/*
* Copyright (c) AXA Shared Services 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('brain.js');
/**
* Classifier using Brain.js Neural Network
*/
class BinaryNeuralNetworkClassifier {
/**
* Constructor of the class.
* @param {Object} settings Settings for the instance.
*/
constructor(settings) {
this.settings = 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);
Iif (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 Eif (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] = [];
}
datasetMap[label].push({
input: item.input,
output: [item.output === label ? 1 : 0],
});
});
});
const promises = [];
Object.keys(datasetMap).forEach(label => {
promises.push(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 = [];
Object.keys(this.classifierMap).forEach(label => {
const score = this.classifierMap[label].run(sample);
scores.push({ label, value: score[0] });
});
return scores.sort((x, y) => y.value - x.value);
}
}
module.exports = BinaryNeuralNetworkClassifier;
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