wip

.gitignore

@@ -3,6 +3,7 @@
 /node_modules
 /built
 /uploads
+/data
 npm-debug.log
 *.pem
 run.bat

package.json

@@ -64,6 +64,7 @@
     "@types/webpack": "3.0.10",
     "@types/webpack-stream": "3.2.7",
     "@types/websocket": "0.0.34",
+    "@types/msgpack-lite": "^0.1.5",
     "chai": "4.1.2",
     "chai-http": "3.0.0",
     "css-loader": "0.28.7",
@@ -97,7 +98,6 @@
     "accesses": "2.5.0",
     "animejs": "2.0.2",
     "autwh": "0.0.1",
-    "bayes": "0.0.7",
     "bcryptjs": "2.4.3",
     "body-parser": "1.17.2",
     "cafy": "2.4.0",
@@ -126,6 +126,7 @@
     "monk": "6.0.3",
     "morgan": "1.8.2",
     "ms": "2.0.0",
+    "msgpack-lite": "^0.1.26",
     "multer": "1.3.0",
     "nprogress": "0.2.0",
     "os-utils": "0.0.14",

src/config.ts

@@ -68,6 +68,9 @@ type Source = {
 		hook_secret: string;
 		username: string;
 	};
+	categorizer?: {
+		mecab_command?: string;
+	};
 };
 
 /**

src/tools/ai/categorizer.ts

@@ -1,36 +1,42 @@
 import * as fs from 'fs';
-const bayes = require('bayes');
+
+const bayes = require('./naive-bayes.js');
 const MeCab = require('mecab-async');
+import * as msgpack from 'msgpack-lite';
+
 import Post from '../../api/models/post';
+import config from '../../conf';
 
+/**
+ * 投稿を学習したり与えられた投稿のカテゴリを予測します
+ */
 export default class Categorizer {
-	classifier: any;
+	private classifier: any;
-	categorizerDbFilePath: string;
+	private categorizerDbFilePath: string;
-	mecab: any;
+	private mecab: any;
 
-	constructor(categorizerDbFilePath: string, mecabCommand: string = 'mecab -d /usr/share/mecab/dic/mecab-ipadic-neologd') {
+	constructor() {
-		this.categorizerDbFilePath = categorizerDbFilePath;
+		this.categorizerDbFilePath = `${__dirname}/../../../data/category`;
 
 		this.mecab = new MeCab();
-		this.mecab.command = mecabCommand;
+		if (config.categorizer.mecab_command) this.mecab.command = config.categorizer.mecab_command;
 
 		// BIND -----------------------------------
 		this.tokenizer = this.tokenizer.bind(this);
 	}
 
-	tokenizer(text: string) {
+	private tokenizer(text: string) {
 		return this.mecab.wakachiSync(text);
 	}
 
-	async init() {
+	public async init() {
 		try {
-			const db = fs.readFileSync(this.categorizerDbFilePath, {
+			const buffer = fs.readFileSync(this.categorizerDbFilePath);
-				encoding: 'utf8'
+			const db = msgpack.decode(buffer);
-			});
 
-			this.classifier = bayes.fromJson(db);
+			this.classifier = bayes.import(db);
 			this.classifier.tokenizer = this.tokenizer;
-		} catch(e) {
+		} catch (e) {
 			this.classifier = bayes({
 				tokenizer: this.tokenizer
 			});
@@ -49,7 +55,7 @@ export default class Categorizer {
 		}
 	}
 
-	async learn(id, category) {
+	public async learn(id, category) {
 		const post = await Post.findOne({ _id: id });
 
 		Post.update({ _id: id }, {
@@ -64,7 +70,7 @@ export default class Categorizer {
 		this.save();
 	}
 
-	async categorize(id) {
+	public async categorize(id) {
 		const post = await Post.findOne({ _id: id });
 
 		const category = this.classifier.categorize(post.text);
@@ -76,14 +82,12 @@ export default class Categorizer {
 		});
 	}
 
-	async test(text) {
+	public async test(text) {
 		return this.classifier.categorize(text);
 	}
 
-	save() {
+	private save() {
-		fs.writeFileSync(this.categorizerDbFilePath, this.classifier.toJson(), {
+		const buffer = msgpack.encode(this.classifier.export());
-			encoding: 'utf8'
+		fs.writeFileSync(this.categorizerDbFilePath, buffer);
-		});
 	}
 }
-

src/tools/ai/naive-bayes.js

@@ -0,0 +1,302 @@
+// 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
+}