Non-Parametric Inference of Relational Dependence (NIRD)

---

Requirements

Prepare Ubuntu environment:

sudo apt-get update
sudo apt-get install build-essential
sudo apt-get install zip

This project requires Python 3. Follow the instructions to install all the dependencies.

Install Anaconda.

a. Create a conda venv:

conda create --name rci python=3.6 --yes

b. Activate the venv:

conda activate rci

If it doesn't work, try:

source activate rci

c. Make sure you are using the python inside the venv by:

which python

d. Note for MacOS: Make sure wget is installed in the system. You can install it by brew.

e. Run the following command which automates the download and installation of dependencies.

	sh ./install.sh

f. Install torch and gpytorch (only needed for NIRD conditional test):

	pip install torch
	pip install --upgrade git+https://github.com/cornellius-gp/gpytorch.git

Sample Usage

• Run the sample script just to check everything is working fine:

	./sample.sh

• It'll take less than a minute to finish. It'll create a zip called sample.zip containing the outputs, logs and plots created for the sample run.

Reproduce reported results

• Run the following script to reproduce the reported results:

   sh ./neurips21.sh 100
  

Usage for custom experiments

• Enter your preferred configuration for experiment in a config json file inside conf directory

  • conf/exp_1a_dep.json is a config file for case 1a. It includes both synthetic graph parameters as well as experiment parameters. Refer to experiments.md for detailed explanation of experiment configs.

• Then run the experiment using the following command:

   python experiment.py -config conf/exp_1a_dep.json -o out/exp_1a_dep.csv 
  

  -config: Input config

  -o: Output csv path. By default it will be stored in out directory.

  Following files will be created:

  • out/exp_1a_dep_type_i.csv

  • out/exp_1a_dep_type_ii.csv

• There are several other arguments you can use:

   python experiment.py -config conf/sample.json -nt 2             # override number of trials
   python experiment.py -config conf/sample.json -jobs 2           # specifiy job for parallel
   python experiment.py -config conf/sample.json --nop             # no parallel run
   python experiment.py -config conf/sample.json -a 0.01 --cache   # specify alpha, get results from logged p-values, no exp done
  

• To plot results run the following:

   python plotter.py -fmt png -res out/exp_1a_dep_type_ii.csv -sres out/exp_1a_dep_type_i.csv
  

  -res: Input results csv file (type ii)

  -sres: Input results csv file (type i)

  -fmt: Output image format (png / eps)