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README.md

The Finite Set Independence Criterion (FSIC)

Build Status license

This repository contains a Python 2.7 implementation of the normalized FSIC (NFSIC) test as described in our paper

An Adaptive Test of Independence with Analytic Kernel Embeddings
Wittawat Jitkrittum, Zoltán Szabó, Arthur Gretton
ICML 2017

How to install?

If you plan to reproduce experimental results, you will probably want to modify our code. It is best to install by:

  1. Clone the repository by git clone https://github.com/wittawatj/fsic-test.

  2. cd to the folder that you get, and install our package by

    pip install -e .

Alternatively, if you only want to use the developed package, you can do the following without cloning the repository.

pip install git+https://github.com/wittawatj/fsic-test.git

Either way, once installed, you should be able to do import fsic without any error.

Dependency

We rely on the following Python packages during development. Please make sure that you use the packages with the specified version numbers or newer.

numpy==1.11.0
matplotlib==1.5.1
scipy==0.18.0
theano==0.8.

Note that theano is not enabled in Anaconda by default. See this page for how to install it.

Demo scripts

To get started, check demo_nfsic.ipynb which will guide you through from the beginning. There are many Jupyter notebooks in ipynb folder. Be sure to check them if you would like to explore more.

Reproduce experimental results

Experiments on test powers

All experiments which involve test powers on toy problems can be found in fsic/ex/ex1_vary_n.py, fsic/ex/ex2_prob_params.py. fsic/ex/ex4_real_data.py, and fsic/ex/ex5_real_vary_n.py are for experiments on real data. Each file is runnable with a command line argument. For example in ex1_vary_n.py, we aim to check the test power of each test as a function of the sample size n. The script ex1_vary_n.py takes a dataset name as its argument. See run_ex1.sh which is a standalone Bash script on how to execute ex1_power_vs_n.py.

We used independent-jobs package to parallelize our experiments over a Slurm cluster (the package is not needed if you just need to use our developed tests). For example, for ex1_vary_n.py, a job is created for each combination of

(dataset, test algorithm, n, trial)

If you do not use Slurm, you can change the line

engine = SlurmComputationEngine(batch_parameters)

to

engine = SerialComputationEngine()

which will instruct the computation engine to just use a normal for-loop on a single machine (will take a lot of time). Other computation engines that you use might be supported. Running simulation will create a lot of result files (one for each tuple above) saved as Pickle. Also, the independent-jobs package requires a scratch folder to save temporary files for communication among computing nodes. Path to the folder containing the saved results (after running the experiments) is fsic/result. Real data should be placed in fsic/data.

The scratch folder needed by the independent-jobs package can be specified in fsic/config.py. To plot the results, see the experiment's corresponding Jupyter notebook in the ipynb/ folder. For example, for ex1_vary_n.py see ipynb/ex1_results.ipynb to plot the results.

License

MIT license.

If you have questions or comments about anything regarding this work or code, please do not hesitate to contact Wittawat Jitkrittum.

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ICML 2017. Kernel-based adaptive linear-time independence test.

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