Codebase for Attentive Neural Hawkes Process (A-NHP) and Attentive Neural Datalog Through Time (A-NDTT)

Overview

Introduction

Codebase for the paper Transformer Embeddings of Irregularly Spaced Events and Their Participants.

This codebase contains two packages:

  1. anhp: Attentive-Neural Hawkes Process (A-NHP)
  2. andtt: Attentive-Neural Datalog Through Time (A-NDTT).

Author: Chenghao Yang ([email protected])

Reference

If you use this code as part of any published research, please acknowledge the following paper (it encourages researchers who publish their code!):

@article{yang-2021-transformer,
  author =      {Chenghao Yang and Hongyuan Mei and Jason Eisner},
  title =       {Transformer Embeddings of Irregularly Spaced Events and Their Participants},
  journal =     {arXiv preprint arxiv:2201.00044},
  year =        {2021}
}

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 that provides almost all the Python-related dependencies;

This project relies on Datalog Utilities in NDTT project, 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.

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 file for details.

Acknowledgements

  1. The transformer component implementation used in this repo is based on widely-recognized Annotated Transformer.
  2. The code structure is inspired by Prof. Hongyuan Mei's Neural Datalog Through Time
Owner
Alan Yang
AWS Applied Scientist Intern. [email protected] CLSP; M.S. & RA @columbia; Ex-intern @IBM Watson; B.S.
Alan Yang
StyleGAN2 with adaptive discriminator augmentation (ADA) - Official TensorFlow implementation

StyleGAN2 with adaptive discriminator augmentation (ADA) — Official TensorFlow implementation Training Generative Adversarial Networks with Limited Da

NVIDIA Research Projects 1.7k Dec 29, 2022
Code for the CVPR2021 paper "Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition"

Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition This repository contains code for the CVPR2021 paper "Patch-NetV

QVPR 368 Jan 06, 2023
This project implements "virtual speed" from heart rate monito

ANT+ Virtual Stride Based Speed and Distance Monitor Overview This project imple

2 May 20, 2022
Laser device for neutralizing - mosquitoes, weeds and pests

Laser device for neutralizing - mosquitoes, weeds and pests (in progress) Here I will post information for creating a laser device. A warning!! How It

Ildaron 1k Jan 02, 2023
Parametric Contrastive Learning (ICCV2021)

Parametric-Contrastive-Learning This repository contains the implementation code for ICCV2021 paper: Parametric Contrastive Learning (https://arxiv.or

DV Lab 156 Dec 21, 2022
Code for the paper "Improving Vision-and-Language Navigation with Image-Text Pairs from the Web" (ECCV 2020)

Improving Vision-and-Language Navigation with Image-Text Pairs from the Web Arjun Majumdar, Ayush Shrivastava, Stefan Lee, Peter Anderson, Devi Parikh

Arjun Majumdar 44 Dec 14, 2022
Multiband spectro-radiometric satellite image analysis with K-means cluster algorithm

Multi-band Spectro Radiomertric Image Analysis with K-means Cluster Algorithm Overview Multi-band Spectro Radiomertric images are images comprising of

Chibueze Henry 6 Mar 16, 2022
This repository is a series of notebooks that show solutions for the projects at Dataquest.io.

Dataquest Project Solutions This repository is a series of notebooks that show solutions for the projects at Dataquest.io. Of course, there are always

Dataquest 1.1k Dec 30, 2022
Train a deep learning net with OpenStreetMap features and satellite imagery.

DeepOSM Classify roads and features in satellite imagery, by training neural networks with OpenStreetMap (OSM) data. DeepOSM can: Download a chunk of

TrailBehind, Inc. 1.3k Nov 24, 2022
The official repository for our paper "The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization".

Codebase for learning control flow in transformers The official repository for our paper "The Neural Data Router: Adaptive Control Flow in Transformer

Csordás Róbert 24 Oct 15, 2022
FastReID is a research platform that implements state-of-the-art re-identification algorithms.

FastReID is a research platform that implements state-of-the-art re-identification algorithms.

JDAI-CV 2.8k Jan 07, 2023
This is RFA-Toolbox, a simple and easy-to-use library that allows you to optimize your neural network architectures using receptive field analysis (RFA) and create graph visualizations of your architecture.

ReceptiveFieldAnalysisToolbox This is RFA-Toolbox, a simple and easy-to-use library that allows you to optimize your neural network architectures usin

84 Nov 23, 2022
Learning where to learn - Gradient sparsity in meta and continual learning

Learning where to learn - Gradient sparsity in meta and continual learning In this paper, we investigate gradient sparsity found by MAML in various co

Johannes Oswald 28 Dec 09, 2022
Semantic Segmentation for Aerial Imagery using Convolutional Neural Network

This repo has been deprecated because whole things are re-implemented by using Chainer and I did refactoring for many codes. So please check this newe

Shunta Saito 27 Sep 23, 2022
Wider-Yolo Kütüphanesi ile Yüz Tespit Uygulamanı Yap

WIDER-YOLO : Yüz Tespit Uygulaması Yap Wider-Yolo Kütüphanesinin Kullanımı 1. Wider Face Veri Setini İndir Train Dataset Val Dataset Test Dataset Not:

Kadir Nar 6 Aug 22, 2022
Churn prediction

Churn-prediction Churn-prediction Data preprocessing:: Label encoder is used to normalize the categorical variable Data Transformation:: For each data

1 Sep 28, 2022
A simple API wrapper for Discord interactions.

Your ultimate Discord interactions library for discord.py. About | Installation | Examples | Discord | PyPI About What is discord-py-interactions? dis

james 641 Jan 03, 2023
Collision risk estimation using stochastic motion models

collision_risk_estimation Collision risk estimation using stochastic motion models. This is a new approach, based on stochastic models, to predict the

Unmesh 7 Jun 26, 2022
Training a deep learning model on the noisy CIFAR dataset

Training-a-deep-learning-model-on-the-noisy-CIFAR-dataset This repository contai

1 Jun 14, 2022
A complete, self-contained example for training ImageNet at state-of-the-art speed with FFCV

ffcv ImageNet Training A minimal, single-file PyTorch ImageNet training script designed for hackability. Run train_imagenet.py to get... ...high accur

FFCV 92 Dec 31, 2022