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        "author": "Chris Emmery",
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        "description": "Omesa\n=====\n\n.. image:: https://travis-ci.org/cmry/omesa.svg?branch=master\n    :target: https://travis-ci.org/cmry/omesa\n    :alt: Travis-CI\n\n.. image:: https://readthedocs.org/projects/omesa/badge/?version=latest\n    :target: http://omesa.readthedocs.org/en/latest/?badge=latest\n    :alt: Docs\n\n.. image:: https://landscape.io/github/cmry/omesa/master/landscape.svg?style=flat\n    :target: https://landscape.io/github/cmry/omesa/master\n    :alt: Landscape\n\n.. image:: https://img.shields.io/badge/license-GPLv3-blue.svg\n    :alt: GPLv3\n\n.. image:: https://img.shields.io/badge/python-3.5-blue.svg\n    :alt: Python 3.4\n\n.. _scikit-learn: http://scikit-learn.org/stable/\n.. _readthedocs: http://omesa.readthedocs.org/\n\nA small framework for reproducible Text Mining research that largely builds\non top of scikit-learn_. Its goal is to make common research procedures fully\nautomated, optimized, and well recorded. To this end it features:\n\n- Exhaustive search over best features, pipeline options, to classifier optimization.\n- Flexible wrappers to plug in your tools and features of choice.\n- Completely sparse pipeline through hashing - from data to feature space.\n- Record of all settings and fitted parts of the entire experiment, promoting reproducibility.\n- Dump an easily deployable version of the final model for plug-and-play demos.\n\nRead the documentation at readthedocs_.\n\n.. image:: http://www.cmry.nl/dump/shed.png\n    :alt: Pipeline\n\nImportant Note\n''''''''''''''\n\nThis repository is currently in alpha development, so don't expect any stable\nfunctionality until this part is removed. The `dev` branch will usually have the\nlatest (not always stable) version.\n\nFront-end Preview\n'''''''''''''''''''\n\n.. _dev: https://github.com/cmry/omesa/tree/dev\n.. _lime: https://github.com/marcotcr/lime\n\nIn 'front' a web front-end is being developed that uses a standalone\ndatabase for storing models. This provides visualization and comparison of\nmodel performance. Some extra dependencies are introduced, such as ``bottle``,\n``blitzdb``, ``plotly`` and lime_. Currently only the 'Results' section works,\npreview below:\n\n.. image:: http://www.cmry.nl/dump/omesa.png\n    :alt: Front\n\n.. image:: http://www.cmry.nl/dump/omesa_prop.png\n    :alt: Front Prop\n\nIf you want to take a peek, install all above dependencies, do the following:\n\n.. code-block:: shell\n\n    $ cd /dir/to/omesa/examples\n    $ python3 n_gram.py\n    $ cd ../front\n    $ python3 ./app.wsgi\n\nAnd follow the ``localhost`` link that is shown to access the web app. Please\nnote that this part can be quite unstable. Bug reports are welcome.\n\n\nDependencies\n''''''''''''\n\n.. _Frog: https://languagemachines.github.io/frog/\n.. _LaMachine: https://proycon.github.io/LaMachine/\n.. _spaCy: https://spacy.io/\n\nOmesa currently heavily relies on ``numpy``, ``scipy`` and ``sklearn``. To use\nFrog_ as a Dutch back-end, we strongly recommend using LaMachine_. For\nEnglish, there is a spaCy_ wrapper available.\n\nOmesa Only - End-To-End In 2 Minutes\n------------------------------------\n\nWith the end-to-end ``Experiment`` pipeline and a configuration dictionary,\nseveral experiments or set-ups can be ran and evaluated with a very minimal\npiece of code. One of the test examples provided is that of n-gram\nclassification of Wikipedia documents. In this experiment, we are provided with\na toy set n_gram.csv that features 10 articles about Machine Learning, and 10\nrandom other articles. To run the experiment, the following configuration is used:\n\nExample\n'''''''\n\n.. _`n-gram classification`: https://github.com/cmry/omesa/blob/master/examples/n_gram.py\n.. _`n_gram.csv`: https://github.com/cmry/omesa/blob/master/examples/n_gram.csv\n\nWith the end-to-end ``Experiment`` pipeline and a configuration dictionary,\nseveral experiments or set-ups can be ran and evaluated with a very minimal\npiece of code. One of the test examples provided is that of `n-gram classification`_\nof Wikipedia documents. In this experiment, we are provided with a toy set\n`n_gram.csv`_ that features 10 articles about Machine Learning, and 10 random\nother articles. To run the experiment, the following configuration is used:\n\n.. code-block:: python\n\n    from omesa.experiment import Experiment\n    from omesa.featurizer import Ngrams\n    from omesa.containers import CSV\n    from sklearn.naive_bayes import MultinomialNB\n\n    Experiment(\n        project=\"unit_tests\",\n        name=\"gram_experiment\",\n        train_data=CSV(\"n_gram.csv\", data=\"gram\", label=\"label\"),\n        lime_data=CSV(\"n_gram.csv\", data=\"gram\", label=\"label\"),\n        features=[Ngrams(level='char', n_list=[3])],\n        classifiers=[\n            {'clf': MultinomialNB()}\n        ],\n        \"save\": (\"log\")\n    )\n\nThis will cross validate performance on the ``.csv``, selecting text\nand label columns and indicating a header is present in the ``.csv`` document.\nWe provide the ``Ngrams`` function and parameters to be used as features, and\nstore the log.\n\nOutput\n''''''\n\nThe log file will be printed during run time, as well as stored in the\nscript's directory. A sample from the output of the current experiment is as\nfollows:\n\n.. code-block:: shell\n\n    ---- Omesa ----\n\n     Config:\n\n            feature:   char_ngram\n            n_list:    [3]\n\n    \tname: gram_experiment\n    \tseed: 42\n\n     Sparse train shape: (20, 1301)\n\n     Performance on test set:\n\n                 precision    recall  f1-score   support\n\n             DF       0.83      0.50      0.62        10\n             ML       0.64      0.90      0.75        10\n\n    avg / total       0.74      0.70      0.69        20\n\n\n     Experiment took 0.2 seconds\n\n    ----------\n\nAdding own Features\n-------------------\n\nHere's an example of the most minimum word frequency feature class:\n\n.. code-block:: python\n\n    class SomeFeaturizer(object):\n\n        def __init__(self, some_params):\n            \"\"\"Set parameters for SomeFeaturizer.\"\"\"\n            self.name = 'hookname'\n            self.some_params = some_params\n\n        def transform(self, raw, parse):\n            \"\"\"Return a dictionary of feature values.\"\"\"\n            return Counter([x for x in raw])\n\nThis returns a ``{word: frequency}`` dict per instance that can easily be\ntransformed into a sparse matrix.\n\nAcknowledgements\n----------------\n\n.. _AMiCA: http://www.amicaproject.be/\n\nPart of the work on Omesa was carried out in the context of the\nAMiCA_ (IWT SBO-project 120007) project, funded by the government agency for\nInnovation by Science and Technology (IWT).\n",
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