Introduction

EasyTPP provides an open-source library for Neural TPP, with a fully automated pipeline for model training and prediction.

Framework

EasyTPP supports both Tensorflow and PyTorch: each model has two equivalent versions implemented in Tensorflow 1.13 and Pytorch 1.8 respectively. The data processing and model training / prediction pipeline are compatible with both Tensorflow and Pytorch as well.

At the module level, EasyTPP is a package that consists of the following components, which are designed as loose-coupled modules that provide flexibility for users to develop customized functionalities.

Name	Description
Preprocess module	Provides data batch-wise padding, inter-time processing and other related work for raw sequence.
Model module	Implements a list of SOTA TPP models. Please refer to Model Validation for more details.
Config module	Encapsulate the construction of the configuration needed to run the pipeline.
Runner module	Controls the training and prediction pipeline.

Install

EasyTPP can be installed either by pip or the source. By default it is built based on PyTorch. If one wants to run with the Tensorflow backend, one needs to install Tensorflow additionally.

Please see Installation for details of requirement and installation.

Prepare Data

By default, we use the data in Gatech format, i.e., each dataset is a dict containing the keys such as time_since_last_event, time_since_start and type_event. Preprocess module will preprocess the data and feed it into the model.

An example of building a pseudo dataloader can be found at examples. Please refer to Datatset for more explanations of the TPP dataset iterator.

Model Training and Prediction

The training and prediction pipeline consists of two steps:

1. Setup the config file, which specifies the dataset dir, model params and pipeline settings.

2. Launch the python script to run the whole pipeline.

Please see Training Pipeline and Evaluation Pipeline for more details.