{
    "info": {
        "author": "Lars Buitinck",
        "author_email": "larsmans@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Text Processing"
        ],
        "description": ".. -*- mode: rst -*-\n\nseqlearn\n========\n\nseqlearn is a sequence classification toolkit for Python. It is designed to\nextend `scikit-learn <http://scikit-learn.org>`_ and offer as similar as\npossible an API.\n\n\nCompiling and installing\n------------------------\n\nGet NumPy >=1.6, SciPy >=0.11, Cython >=0.20.2 and a recent version of\nscikit-learn. Then issue::\n\n    python setup.py install\n\nto install seqlearn.\n\nIf you want to use seqlearn from its source directory without installing,\nyou have to compile first::\n\n    python setup.py build_ext --inplace\n\n\nGetting started\n---------------\n\nThe easiest way to start using seqlearn is to fetch a dataset in CoNLL 2000\nformat. Define a task-specific feature extraction function, e.g.::\n\n    >>> def features(sequence, i):\n    ...     yield \"word=\" + sequence[i].lower()\n    ...     if sequence[i].isupper():\n    ...         yield \"Uppercase\"\n    ...\n\nLoad the training file, say ``train.txt``::\n\n    >>> from seqlearn.datasets import load_conll\n    >>> X_train, y_train, lengths_train = load_conll(\"train.txt\", features)\n\nTrain a model::\n\n    >>> from seqlearn.perceptron import StructuredPerceptron\n    >>> clf = StructuredPerceptron()\n    >>> clf.fit(X_train, y_train, lengths_train)\n\nCheck how well you did on a validation set, say ``validation.txt``::\n\n    >>> X_test, y_test, lengths_test = load_conll(\"validation.txt\", features)\n    >>> from seqlearn.evaluation import bio_f_score\n    >>> y_pred = clf.predict(X_test, lengths_test)\n    >>> print(bio_f_score(y_test, y_pred))\n\nFor more information, see the `documentation\n<http://larsmans.github.io/seqlearn>`_.\n\n\n|Travis|_\n\n.. |Travis| image:: https://api.travis-ci.org/larsmans/seqlearn.png?branch=master\n.. _Travis: https://travis-ci.org/larsmans/seqlearn\n",
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        "keywords": null,
        "license": "MIT",
        "maintainer": null,
        "maintainer_email": null,
        "name": "seqlearn",
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        "summary": "Sequence learning toolkit",
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