authors: Yu-An Chung, Shao-Chuan Lee, Yao-Yuan Yang, Tung-En Wu, 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.

Documentation for the latest release is hosted here. Comments and questions on the package is welcomed at libact-users@googlegroups.com. If you find this package useful, please cite the original works (see Reference of each strategy) as well as (temporarily)

@TechReport{libact,
  author =   {Yao-Yuan Yang and Yu-An Chung and Shao-Chuan Lee and Tung-En Wu and Hsuan-Tien Lin},
  title =    {libact: Pool-based Active Learning in Python},
  url = {https://github.com/ntucllab/libact},
  year = {2015}
}

• 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

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

To build and install from souce in your home directory:

python setup.py install --user

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

python setup.py build
sudo python setup.py install

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:

• examples/plot.py: 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.
• examples/label_digits.py: This example shows how to use libact in the case that you want a human to label the selected sample for your algorithm.
• examples/albl_plot.py: This example compares the performance of ALBL with other active learning algorithms.

To run the test suite:

python setup.py 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/* setup.py test
coverage report

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.