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https://github.com/yangalan123/anhp-andtt
Codebase for Attentive Neural Hawkes Process (A-NHP) and Attentive Neural Datalog Through Time (A-NDTT)
https://github.com/yangalan123/anhp-andtt
Last synced: about 2 months ago
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Codebase for Attentive Neural Hawkes Process (A-NHP) and Attentive Neural Datalog Through Time (A-NDTT)
- Host: GitHub
- URL: https://github.com/yangalan123/anhp-andtt
- Owner: yangalan123
- License: mit
- Created: 2021-12-30T15:16:08.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2023-11-08T00:08:03.000Z (about 1 year ago)
- Last Synced: 2024-04-20T13:04:22.017Z (9 months ago)
- Language: Python
- Homepage:
- Size: 218 KB
- Stars: 49
- Watchers: 3
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Maintainance Status: Arhical Mode (2024)
If you are seeking more up-to-date efficient implementations of ANHP, THP, and SAHP, I recommend checking out [EasyTPP](https://github.com/ant-research/EasyTemporalPointProcess). They also provide better community support for your TPP usage. This repository is currently under archival (Read-Only) mode.
# Introduction
Codebase for the paper [**Transformer Embeddings of Irregularly Spaced Events and Their Participants**](https://arxiv.org/abs/2201.00044).
Author: Chenghao Yang ([email protected])
This codebase contains several packages:
1. `anhp`: Attentive-Neural Hawkes Process (A-NHP)
2. `andtt`: Attentive-Neural Datalog Through Time (A-NDTT).
3. `thp`: Our corrected version of [Transformer Hawkes Process (THP)](https://arxiv.org/abs/2002.09291).
4. `sahp`: Our corrected version of [Self-Attentive Hawkes Process (SAHP)](https://arxiv.org/abs/1907.07561).
For `thp` and `sahp`, our code includes certain corrections that have been discussed with and agreed by the authors of those papers.
## Reference
If you use this code as part of any published research, please acknowledge the following paper (it encourages researchers who publish their code!):
```
@inproceedings{yang-2021-transformer,
author = {Chenghao Yang and Hongyuan Mei and Jason Eisner},
title = {Transformer Embeddings of Irregularly Spaced Events and Their Participants},
booktitle = {International Conference on Learning Representations},
year = {2022}
}
```
## Instructions
Here are the instructions to use the code base.
### Dependencies and Installation
This code is written in Python 3, and I recommend you to install:
* [Anaconda](https://www.continuum.io/) that provides almost all the Python-related dependencies;
This project relies on Datalog Utilities in [NDTT project](https://github.com/hongyuanmei/neural-datalog-through-time), please first install it.
(**please remove the `torch` version (`1.1.0`) in `setup.py` of NDTT project, because that is not the requirement of this project and we only use non-pytorch part of NDTT. We recommend using `torch>=1.7` for this project.**).
Then run the command line below to install the package (add `-e` option if you need an editable installation):
```
pip install .
```
### Dataset Preparation
Download datasets and programs from [here](https://drive.google.com/drive/folders/17vtQdx3d1wR-SADSMamt4E2mqHfEOu9q).
Organize your domain datasets as follows:
```
domains/YOUR_DOMAIN/YOUR_PROGRAMS_AND_DATA
```
### (A-NDTT-only) Build Dynamic Databases
Go to the `andtt/run` directory.
To build the dynamic databases for your data, try the command line below for detailed guide:
```
python build.py --help
```
The generated dynamic model architectures (represented by database facts) are stored in this directory:
```
domains/YOUR_DOMAIN/YOUR_PROGRAMS_AND_DATA/tdbcache
```
### Train Models
To train the model specified by your Datalog probram, try the command line below for detailed guide:
```
python train.py --help
```
The training log and model parameters are stored in this directory:
```
# A-NHP
domains/YOUR_DOMAIN/YOUR_PROGRAMS_AND_DATA/ContKVLogs
# A-NDTT
domains/YOUR_DOMAIN/YOUR_PROGRAMS_AND_DATA/Logs
```
Example command line for training:
```
# A-NHP
python train.py -d YOUR_DOMAIN -ps ../../ -bs BATCH_SIZE -me 50 -lr 1e-4 -d_model 32 -teDim 10 -sd 1111 -layer 1
# A-NDTT
python train.py -d YOUR_DOMAIN -db YOUR_PROGRAM -ps ../../ -bs BATCH_SIZE -me 50 -lr 1e-4 -d_model 32 -teDim 10 -sd 1111 -layer 1
```
### Test Models
To test the trained model, use the command line below for detailed guide:
```
python test.py --help
```
Example command line for testing:
```
python test.py -d YOUR_DOMAIN -fn FOLDER_NAME -s test -sd 12345 -pred
```
To evaluate the model predictions, use the command line below for detailed guide:
```
python eval.py --help
```
Example command line for testing:
```
python eval.py -d YOUR_DOMAIN -fn FOLDER_NAME -s test
```
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgements
1. The transformer component implementation used in this repo is based on widely-recognized [Annotated Transformer](https://nlp.seas.harvard.edu/2018/04/03/attention.html
).
1. The code structure is inspired by Prof. Hongyuan Mei's [Neural Datalog Through Time](https://github.com/HMEIatJHU/neural-datalog-through-time.git)