misskey/src/tools/analysis/naive-bayes.js

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2017-09-06 12:29:56 +00:00
// Original source code: https://github.com/ttezel/bayes/blob/master/lib/naive_bayes.js (commit: 2c20d3066e4fc786400aaedcf3e42987e52abe3c)
// CUSTOMIZED BY SYUILO
/*
Expose our naive-bayes generator function
*/
module.exports = function (options) {
return new Naivebayes(options)
}
// keys we use to serialize a classifier's state
var STATE_KEYS = module.exports.STATE_KEYS = [
'categories', 'docCount', 'totalDocuments', 'vocabulary', 'vocabularySize',
'wordCount', 'wordFrequencyCount', 'options'
];
/**
* Initializes a NaiveBayes instance from a JSON state representation.
* Use this with classifier.toJson().
*
* @param {String} jsonStr state representation obtained by classifier.toJson()
* @return {NaiveBayes} Classifier
*/
module.exports.fromJson = function (jsonStr) {
var parsed;
try {
parsed = JSON.parse(jsonStr)
} catch (e) {
throw new Error('Naivebayes.fromJson expects a valid JSON string.')
}
// init a new classifier
var classifier = new Naivebayes(parsed.options)
// override the classifier's state
STATE_KEYS.forEach(function (k) {
if (!parsed[k]) {
throw new Error('Naivebayes.fromJson: JSON string is missing an expected property: `'+k+'`.')
}
classifier[k] = parsed[k]
})
return classifier
}
/**
* Given an input string, tokenize it into an array of word tokens.
* This is the default tokenization function used if user does not provide one in `options`.
*
* @param {String} text
* @return {Array}
*/
var defaultTokenizer = function (text) {
//remove punctuation from text - remove anything that isn't a word char or a space
var rgxPunctuation = /[^(a-zA-ZA-Яa-я0-9_)+\s]/g
var sanitized = text.replace(rgxPunctuation, ' ')
return sanitized.split(/\s+/)
}
/**
* Naive-Bayes Classifier
*
* This is a naive-bayes classifier that uses Laplace Smoothing.
*
* Takes an (optional) options object containing:
* - `tokenizer` => custom tokenization function
*
*/
function Naivebayes (options) {
// set options object
this.options = {}
if (typeof options !== 'undefined') {
if (!options || typeof options !== 'object' || Array.isArray(options)) {
throw TypeError('NaiveBayes got invalid `options`: `' + options + '`. Pass in an object.')
}
this.options = options
}
this.tokenizer = this.options.tokenizer || defaultTokenizer
//initialize our vocabulary and its size
this.vocabulary = {}
this.vocabularySize = 0
//number of documents we have learned from
this.totalDocuments = 0
//document frequency table for each of our categories
//=> for each category, how often were documents mapped to it
this.docCount = {}
//for each category, how many words total were mapped to it
this.wordCount = {}
//word frequency table for each category
//=> for each category, how frequent was a given word mapped to it
this.wordFrequencyCount = {}
//hashmap of our category names
this.categories = {}
}
/**
* Initialize each of our data structure entries for this new category
*
* @param {String} categoryName
*/
Naivebayes.prototype.initializeCategory = function (categoryName) {
if (!this.categories[categoryName]) {
this.docCount[categoryName] = 0
this.wordCount[categoryName] = 0
this.wordFrequencyCount[categoryName] = {}
this.categories[categoryName] = true
}
return this
}
/**
* train our naive-bayes classifier by telling it what `category`
* the `text` corresponds to.
