Niklas Stoehr (ETH Zurich), Benjamin Radford (UNC Charlotte), Ryan Cotterell (ETH Zurich), Aaron Schein (University of Chicago)
paper pre-print at https://arxiv.org/pdf/2212.04130.pdf
The code can either be run locally or tinkered with using our colaboratory notebooks. For quick exploration, we recommend the latter. In particular, we provide notebooks showing the following functionality:
Comparison SMD, BMD and OMD
OMD Alpha Prior Comparison
Latent Dirichlet Allocation LDA
Hidden Markov Model HMM
Poisson-Gamma Dynamical System PGDS
Dynamic Possion Tucker Model DPTM
Recovering Latent Patterns with an HMM and the OMD
Latent Parameters of DPTM fitted to ICEWS
When running the code locally, you have to install the user-defined modules omd0configs,omd1data,omd2model. From the root, install the modules by executing
pip install -e omd0configs
pip install -e omd1data
pip install -e omd2model
omd0configs features configuration methods and other helper functions
omd1data features different data loading functionality
omd2model features different models
In addition, we recommend installing the requirements listed in the requirements.txt file:
pip install -r requirements.txt
This repository offers functionalites for two kinds of data: synthetic data and real-world conflict data. For the latter, you need to download the freely accessible ICEWS coded event data from the Harvard Dataverse. In particular, we recommend downloading the event data from 2015 to 2020:
events.2020.20220623.tab.zip
events.2019.20200427085336.tab
events.2018.20200427084805.tab
events.2017.20201119.zip
events.2016.20180710092843.tab
events.2015.20180710092545.tab
Place the data at data/conflict/icews.