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        "author": "Cheng Soon Ong",
        "author_email": "chengsoon.ong@anu.edu.au",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
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            "Topic :: Scientific/Engineering"
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        "description": "Acton - A scientific research assistant\n=======================================\n\nActon is a modular Python library for active learning.\n`Acton <https://en.wikipedia.org/wiki/Acton,_Australian_Capital_Territory>`_\nis a suburb in Canberra, where Australian National University is\nlocated.\n\n|Build Status| |Documentation Status|\n\n.. |Build Status| image:: https://travis-ci.org/chengsoonong/acton.svg?branch=master\n   :target: https://travis-ci.org/chengsoonong/acton\n.. |Documentation Status| image:: http://readthedocs.org/projects/acton/badge/?version=latest\n   :target: http://acton.readthedocs.io/en/latest/?badge=latest\n\nDependencies\n------------\n\nMost dependencies will be installed by pip. You will need to manually install:\n\n- Python 3.4+\n- `Protobuf <https://github.com/google/protobuf/tree/master/python>`_\n\nSetup\n-----\n\nInstall Acton using ``pip3``:\n\n.. code:: bash\n\n    pip install git+https://github.com/chengsoonong/acton.git\n\nThis provides access to a command-line tool ``acton`` as well as the\n``acton`` Python library.\n\nActon CLI\n---------\n\nThe command-line interface to Acton is available through the ``acton``\ncommand. This takes a dataset of features and labels and simulates an\nactive learning experiment on that dataset.\n\nInput\n+++++\n\nActon supports three formats of dataset: ASCII, pandas, and HDF5. ASCII\ntables can be any file read by ``astropy.io.ascii.read``, including many common\nplain-text table formats like CSV. pandas tables are supported if dumped to a\nfile from ``DataFrame.to_hdf``. HDF5 tables are either an HDF5 file with datasets\nfor each feature and a dataset for labels, or an HDF5 file with one\nmultidimensional dataset for features and one dataset for labels.\n\nOutput\n++++++\n\nActon outputs a file containing predictions for each epoch of the simulation.\nThese are encoded as specified in `this notebook\n<https://github.com/chengsoonong/acton/blob/master/docs/protobuf_spec.ipynb>`_.\n\nQuickstart\n----------\n\nYou will need a dataset. Acton currently supports ASCII tables (anything that can be read by :code:`astropy.io.ascii.read`), HDF5 tables, and Pandas tables saved as HDF5. `Here's a simple classification dataset <https://github.com/chengsoonong/acton/files/603416/classification.txt>`_ that you can use.\n\nTo run Acton to generate a passive learning curve with logistic regression:\n\n.. code:: bash\n\n    acton --data classification.txt --label col20 --feature col10 --feature col11 -o passive.pb --recommender RandomRecommender --predictor LogisticRegression\n\nThis command uses columns ``col10`` and ``col11`` as features, and ``col20`` as labels, a logistic regression predictor, and random recommendations. It outputs all predictions for test data points selected randomly from the input data to :code:`passive.pb`, which can then be used to construct a plot. To output an active learning curve using uncertainty sampling, change :code:`RandomRecommender` to :code:`UncertaintyRecommender`.\n\nTo show the learning curve, use `acton.plot`:\n\n.. code:: bash\n\n    python3 -m acton.plot passive.pb\n\nLook at the directory ``examples`` for more examples.",
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