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https://github.com/ntucllab/libact
Pool-based active learning in Python
https://github.com/ntucllab/libact
active-learning machine-learning machine-learning-library uncertainty-sampling
Last synced: 29 days ago
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Pool-based active learning in Python
- Host: GitHub
- URL: https://github.com/ntucllab/libact
- Owner: ntucllab
- License: bsd-2-clause
- Created: 2015-12-06T17:24:38.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2023-06-18T01:38:20.000Z (over 1 year ago)
- Last Synced: 2024-10-28T16:56:37.182Z (about 1 month ago)
- Topics: active-learning, machine-learning, machine-learning-library, uncertainty-sampling
- Language: Python
- Homepage: http://libact.readthedocs.org/
- Size: 1.85 MB
- Stars: 778
- Watchers: 59
- Forks: 174
- Open Issues: 43
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# libact: Pool-based Active Learning in Python
authors: [Yao-Yuan Yang](http://yyyang.me), Shao-Chuan Lee, Yu-An Chung, Tung-En Wu, Si-An Chen, [Hsuan-Tien Lin](http://www.csie.ntu.edu.tw/~htlin)
[](https://travis-ci.org/ntucllab/libact)
[](http://libact.readthedocs.org/en/latest/?badge=latest)
[](https://badge.fury.io/py/libact)
[](https://codecov.io/github/ntucllab/libact?branch=master)
# 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](http://www.csie.ntu.edu.tw/~htlin/paper/doc/aaai15albl.pdf)
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
The technical report associated with the package is on [arXiv](https://arxiv.org/abs/1710.00379), and the documentation for the latest release is available on [readthedocs](http://libact.readthedocs.org/en/latest/).
Comments and questions on the package is welcomed at `[email protected]`. All contributions to the documentation are greatly appreciated!
# Basic Dependencies
* Python 2.7, 3.3, 3.4, 3.5, 3.6
* 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
```
sudo pacman -S lapacke
```
* macOS
```
brew install 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+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
```
## Installation Options
- `LIBACT_BUILD_HINTSVM`: set this variable to 1 if you would like to build
hintsvm c-extension. If set to 0, you will not be able to use the HintSVM
query strategy. Default=1.
- `LIBACT_BUILD_VARIANCE_REDUCTION`: set this variable to 1 if you would like to
build variance reduction c-extension. If set to 0, you will not be able to use
the VarianceReduction query strategy. Default=1.
Example:
```
LIBACT_BUILD_HINTSVM=1 pip install git+https://github.com/ntucllab/libact.git
```
# Usage
The main usage of `libact` is as follows:
```python
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](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.
- [label_digits](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.
- [albl_plot](examples/albl_plot.py): This example compares the performance of ALBL
with other active learning algorithms.
- [multilabel_plot](examples/multilabel_plot.py): This example compares the performance of
algorithms under multilabel setting.
- [alce_plot](examples/alce_plot.py): This example compares the performance of
algorithms under cost-sensitive multi-class setting.
# Running tests
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
```
# Citing
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
}
```
# Acknowledgments
The authors thank Chih-Wei Chang and other members of the [Computational Learning Lab](https://learner.csie.ntu.edu.tw/) at National Taiwan University for valuable discussions and various contributions to making this package better.