scentamint 1.1.2

A web api providing naïve bayesian text classification and training using simplebayes.

Build Status

Why?

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.

Installation

sudo pip install scentamint

Config

Config 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 = 80

Server Usage

$ sudo scentamint --help

Scentamint Server Help:

    -h, --help
        Show this help

    -p [port], --port [port]
        Set the port the server should listen on

    -d, --debug
        Run the server in debug mode (errors displayed, debug output)

$ sudo scentamint --port 80 --debug
 * Running on http://0.0.0.0:80/ (Press CTRL+C to quit)
 * Restarting with reloader
# CTRL+C pressed

$ sudo scentamint --port 80
 * Running on http://0.0.0.0:80/ (Press CTRL+C to quit)
# CTRL+C pressed

# A simple, no fuss, server execution command.
$ sudo nohup scentamint >> /var/log/scentamint.log 2>&1 &

API Usage

All endpoints accept POST commands and return predictable results depending on what is posted.

Training the Classifier

Endpoint:

/train/<string:category>/ (ex: /train/spam/)

Result Status:

204 No Content

• The 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 Classifier

Endpoint:

/untrain/<string:category>/ (ex: /train/ham/)

Result Status:

204 No Content

• The 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 Text

Endpoint:

/classify/

Result Status:

200 OK

Result JSON Example:

{
    "result": "ham"
}

• The POST payload should contain the raw text that you want to classify.

Scoring Text

Endpoint:

/score/

Result Status:

200 OK

Result 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 Data

Endpoint:

/flush/

Result Status:

204 No Content

• This is a purely destructive, non-reversable action.

License

The 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
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.