├── .coveralls.yml ├── CHANGELOG.md ├── index.js ├── .travis.yml ├── .gitignore ├── package.json ├── test ├── nominalDataset.json ├── mallard-tests.js └── largeContinuousDataset.json ├── gulpfile.js ├── lib ├── kNN.js ├── ID3.js └── naiveBayes.js ├── LICENSE └── README.md /.coveralls.yml: -------------------------------------------------------------------------------- 1 | service_name: travis-ci 2 | -------------------------------------------------------------------------------- /CHANGELOG.md: -------------------------------------------------------------------------------- 1 | ## 1.1.0 (2014-08-27) 2 | 3 | 4 | #### Features 5 | 6 | * Naive Bayes algorithm ([70066711](https://github.com/stevedocious/mallard.git/commit/70066711e3450b1819f143befdcd5d14b1e69b11)) 7 | 8 | -------------------------------------------------------------------------------- /index.js: -------------------------------------------------------------------------------- 1 | 2 | var kNN = require('./lib/kNN'); 3 | var ID3 = require('./lib/ID3'); 4 | var bayes = require('./lib/naiveBayes'); 5 | 6 | module.exports = { 7 | kNN: kNN, 8 | ID3: ID3, 9 | bayes: bayes 10 | }; 11 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | language: node_js 2 | node_js: 3 | - '0.10' 4 | 5 | addons: 6 | code_climate: 7 | repo_token: 563e02929f350f5e74dd8aba026fb6e5176f74a08e15c5e37681703c7631ec22 8 | 9 | before_script: 10 | - 'npm install -g gulp' 11 | - 'npm install -g codeclimate-test-reporter' 12 | 13 | after_script: 14 | - cat ./coverage/lcov.info | codeclimate 15 | 16 | script: 17 | - gulp ci 18 | 19 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Logs 2 | logs 3 | *.log 4 | 5 | # Runtime data 6 | pids 7 | *.pid 8 | *.seed 9 | 10 | # Directory for instrumented libs generated by jscoverage/JSCover 11 | lib-cov 12 | 13 | # Coverage directory used by tools like istanbul 14 | coverage 15 | 16 | # Grunt intermediate storage (http://gruntjs.com/creating-plugins#storing-task-files) 17 | .grunt 18 | 19 | # Compiled binary addons (http://nodejs.org/api/addons.html) 20 | build/Release 21 | 22 | # Dependency directory 23 | # Deployed apps should consider commenting this line out: 24 | # see https://npmjs.org/doc/faq.html#Should-I-check-my-node_modules-folder-into-git 25 | node_modules 26 | -------------------------------------------------------------------------------- /package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "mallard", 3 | "version": "1.1.0", 4 | "description": "Data Classification and Machine Learning algorithms", 5 | "main": "index.js", 6 | "scripts": { 7 | "test": "gulp test" 8 | }, 9 | "repository": { 10 | "type": "git", 11 | "url": "https://github.com/stevedocious/mallard.git" 12 | }, 13 | "keywords": [ 14 | "utility" 15 | ], 16 | "author": "stevedocious", 17 | "license": "LGPLv3", 18 | "bugs": { 19 | "url": "https://github.com/stevedocious/mallard/issues" 20 | }, 21 | "homepage": "https://github.com/stevedocious/mallard", 22 | "devDependencies": { 23 | "gulp": "^3.8.6", 24 | "chai": "^1.9.1", 25 | "sinon": "^1.10.3", 26 | "conventional-changelog": "0.0.11", 27 | "gulp-jshint": "^1.8.0", 28 | "gulp-mocha": "^0.5.2", 29 | "gulp-istanbul": "^0.2.1", 30 | "gulp-coveralls": "^0.1.2", 31 | "jshint-stylish": "^0.4.0", 32 | "gulp-bump": "^0.1.11", 33 | "gulp-git": "^0.5.0" 34 | }, 35 | "dependencies": { 36 | "pocketwrench": "0.0.1" 37 | } 38 | } 39 | -------------------------------------------------------------------------------- /test/nominalDataset.json: -------------------------------------------------------------------------------- 1 | [ 2 | {"category" : "A", "datapoints" : ["red","large","no","old"]}, 3 | {"category" : "B", "datapoints" : ["red","large","no","young"]}, 4 | {"category" : "A", "datapoints" : ["red","large","yes","old"]}, 5 | {"category" : "C", "datapoints" : ["red","large","yes","young"]}, 6 | {"category" : "A", "datapoints" : ["red","small","no","old"]}, 7 | {"category" : "B", "datapoints" : ["red","small","no","young"]}, 8 | {"category" : "A", "datapoints" : ["red","small","yes","old"]}, 9 | {"category" : "C", "datapoints" : ["red","small","yes","young"]}, 10 | {"category" : "A", "datapoints" : ["green","large","no","old"]}, 11 | {"category" : "B", "datapoints" : ["green","large","no","young"]}, 12 | {"category" : "A", "datapoints" : ["green","large","yes","old"]}, 13 | {"category" : "C", "datapoints" : ["green","large","yes","young"]}, 14 | {"category" : "A", "datapoints" : ["green","small","no","old"]}, 15 | {"category" : "B", "datapoints" : ["green","small","no","young"]}, 16 | {"category" : "A", "datapoints" : ["green","small","yes","old"]}, 17 | {"category" : "A", "datapoints" : ["green","small","yes","young"]}, 18 | {"category" : "A", "datapoints" : ["blue","large","no","old"]}, 19 | {"category" : "A", "datapoints" : ["blue","large","no","young"]}, 20 | {"category" : "A", "datapoints" : ["blue","large","yes","old"]}, 21 | {"category" : "C", "datapoints" : ["blue","large","yes","young"]}, 22 | {"category" : "A", "datapoints" : ["blue","small","no","old"]}, 23 | {"category" : "B", "datapoints" : ["blue","small","no","young"]}, 24 | {"category" : "A", "datapoints" : ["blue","small","yes","old"]}, 25 | {"category" : "A", "datapoints" : ["blue","small","yes","young"]} 26 | ] 27 | -------------------------------------------------------------------------------- /gulpfile.js: -------------------------------------------------------------------------------- 1 | var fs = require('fs'); 2 | var gulp = require('gulp'); 3 | var changelog = require('conventional-changelog'); 4 | var jshint = require('gulp-jshint'); 5 | var mocha = require('gulp-mocha'); 6 | var istanbul = require('gulp-istanbul'); 7 | var stylish = require('jshint-stylish'); 8 | var coveralls = require('gulp-coveralls'); 9 | var bump = require('gulp-bump'); 10 | var git = require('gulp-git'); 11 | 12 | // ------------------------ DEV tasks 13 | gulp.task('test', ['lint'], function () { 14 | return gulp.src('test/*.js') 15 | .pipe(mocha({reporter: 'spec'})); 16 | }); 17 | 18 | gulp.task('lint', function() { 19 | return gulp.src(['gulpfile.js', 'index.js', 'lib/*.js', 'test/*.js']) 20 | .pipe(jshint()) 21 | .pipe(jshint.reporter(stylish)) 22 | .pipe(jshint.reporter('fail')); 23 | }); 24 | 25 | gulp.task('coverage', function(cb) { 26 | gulp.src(['lib/**/*.js', 'index.js']) 27 | .pipe(istanbul()) 28 | .on('finish', function () { 29 | gulp.src(['test/*.js']) 30 | .pipe(mocha()) 31 | .pipe(istanbul.writeReports()) 32 | }); 33 | //.on('end', cb); 34 | cb(); 35 | }); 36 | 37 | gulp.task('watch', function() { 38 | return gulp.watch(['lib/*.js', 'index.js'], ['lint', 'test']); 39 | }); 40 | 41 | gulp.task('coveralls', ['coverage'], function() { 42 | return gulp.src('coverage/**/lcov.info') 43 | .pipe(coveralls()); 44 | }); 45 | 46 | // ------------------------ RELEASE tasks 47 | 48 | gulp.task('bump', function(){ 49 | return gulp.src('./package.json') 50 | .pipe(bump({type:'minor'})) 51 | .pipe(gulp.dest('./')); 52 | }); 53 | 54 | gulp.task('tag', ['changelog'], function () { 55 | var pkg = require('./package.json'); 56 | var v = 'v' + pkg.version; 57 | var message = 'Release ' + v; 58 | 59 | return gulp.src('./') 60 | .pipe(git.commit(message)) 61 | .pipe(git.tag(v, message)) 62 | .pipe(git.push('origin', 'master', '--tags')) 63 | .pipe(gulp.dest('./')); 64 | }); 65 | 66 | gulp.task('changelog', ['bump'], function(done){ 67 | function changeParsed(err, log){ 68 | if (err) { 69 | return done(err); 70 | } 71 | fs.writeFile('CHANGELOG.md', log, done); 72 | } 73 | fs.readFile('./package.json', 'utf8', function(err, data){ 74 | var ref$, repository, version; 75 | ref$ = JSON.parse(data); 76 | repository = ref$.repository; 77 | version = ref$.version; 78 | return changelog({ 79 | repository: repository.url, 80 | version: version 81 | }, changeParsed); 82 | }); 83 | }); 84 | 85 | gulp.task('default', ['dev']); 86 | gulp.task('dev', ['watch']); 87 | gulp.task('ci', ['coveralls']); 88 | gulp.task('release', ['tag']); 89 | -------------------------------------------------------------------------------- /lib/kNN.js: -------------------------------------------------------------------------------- 1 | var pocketwrench = require('pocketwrench'); 2 | module.exports = (function() { 3 | 4 | var calculateDistance = function(dp1, dp2) { 5 | var total = 0.0; 6 | 7 | for(var x = 0; x < dp1.length; x++) { 8 | total += Math.pow((dp1[x] - dp2[x]), 2); 9 | } 10 | return Math.sqrt(total); 11 | }; 12 | 13 | var getMins = function(dataset) { 14 | var mins = []; 15 | 16 | for(var i = 0; i < dataset[0].datapoints.length; i++) { 17 | mins.push(999999); 18 | } 19 | 20 | for(var x = 0; x < dataset.length; x++) { 21 | for(var y = 0; y < dataset[x].datapoints.length; y++) { 22 | if(dataset[x].datapoints[y] < mins[y]) { 23 | mins[y] = dataset[x].datapoints[y]; 24 | } 25 | } 26 | } 27 | 28 | return mins; 29 | }; 30 | 31 | var getMaxs = function(dataset) { 32 | var maxs = []; 33 | 34 | for(var i = 0; i < dataset[0].datapoints.length; i++) { 35 | maxs.push(-999999); 36 | } 37 | 38 | for(var x = 0; x < dataset.length; x++) { 39 | for(var y = 0; y < dataset[x].datapoints.length; y++) { 40 | if(dataset[x].datapoints[y] > maxs[y]) { 41 | maxs[y] = dataset[x].datapoints[y]; 42 | } 43 | } 44 | } 45 | 46 | return maxs; 47 | }; 48 | 49 | var normaliseDatapoints = function(dps, mins, maxs) { 50 | var normalised = []; 51 | for(var y = 0; y < dps.length; y++) { 52 | var normalisedValue = (dps[y] - mins[y]) / (maxs[y] - mins[y]); 53 | normalised.push(normalisedValue); 54 | } 55 | 56 | return normalised; 57 | }; 58 | 59 | var normaliseDataset = function(dataset) { 60 | var normalised = []; 61 | var mins = getMins(dataset); 62 | var maxs = getMaxs(dataset); 63 | 64 | for(var x = 0; x < dataset.length; x++) { 65 | var row = { 66 | category: dataset[x].category, 67 | datapoints : normaliseDatapoints(dataset[x].datapoints, mins, maxs) 68 | }; 69 | normalised.push(row); 70 | } 71 | 72 | return normalised; 73 | }; 74 | 75 | var normaliseInput = function(input, dataset) { 76 | var mins = getMins(dataset); 77 | var maxs = getMaxs(dataset); 78 | return normaliseDatapoints(input, mins, maxs); 79 | }; 80 | 81 | var kNN = function(input, dataset, k) { 82 | var distances = []; 83 | 84 | var normalisedDataset = normaliseDataset(dataset); 