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, 
                  verbose=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
      verbose: If False, do not print intermediate time taken in thinning rounds, 
        if True print that info
    """

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

The primary Compress++ function is compresspp in the compress module:

from goodpoints import compress
coreset = compress.compresspp(X, halve, thin, g)
    """Returns Compress++(g) coreset of size sqrt(n) as row indices into X

    Args: 
        X: Input sequence of sample points with shape (n, d)
        halve: Function that takes in an (n', d) numpy array Y and returns 
          floor(n'/2) distinct row indices into Y, identifying a halved coreset
        thin: Function that takes in an (n', d) numpy array Y and returns
          2^g sqrt(n') row indices into Y, identifying a thinned coreset
        g: Oversampling factor
    """

For example uses, please refer to the code examples/compress/construct_compresspp_coresets.py.

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}
}

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, where in the last code block we report the median heuristic based bandwidth parameteters (along with the code to compute it).
After all results have been generated, the notebook examples/kt/plot_results.ipynb can be used to reproduce the figures of Kernel Thinning.

Generalized Kernel Thinning

@inproceedings{dwivedi2022generalized,
  title={Generalized Kernel Thinning},
  author={Raaz Dwivedi and Lester Mackey},
  booktitle={International Conference on Learning Representations},
  year={2022}
}

The script examples/gkt/submit_gkt_jobs.py reproduces the vignette experiments of Generalized Kernel Thinning on a Slurm cluster by executing examples/gkt/run_generalized_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.
Once the coresets are generated, examples/gkt/compute_test_function_errors.ipynb can be used to generate integration errors for different test functions.
After all results have been generated, the notebook examples/gkt/plot_gkt_results.ipynb can be used to reproduce the figures of Generalized Kernel Thinning.

Distribution Compression in Near-linear Time

@inproceedings{shetty2022distribution,
  title={Distribution Compression in Near-linear Time},
  author={Abhishek Shetty and Raaz Dwivedi and Lester Mackey},
  booktitle={International Conference on Learning Representations},
  year={2022}
}

The notebook examples/compress/script_to_deploy_jobs.ipynb reproduces the experiments of Distribution Compression in Near-linear Time in the following manner:
1. It generates various coresets and computes their mmds by executing examples/compress/construct_{THIN}_coresets.py for THIN in {compresspp, kt, st, herding} with appropriate parameters, where the flag kt stands for kernel thinning, st stands for standard thinning (choosing every t-th point), and herding refers to kernel herding.
2. It computes the runtimes of different algorithms by executing examples/compress/run_time.py.
3. 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.
4. The notebook currently deploys these jobs on a slurm cluster, but setting deploy_slurm = False in examples/compress/script_to_deploy_jobs.ipynb will submit the jobs as independent python calls on terminal.
After all results have been generated, the notebook examples/compress/plot_compress_results.ipynb can be used to reproduce the figures of Distribution Compression in Near-linear Time.

Contributing

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

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