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README.md

Combining Active Learning Suggestions

This directory contains the notebooks that are used to replicate the experiments in the paper 'Combining Active Learning Suggestions' by Alasdair Tran, Cheng Soon Ong, and Christian Wolf.

Installation

We recommend using the Anaconda distribution for Python 3. You also need to have the mclearn installed. To install it from source:

git clone https://github.com/chengsoonong/mclass-sky.git
cd mclass-sky; python3 setup.py develop

# We also need a few more dependencies
pip3 install joblib  # or conda install joblib

# Download the results
git submodule update --init
git submodule foreach git pull origin master

Replicating the Experiments

There are two main notebooks used to replicate the experiments.

  1. UCI Datasets: Run this notebook first to generate process the UCI data and put them in the right format for the experiments. In addition to the UCI data, you also need to have the SDSS dataset, which can be manually downloaded from Zenodo.

  2. Active Learning Suggestions: The second notebook contains the code used to replicate the experiments and produce all the plots used in the paper. Note that with the larger datasets, it can take up to a day to run. For testing purposes, we recommend running with the small datasets such as wine. If you would like to reproduce the plots used in the paper without re-running the experiments, set the RUN_EXPERIMENTS constant to False in the notebook and pull the latest result data from the subdirectory results (which is a git submodule).