# ALiPy: Active Learning in Python

arXiv: https://arxiv.org/abs/1901.03802 · HTML: https://mlsanity.com/paper/1901.03802/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2019.

## Built on by (most cited first, 11 of 11)

- [Small-Text: Active Learning for Text Classification in Python](https://mlsanity.com/paper/2107.10314/) (2021)
- [Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation](https://mlsanity.com/paper/2207.00845/) (2022)
- [An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets](https://mlsanity.com/paper/2306.08954/) (2023)
- [ImitAL: Learning Active Learning Strategies from Synthetic Data](https://mlsanity.com/paper/2108.07670/) (2021)
- [Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios](https://mlsanity.com/paper/2207.01190/) (2022)
- [PyRelationAL: a python library for active learning research and development](https://mlsanity.com/paper/2205.11117/) (2022)
- [ImitAL: Learned Active Learning Strategy on Synthetic Data](https://mlsanity.com/paper/2208.11636/) (2022)
- [ALANNO: An Active Learning Annotation System for Mortals](https://mlsanity.com/paper/2211.06224/) (2022)
- [Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI](https://mlsanity.com/paper/2207.09109/) (2022)
- [regAL: Python Package for Active Learning of Regression Problems](https://mlsanity.com/paper/2410.17917/) (2024)
- [Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid](https://mlsanity.com/paper/2506.03817/) (2025)

## Abstract (arXiv)

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.