| 2500/2500 [00:03<00:00, 692.34it/s]
test_acc: 0.98265. best_test_acc: 0.98365
2022-10-28 23:17:21 - Epoch: 5/5
5000/5000 [==============================] - 14s 3ms/step - loss: 0.0129 - acc: 0.9958
Evaluate: 100%|██████████████████████████████████████████████████| 2500/2500 [00:03<00:00, 701.77it/s]
test_acc: 0.98585. best_test_acc: 0.98585
2022-10-28 23:17:37 - Finish Training
```
## 3. 快速上手
- 参考了[deepctr-torch](https://github.com/shenweichen/DeepCTR-Torch), 使用[torch4keras](https://github.com/Tongjilibo/torch4keras)中作为Trainer
- [测试用例](https://github.com/Tongjilibo/rec4torch/tree/master/examples)
## 4. 版本说明
- **v0.0.2**：20240204 更新依赖项torch4keras版本
- **v0.0.1**：20221027 dcn, deepcrossing, deepfm, din, dien, wide&deep, ncf等模型，训练过程修改为传入dataloader，合并models和layers，合并简化embedding_lookup，去掉一些重复的embedding过程(提速)
## 5. 更新：
- **20240204**：更新依赖项torch4keras版本
- **20221110**：增加自定义的TensorDataset和collate_fn_device，支持指定device，防止显存占用多大，用out_dim和loss来替代task参数，兼容多分类
- **20221027**：增加deepcrossing, ncf, din, dien算法，使用torch4keras作为trainer
- **20220930**：初版提交, 训练过程修改为传入dataloader(参考bert4torch)，合并models和layers(模型结构较简单)，合并简化embedding_lookup，去掉一些重复的embedding过程(提速)