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    "info": {
        "author": "Michael Aquilina",
        "author_email": "michaelaquilina@gmail.com",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "===============================\nhashedindex\n===============================\n\n|TravisCI| |AppVeyor| |CodeCov| |PyPi|\n\n\nFast and simple InvertedIndex implementation using hash lists (python dictionaries).\n\nSupports Python 3.5+\n\nFree software: BSD license\n\n* Installing_\n* Features_\n* `Text Parsing`_\n* `Integration with Numpy and Pandas`_\n* `Reporting Bugs`_\n\n\nInstalling\n----------\n\nThe easiest way to install hashindex is through pypi\n\n::\n\n    pip install hashedindex\n\n\nFeatures\n--------\n\n``hashedindex`` provides a simple to use inverted index structure that is flexible enough to work with all kinds of use cases.\n\nBasic Usage:\n\n.. code-block:: python\n\n    import hashedindex\n    index = hashedindex.HashedIndex()\n\n    index.add_term_occurrence('hello', 'document1.txt')\n    index.add_term_occurrence('world', 'document1.txt')\n\n    index.get_documents('hello')\n    Counter({'document1.txt': 1})\n\n    index.items()\n    {'hello': Counter({'document1.txt': 1}),\n    'world': Counter({'document1.txt': 1})}\n\n    example = 'The Quick Brown Fox Jumps Over The Lazy Dog'\n\n    for term in example.split():\n        index.add_term_occurrence(term, 'document2.txt')\n\n``hashedindex`` is not limited to strings, any hashable object can be indexed.\n\n.. code-block:: python\n\n   index.add_term_occurrence('foo', 10)\n   index.add_term_occurrence(('fire', 'fox'), 90.2)\n\n   index.items()\n   {'foo': Counter({10: 1}), ('fire', 'fox'): Counter({90.2: 1})}\n\nText Parsing\n------------\n\nThe ``hashedindex`` module comes included with a powerful textparser module with methods to split\ntext into tokens.\n\n.. code-block:: python\n\n   from hashedindex import textparser\n   list(textparser.word_tokenize(\"hello cruel world\"))\n   [('hello',), ('cruel',), ('world',)]\n\nTokens are wrapped within tuples due to the ability to specify any number of n-grams required:\n\n.. code-block:: python\n\n   list(textparser.word_tokenize(\"Life is about making an impact, not making an income.\", ngrams=2))\n   [(u'life', u'is'), (u'is', u'about'), (u'about', u'making'), (u'making', u'an'), (u'an', u'impact'),\n    (u'impact', u'not'), (u'not', u'making'), (u'making', u'an'), (u'an', u'income')]\n\nTake a look at the function's docstring for information on how to use ``stopwords``, specify a\n``min_length`` or ``ignore_numeric`` terms.\n\nIntegration with Numpy and Pandas\n---------------------------------\n\nThe idea behind ``hashedindex`` is to provide a really quick and easy way of generating\nmatrices for machine learning with the additional use of numpy, pandas and scikit-learn.\nFor example:\n\n.. code-block:: python\n\n   from hashedindex import textparser\n   import hashedindex\n   import numpy as np\n\n   index = hashedindex.HashedIndex()\n\n   documents = ['spam1.txt', 'ham1.txt', 'spam2.txt']\n   for doc in documents:\n       with open(doc, 'r') as fp:\n            for term in textparser.word_tokenize(fp.read()):\n                index.add_term_occurrence(term, doc)\n\n   # You *probably* want to use scipy.sparse.csr_matrix for better performance\n   X = np.as_matrix(index.generate_feature_matrix(mode='tfidf'))\n\n   y = []\n   for doc in index.documents():\n       y.append(1 if 'spam' in doc else 0)\n   y = np.asarray(doc)\n\n   from sklearn.svm import SVC\n   classifier = SVC(kernel='linear')\n   classifier.fit(X, y)\n\nYou can also extend your feature matrix to a more verbose pandas DataFrame:\n\n.. code-block:: python\n\n   import pandas as pd\n   X  = index.generate_feature_matrix(mode='tfidf')\n   df = pd.DataFrame(X, columns=index.terms(), index=index.documents())\n\nAll methods within the code have high test coverage so you can be sure everything works as expected.\n\nReporting Bugs\n--------------\n\nFound a bug? Nice, a bug found is a bug fixed. Open an Issue or better yet, open a pull request.\n\n.. |TravisCI| image:: https://travis-ci.org/MichaelAquilina/hashedindex.svg?branch=master\n   :target: https://travis-ci.org/MichaelAquilina/hashedindex\n\n.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/qkhn4bub2pye7skm?svg=true\n   :target: https://ci.appveyor.com/project/MichaelAquilina/hashedindex\n\n.. |PyPi| image:: https://badge.fury.io/py/hashedindex.svg\n   :target: https://badge.fury.io/py/hashedindex\n\n.. |CodeCov| image:: https://codecov.io/gh/MichaelAquilina/hashedindex/branch/master/graph/badge.svg\n   :target: https://codecov.io/gh/MichaelAquilina/hashedindex\n\n\n\n\nHistory\n-------\n\n0.5.0 (2019-07-21)\n--------------------\n* Drop support for python 2.7 and 3.4\n\n0.1.0 (2015-01-11)\n---------------------\n\n* First release on PyPI.\n",
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