85 | var normalisedInput = normaliseInput(input, dataset); 86 | 87 | for(var x = 0; x < normalisedDataset.length; x++) { 88 | var distance = calculateDistance(normalisedInput, normalisedDataset[x].datapoints); 89 | distances.push({category: normalisedDataset[x].category, distance: distance}); 90 | } 91 | 92 | var sortedDistances = pocketwrench.array.sortByField(distances, 'distance'); 93 | return sortedDistances.slice(0,k); 94 | }; 95 | 96 | var classify = function(input, dataset, k) { 97 | var knn = kNN(input, dataset, k); 98 | return pocketwrench.array.mostFrequentElement(knn, 'category'); 99 | }; 100 | 101 | return { 102 | kNN: kNN, 103 | classify: classify 104 | }; 105 | }()); 106 | -------------------------------------------------------------------------------- /lib/ID3.js: -------------------------------------------------------------------------------- 1 | var pocketwrench = require('pocketwrench'); 2 | 3 | module.exports = function() { 4 | 5 | var uniqueValues = function(dataset, column) { 6 | var unique = {}; 7 | 8 | for(var x = 0; x < dataset.length; x++) { 9 | var value = dataset[x].datapoints[column]; 10 | if(!unique[value]) { 11 | unique[value] = value; 12 | } 13 | } 14 | 15 | var uniqueVals = []; 16 | 17 | for(var y = 0; y < Object.keys(unique).length; y++) { 18 | uniqueVals.push(unique[Object.keys(unique)[y]]); 19 | } 20 | 21 | return uniqueVals; 22 | }; 23 | 24 | var uniqueCategories = function(dataset) { 25 | var unique = {}; 26 | 27 | for(var x = 0; x < dataset.length; x++){ 28 | var category = dataset[x].category; 29 | if(!unique[category]) { 30 | unique[category] = category; 31 | } 32 | } 33 | 34 | var uniqueVals = []; 35 | 36 | for(var y = 0; y < Object.keys(unique).length; y++) { 37 | uniqueVals.push(unique[Object.keys(unique)[y]]); 38 | } 39 | 40 | return uniqueVals; 41 | }; 42 | 43 | var getShannonEntropy = function(dataset) { 44 | var categoryCounts = {}; 45 | 46 | for(var x = 0; x < dataset.length; x++) { 47 | if(categoryCounts[dataset[x].category]) { 48 | categoryCounts[dataset[x].category] += 1; 49 | } 50 | else { 51 | categoryCounts[dataset[x].category] = 1; 52 | } 53 | } 54 | 55 | var ent = 0.0; 56 | 57 | for(var y = 0; y < Object.keys(categoryCounts).length; y++) { 58 | var probability = categoryCounts[Object.keys(categoryCounts)[y]] / dataset.length; 59 | ent -= probability * pocketwrench.math.logx(probability, 2); 60 | } 61 | 62 | return ent; 63 | 64 | }; 65 | 66 | var splitDataset = function(dataset, column, value) { 67 | var splitSet = []; 68 | 69 | for(var x = 0; x < dataset.length; x++) { 70 | if(dataset[x].datapoints[column] == value) { 71 | var entry = { 72 | category : dataset[x].category, 73 | datapoints : dataset[x].datapoints.slice() 74 | }; 75 | entry.datapoints.splice(column, 1); 76 | splitSet.push(entry); 77 | } 78 | } 79 | 80 | return splitSet; 81 | }; 82 | 83 | var getBestSplit = function(dataset) { 84 | var baseEntropy = getShannonEntropy(dataset); 85 | var numberOfFeatures = dataset[0].datapoints.length; 86 | var bestInfoGain = 0.0; 87 | var bestColumn = -1; 88 | 89 | for(var col = 0; col < numberOfFeatures; col++) { 90 | var uniqueVals = uniqueValues(dataset, col); 91 | var newEntropy = 0.0; 92 | for(var y = 0; y < uniqueVals.length; y++) { 93 | var subset = splitDataset(dataset, col, uniqueVals[y]); 94 | var prob = subset.length / dataset.length; 95 | newEntropy += prob * getShannonEntropy(subset); 96 | } 97 | var infogain = baseEntropy - newEntropy; 98 | if(infogain > bestInfoGain) { 99 | bestInfoGain = infogain; 100 | bestColumn = col; 101 | } 102 | } 103 | return bestColumn; 104 | }; 105 | 106 | var createTree = function(dataset, columnLabels) { 107 | var uniqueCats = uniqueCategories(dataset); 108 | 109 | if(uniqueCats.length == 1) { 110 | return uniqueCats[0]; 111 | } 112 | 113 | if(dataset.length == 1) { 114 | return dataset[0].category; 115 | } 116 | 117 | var bestSplit = getBestSplit(dataset); 118 | var tree = {}; 119 | tree[columnLabels[bestSplit]] = {}; 120 | 121 | var subLabels = columnLabels.slice(); 122 | subLabels.splice(bestSplit, 1); 123 | var uniqueVals = uniqueValues(dataset, bestSplit); 124 | 125 | for(var x = 0; x < uniqueVals.length; x++) { 126 | tree[columnLabels[bestSplit]][uniqueVals[x]] = createTree(splitDataset(dataset, bestSplit, uniqueVals[x]), subLabels); 127 | } 128 | 129 | return tree; 130 | }; 131 | 132 | var classify = function(input, tree, labels) { 133 | var rootLabel = Object.keys(tree)[0]; 134 | var rootFeatureIndex = labels.indexOf(rootLabel); 135 | var subtree = tree[rootLabel]; 136 | 137 | for(var x = 0; x < Object.keys(subtree).length; x++) { 138 | if(input[rootFeatureIndex] == Object.keys(subtree)[x]) { 139 | if(pocketwrench.object.isDictionary(subtree[Object.keys(subtree)[x]])) { 140 | return classify(input, subtree[Object.keys(subtree)[x]], labels); 141 | } 142 | else { 143 | return subtree[Object.keys(subtree)[x]]; 144 | } 145 | } 146 | } 147 | }; 148 | 149 | return { 150 | createTree : createTree, 151 | classify: classify 152 | }; 153 | 154 | }(); 155 | -------------------------------------------------------------------------------- /lib/naiveBayes.js: -------------------------------------------------------------------------------- 1 | var pocketwrench = require('pocketwrench'); 2 | 3 | module.exports = function() { 4 | 5 | var defaultArray = function(count, defaultValue) { 6 | var ds = []; 7 | 8 | for(var x = 0; x < count; x++) { 9 | ds.push(defaultValue); 10 | } 11 | 12 | return ds; 13 | }; 14 | 15 | var sum = function(list) { 16 | return list.reduce( 17 | function(previous, current, index, array) 18 | { 19 | return previous + current; 20 | }, 0); 21 | }; 22 | 23 | var multiplyArray = function(array1, array2) { 24 | if(array1.length != array2.length) { 25 | return []; 26 | } 27 | 28 | var result = []; 29 | 30 | for(var x = 0; x < array1.length; x++) { 31 | result.push(array1[x] * array2[x]); 32 | } 33 | 34 | return result; 35 | }; 36 | 37 | var countOccurrences = function(dataset, fieldName, value) { 38 | var x = dataset.filter(function(elem, pos) { 39 | return elem[fieldName] == value; 40 | }); 41 | 42 | return x.length; 43 | }; 44 | 45 | var maxEntity = function(collection, fieldName) { 46 | var maxElement = null; 47 | 48 | for(var x = 0; x < collection.length; x++) { 49 | if(maxElement === null || collection[x][fieldName] > maxElement[fieldName]) { 50 | maxElement = collection[x]; 51 | } 52 | } 53 | 54 | return maxElement; 55 | }; 56 | 57 | var uniqueValues = function(input) { 58 | return input.filter(function(elem, pos) { 59 | return input.indexOf(elem) == pos; 60 | }); 61 | }; 62 | 63 | var uniqueCategories = function(dataset) { 64 | var categories = []; 65 | 66 | for(var x = 0; x < dataset.length; x++) { 67 | categories.push(dataset[x].category); 68 | } 69 | 70 | return uniqueValues(categories); 71 | }; 72 | 73 | var buildVocabulary = function(dataset) { 74 | var vocab = []; 75 | 76 | for(var x = 0; x < dataset.length; x++) { 77 | vocab = vocab.concat(dataset[x].datapoints); 78 | } 79 | 80 | return uniqueValues(vocab); 81 | }; 82 | 83 | var convertTokenList = function(tokenList, vocabulary) { 84 | var tokenVector = []; 85 | 86 | for(var y = 0; y < vocabulary.length; y++) { 87 | if(tokenList.indexOf(vocabulary[y]) == -1) { 88 | tokenVector.push(0); 89 | } 90 | else { 91 | tokenVector.push(1); 92 | } 93 | } 94 | 95 | return tokenVector; 96 | }; 97 | 98 | var buildTrainingMatrix = function(dataset, vocabulary) { 99 | var trainingMatrix = []; 100 | 101 | for(var x = 0; x < dataset.length; x++) { 102 | trainingMatrix.push( 103 | { 104 | category : dataset[x].category, 105 | tokens : convertTokenList(dataset[x].datapoints, vocabulary) 106 | }); 107 | } 108 | 109 | return trainingMatrix; 110 | }; 111 | 112 | var train = function(dataset) { 113 | var trainedDataset = { 114 | vocabulary : buildVocabulary(dataset), 115 | probabilities : [] 116 | }; 117 | var uniqueCats = uniqueCategories(dataset); 118 | var trainingMatrix = buildTrainingMatrix(dataset, trainedDataset.vocabulary); 119 | 120 | for(var x = 0; x < uniqueCats.length; x++) { 121 | trainedDataset.probabilities.push({ 122 | category : uniqueCats[x], 123 | probability : countOccurrences(dataset, 'category', uniqueCats[x]) / dataset.length, 124 | tokenProbNum : defaultArray(trainingMatrix[0].tokens.length, 1), 125 | tokenProbDenum : 2.0 126 | }); 127 | } 128 | 129 | for(var y = 0; y < trainingMatrix.length; y++) { 130 | var cat = pocketwrench.array.findFirstByField(trainedDataset.probabilities, 'category', trainingMatrix[y].category); 131 | cat.tokenProbDenum += sum(trainingMatrix[y].tokens); 132 | for(var z = 0; z < trainingMatrix[0].tokens.length; z++) { 133 | cat.tokenProbNum[z] += trainingMatrix[y].tokens[z]; 134 | } 135 | } 136 | 137 | for(var i = 0; i < trainedDataset.probabilities.length; i++) { 138 | for(var j = 0; j < trainedDataset.probabilities[0].tokenProbNum.length; j++) { 139 | trainedDataset.probabilities[i].tokenProbNum[j] = 140 | Math.log(trainedDataset.probabilities[i].tokenProbNum[j] / trainedDataset.probabilities[i].tokenProbDenum); 141 | } 142 | } 143 | 144 | return trainedDataset; 145 | }; 146 | 147 | var tokenize = function(text) { 148 | var wordsList = text.toLowerCase().match(/\b\w+\b/g); 149 | return uniqueValues(wordsList); 150 | }; 151 | 152 | var classify = function(input, trainedDataset) { 153 | if(!Array.isArray(input)) { 154 | input = tokenize(input); 155 | } 156 | 157 | var inputTokens = convertTokenList(input, trainedDataset.vocabulary); 158 | 159 | var probs = []; 160 | 161 | for(var x = 0; x < trainedDataset.probabilities.length; x++) { 162 | probs.push( 163 | { 164 | category: trainedDataset.probabilities[x].category, 165 | probability : sum(multiplyArray(inputTokens, trainedDataset.probabilities[x].tokenProbNum)) + Math.log(trainedDataset.probabilities[x].probability) 166 | } 167 | ); 168 | } 169 | 170 | return maxEntity(probs, 'probability'); 171 | 172 | }; 173 | 174 | return { 175 | tokenize : tokenize, 176 | train: train, 177 | classify : classify 178 | }; 179 | }(); 180 | -------------------------------------------------------------------------------- /test/mallard-tests.js: -------------------------------------------------------------------------------- 1 | var fs = require('fs'); 2 | var mallard = require('../index'); 3 | var expect = require('chai').expect; 4 | 5 | describe('Mallard Unit Tests', function () { 6 | 'use strict'; 7 | 8 | var nominalDataset = [ 9 | {"category" : "A", "datapoints" : ["red","large","no","old"]}, 10 | {"category" : "B", "datapoints" : ["red","large","no","young"]}, 11 | {"category" : "A", "datapoints" : ["red","large","yes","old"]}, 12 | {"category" : "C", "datapoints" : ["red","large","yes","young"]}, 13 | {"category" : "A", "datapoints" : ["red","small","no","old"]}, 14 | {"category" : "B", "datapoints" : ["red","small","no","young"]}, 15 | {"category" : "A", "datapoints" : ["red","small","yes","old"]}, 16 | {"category" : "C", "datapoints" : ["red","small","yes","young"]}, 17 | {"category" : "A", "datapoints" : ["green","large","no","old"]}, 18 | {"category" : "B", "datapoints" : ["green","large","no","young"]}, 19 | {"category" : "A", "datapoints" : ["green","large","yes","old"]}, 20 | {"category" : "C", "datapoints" : ["green","large","yes","young"]}, 21 | {"category" : "A", "datapoints" : ["green","small","no","old"]}, 22 | {"category" : "B", "datapoints" : ["green","small","no","young"]}, 23 | {"category" : "A", "datapoints" : ["green","small","yes","old"]}, 24 | {"category" : "A", "datapoints" : ["green","small","yes","young"]}, 25 | {"category" : "A", "datapoints" : ["blue","large","no","old"]}, 26 | {"category" : "A", "datapoints" : ["blue","large","no","young"]}, 27 | {"category" : "A", "datapoints" : ["blue","large","yes","old"]}, 28 | {"category" : "C", "datapoints" : ["blue","large","yes","young"]}, 29 | {"category" : "A", "datapoints" : ["blue","small","no","old"]}, 30 | {"category" : "B", "datapoints" : ["blue","small","no","young"]}, 31 | {"category" : "A", "datapoints" : ["blue","small","yes","old"]}, 32 | {"category" : "A", "datapoints" : ["blue","small","yes","young"]} 33 | ]; 34 | 35 | var nominalDatasetLabels = ['Feature 1', 'Feature 2', 'Feature 3', 'Feature 4']; 36 | 37 | var continuousDataset; 38 | 39 | before(function() { 40 | var buffer = fs.readFileSync('test/largeContinuousDataset.json', 'utf8'); 41 | continuousDataset = JSON.parse(buffer); 42 | }); 43 | 44 | describe('kNN Tests', function() { 45 | this.timeout(15000); 46 | it('Should return k neighbours', function() { 47 | var input = [10311, 2.654, 0.7932]; 48 | var kNN = mallard.kNN.kNN(input, continuousDataset, 3); 49 | expect(kNN.length).to.equal(3); 50 | }); 51 | 52 | it('Should have at least a 90% accuracy rate', function() { 53 | var correctlyIdentified = 0; 54 | for(var x = 0; x < continuousDataset.length; x++) { 55 | var category = mallard.kNN.classify(continuousDataset[x].datapoints, continuousDataset, 3); 56 | if(category == continuousDataset[x].category) { 57 | correctlyIdentified++; 58 | } 59 | } 60 | var percentageCorrect = (correctlyIdentified / continuousDataset.length) * 100.0; 61 | expect(percentageCorrect).to.be.at.least(90); 62 | }); 63 | }); 64 | 65 | describe('ID3 Tests', function() { 66 | it('Should split the dataset on the most appropriate feature', function() { 67 | var tree = mallard.ID3.createTree(nominalDataset, nominalDatasetLabels); 68 | var topNodeSplit = Object.keys(tree)[0]; 69 | expect(topNodeSplit).to.equal('Feature 4'); 70 | }); 71 | it('Should have at least a 90% accuracy rate', function() { 72 | var tree = mallard.ID3.createTree(nominalDataset, nominalDatasetLabels); 73 | var correctlyIdentified = 0; 74 | for(var x = 0; x < nominalDataset.length; x++) { 75 | var category = mallard.ID3.classify(nominalDataset[x].datapoints, tree, nominalDatasetLabels); 76 | if(category == nominalDataset[x].category) { 77 | correctlyIdentified++; 78 | } 79 | } 80 | var percentageCorrect = (correctlyIdentified / nominalDataset.length) * 100.0; 81 | expect(percentageCorrect).to.be.at.least(90); 82 | }); 83 | 84 | }); 85 | 86 | // The male birds have a glossy green head and are grey on wings and belly, while the females have mainly brown-speckled plumage. 87 | // The male has a dark green head, a yellow bill, is mainly purple-brown on the breast and grey on the body. 88 | // The female is mainly brown with an orange bill 89 | // The males sport a glossy green head and white neck ring, and what the females lack in colour they make up for in noise. 90 | // The green head and yellow bill of the mallard duck is a familiar sight to many people living in the Northern hemisphere 91 | 92 | // The bird is predominantly black and white, with the back feathers being iridescent and glossy in males, while the females are more drab. 93 | // Wild Muscovy ducks are all black with white patches on the upper and under wing. 94 | // They may be black, blue, chocolate, lavender or white 95 | // The original, wild type, coloration is black and white, but domestication has produced many more colors, including white, black, chocolate, and blue 96 | 97 | 98 | describe('Naive Bayes Tests', function() { 99 | it('Should remove duplicates when tokenizing', function() { 100 | var testString = 'This string has some duplicates, duplicates are in this string.'; 101 | var tokens = mallard.bayes.tokenize(testString); 102 | expect(tokens.length).to.equal(7); 103 | expect(tokens[0]).to.equal('this'); 104 | expect(tokens[1]).to.equal('string'); 105 | expect(tokens[2]).to.equal('has'); 106 | expect(tokens[3]).to.equal('some'); 107 | expect(tokens[4]).to.equal('duplicates'); 108 | expect(tokens[5]).to.equal('are'); 109 | expect(tokens[6]).to.equal('in'); 110 | }); 111 | it('Should remove punctuation when tokenizing', function() { 112 | var testString = 'This string, has some, punctuation!'; 113 | var tokens = mallard.bayes.tokenize(testString); 114 | var tokenizedString = tokens.join(' '); 115 | var indexOfComma = tokenizedString.indexOf(','); 116 | expect(indexOfComma).to.equal(-1); 117 | }); 118 | it('Should use the training dataset to create a vocabulary with unique values', function() { 119 | var trainedDataset = mallard.bayes.train(nominalDataset); 120 | expect(trainedDataset.vocabulary.length).to.equal(9); 121 | }); 122 | it('Should use the training dataset to create a probability row for each category', function() { 123 | var trainedDataset = mallard.bayes.train(nominalDataset); 124 | expect(trainedDataset.probabilities.length).to.equal(3); 125 | }); 126 | it('Should have at least a 85% accuracy rate', function() { 127 | var trainedDataset = mallard.bayes.train(nominalDataset); 128 | var correctlyIdentified = 0; 129 | for(var x = 0; x < nominalDataset.length; x++) { 130 | var result = mallard.bayes.classify(nominalDataset[x].datapoints, trainedDataset); 131 | if(result.category == nominalDataset[x].category) { 132 | correctlyIdentified++; 133 | } 134 | } 135 | var percentageCorrect = (correctlyIdentified / nominalDataset.length) * 100.0; 136 | expect(percentageCorrect).to.be.at.least(85); 137 | }); 138 | }); 139 | }); 140 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU LESSER GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | 9 | This version of the GNU Lesser General Public License incorporates 10 | the terms and conditions of version 3 of the GNU General Public 11 | License, supplemented by the additional permissions listed below. 12 | 13 | 0. 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Revised Versions of the GNU Lesser General Public License. 145 | 146 | The Free Software Foundation may publish revised and/or new versions 147 | of the GNU Lesser General Public License from time to time. Such new 148 | versions will be similar in spirit to the present version, but may 149 | differ in detail to address new problems or concerns. 150 | 151 | Each version is given a distinguishing version number. If the 152 | Library as you received it specifies that a certain numbered version 153 | of the GNU Lesser General Public License "or any later version" 154 | applies to it, you have the option of following the terms and 155 | conditions either of that published version or of any later version 156 | published by the Free Software Foundation. If the Library as you 157 | received it does not specify a version number of the GNU Lesser 158 | General Public License, you may choose any version of the GNU Lesser 159 | General Public License ever published by the Free Software Foundation. 160 | 161 | If the Library as you received it specifies that a proxy can decide 162 | whether future versions of the GNU Lesser General Public License shall 163 | apply, that proxy's public statement of acceptance of any version is 164 | permanent authorization for you to choose that version for the 165 | Library. 166 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | mallard 2 | ======== 3 | 4 | Data Classification and Machine Learning algorithms. 5 | 6 | [![NPM version](https://badge.fury.io/js/mallard.svg)](http://badge.fury.io/js/mallard) 7 | [![Build Status](https://travis-ci.org/tophatsteve/mallard.svg?branch=master)](https://travis-ci.org/tophatsteve/mallard) 8 | [![Code Climate](https://codeclimate.com/github/tophatsteve/mallard/badges/gpa.svg)](https://codeclimate.com/github/tophatsteve/mallard) 9 | [![Test Coverage](https://codeclimate.com/github/tophatsteve/mallard/badges/coverage.svg)](https://codeclimate.com/github/tophatsteve/mallard) 10 | 11 | 12 | ### Module Overview 13 | 14 | Based on the book [Machine Learning in Action](http://www.manning.com/pharrington/) by Peter Harrington, *mallard* contains a 15 | translation of the algorithms from the orignal Python into JavaScript. The algorithms implemented so far are: 16 | 17 | - [kNN](http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) - The k nearest neighbours algorithm classifies an unknown 18 | entity by working out the closest neighbours to it in a known dataset. The known data needs to be numerical so that distances 19 | between neighbours can be calculated. 20 | - [ID3](http://en.wikipedia.org/wiki/ID3_algorithm) - The ID3 decision tree algorithm uses known data to produce a decision tree 21 | that can then be used to classify an unknown entity. The known data for this algorithm needs to be discrete values because 22 | the generated decision tree checks for exactly matched values. 23 | - [Naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier) - The Naive Bayes algorithm uses a training dataset to calculate 24 | the probabilities of a datapoint occurring in each category. These probabilities are then mapped onto the datapoints in the 25 | input, and a most probable category computed. 