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        "author": "Shawn",
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        "description": "MFNBC\n=====\n\nLinks\n~~~~~\n\n- PyPi - https://pypi.python.org/pypi/mfnbc/\n- Github - https://github.com/wharton/mfnbc\n\nAbout The Math\n~~~~~~~~~~~~~~\nThis package computes a set of simple arithmetic to calculate the final posterior probabilities for a set of features over a set of texts within a corpus.\n\nSo for each text in the corpus, the package looks to see if the word in contained in the provided likelihood table. If it is found, the posterior probabilities for each feature is updated as using `Bayesian statistics\n<https://en.wikipedia.org/wiki/Posterior_probability>`_.\n\n\n.. image:: https://research-it.wharton.upenn.edu/wp-content/uploads/2016/12/eq1.gif\n\nwhere\n\n.. image:: https://research-it.wharton.upenn.edu/wp-content/uploads/2016/12/eq2.gif\n\nis:\n\n.. image:: https://research-it.wharton.upenn.edu/wp-content/uploads/2016/12/eq3.gif\n\nRequirements\n~~~~~~~~~~~~\n\nPython >= 3.3\n\nInstall\n~~~~~~~\n\n``pip install mfnbc``\n\nSetup (Likelihood Input File)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nIt is assumed you have a word based likelihood table (csv file) where\nthe headers consists of the literal word ``Word`` and the remaining\ncolumns are the features you would like to classify.\n\nFor example:\n\n+----------+----------+---------+---------+\n| Word     | Animal   | Human   | Plant   |\n+----------+----------+---------+---------+\n| cat      | 0.33     | 0.03    | 0.05    |\n+----------+----------+---------+---------+\n| dog      | 0.33     | 0.02    | 0.05    |\n+----------+----------+---------+---------+\n| leaves   | 0.05     | 0.03    | 0.4     |\n+----------+----------+---------+---------+\n| tree     | 0.05     | 0.02    | 0.4     |\n+----------+----------+---------+---------+\n| man      | 0.12     | 0.45    | 0.05    |\n+----------+----------+---------+---------+\n| women    | 0.12     | 0.45    | 0.05    |\n+----------+----------+---------+---------+\n\nSetup (Unlabeled Data File)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n+----+-----------------------------------------------------------------------+\n| ID | Text                                                                  |\n+----+-----------------------------------------------------------------------+\n| 1  | The cat is my pet and he is lovely. A dog will not do.                |\n+----+-----------------------------------------------------------------------+\n| 2  | The man and women had a cat and lived under a tree                    |\n+----+-----------------------------------------------------------------------+\n| 3  | The tree had lots of leaves                                           |\n+----+-----------------------------------------------------------------------+\n| 4  | A man lives under a tree with many leaves. A women has a cat as a pet |\n+----+-----------------------------------------------------------------------+\n| 5  | The dog and cat chase the man under the tree                          |\n+----+-----------------------------------------------------------------------+\n| 6  | The man and women live in a house.                                    |\n+----+-----------------------------------------------------------------------+\n\nThe key is having the header titled ``Text`` any other fields will be\nincluded unmodified in the output file.\n\n\nImport\n~~~~~~\n\n.. code:: python\n\n    from mfnbc import MFNBC\n\nInstantiate\n~~~~~~~~~~~\n\n.. code-block:: python\n\n    m = MFNBC(<likelihoods_input_file - location of Likelihood table (str)>,\n              <unlabeled_data_file - Location of unlabeled data file (str)>,\n              <verbose output - Turn on of off verbose output, default: off>\n              <output file name - defaults to out.csv, (str)>\n\nExample\n~~~~~~~\n\n.. code:: python\n\n    m = MFNBC('likeli_sample.csv', 'input_sample.csv', False, 'my_output.csv')\n    m.read_likelihoods()\n    m.calc_posteriors()\n    m.write_csv()\n\nor you can do it all in a single command\n\n.. code:: python\n\n    m = MFNBC('likeli_sample.csv', 'input_sample.csv', False).write_csv()\n\n\nExample Results\n~~~~~~~~~~~~~~~\n\n+----+-----------------------------------------------------------------------+-------------+-------------+-------------+\n| ID | Text                                                                  | Animal      | Human       | Plant       |\n+----+-----------------------------------------------------------------------+-------------+-------------+-------------+\n| 1  | The cat is my pet and he is lovely. 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