goodpoints 0.0.1

Tools to generate concice high-quality summaries of a probability distribution

GoodPoints

A Python package for generating concise, high-quality summaries of a probability distribution

GoodPoints is a collection of tools for compressing a distribution more effectively than independent sampling:

• Given an initial summary of n input points, kernel thinning returns s << n output points with comparable integration error across a reproducing kernel Hilbert space
• Compress++ reduces the runtime of generic thinning algorithms with minimal loss in accuracy

Installation

To install the goodpoints package, use the following pip command:

pip install goodpoints

Getting started

The primary kernel thinning function is thin in the kt module:

from goodpoints import kt
coreset = kt.thin(X, m, split_kernel, swap_kernel, delta=0.5, seed=123, store_K=False)
    """Returns kernel thinning coreset of size floor(n/2^m) as row indices into X

    Args:
      X: Input sequence of sample points with shape (n, d)
      m: Number of halving rounds
      split_kernel: Kernel function used by KT-SPLIT (typically a square-root kernel, krt);
        split_kernel(y,X) returns array of kernel evaluations between y and each row of X
      swap_kernel: Kernel function used by KT-SWAP (typically the target kernel, k);
        swap_kernel(y,X) returns array of kernel evaluations between y and each row of X
      delta: Run KT-SPLIT with constant failure probabilities delta_i = delta/n
      seed: Random seed to set prior to generation; if None, no seed will be set
      store_K: If False, runs O(nd) space version which does not store kernel
        matrix; if True, stores n x n kernel matrix
    """

For example uses, please refer to the notebook examples/kt/run_kt_experiment.ipynb.

Examples

Code in the examples directory uses the goodpoints package to recreate the experiments of the following research papers.

---

Kernel Thinning

@article{dwivedi2021kernel,
  title={Kernel Thinning},
  author={Raaz Dwivedi and Lester Mackey},
  journal={arXiv preprint arXiv:2105.05842},
  year={2021}
}

1. The script examples/kt/submit_jobs_run_kt.py reproduces the vignette experiments of Kernel Thinning on a Slurm cluster by executing examples/kt/run_kt_experiment.ipynb with appropriate parameters. For the MCMC examples, it assumes that necessary data was downloaded and pre-processed following the steps listed in examples/kt/preprocess_mcmc_data.ipynb.
2. After all results have been generated, the notebook plot_results.ipynb can be used to reproduce the figures of Kernel Thinning.

Generalized Kernel Thinning

@article{dwivedi2021generalized,
  title={Generalized Kernel Thinning},
  author={Dwivedi, Raaz and Mackey, Lester},
  journal={arXiv preprint arXiv:2110.01593},
  year={2021}
}

1. The script examples/gkt/ADDME reproduces the experiments of Generalized Kernel Thinning on a Slurm cluster by executing examples/gkt/ADDME with appropriate parameters. For the MCMC examples, it assumes that necessary data was downloaded and pre-processed following the steps listed in examples/kt/preprocess_mcmc_data.ipynb.
2. After all results have been generated, the notebook examples/gkt/ADDME can be used to reproduce the figures of Generalized Kernel Thinning.

Distribution Compression in Near-linear Time

@article{shetti2021distribution,
  title={Distribution Compression in Near-linear Time},
  author={Abhishek Shetty and Raaz Dwivedi and Lester Mackey},
  journal={arXiv preprint to appear},
  year={2021}
}

1. The script examples/compress/ADDME reproduces the experiments of Distribution Compression in Near-linear Time on a Slurm cluster by executing examples/compress/ADDME with appropriate parameters. For the MCMC examples, it assumes that necessary data was downloaded and pre-processed following the steps listed in examples/kt/preprocess_mcmc_data.ipynb.
2. After all results have been generated, the notebook examples/compress/ADDME can be used to reproduce the figures of Distribution Compression in Near-linear Time.

Contributing

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