26 | 27 | ### Usage 28 | 29 | To load *mallard*, just require it: 30 | 31 | ```javascript 32 | var mallard = require('mallard'); 33 | ``` 34 | 35 | In order to correctly classify an entity, the algorithms require a dataset of already known data. This is provided in the 36 | format of an array of objects, each object having two properties. The first property is *category*, which gives the 37 | classification of the object. The second property is *datapoints* which is an array of the values of the known features 38 | of that object. 39 | 40 | ```javascript 41 | var dataset = [ 42 | { "category" : "A", "datapoints" : ["x", "y", "z"]}, 43 | { "category" : "B", "datapoints" : ["s", "t", "u"]} 44 | ]; 45 | ``` 46 | 47 | #### kNN 48 | 49 | Suppose we have a mystery duck (just go with me on this), and we want to know whether it is a member of the Mallard family or the Muscovy family. Now, 50 | we know the length, wingspan, weight and type of a bunch of ducks, so we load all the data into a dataset as follows: 51 | 52 | *Note: This data is entirely fictional, so please do not use it in any live duck classification schenarios.* 53 | 54 | ```javascript 55 | var continuousDataset = [ 56 | { "category" : "Mallard", "datapoints" : [55, 87, 0.953952]}, 57 | { "category" : "Mallard", "datapoints" : [51, 81, 0.917523]}, 58 | { "category" : "Muscovy", "datapoints" : [72, 140, 1.126542]}, 59 | { "category" : "Mallard", "datapoints" : [60, 90, 1.351631]}, 60 | { "category" : "Mallard", "datapoints" : [67, 95, 1.512341]}, 61 | { "category" : "Muscovy", "datapoints" : [66, 150, 2.354634]}, 62 | { "category" : "Muscovy", "datapoints" : [81, 145, 4.344334]}, 63 | { "category" : "Muscovy", "datapoints" : [84, 137, 3.123083]}, 64 | { "category" : "Mallard", "datapoints" : [57, 89, 0.812545]}, 65 | { "category" : "Muscovy", "datapoints" : [65, 150, 2.124541]}, 66 | { "category" : "Mallard", "datapoints" : [61, 90, 1.0065432]}, 67 | { "category" : "Muscovy", "datapoints" : [70, 139, 1.7846383]} 68 | ]; 69 | ``` 70 | 71 | Now we measure our mystery duck and find that it is 72cm long, has a wingspan of 105cm and weighs 2.123234kg, so we use the *kNN* function 72 | to find the nearest k matching ducks to these measurements. In this case we set k to 3. 73 | 74 | ```javascript 75 | mallard.kNN.kNN([72, 105, 2.123234], continuousDataset, 3) 76 | 77 | ==> [ { category: 'Mallard', 78 | distance: 0.2718076518009722 }, 79 | { category: 'Mallard', 80 | distance: 0.47667718685043636 }, 81 | { category: 'Muscovy', 82 | distance: 0.505638657863494 } ] 83 | ``` 84 | 85 | The *kNN* function returns the 3 nearest ducks, which are 2 Mallards and 1 Muscovy, so we can take the majority vote and say that our duck is 86 | a Mallard. If we just want the classification and don't really want to see the neighbours, we can just use the *classify* function: 87 | 88 | ```javascript 89 | mallard.kNN.classify([72, 105, 2.123234], continuousDataset, 3) 90 | 91 | ==> "Mallard" 92 | ``` 93 | 94 | #### ID3 Decision Tree 95 | 96 | Suppose we can't catch our mystery duck in order to measure it, but we do at least know what it looks like. If we know what typical Mallard and Muscovy 97 | ducks look like we can use an ID3 decision tree to work through the possibilities. We load the data for the decision tree the same way as we load 98 | the data for the kNN algorithm: 99 | 100 | ```javascript 101 | var discreteDataset = [ 102 | {"category" : "Mallard", "datapoints" : ["green", "male", "adult"]}, 103 | {"category" : "Mallard", "datapoints" : ["brown", "female", "adult"]}, 104 | {"category" : "Mallard", "datapoints" : ["green", "male", "child"]}, 105 | {"category" : "Mallard", "datapoints" : ["brown", "female", "child"]}, 106 | {"category" : "Muscovy", "datapoints" : ["black", "male", "adult"]}, 107 | {"category" : "Muscovy", "datapoints" : ["black", "female", "adult"]}, 108 | {"category" : "Muscovy", "datapoints" : ["white", "male", "child"]}, 109 | {"category" : "Muscovy", "datapoints" : ["white", "female", "child"]} 110 | ]; 111 | ``` 112 | 113 | In addition we also specify what each column represents. This is used in building the tree, and can be used to visually verify the tree is 114 | structured correctly. 115 | 116 | ```javascript 117 | var featureNames = ["head colour", "sex", "age"]; 118 | 119 | ``` 120 | 121 | Before we can classify our duck using ID3, we first need to build a decision tree from our sample data using the *createTree* function: 122 | 123 | ```javascript 124 | var tree = mallard.ID3.createTree(discreteDataset, featureNames); 125 | ``` 126 | 127 | We can then use that generated tree to examine our mystery duck. If we know our mystery duck is a male child with a white head then we can 128 | use the *classify* function as follows: 129 | 130 | ```javascript 131 | mallard.ID3.classify(["white", "male", "child"], tree, featureNames) 132 | 133 | ==> "Muscovy" 134 | ``` 135 | 136 | #### Naive Bayes 137 | 138 | Suppose we have a set of descriptions for our two different types of duck, and then a description of a mystery duck, and we want to 139 | find out what our mystery duck is. Naive Bayes excels at classifying items based on the probability that certain tokens (words) 140 | are associated with a category. 