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.
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.
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?
You'll need node 5.0+
npm install bayes-probas
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)
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(' ') }
})
Teach your classifier what category
should be associated with an array text
of words.
The classifier will unlearn the text
that was associated with category
.
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.
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.
Returns the JSON representation of a classifier.
Returns a classifier instance from the JSON representation. Use this with the JSON representation obtained from classifier.toJson()
(The MIT License)
Original work Copyright (c) ttzel tolgatezel11@gmail.com Modified work Copyright (c) Wozacosta wozacosta@gmail.com
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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