lib/logistic_regression.mjs
import { BaseNeuralNetwork, } from './deep_learning';
/**
* Logistic Regression Classification with Tensorflow
* @class LogisticRegression
* @implements {BaseNeuralNetwork}
*/
export class LogisticRegression extends BaseNeuralNetwork {
/**
* @param {{layers:Array<Object>,compile:Object,fit:Object}} options - neural network configuration and tensorflow model hyperparameters
* @param {{model:Object,tf:Object,}} properties - extra instance properties
*/
constructor(options = {}, properties) {
const config = Object.assign({
layers: [],
type:'simple',
compile: {
loss: 'meanSquaredError',
optimizer: 'rmsprop',
},
fit: {
epochs: 100,
batchSize: 5,
},
}, options);
super(config, properties);
return this;
}
/**
* Adds dense layers to tensorflow classification model
* @override
* @param {Array<Array<number>>} x_matrix - independent variables
* @param {Array<Array<number>>} y_matrix - dependent variables
* @param {Array<Object>} layers - model dense layer parameters
* @param {Array<Array<number>>} x_test - validation data independent variables
* @param {Array<Array<number>>} y_test - validation data dependent variables
*/
generateLayers(x_matrix, y_matrix, layers, x_test, y_test) {
const xShape = this.getInputShape(x_matrix);
const yShape = this.getInputShape(y_matrix);
this.yShape = yShape;
this.xShape = xShape;
const denseLayers = [];
if (layers) {
denseLayers.push(...layers);
} else if (this.settings.type==='class') {
denseLayers.push({ units: 1, inputDim: xShape[ 1 ], activation: 'sigmoid', });
this.settings.compile.loss = 'binaryCrossentropy';
} else if (this.settings.type === 'l1l2') {
const kernelRegularizer = this.tf.regularizers.l1l2({ l1: 0.01, l2: 0.01, });
denseLayers.push({ units: 1, inputDim: xShape[ 1 ], activation: 'sigmoid', kernelRegularizer, });
this.settings.compile.loss = 'binaryCrossentropy';
} else {
denseLayers.push({ units: 1, inputShape: [xShape[1], ], });
}
this.layers = denseLayers;
denseLayers.forEach(layer => {
this.model.add(this.tf.layers.dense(layer));
});
/* istanbul ignore next */
if (x_test && y_test) {
this.settings.fit.validationData = [x_test, y_test];
}
}
}