classificator

Naive Bayes classifier for node.js

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

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

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?

More here: https://en.wikipedia.org/wiki/Naive_Bayes_classifier

Installing

Recommended: Node v6.0.0 +

npm install --save classificator

Differences with bayes-proba (original package)

For now, apart from a less misleading package name, I also changed misnommers in categorizeObj

probas -> likelihoods proba -> logLikelihood probaH -> scaledLogLikelihood chosenCategory -> predictedCategory

Usage

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

Teach your classifier

classifier.learn('amazing, awesome movie! Had a good time', 'positive')
classifier.learn('Buy my free viagra pill and get rich!', 'spam')
classifier.learn('I really hate dust and annoying cats', 'negative')
classifier.learn('LOL this sucks so hard', 'troll')

Make your classifier unlearn

classifier.learn('i hate mornings', 'positive');
// uh oh, that was mistake. Time to unlearn
classifier.unlearn('i hate mornings', 'positive');

Remove a category

    classifier.removeCategory('troll');

categorization

classifier.categorize("I've always hated Martians"); // simple
// => 'negative'

classifier.categorize("I've always hated Martians", true); // verbose
// =>
// =>
{
    likelihoods: [
            {
                category: 'negative',
                logLikelihood: 0.008489, // log likelihood
                scaledLogLikelihood: 100 // log likelihood on a [0-100] scale, not probability (100 doesn't mean it's 100% certain)
            },
            {
                category: 'troll',
                logLikelihood: 0.00412,
                scaledLogLikelihood: 43
            },
            {
                category: 'spam',
                logLikelihood: 0.00152,
                scaledLogLikelihood: 18
            },
            {
                category: 'positive',
                logLikelihood: 0.000074,
                scaledLogLikelihood: 0
            },
      predictedCategory : 'negative'
}

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

let 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.removeCategory(category)

The category is removed and the classifier data are updated accordingly.

classifier.categorize(text, verbose)

Parameters text {String} verbose {Boolean} whether or not it should returns more data associated with the categorization.

If not verbose :

*Returns*
   `{String}` An object with the `category` it thinks `text` belongs to. Based on what it learned with `classifier.learn()`.
```
{
  predictedCategory: 'positive'
}
```

If verbose

*Returns*
 `{Object}` An object with the `predictedCategory` and an array of the categories ordered by likelihood (most likely first).

```
{
    likelihoods :  [
                    ...
                      {
                        category: 'spam',
                        logLikelihood: 0.0047591, // logarithmic likelihood
                        scaledLogLikelihood: 84 // likelihood on a scale from 0 to 100
                      },
                      ...
                   ]
    predictedCategory : 'negative'  //--> the main category bayes thinks the text belongs to. As a string
}
```

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)

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.