From 4e250b66d482ea3b1388b6c2f24cc66850f71038 Mon Sep 17 00:00:00 2001 From: syuilo Date: Wed, 6 Sep 2017 21:29:56 +0900 Subject: [PATCH] wip --- .gitignore | 1 + package.json | 3 +- src/config.ts | 3 + src/tools/ai/categorizer.ts | 48 +++--- src/tools/ai/naive-bayes.js | 302 ++++++++++++++++++++++++++++++++++++ 5 files changed, 334 insertions(+), 23 deletions(-) create mode 100644 src/tools/ai/naive-bayes.js diff --git a/.gitignore b/.gitignore index 42b1bde94..2ae0f98c5 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ /node_modules /built /uploads +/data npm-debug.log *.pem run.bat diff --git a/package.json b/package.json index ae959d1b1..31cf7a02c 100644 --- a/package.json +++ b/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", diff --git a/src/config.ts b/src/config.ts index 8f4ada5af..f333a1f5a 100644 --- a/src/config.ts +++ b/src/config.ts @@ -68,6 +68,9 @@ type Source = { hook_secret: string; username: string; }; + categorizer?: { + mecab_command?: string; + }; }; /** diff --git a/src/tools/ai/categorizer.ts b/src/tools/ai/categorizer.ts index f70ce1b7d..c13374161 100644 --- a/src/tools/ai/categorizer.ts +++ b/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; - categorizerDbFilePath: string; - mecab: any; + private classifier: any; + private categorizerDbFilePath: string; + private mecab: any; - constructor(categorizerDbFilePath: string, mecabCommand: string = 'mecab -d /usr/share/mecab/dic/mecab-ipadic-neologd') { - this.categorizerDbFilePath = categorizerDbFilePath; + constructor() { + 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, { - encoding: 'utf8' - }); + const buffer = fs.readFileSync(this.categorizerDbFilePath); + 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() { - fs.writeFileSync(this.categorizerDbFilePath, this.classifier.toJson(), { - encoding: 'utf8' - }); + private save() { + const buffer = msgpack.encode(this.classifier.export()); + fs.writeFileSync(this.categorizerDbFilePath, buffer); } } - diff --git a/src/tools/ai/naive-bayes.js b/src/tools/ai/naive-bayes.js new file mode 100644 index 000000000..78f07153c --- /dev/null +++ b/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 +}