141 | 142 | To use Naive Bayes, first we need to train the algorithm using our set of known descriptions. *tokenize* is a 143 | convenience function which will convert a sentence into a set of tokens to be used as the datapoints in the algorithm. 144 | *train* will take our training dataset and calculate the probabilities that each token appears in each category. 145 | 146 | ```javascript 147 | var ducks = [ 148 | {"category" : "Mallard", "datapoints" : mallard.bayes.tokenize('The male birds have a glossy green head and are grey on wings and belly, while the females have mainly brown-speckled plumage.')}, 149 | {"category" : "Mallard", "datapoints" : mallard.bayes.tokenize('The male has a dark green head, a yellow bill, is mainly purple-brown on the breast and grey on the body.')}, 150 | {"category" : "Mallard", "datapoints" : mallard.bayes.tokenize('The female is mainly brown with an orange bill')}, 151 | {"category" : "Mallard", "datapoints" : mallard.bayes.tokenize('The males sport a glossy green head and white neck ring, and what the females lack in colour they make up for in noise.')}, 152 | {"category" : "Mallard", "datapoints" : mallard.bayes.tokenize('The green head and yellow bill of the mallard duck is a familiar sight to many people living in the Northern hemisphere.')}, 153 | {"category" : "Muscovy", "datapoints" : mallard.bayes.tokenize('The bird is predominantly black and white, with the back feathers being iridescent and glossy in males, while the females are more drab.')}, 154 | {"category" : "Muscovy", "datapoints" : mallard.bayes.tokenize('Wild Muscovy ducks are all black with white patches on the upper and under wing.')}, 155 | {"category" : "Muscovy", "datapoints" : mallard.bayes.tokenize('They may be black, blue, chocolate, lavender or white.')}, 156 | {"category" : "Muscovy", "datapoints" : mallard.bayes.tokenize('The original, wild type, coloration is black and white, but domestication has produced many more colors, including white, black, chocolate, and blue')}, 157 | ]; 158 | 159 | var trainedDataset = mallard.bayes.train(ducks); 160 | ``` 161 | 162 | After training the algorithm, we just need to call the *classify* function with our unknown sentence to get a prediction for 163 | what it is. 164 | 165 | ```javascript 166 | mallard.bayes.classify('A small duck with a green head', trainedDataset); 167 | 168 | ==> { category: 'Mallard', probability: -16.872226918131254 } 169 | ``` 170 | 171 | So using Naive Bayes, our mystery duck is predicted to be a mallard, which is correct. 172 | -------------------------------------------------------------------------------- /test/largeContinuousDataset.json: -------------------------------------------------------------------------------- 1 | [ 2 | { "category" : "A", "datapoints" : [40920,8.326976,0.953952]}, 3 | { "category" : "B", "datapoints" : [14488,7.153469,1.673904]}, 4 | { "category" : "C", "datapoints" : [26052,1.441871,0.805124]}, 5 | { "category" : "C", "datapoints" : [75136,13.147394,0.428964]}, 6 | { "category" : "C", "datapoints" : [38344,1.669788,0.134296]}, 7 | { "category" : "C", "datapoints" : [72993,10.14174,1.032955]}, 8 | { "category" : "A", "datapoints" : [35948,6.830792,1.213192]}, 9 | { "category" : "A", "datapoints" : [42666,13.276369,0.54388]}, 10 | { "category" : "C", "datapoints" : [67497,8.631577,0.749278]}, 11 | { "category" : "A", "datapoints" : [35483,12.273169,1.508053]}, 12 | { "category" : "C", "datapoints" : [50242,3.723498,0.831917]}, 13 | { "category" : "C", "datapoints" : [63275,8.385879,1.669485]}, 14 | { "category" : "B", "datapoints" : [5569,4.875435,0.728658]}, 15 | { "category" : "C", "datapoints" : [51052,4.680098,0.625224]}, 16 | { "category" : "C", "datapoints" : [77372,15.29957,0.331351]}, 17 | { "category" : "C", "datapoints" : [43673,1.889461,0.191283]}, 18 | { "category" : "C", "datapoints" : [61364,7.516754,1.269164]}, 19 | { "category" : "C", "datapoints" : [69673,14.239195,0.261333]}, 20 | { "category" : "B", "datapoints" : [15669,0,1.250185]}, 21 | { "category" : "A", "datapoints" : [28488,10.528555,1.304844]}, 22 | { "category" : "B", "datapoints" : [6487,3.540265,0.822483]}, 23 | { "category" : "C", "datapoints" : [37708,2.991551,0.83392]}, 24 | { "category" : "B", "datapoints" : [22620,5.297865,0.638306]}, 25 | { "category" : "A", "datapoints" : [28782,6.593803,0.187108]}, 26 | { "category" : "B", "datapoints" : [19739,2.81676,1.686209]}, 27 | { "category" : "A", "datapoints" : [36788,12.458258,0.649617]}, 28 | { "category" : "B", "datapoints" : [5741,0,1.656418]}, 29 | { "category" : "A", "datapoints" : [28567,9.968648,0.731232]}, 30 | { "category" : "B", "datapoints" : [6808,1.364838,0.640103]}, 31 | { "category" : "C", "datapoints" : [41611,0.230453,1.151996]}, 32 | { "category" : "A", "datapoints" : [36661,11.865402,0.88281]}, 33 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