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rsw

Optimal representative sample weighting (rsw) in Python. This package implements the methods described in the paper Optimal Representative Sample Weighting. At a high level, the package takes in a dataset assigns to each data point a nonnegative weight, so as to make weighted sample averages equal or close to some desired averages. For more details behind the math, we highly recommend checking out the paper.

Installation

We highly recommend upgrading your version of pip before installing rsw:

$ pip install --upgrade pip

Clone the repository, then run:

$ python setup.py install

API

rsw exposes one method, with the signature

def rsw(df, funs, losses, regularizer, lam=1, **kwargs):
    """Optimal representative sample weighting.

    Arguments:
        - df: Pandas dataframe
        - funs: functions to apply to each row of df.
        - losses: list of losses, each one of rsw.EqualityLoss, rsw.InequalityLoss, rsw.LeastSquaresLoss,
            or rsw.KLLoss()
        - regularizer: One of rsw.ZeroRegularizer, rsw.EntropyRegularizer,
            or rsw.KLRegularizer, rsw.BooleanRegularizer
        - lam (optional): Regularization hyper-parameter (default=1).
        - kwargs (optional): additional arguments to be sent to solver. For example: verbose=False,
            maxiter=5000, rho=50, eps_rel=1e-5, eps_abs=1e-5.

    Returns:
        - w: Final sample weights.
        - out: Final induced expected values as a list of numpy arrays.
        - sol: Dictionary of final ADMM variables. Can be ignored.
    """

Running the examples

There are two examples, one on simulated data and one on the CDC BRFSS dataset.

Simulated

To run the simulated example, after installing rsw, navigate to the examples folder and run:

$ python simulated.py

CDC BRFSS

To run the CDC BRFSS example, first download the data:

$ cd examples/data
$ wget https://www.cdc.gov/brfss/annual_data/2018/files/LLCP2018XPT.zip
$ unzip LLCP2018XPT.zip

In the examples folder, to run all the examples in the paper, execute the following command:

$ python brfss.py

Citing

If you use rsw in your research, please consider citing us by using the following bibtex:

@misc{barratt2020optimal,
  title={Optimal Representative Sample Weighting},
  author={Barratt, Shane and Angeris, Guillermo and Boyd, Stephen},
  month={May},
  year={2020},
  howpublished={\texttt{https://stanford.edu/~boyd/papers/optimal_representative_sampling.html}}
}

License

This repository carries a permissive Apache 2.0 license.

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rsw: optimal representative sample weighting.

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