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        "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",
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            "Programming Language :: Python :: 2.7",
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        "description": "# libact: Pool-based Active Learning in Python\n\nauthors: Yao-Yuan Yang, Shao-Chuan Lee, Yu-An Chung, Tung-En Wu, Si-An Chen, [Hsuan-Tien Lin](http://www.csie.ntu.edu.tw/~htlin)\n\n[![Build Status](https://travis-ci.org/ntucllab/libact.svg)](https://travis-ci.org/ntucllab/libact)\n[![Documentation Status](https://readthedocs.org/projects/libact/badge/?version=latest)](http://libact.readthedocs.org/en/latest/?badge=latest)\n[![PyPI version](https://badge.fury.io/py/libact.svg)](https://badge.fury.io/py/libact)\n[![codecov.io](https://codecov.io/github/ntucllab/libact/coverage.svg?branch=master)](https://codecov.io/github/ntucllab/libact?branch=master)\n\n# Introduction\n\n`libact` is a Python package designed to make active learning easier for\nreal-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)\nmeta-algorithm that assists the users to automatically select the best strategy\non 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.\n\n# Documentation\n\nThe 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/).\nComments and questions on the package is welcomed at `libact-users@googlegroups.com`. All contributions to the documentation are greatly appreciated!\n\n# Basic Dependencies\n\n* Python 2.7, 3.3, 3.4, 3.5, 3.6\n\n* Python dependencies\n```\npip install -r requirements.txt\n```\n\n* Debian (>= 7) / Ubuntu (>= 14.04)\n```\nsudo apt-get install build-essential gfortran libatlas-base-dev liblapacke-dev python3-dev\n```\n\n* Arch\n```\nsudo pacman -S lapacke\n```\n\n* macOS\n```\nbrew install openblas\n```\n\n# Installation\n\nAfter resolving the dependencies, you may install the package via pip (for all users):\n```\nsudo pip install libact\n```\n\nor pip install in home directory:\n```\npip install --user libact\n```\n\nor pip install from github repository for latest source:\n```\npip install git+https://github.com/ntucllab/libact.git\n```\n\nTo build and install from souce in your home directory:\n```\npython setup.py install --user\n```\n\nTo build and install from souce for all users on Unix/Linux:\n```\npython setup.py build\nsudo python setup.py install\n```\n\n## Installation Options\n\n- `LIBACT_BUILD_HINTSVM`: set this variable to 1 if you would like to build\n  hintsvm c-extension. If set to 0, you will not be able to use the HintSVM\n  query strategy. Default=1.\n- `LIBACT_BUILD_VARIANCE_REDUCTION`: set this variable to 1 if you would like to\n  build variance reduction c-extension. If set to 0, you will not be able to use\n  the VarianceReduction query strategy. Default=1.\n\nExample:\n```\nLIBACT_BUILD_HINTSVM=1 pip install git+https://github.com/ntucllab/libact.git\n```\n\n# Usage\n\nThe main usage of `libact` is as follows:\n\n```python\nqs = UncertaintySampling(trn_ds, method='lc') # query strategy instance\n\nask_id = qs.make_query() # let the specified query strategy suggest a data to query\nX, y = zip(*trn_ds.data)\nlb = lbr.label(X[ask_id]) # query the label of unlabeled data from labeler instance\ntrn_ds.update(ask_id, lb) # update the dataset with newly queried data\n```\n\nSome examples are available under the `examples` directory. Before running, use\n`examples/get_dataset.py` to retrieve the dataset used by the examples.\n\nAvailable examples:\n\n  - [plot](examples/plot.py) : This example performs basic usage of libact. It splits\n    a fully-labeled dataset and remove some label from dataset to simulate\n    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.\n  - [label_digits](examples/label_digits.py) : This example shows how to use libact in the case\n    that you want a human to label the selected sample for your algorithm.\n  - [albl_plot](examples/albl_plot.py): This example compares the performance of ALBL\n    with other active learning algorithms.\n  - [multilabel_plot](examples/multilabel_plot.py): This example compares the performance of\n    algorithms under multilabel setting.\n  - [alce_plot](examples/alce_plot.py): This example compares the performance of\n    algorithms under cost-sensitive multi-class setting.\n\n# Running tests\n\nTo run the test suite:\n\n```\npython setup.py test\n```\n\nTo run pylint, install pylint through ```pip install pylint``` and run the following command in root directory:\n\n```\npylint libact\n```\n\nTo measure the test code coverage, install coverage through ```pip install coverage``` and run the following commands in root directory:\n\n```\ncoverage run --source libact --omit */tests/* setup.py test\ncoverage report\n```\n\n# Citing\nIf you find this package useful, please cite the original works (see Reference of each strategy) as well as the following\n\n```\n@techreport{YY2017,\n  author = {Yao-Yuan Yang and Shao-Chuan Lee and Yu-An Chung and Tung-En Wu and Si-An Chen and Hsuan-Tien Lin},\n  title = {libact: Pool-based Active Learning in Python},\n  institution = {National Taiwan University},\n  url = {https://github.com/ntucllab/libact},\n  note = {available as arXiv preprint \\url{https://arxiv.org/abs/1710.00379}},\n  month = oct,\n  year = 2017\n}\n```\n\n\n# Acknowledgments\n\nThe 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.",
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