*
* @param {String} text
* @param {String} class
*/
Naivebayes.prototype.learn = function (text, category) {
var self = this
//initialize category data structures if we've never seen this category
self.initializeCategory(category)
//update our count of how many documents mapped to this category
self.docCount[category]++
//update the total number of documents we have learned from
self.totalDocuments++
//normalize the text into a word array
var tokens = self.tokenizer(text)
//get a frequency count for each token in the text
var frequencyTable = self.frequencyTable(tokens)
/*
Update our vocabulary and our word frequency count for this category
*/
Object
.keys(frequencyTable)
.forEach(function (token) {
//add this word to our vocabulary if not already existing
if (!self.vocabulary[token]) {
self.vocabulary[token] = true
self.vocabularySize++
}
var frequencyInText = frequencyTable[token]
//update the frequency information for this word in this category
if (!self.wordFrequencyCount[category][token])
self.wordFrequencyCount[category][token] = frequencyInText
else
self.wordFrequencyCount[category][token] += frequencyInText
//update the count of all words we have seen mapped to this category
self.wordCount[category] += frequencyInText
})
return self
}
/**
* Determine what category `text` belongs to.
*
* @param {String} text
* @return {String} category
*/
Naivebayes.prototype.categorize = function (text) {
var self = this
, maxProbability = -Infinity
, chosenCategory = null
var tokens = self.tokenizer(text)
var frequencyTable = self.frequencyTable(tokens)
//iterate thru our categories to find the one with max probability for this text
Object
.keys(self.categories)
.forEach(function (category) {
//start by calculating the overall probability of this category
//=> out of all documents we've ever looked at, how many were
// mapped to this category
var categoryProbability = self.docCount[category] / self.totalDocuments
//take the log to avoid underflow
var logProbability = Math.log(categoryProbability)
//now determine P( w | c ) for each word `w` in the text
Object
.keys(frequencyTable)
.forEach(function (token) {
var frequencyInText = frequencyTable[token]
var tokenProbability = self.tokenProbability(token, category)
// console.log('token: %s category: `%s` tokenProbability: %d', token, category, tokenProbability)
//determine the log of the P( w | c ) for this word
logProbability += frequencyInText * Math.log(tokenProbability)
})
if (logProbability > maxProbability) {
maxProbability = logProbability
chosenCategory = category
}
})
return chosenCategory
}
/**
* Calculate probability that a `token` belongs to a `category`
*
* @param {String} token
* @param {String} category
* @return {Number} probability
*/
Naivebayes.prototype.tokenProbability = function (token, category) {
//how many times this word has occurred in documents mapped to this category
var wordFrequencyCount = this.wordFrequencyCount[category][token] || 0
//what is the count of all words that have ever been mapped to this category
var wordCount = this.wordCount[category]
//use laplace Add-1 Smoothing equation
return ( wordFrequencyCount + 1 ) / ( wordCount + this.vocabularySize )
}
/**
* Build a frequency hashmap where
* - the keys are the entries in `tokens`
* - the values are the frequency of each entry in `tokens`
*
* @param {Array} tokens Normalized word array
* @return {Object}
*/
Naivebayes.prototype.frequencyTable = function (tokens) {
var frequencyTable = Object.create(null)
tokens.forEach(function (token) {
if (!frequencyTable[token])
frequencyTable[token] = 1
else
frequencyTable[token]++
})
return frequencyTable
}
/**
* Dump the classifier's state as a JSON string.
* @return {String} Representation of the classifier.
*/
Naivebayes.prototype.toJson = function () {
var state = {}
var self = this
STATE_KEYS.forEach(function (k) {
state[k] = self[k]
})
var jsonStr = JSON.stringify(state)
return jsonStr
}
// (original method)
Naivebayes.prototype.export = function () {
var state = {}
var self = this
STATE_KEYS.forEach(function (k) {
state[k] = self[k]
})
return state
}
module.exports.import = function (data) {
var parsed = data
// init a new classifier
var classifier = new Naivebayes()
// override the classifier's state
STATE_KEYS.forEach(function (k) {
if (!parsed[k]) {
throw new Error('Naivebayes.import: data is missing an expected property: `'+k+'`.')
}
classifier[k] = parsed[k]
})
return classifier
}