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

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.

The technical report associated with the package is on arXiv, and the documentation for the latest release is available on readthedocs. Comments and questions on the package is welcomed at [email protected]. All contributions to the documentation are greatly appreciated!

• Python 3.9, 3.10, 3.11, 3.12

  • Note. We will soon release Python 2.7, 3.3, 3.4, 3.5, and 3.6 installations in the new branch.

• 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

• Arch Linux

sudo pacman -S lapacke

• macOS

brew install openblas

• Others: refer to the BLAS/LAPACKE installation guides.

• Install the official release (from PyPI):

pip install libact

• Install the latest development version

pip install git+https://github.com/ntucllab/libact.git

This package supports the following build options:

• blas: BLAS library to use (default='auto'). Options: auto, openblas, Accelerate, mkl, lapack, blis.
• lapack: LAPACK library to use (default=auto). Options: auto, openblas, Accelerate, mkl, lapack, blis.
• variance_reduction: Build variance reduction module (default: true)
• hintsvm: Build hintsvm module (default: true)

To install libact with the default configuration, run:

pip install libact

Install without optional modules:

pip install libact --config-settings=setup-args="-Dvariance_reduction=false" \
                    --config-settings=setup-args="-Dhintsvm=false"

This project utilizes meson and meson-python as the build backend. To build from source, ensure you have the aforementioned dependencies installed on your system. The building procedure additionally requires the following dependencies:

• meson-python
• ninja
• cython
• numpy

To simplify the environment setup, we provide a pre-configured environment.yml located at the root directory of the project. Install with conda/mamba to get a head start.

# Clone the repository
git clone https://github.com/ntucllab/libact.git
cd libact

# Create and activate conda environment
conda env create -f environment.yml
conda activate libact

# Install in development mode
pip install --no-build-isolation -e .

# Or build and install
pip install --no-build-isolation .

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(*trn_ds.data)
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/get_dataset.py to retrieve the dataset used by the examples.

Available examples:

• plot : 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 : 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: This example compares the performance of ALBL with other active learning algorithms.
• multilabel_plot: This example compares the performance of algorithms under multilabel setting.
• alce_plot: This example compares the performance of algorithms under cost-sensitive multi-class setting.

To run the test suite:

python -m unittest -v

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:

python -m coverage run --source libact --omit */tests/* -m unittest
python -m coverage report

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

@techreport{YY2017,
  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},
  institution = {National Taiwan University},
  url = {https://github.com/ntucllab/libact},
  note = {available as arXiv preprint \url{https://arxiv.org/abs/1710.00379}},
  month = oct,
  year = 2017
}

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.