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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | 42x 42x 41x 41x 40x 41x 41x 41x 41x 115x 40x 40x 6x 3x 3x 34x 31x 40x 15x 110x 15x 110x 273x 35x 273x 273x 273x 15x 35x 15x 6x 6x 1x 3x 5x 15x 15x 17x 6x 6x 6x 1x 3x 5x 10x 10x 7x 13x 2x 2x 2x 2x 2x 5x 2x 2x 2x 2x 2x 5x 5x 42x 42x | /* * Copyright (c) AXA Group Operations Spain S.A. * * 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. */ const NeuralNetwork = require('./neural-network'); const Classifier = require('./classifier'); /** * Classifier using Binary Relevance Neural Network */ class BinaryNeuralNetworkClassifier extends Classifier { /** * Constructor of the class. * @param {Object} settings Settings for the instance. */ constructor(settings) { super(settings); if (!this.settings.config) { this.settings.config = { activation: 'leaky-relu', hiddenLayers: [], learningRate: 0.1, errorThresh: 0.0005, }; } this.totalTimeout = this.settings.totalTimeout || 2 * 60 * 1000; this.labelTimeout = this.settings.labelTimeout; this.labels = []; this.classifierMap = {}; } /** * If a trainer does not exists for a label, create it. * @param {*} label */ addTrainer(label) { if (!this.classifierMap[label]) { this.labels.push(label); if (this.labelTimeout && this.labelTimeout > 0) { if (this.totalTimeout && this.totalTimeout > 0) { const partialTimeout = this.totalTimeout / this.labels.length; this.settings.config.timeout = Math.min( this.totalTimeout, partialTimeout ); } } else if (this.totalTimeout && this.totalTimeout > 0) { this.settings.config.timeout = this.totalTimeout / this.labels.length; } this.classifierMap[label] = new NeuralNetwork(this.settings.config); } } /** * Train the classifier given a dataset. * @param {Object} dataset Dataset with features and outputs. */ async trainBatch(dataset) { const datasetMap = {}; dataset.forEach(item => this.addTrainer(item.output)); dataset.forEach(item => { this.labels.forEach(label => { if (!datasetMap[label]) { datasetMap[label] = []; } const obj = { input: item.input, output: {}, }; obj.output[item.output === label ? 'true' : 'false'] = 1; datasetMap[label].push(obj); }); }); const promises = Object.keys(datasetMap).map(label => this.classifierMap[label].train(datasetMap[label]) ); return Promise.all(promises); } /** * Given a sample, return the classification. * @param {Object} sample Input sample. * @returns {Object} Classification output. */ classify(sample) { const scores = []; if (Object.keys(sample).length === 0) { this.labels.forEach(label => { scores.push({ label, value: 0.5 }); }); } else { Object.keys(this.classifierMap).forEach(label => { const score = this.classifierMap[label].run(sample); scores.push({ label, value: score.true }); }); } scores.sort((x, y) => y.value - x.value); return scores; } /** * Given an observation and an array for inserting the results, * it calculates the score of the observation for each of the classifications * and fills the array with the result objects. * @param {Object} srcObservation Source observation. * @param {Object[]} classifications Array of classifications. * @memberof BinaryNeuralNetworkClassifier */ classifyObservation(observation, classifications) { const scores = []; if (Object.keys(observation).length === 0) { this.labels.forEach(label => { scores.push({ label, value: 0.5 }); }); } else { Object.keys(this.classifierMap).forEach(label => { const score = this.classifierMap[label].run(observation); scores.push({ label, value: score.true }); }); } const sortedScores = scores.sort((x, y) => y.value - x.value); sortedScores.forEach(x => classifications.push(x)); } /** * Clone the object properties. * @returns {Object} Cloned object. */ toObj() { const result = {}; result.className = this.constructor.name; result.settings = this.settings; result.classifierMap = {}; Object.keys(this.classifierMap).forEach(key => { result.classifierMap[key] = this.classifierMap[key].toJSON(); }); result.labels = this.labels; return result; } /** * Fills the instance from another object. * @param {Object} obj Source object. */ fromObj(obj) { this.settings = obj.settings; this.labels = obj.labels; Object.keys(obj.classifierMap).forEach(label => { this.addTrainer(label); this.classifierMap[label].fromJSON(obj.classifierMap[label]); }); } } Classifier.classes.BinaryNeuralNetworkClassifier = BinaryNeuralNetworkClassifier; module.exports = BinaryNeuralNetworkClassifier; |