ALiPy: Active Learning in Python

TL;DR

ALiPy is an open-source Python toolbox that facilitates the implementation, evaluation, and comparison of various active learning algorithms, supporting diverse settings like multi-label data and noisy annotators.

Contribution

It introduces a comprehensive, modular framework for active learning in Python, including over 20 algorithms and customization options for different scenarios.

Findings

Supports evaluation and comparison of active learning methods

Includes implementations of 20+ state-of-the-art algorithms

Enables customization for various active learning settings

Abstract

Supervised machine learning methods usually require a large set of labeled examples for model training. However, in many real applications, there are plentiful unlabeled data but limited labeled data; and the acquisition of labels is costly. Active learning (AL) reduces the labeling cost by iteratively selecting the most valuable data to query their labels from the annotator. This article introduces a Python toobox ALiPy for active learning. ALiPy provides a module based implementation of active learning framework, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods. In the toolbox, multiple options are available for each component of the learning framework, including data process, active selection, label query, results visualization, etc. In addition to the implementations of more than 20 state-of-the-art active learning algorithms, ALiPy also supports users to easily configure and implement their own approaches under different active learning settings, such as AL for multi-label data, AL with noisy annotators, AL with different costs and so on. The toolbox is well-documented and open-source on Github, and can be easily installed through PyPI.