A Lighting Pytorch Framework for Recommendation System, Easy-to-use and Easy-to-extend.

Last update: Jan 04, 2023

Overview

Torch-RecHub

A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.

安装

pip install torch-rechub

主要特性

scikit-learn风格易用的API（fit、predict），即插即用
训练过程与模型定义解耦，易拓展，可针对不同类型的模型设置不同的训练机制
使用Pytorch原生Dataset、DataLoader，易修改，自定义数据
高度模块化，支持常见Layer（MLP、FM、FFM、target-attention、self-attention、transformer等），容易调用组装成新模型
支持常见排序模型（WideDeep、DeepFM、DIN、DCN、xDeepFM等）
支持常见召回模型（DSSM、YoutubeDNN、MIND、SARSRec等）
丰富的多任务学习支持
- SharedBottom、ESMM、MMOE、PLE、AITM等模型
- GradNorm、UWL等动态loss加权机制
聚焦更生态化的推荐场景
- 冷启动
- 延迟反馈
- 去偏
支持丰富的训练机制（对比学习、蒸馏学习等）
第三方高性能开源Trainer支持（Pytorch Lighting等）
更多模型正在开发中

快速使用

from torch_rechub.rmodels.ranking import WideDeep, DeepFM, DIN
from torch_rechub.trainers import CTRTrainer
from torch_rechub.basic.utils import DataGenerator

dg = DataGenerator(x, y)
train_dataloader, val_dataloader, test_dataloader = dg.generate_dataloader()

model = DeepFM(deep_features=deep_features, fm_features=fm_features, mlp_params={"dims": [256, 128], "dropout": 0.2, "activation": "relu"})

ctr_trainer = CTRTrainer(model)
ctr_trainer.fit(train_dataloader, val_dataloader)
auc = ctr_trainer.evaluate(ctr_trainer.model, test_dataloader)

多任务学习

from torch_rechub.models.multi_task import SharedBottom, ESMM, MMOE, PLE, AITM
from torch_rechub.trainers import MTLTrainer

model = MMOE(features, task_types, n_expert=3, expert_params={"dims": [64,32,16]}, tower_params_list=[{"dims": [8]}, {"dims": [8]}])

ctr_trainer = MTLTrainer(model)
ctr_trainer.fit(train_dataloader, val_dataloader)
auc = ctr_trainer.evaluate(ctr_trainer.model, test_dataloader)

Note:

所有模型均在大多数论文提及的多个知名公开数据集中测试，达到或者接近论文性能。

使用案例：Examples

每个数据集将会提供

一个使用脚本，包含样本生成、模型训练与测试，并提供一套测评所用参数。

一个预处理脚本，将原始数据进行预处理，转化成csv。

数据格式参考文件（100条）。

全量数据，统一的csv文件，提供高速网盘下载链接和原始数据链接。

初步规划TODO清单

A Lighting Pytorch Framework for Recommendation System, Easy-to-use and Easy-to-extend.

Related tags

Overview

Torch-RecHub

安装

主要特性

快速使用

Owner

Mincai Lai

Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Official DGL implementation of "Rethinking High-order Graph Convolutional Networks"

Ejemplo Algoritmo Viterbi - Example of a Viterbi algorithm applied to a hidden Markov model on DNA sequence

Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time

This is a code repository for the paper "Graph Auto-Encoders for Financial Clustering".

A model to classify a piece of news as REAL or FAKE

YOLO-v5 기반 단안 카메라의 영상을 활용해 차간 거리를 일정하게 유지하며 주행하는 Adaptive Cruise Control 기능 구현

Code for Overinterpretation paper Overinterpretation reveals image classification model pathologies

Simulator for FRC 2022 challenge: Rapid React

This repository contains the source code and data for reproducing results of Deep Continuous Clustering paper

SGPT: Multi-billion parameter models for semantic search

Implementation for Homogeneous Unbalanced Regularized Optimal Transport

Methods to get the probability of a changepoint in a time series.

MAUS: A Dataset for Mental Workload Assessment Using Wearable Sensor - Baseline system

Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition"

Personalized Federated Learning using Pytorch (pFedMe)

Brax is a differentiable physics engine that simulates environments made up of rigid bodies, joints, and actuators

ATAC: Adversarially Trained Actor Critic

Unofficial PyTorch implementation of Guided Dropout

sense-py-AnishaBaishya created by GitHub Classroom