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kcgof

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This repository contains a Python 3 implementation of two nonparametric goodness-of-fit testing of conditional density models as proposed in our paper

Testing Goodness of Fit of Conditional Density Models with Kernels
Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf
UAI 2020
https://arxiv.org/abs/2002.10271
  • Presentation slides for UAI 2020 can be found here.
  • Video presentation here (TODO: link not available yet).

Dependency

  • dill == 0.3.1.1
  • matplotlib == 3.1.3
  • numpy == 1.18.1
  • scipy == 1.4.1
  • torch == 1.4.0

Version numbers for everything else except torch should not matter much as long as they are not too old.

Demo

See the Jupyter notebook ipynb/demo_proposed_tests.ipynb for how to start using our tests. This notebook describes how to use the proposed Kernel Conditional Stein Discrepancy (KCSD) test, which is one of the two test we proposed.

A notebook that introduces the other test, the Finite Set Conditional Discrepancy (FSCD), is coming soon.

Development

We recommend Anaconda. To install our package kcgof for development purpose, follow the following steps:

  1. Make a new Anaconda environment for this project. We will need Pytorch. Switch to this environment.

  2. Install kernel-gof (dependency). See https://github.com/wittawatj/kernel-gof.

  3. (Optional. Only needed if you use the MMD test in this repository.) Install freqopttest. See https://github.com/wittawatj/interpretable-test.

  4. Install the above dependencies with conda install or pip install in your environment.

  5. Clone this repository to your local folder.

  6. Run the following command in a terminal to install the kcgof package from this repository.

     pip install -e /path/to/the/local/folder
    
  7. In a Python shell under the same conda environment, make sure that you can import kcgof without any error.

The -e flag offers an "edit mode", so that changes to any files in this repo will be reflected immediately in the imported package.

Reproduce experimental results

All experiments which involve test powers can be found in kcgof/ex/ex1_vary_n.py. To run the experiment, use kcgof/ex/run_ex1.sh.

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 without reproducing our results). 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, in kcgof/config.py, set

'ex_use_slurm_cluster': False,

which will instruct the computation engine to just use a normal for-loop on a single machine (will take a lot of time). 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 can be specified in the same config file by changing the value of ex_results_path. The scratch folder needed by the independent-jobs package can be specified in the same file by changing the value of ex_scratch_path.

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.


If you have questions or comments about anything related to this work, please do not hesitate to contact Wittawat Jitkrittum and Heishiro Kanagawa

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UAI 2020. Kernel goodness-of-fit tests for conditional density models.

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