Introduction

This is an implementation of the unlearning algorithms presented in Toward Making Systems Forget with Machine Learning, written by Cao, Yinzhi and Yang, Junfeng. Specifically, we are implementing the unlearning algorithm for LensKit's ItemItem similarity algorithm.

SetUp

• Python 3 is required
• Download the MovesLens Dataset from https://grouplens.org/datasets/movielens/100k/
  • Unzip and save ./ml-100k at ./
• Run ./unlearn/basic.py to start the pipline of time cost evaluations.

Modification

Modifications are made in ./lenskit/algorithms/item_knn.py from line 221 to 691.
Codes are injected in ./lenskit/algorithms/item_knn.py method fit to run time cost evaluation and save results in .csv
A pipeline is written in ./unlearn/basic.py to run produce time cost evaluation for different input size
A visualization is written in ./unlearn/visualization.py to graph the time cost evaluation stored in .csv

LensKit Python Implementation

LensKit is a set of Python tools for experimenting with and studying recommender systems. It provides support for training, running, and evaluating recommender algorithms in a flexible fashion suitable for research and education.

Python LensKit (LKPY) is the successor to the Java-based LensKit project.