ksu 0.1.2

Implementation of the KSU compression algorithm https://www.cs.bgu.ac.il/~karyeh/compression-arxiv.pdf

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, [gramPath, prune])

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)

gramPath (optional, default=None) a path to a precomputed gramian matrix

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)

KSU provides a method makePredictor([delta])

Which returns a 1-NN Classifer (based on sklearn's K-NN) fitted to the compressed data, where delta (optional, default=5%) is the required confidence of said classifier.