ksu 0.2.1

KSU Compression Algorithm Implementation

Algortihm 1 from Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions

Installation

• With pip: pip install ksu
• From source:
  • git clone --recursive https://github.com/nimroha/ksu_classifier.git
  • cd ksu_classifier
  • python setup.py install

Usage

This package provides a class KSU(Xs, Ys, metric, [gram, prune, logLevel, n_jobs])

Xs and Ys are the data points and their respective labels as numpy arrays

metric is either a callable to compute the metric or a string that names one of our provided metrics (print ksu.KSU.METRICS.keys() for the full list)

gram (optional, default=None) a precomputed gramian matrix, will be calculated if not provided.

prune (optional, default=False) a boolean indicating whether to prune the compressed set or not (Algorithm 2 from Near-optimal sample compression for nearest neighbors)

`logLevel (optional, default='CRITICAL') a string indicating the logging level (set to 'INFO' or 'DEBUG' to get more information)

n_jobs (optional, default=1) an integer defining how many cpus to use, pass -1 to use all. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.

KSU provides a method compressData([delta])

Which selects the subset with the lowest estimated error with confidence 1 - delta.

You can then run getClassifier() which returns a 1-NN Classifer (based on sklearn's K-NN) fitted to the compressed data.

Or, run getCompressedSet() to get the compressed data as a tuple of numpy arrays (compressedXs, compressedYs).

See scripts/ for example usage