Package: scentamint
A web api providing naïve bayesian text classification and training using simplebayes.Build StatusWhy?Bayesian text classification is often used for things like
spam detection, sentiment determination, or general categorization.

Essentially you collect samples of text that you know are of a certain
"type" or "category," then you use it to train a bayesian classifier.
Once you have trained the classifier with many samples of various
categories, you can begin to classify and/or score text samples to see
which category they fit best in.

You could, for instance, set up classification of sentiment by finding
samples of text that are happy, sad, angry, sarcastic, and so on, then
train a classifier using those samples. Once your classifier is trained,
you can begin to classify other text into one of those categories.

What a classifier does is look at text and tell you how much that text
"looks like" other categories of text that it has been trained for.Installationsudo pip install scentamintConfigConfig file location is /etc/scentamint.ini:[scentamint]

; set the location that we want to store the bayes training cache
persist_location = /var/lib/scentamint/

; the default port this server will run on
listen_port = 80Server Usage$sudoscentamint--helpScentamintServerHelp:-h,--helpShowthishelp-p[port],--port[port]Settheporttheservershouldlistenon-d,--debugRuntheserverindebugmode(errorsdisplayed,debugoutput)$sudoscentamint--port80--debug*Runningonhttp://0.0.0.0:80/(PressCTRL+Ctoquit)*Restartingwithreloader# CTRL+C pressed$sudoscentamint--port80*Runningonhttp://0.0.0.0:80/(PressCTRL+Ctoquit)# CTRL+C pressed# A simple, no fuss, server execution command.$sudonohupscentamint>>/var/log/scentamint.log2>&1&API UsageAll endpoints accept POST commands and return predictable results depending on what is posted.Training the ClassifierEndpoint:/train/<string:category>/ (ex: /train/spam/)Result Status:204 No ContentThe POST payload should contain the raw text that will train the classifier.You can train a category as many times as you want.Untraining the ClassifierEndpoint:/untrain/<string:category>/ (ex: /train/ham/)Result Status:204 No ContentThe POST payload should contain the raw text that will train the classifier.You can untrain a category as many times as you want, but a token’s value will not go below zero.This action carries out the inverse operation of training so unintentional trains can be reversed.Classifying TextEndpoint:/classify/Result Status:200 OKResult JSON Example:{
    "result": "ham"
}The POST payload should contain the raw text that you want to classify.Scoring TextEndpoint:/score/Result Status:200 OKResult JSON Example:{
    "scores": {
        "ham": 268.4685238156538,
        "spam": 44.531476184346225
    }
}The POST payload should contain the raw text that you want to score.Emptying All Classifier Training DataEndpoint:/flush/Result Status:204 No ContentThis is a purely destructive, non-reversable action.LicenseThe MIT License (MIT)

Copyright (c) 2015 Ryan Vennell

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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