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bayes-probas

0.2.4 • Public • Published

The project has now moved to classificator

If you depend on this package for now, feel free to issue a PR or leave an issue if there's anything you might need. The move was mainly to avoid conflict whilst renaming misnommers variables in the code. So far classificator also has a few more functionallity (removing a label properly). And Im trying out some better scoring mechanism.

bayes-probas : Bayes classifier for node.js

Forked from https://www.npmjs.com/package/bayes, adds some functionnalities upon it (returning more informations when categorizing, unlearning)

bayes takes a document (piece of text), and tells you what category that document belongs to.

What can I use this for?

You can use this for categorizing any text content into any arbitrary set of categories. For example:

  • is an email spam, or not spam ?
  • is a news article about technology, politics, or sports ?
  • is a piece of text expressing positive emotions, or negative emotions?

Installing

You'll need node 5.0+

npm install bayes-probas

Usage

const bayes = require('bayes-probas')
const classifier = bayes()

// teach it positive phrases

classifier.learn('amazing, awesome movie!! Yeah!! Oh boy.', 'positive')
classifier.learn('Sweet, this is incredibly, amazing, perfect, great!!', 'positive')

// teach it a negative phrase

classifier.learn('terrible, shitty thing. Damn. Sucks!!', 'negative')

// unlearn something
classifier.learn('i hate mornings', 'positive');
... // uh oh, inadvertently associated 'i hate mornings' as being 'positive'.
classifier.unlearn('i hate mornings', 'positive');


// now ask it to categorize a document it has never seen before

classifier.categorize('awesome, cool, amazing!! Yay.')
// => 'positive'

// serialize the classifier's state as a JSON string.
let stateJson = classifier.toJson()

// load the classifier back from its JSON representation.
let revivedClassifier = bayes.fromJson(stateJson)

API

var classifier = bayes([options])

Returns an instance of a Naive-Bayes Classifier.

Pass in an optional options object to configure the instance. If you specify a tokenizer function in options, it will be used as the instance's tokenizer. It receives a (string) text argument - this is the string value that is passed in by you when you call .learn() or .categorize(). It must return an array of tokens. The default tokenizer removes punctuation and splits on spaces.

Eg.

let classifier = bayes({
    tokenizer: function (text) { return text.split(' ') }
})

classifier.learn(text, category)

Teach your classifier what category should be associated with an array text of words.

classifier.unlearn(text, category)

The classifier will unlearn the text that was associated with category.

classifier.categorize(text)

Returns the category it thinks text belongs to. Its judgement is based on what you have taught it with classifier.learn(). And an array of the categories sorted from most pertinent to less pertinent.

classifier.categorizeObj(text)

Returns an array of categories ordered by likelihood to the text parameter.

The returned object is as such : { probas, //---> [ {proba: logProbaCategoryA, probaH: humanReadableProbaCategoryA}, {proba: logProbaCategoryB, probaH: humanReadableProbaCategoryB}... ] chosenCategory //--> the main category bayes thinks the text belongs to. As a string }

probas[0].proba = logarithmic likelihood of the most pertinent category probas[0].probaH = likelihood on a scale from 0 to 100, 0 and 100. With 0 being the least likely category and 100 being the most likely.

classifier.toJson()

Returns the JSON representation of a classifier.

let classifier = bayes.fromJson(jsonStr)

Returns a classifier instance from the JSON representation. Use this with the JSON representation obtained from classifier.toJson()

License

(The MIT License)

Original work Copyright (c) ttzel tolgatezel11@gmail.com Modified work Copyright (c) Wozacosta wozacosta@gmail.com

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.

Install

npm i bayes-probas

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