alipy

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ALiPy

Active Learning in Python

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. It implementations more than 20 algorithms and also supports users to easily implement their own approaches under different settings.

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Citation

Tang, Y.-P.; Li, G.-X.; and Huang, S.-J. ALiPy: Active learning in python. https://arxiv.org/abs/1901.03802 . 2019.

Features of ALiPy

Model independent

There is no limitation to the classification model. One can use SVM in sklearn or deep model in tensorflow as you need.

Module independent

One can freely modify one or more modules of the toolbox without affection to the others.

Implement your own algorithm without inheriting anything

There are few limitations of the user-defined functions, such as the parameters or names.

Variant Settings supported

Noisy oracles, Multi-label, Cost effective, Feature querying, etc.

Powerful tools

Intermediate results saving & loading; multi-threading; experiement result analysing, etc.

Quick start

You can get alipy simply by:

pip install alipy
				

For detailed installation instruction, please refer to installation guide .

ALiPy provides several optional usages for different users.


For the users who are less familiar with active learning and want to simply apply a method to a dataset , Please refer to AlExperiment to run the active learning process in a few lines of codes without knowing about any background knowledge.


For the users who want to experimentally evaluate the performance of existing active learning methods , We recommend you to the ALiPy overview to learn the usage of more than 20 state-of-the-art algorithms in 7 different settings.


For the users who want to implement their own idea and perform active learning experiments , We recommend you to the 10 minutes to alipy to learn how to easily perform active learning experiment with ALiPy.

Implemented strategies

ALiPy provide several commonly used strategies in different active learning settings for now, and new algorithms will be added in subsequent updates.

AL with Instance Selection
Uncertainty (SIGIR 1994) Graph Density (CVPR 2012) QUIRE (TPAMI 2014) SPAL (AAAI 2019)
Query By Committee (ICML 1998) Random BMDR (KDD 2013) LAL (NIPS 2017)
Expected Error Reduction (ICML 2001)
AL for Multi-Label Data
AUDI (ICDM 2013) QUIRE (TPAMI 2014) Random MMC (KDD 2009)
Adaptive (IJCAI 2013)
AL by Querying Features
AFASMC (KDD 2018) Stability (ICDM 2013) Random
AL with Different Costs
HALC (IJCAI 2018) Random Cost performance
AL with Noisy Oracles
CEAL (IJCAI 2017) IEthresh (KDD 2009) All Random
AL with Novel Query Types
AURO (IJCAI 2015)
AL for Large Scale Tasks
Subsampling

Meet useful tools in alipy

Data process

Preprocessing & spliting

Query strategy

Select what to query

Oracle

Clean or noisy oracle

IO tool

Save/load queries anytime

Multi threading

Parallel your experiment

Result analyser

Plotting & paired t-test

Copyright © 2018, alipy developers (BSD 3 License).