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            "Development Status :: 5 - Production/Stable",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: Japanese",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "DocumentFeatureSelection\n==========================\n\n# what's this?\n\nThis is set of feature selection codes from text data.\n(About feature selection, see [here](http://nlp.stanford.edu/IR-book/html/htmledition/feature-selection-1.html) or [here](http://stackoverflow.com/questions/13603882/feature-selection-and-reduction-for-text-classification))\n\nThe Feature selection is really important when you use machine learning metrics on natural language data.\nThe natural language data usually contains a lot of noise information, thus machine learning metrics are weak if you don't process any feature selection.\n(There is some exceptions of algorithms like _Decision Tree_ or _Random forest_ . They have feature selection metric inside the algorithm itself)\n\nThe feature selection is also useful when you observe your text data.\nWith the feature selection, you can get to know which features really contribute to specific labels.\n\nPlease visit [project page on github](https://github.com/Kensuke-Mitsuzawa/DocumentFeatureSelection).\n\nIf you find any bugs and you report it to github issue, I'm glad.\n\nAny pull-requests are welcomed.\n\n## Supporting methods\n\nThis package provides you some feature selection metrics.\nCurrently, this package supports following feature selection methods\n\n* TF-IDF\n* Pointwise mutual information (PMI)\n* Strength of Association (SOA)\n* Bi-Normal Separation (BNS)\n\n## Contribution of this package\n\n* Easy interface for pre-processing\n* Easy interface for accessing feature selection methods\n* Fast speed computation thanks to sparse matrix and multi-processing\n\n# Overview of methods\n\n## TF-IDF\n\nThis method, in fact, just calls `TfidfTransformer` of the scikit-learn.\n\nSee [scikit-learn document](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html) about detailed information.\n\n## PMI\n\nPMI is calculated by correlation between _feature_ (i.e. token) and _category_ (i.e. label).\nConcretely, it makes _cross-table_ (or called _contingency table_) and calculates joint probability and marginal probability on it.\n\nTo know more, see [reference](https://www.eecis.udel.edu/~trnka/CISC889-11S/lectures/philip-pmi.pdf)\n\nIn python world, [NLTK](http://www.nltk.org/howto/collocations.html) and [Other package](https://github.com/Bollegala/svdmi) also provide PMI.\nCheck them and choose based on your preference and usage.\n\n\n## SOA\n\nSOA is improved feature-selection method from PMI.\nPMI is weak when feature has low word frequency.\nSOA is based on PMI computing, however, it is feasible on such low frequency features.\nMoreover, you can get anti-correlation between features and categories.\n\nIn this package, SOA formula is from following paper,\n\n`Saif Mohammad and Svetlana Kiritchenko, \"Using Hashtags to Capture Fine Emotion Categories from Tweets\", Computational Intelligence, 01/2014; 31(2).`\n\n```\nSOA(w, e)\\ =\\ log_2\\frac{freq(w, e) * freq(\\neg{e})}{freq(e) * freq(w, \\neg{e})}\n```\n\nWhere\n\n* freq(w, e) is the number of times _w_ occurs in an unit(sentence or document) with label _e_\n* freq(w,\u00ace) is the number of times _w_ occurs in units that does not have the label _e_\n* freq(e) is the number of units having the label _e_\n* freq(\u00ace) is the number of units having NOT the label _e_\n\n## BNS\n\nBNS is a feature selection method for binary class data.\nThere is several methods available for binary class data, such as _information gain (IG)_, _chi-squared\n(CHI)_, _odds ratio (Odds)_.\n\nThe problem is when you execute your feature selection on skewed data.\nThese methods are weak for such skewed data, however, _BNS_ is feasible only for skewed data.\nThe following paper shows how BNS is feasible for skewed data.\n\n```Lei Tang and Huan Liu, \"Bias Analysis in Text Classification for Highly Skewed Data\", 2005```\n\nor \n\n```George Forman, \"An Extensive Empirical Study of Feature Selection Metrics for Text Classification\",Journal of Machine Learning Research 3 (2003) 1289-1305```\n\n\n# Requirement\n\n* Python 3.x(checked under Python 3.5)\n\n\n# Setting up\n\n## install\n\n`python setup.py install`\n\n### Note\n\nYou might see error message during running this command, such as\n\n```\nWe failed to install numpy automatically. Try installing numpy manually or Try anaconda distribution.\n```\n\nThis is because `setup.py` tries to instal numpy and scipy with `pip`, however it fails. \nWe need numpy and scipy before we install `scikit-learn`.\n\nIn this case, you take following choice\n\n* You install `numpy` and `scipy` manually\n* You use `anaconda` python distribution. Please visit [their site](https://www.continuum.io/downloads).\n\n# Example\n\n```python\ninput_dict = {\n    \"label_a\": [\n        [\"I\", \"aa\", \"aa\", \"aa\", \"aa\", \"aa\"],\n        [\"bb\", \"aa\", \"aa\", \"aa\", \"aa\", \"aa\"],\n        [\"I\", \"aa\", \"hero\", \"some\", \"ok\", \"aa\"]\n    ],\n    \"label_b\": [\n        [\"bb\", \"bb\", \"bb\"],\n        [\"bb\", \"bb\", \"bb\"],\n        [\"hero\", \"ok\", \"bb\"],\n        [\"hero\", \"cc\", \"bb\"],\n    ],\n    \"label_c\": [\n        [\"cc\", \"cc\", \"cc\"],\n        [\"cc\", \"cc\", \"bb\"],\n        [\"xx\", \"xx\", \"cc\"],\n        [\"aa\", \"xx\", \"cc\"],\n    ]\n}\n\nfrom DocumentFeatureSelection import interface\ninterface.run_feature_selection(input_dict, method='pmi', use_cython=True).convert_score_matrix2score_record()\n```\nThen, you get the result\n\n```python\n[{'score': 0.14976146817207336, 'label': 'label_c', 'feature': 'bb', 'frequency': 1.0}, ...]\n```\n\nSee scripts in `examples/`\n\n# For developers\n\nYou could set up dev environment with docker-compose.\n\nThis command runs test with the docker container.\n\n```bash\n$ cd tests/\n$ docker-compose build\n$ docker-compose up\n```\n\n\n\n",
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