This repo contains examples of use of the JsonGrinder.jl library from JsonGrinder.jl: automated differentiable neural architecture for embedding arbitrary JSON data, Mandlík, Račinský, Lisý, and Tomáš Pevný, 2022.
Julia v1.7.2 was used in all experiments. Each directory contains the Project.toml and Manifest.toml for reproducibility. To make sure that all dependencies have same versions, run
julia --project=. -e "using Pkg; Pkg.instantiate()"
in the directory.
To reproduce the results, it is sufficient to run in appropriate directory. For baseline results, run
julia --project=. baseline.jl
for tuned results, run
julia --project=. tuned.jl
Mutagenesis is a small dataset from biology, which describes molecules trialed for mutagenicity on Salmonella typhimurium. It is very small and contains only 100 training samples.
Ember is a problem from a computer security, where the goal is to classify samples to malware and clean. Data are available at https://github.com/endgameinc/ember. The problem is large, therefore the scripts are adapted to take advantage of multi-threadding in the data preparation phase (hence run julia with -t option with correct number of threads). Because of this, scripts depart a bit from the usual examples.
Warning: The ember dataset is large. By default, it will be downloaded to a temporary directory in /tmp. Make sure you have at least 10Gb of free space.
Execute julia -t auto --project=. -e 'include("prepare_data.jl"); prepare_data()' to download the data and cache the result of extraction.