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Pool-based active learning in Python

Project description

# libact: Pool-based Active Learning in Python

authors: Yao-Yuan Yang, Shao-Chuan Lee, Yu-An Chung, Tung-En Wu, Si-An Chen, [Hsuan-Tien Lin](

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# Introduction

`libact` is a Python package designed to make active learning easier for
real-world users. The package not only implements several popular active learning strategies, but also features the [active-learning-by-learning](
meta-algorithm that assists the users to automatically select the best strategy
on the fly. Furthermore, the package provides a unified interface for implementing more strategies, models and application-specific labelers. The package is open-source along with issue trackers on github, and can be easily installed from Python Package Index repository.

# Documentation

[Documentation for the latest release is available online](
Comments and questions on the package is welcomed at ``. All contributions to the documentation are greatly appreciated!

# Basic Dependencies

* Python 2.7, 3.3, 3.4, 3.5

* Python dependencies
pip install -r requirements.txt

* Debian (>= 7) / Ubuntu (>= 14.04)
sudo apt-get install build-essential gfortran libatlas-base-dev liblapacke-dev python3-dev

* macOS
brew install homebrew/science/openblas

# Installation

After resolving the dependencies, you may install the package via pip (for all users):
sudo pip install libact

or pip install in home directory:
pip install --user libact

or pip install from github repository for latest source:
pip install git+

To build and install from souce in your home directory:
python install --user

To build and install from souce for all users on Unix/Linux:
python build
sudo python install

# Usage

The main usage of `libact` is as follows:

qs = UncertaintySampling(trn_ds, method='lc') # query strategy instance

ask_id = qs.make_query() # let the specified query strategy suggest a data to query
X, y = zip(*
lb = lbr.label(X[ask_id]) # query the label of unlabeled data from labeler instance
trn_ds.update(ask_id, lb) # update the dataset with newly queried data

Some examples are available under the `examples` directory. Before running, use
`examples/` to retrieve the dataset used by the examples.

Available examples:

- [plot](examples/ : This example performs basic usage of libact. It splits
a fully-labeled dataset and remove some label from dataset to simulate
the pool-based active learning scenario. Each query of an unlabeled dataset is then equivalent to revealing one labeled example in the original data set.
- [label_digits](examples/ : This example shows how to use libact in the case
that you want a human to label the selected sample for your algorithm.
- [albl_plot](examples/ This example compares the performance of ALBL
with other active learning algorithms.
- [multilabel_plot](examples/ This example compares the performance of
algorithms under multilabel setting.
- [alce_plot](examples/ This example compares the performance of
algorithms under cost-sensitive multi-class setting.

# Running tests

To run the test suite:

python test

To run pylint, install pylint through ```pip install pylint``` and run the following command in root directory:

pylint libact

To measure the test code coverage, install coverage through ```pip install coverage``` and run the following commands in root directory:

coverage run --source libact --omit */tests/* test
coverage report

# Citing
If you find this package useful, please cite the original works (see Reference of each strategy) as well as the following (temporarily)

author = {Yao-Yuan Yang and Shao-Chuan Lee and Yu-An Chung and Tung-En Wu and Si-An Chen and Hsuan-Tien Lin},
title = {libact: Pool-based Active Learning in Python},
url = {},
year = {2015}

# Acknowledgments

The authors thank Chih-Wei Chang and other members of the [Computational Learning Lab]( at National Taiwan University for valuable discussions and various contributions to making this package better.

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