DecisionTreeClassifier.js

import { Matrix } from './indexMatrix.js';

import Tree from './TreeNode.js';

const defaultOptions = {
  gainFunction: 'gini',
  splitFunction: 'mean',
  minNumSamples: 3,
  maxDepth: Infinity,
  gainThreshold: 0.01,
};

export class DecisionTreeClassifier {
  /**
   * Create new Decision Tree Classifier with CART implementation with the given options
   * @param {object} options
   * @param {string} [options.gainFunction="gini"] - gain function to get the best split, "gini" the only one supported.
   * @param {string} [options.splitFunction="mean"] - given two integers from a split feature, get the value to split, "mean" the only one supported.
   * @param {number} [options.minNumSamples=3] - minimum number of samples to create a leaf node to decide a class.
   * @param {number} [options.maxDepth=Infinity] - Max depth of the tree.
   * @param {object} model - for load purposes.
   * @constructor
   */
  constructor(options, model) {
    if (options === true) {
      this.options = model.options;
      this.root = new Tree(model.options);
      this.root.setNodeParameters(model.root);
    } else {
      this.options = Object.assign({}, defaultOptions, options);
      this.options.kind = 'classifier';
    }
  }

  /**
   * Train the decision tree with the given training set and labels.
   * @param {Matrix|MatrixTransposeView|Array} trainingSet
   * @param {Array} trainingLabels
   */
  train(trainingSet, trainingLabels) {
    this.root = new Tree(this.options);
    trainingSet = Matrix.checkMatrix(trainingSet);
    this.root.train(trainingSet, trainingLabels, 0, null);
  }

  /**
   * Predicts the output given the matrix to predict.
   * @param {Matrix|MatrixTransposeView|Array} toPredict
   * @return {Array} predictions
   */
  predict(toPredict) {
    toPredict = Matrix.checkMatrix(toPredict);
    let predictions = new Array(toPredict.rows);

    for (let i = 0; i < toPredict.rows; ++i) {
      predictions[i] = this.root
        .classify(toPredict.getRow(i))
        .maxRowIndex(0)[1];
    }

    return predictions;
  }

  /**
   * Export the current model to JSON.
   * @return {object} - Current model.
   */
  toJSON() {
    return {
      options: this.options,
      root: this.root,
      name: 'DTClassifier',
    };
  }

  /**
   * Load a Decision tree classifier with the given model.
   * @param {object} model
   * @return {DecisionTreeClassifier}
   */
  static load(model) {
    if (model.name !== 'DTClassifier') {
      throw new RangeError(`Invalid model: ${model.name}`);
    }

    return new DecisionTreeClassifier(true, model);
  }
}