Metadata-Version: 1.1
Name: libact
Version: 0.1.3b0
Summary: Pool-based active learning in Python
Home-page: https://github.com/ntucllab/libact
Author: Y.-Y. Yang, S.-C. Lee, Y.-A. Chung, T.-E. Wu, H.-T. Lin
Author-email: b01902066@csie.ntu.edu.tw, b01902010@csie.ntu.edu.tw, b01902040@csie.ntu.edu.tw, r00942129@ntu.edu.tw, htlin@csie.ntu.edu.tw
License: UNKNOWN
Description: # libact: Pool-based Active Learning in Python
        
        authors: Yu-An Chung, Shao-Chuan Lee, Yao-Yuan Yang, Tung-En Wu, [Hsuan-Tien Lin](http://www.csie.ntu.edu.tw/~htlin)
        
        [![Build Status](https://travis-ci.org/ntucllab/libact.svg)](https://travis-ci.org/ntucllab/libact)
        [![Documentation Status](https://readthedocs.org/projects/libact/badge/?version=latest)](http://libact.readthedocs.org/en/latest/?badge=latest)
        [![PyPI version](https://badge.fury.io/py/libact.svg)](https://badge.fury.io/py/libact)
        [![codecov.io](https://codecov.io/github/ntucllab/libact/coverage.svg?branch=master)](https://codecov.io/github/ntucllab/libact?branch=master)
        [![Week Stars](http://starveller.sigsev.io/api/repos/ntucllab/libact/badge)](http://starveller.sigsev.io/ntucllab/libact)
        
        # 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 for the latest release is hosted [here](http://libact.readthedocs.org/en/latest/).
        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}
        }
        ```
        
        # 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+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
        ```
        
        # 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
        ```
        
        # 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.
        
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
