remedian

The Remedian: A Robust Averaging Method for Large Data Sets - Python implementation

This algorithm is used to approximate the median of several data chunks if these data chunks cannot (or should not) be loaded into memory at once.

Given a data chunk of size obs_size, and t data chunks overall, the Remedian class sets up a number k_arrs of arrays of length n_obs.

The median of the t data chunks of size obs_size is then approximated as follows: One data chunk after another is fed into the n_obs positions of the first array. When the first array is full, its median is calculated and stored in the first position of the second array. After this, the first array is re-used to fill the second position of the second array, etc. When the second array is full, the median of its values is stored in the first position of the third array, and so on.

The final “Remedian” is the median of the last array, after all t data chunks have been fed into the object.

Installation

remedian runs on Python 3 with numpy as its only dependency. You can install remedian with pip pip install remedian.

Installation of development version

  1. activate your python environment
  2. git clone https://www.github.com/sappelhoff/remedian
  3. cd remedian
  4. pip install -r requirements-dev.txt
  5. pip install -r requirements.txt
  6. pip install -e .
  7. then you should be able to from remedian import Remedian

Usage

See the examples.

CONTRIBUTIONS WELCOME

This is a very basic package currently and there are many enhancements that could be made. If you want to work on this, please write a GitHub issue or submit a Pull Request.

References

  1. P.J. Rousseeuw, G.W. Bassett Jr., “The remedian: A robust averaging method for large data sets”, Journal of the American Statistical Association, vol. 85 (1990), pp. 97-104
  2. M. Chao, G. Lin, “The asymptotic distributions of the remedians”, Journal of Statistical Planning and Inference, vol. 37 (1993), pp. 1-11
  3. Domenico Cantone, Micha Hofri, “Further analysis of the remedian algorithm”, Theoretical Computer Science, vol. 495 (2013), pp. 1-16