/*
* Copyright 2016 IBM Corp.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
module.exports = function(kernelP) {
return (function() {
var Utils = require('../../utils.js');
var ProbabilisticClassificationModel = require('./ProbabilisticClassificationModel')();
var gKernelP = kernelP;
/**
* @classdesc
* [Random Forest]{@link http://en.wikipedia.org/wiki/Random_forest} model for classification.
* It supports both binary and multiclass labels, as well as both continuous and categorical
* features.
* @class
* @extends module:eclairjs/ml/classification.ProbabilisticClassificationModel
* @memberof module:eclairjs/ml/classification
*/
function RandomForestClassificationModel() {
Utils.handleConstructor(this, arguments, gKernelP);
}
RandomForestClassificationModel.prototype = Object.create(ProbabilisticClassificationModel.prototype);
RandomForestClassificationModel.prototype.constructor = RandomForestClassificationModel;
/**
* @returns {module:eclairjs/ml/tree.DecisionTreeModel[]}
*/
RandomForestClassificationModel.prototype.trees = function() {
var DecisionTreeModel = require('../../mllib/tree/model/DecisionTreeModel')();
var args = {
target: this,
method: 'trees',
args: Utils.wrapArguments(arguments),
returnType: [DecisionTreeModel]
};
return Utils.generate(args);
};
/**
* @returns {Promise.<number[]>}
*/
RandomForestClassificationModel.prototype.treeWeights = function() {
var args = {
target: this,
method: 'treeWeights',
args: Utils.wrapArguments(arguments),
returnType: [Number]
};
return Utils.generate(args);
};
/**
* @param {module:eclairjs/ml/param.ParamMap} extra
* @returns {module:eclairjs/ml/classification.RandomForestClassificationModel}
*/
RandomForestClassificationModel.prototype.copy = function(extra) {
var args = {
target: this,
method: 'copy',
args: Utils.wrapArguments(arguments),
returnType: RandomForestClassificationModel
};
return Utils.generate(args);
};
/**
* @returns {Promise.<string>}
*/
RandomForestClassificationModel.prototype.toString = function() {
var args = {
target: this,
method: 'toString',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
* @returns {Promise.<string>}
*/
RandomForestClassificationModel.prototype.toDebugString = function () {
return this.getJavaObject().toDebugString();
};
/**
* Estimate of the importance of each feature.
* This generalizes the idea of "Gini" importance to other losses, following the explanation of Gini importance
* from "Random Forests" documentation by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.
* This feature importance is calculated as follows: - Average over trees: - importance(feature j) = sum
* (over nodes which split on feature j) of the gain, where gain is scaled by the number of instances passing
* through node - Normalize importances for tree based on total number of training instances used to build tree. -
* Normalize feature importance vector to sum to 1.
*
* @returns {module:eclairjs/mllib/linalg.Vector}
*/
RandomForestClassificationModel.prototype.featureImportances = function () {
var Vector = require('../../mllib/linalg/Vector');
var args = {
target: this,
method: 'featureImportances',
args: Utils.wrapArguments(arguments),
returnType: Vector
};
return Utils.generate(args);
};
/**
* Param for raw prediction (a.k.a. confidence) column name.
* @returns {module:eclairjs/ml/param.Param}
*/
RandomForestClassificationModel.prototype.rawPredictionCol = function () {
var Param = require('../param/Param')();
var args = {
target: this,
method: 'rawPredictionCol',
args: Utils.wrapArguments(arguments),
returnType: Param
};
return Utils.generate(args);
};
/**
* @returns {Promise.<string>}
*/
RandomForestClassificationModel.prototype.getRawPredictionCol = function () {
var args = {
target: this,
method: 'getRawPredictionCol',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
* Param for label column name.
* @returns {module:eclairjs/ml/param.Param}
*/
RandomForestClassificationModel.prototype.labelCol = function () {
var Param = require('../param/Param')();
var args = {
target: this,
method: 'labelCol',
args: Utils.wrapArguments(arguments),
returnType: Param
};
return Utils.generate(args);
};
/**
* @returns {Promise.<string>}
*/
RandomForestClassificationModel.prototype.getLabelCol = function () {
var args = {
target: this,
method: 'getLabelCol',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
* Param for features column name.
* @returns {module:eclairjs/ml/param.Param}
*/
RandomForestClassificationModel.prototype.featuresCol = function () {
var Param = require('../param/Param')();
var args = {
target: this,
method: 'featuresCol',
args: Utils.wrapArguments(arguments),
returnType: Param
};
return Utils.generate(args);
};
/**
* @returns {Promise.<string>}
*/
RandomForestClassificationModel.prototype.getFeaturesCol = function () {
var args = {
target: this,
method: 'getFeaturesCol',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
* Param for prediction column name.
* @returns {module:eclairjs/ml/param.Param}
*/
RandomForestClassificationModel.prototype.predictionCol = function () {
var Param = require('../param/Param')();
var args = {
target: this,
method: 'predictionCol',
args: Utils.wrapArguments(arguments),
returnType: Param
};
return Utils.generate(args);
};
/**
* @returns {Promise.<string>}
*/
RandomForestClassificationModel.prototype.getPredictionCol = function () {
var args = {
target: this,
method: 'getPredictionCol',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
* @returns {MLWriter}
*/
RandomForestClassificationModel.prototype.write = function() {
var MLWriter = require('../../ml/util/MLWriter.js');
var args ={
target: this,
method: 'write',
returnType: MLWriter
};
return Utils.generate(args);
};
//
// static methods
//
/**
* @returns {MLReader}
*/
RandomForestClassificationModel.read = function() {
var MLReader = require('../../ml/util/MLReader.js');
var args ={
target: RandomForestClassificationModel,
method: 'read',
static: true,
returnType: MLReader
};
return Utils.generate(args);
};
/**
* @param {string} path
* @returns {RandomForestClassificationModel}
*/
RandomForestClassificationModel.load = function(path) {
var RandomForestClassificationModel = require('../../ml/classification/RandomForestClassificationModel.js');
var args ={
target: RandomForestClassificationModel,
method: 'load',
args: Utils.wrapArguments(arguments),
static: true,
returnType: RandomForestClassificationModel
};
return Utils.generate(args);
};
RandomForestClassificationModel.moduleLocation = '/ml/classification/RandomForestClassificationModel';
return RandomForestClassificationModel;
})();
};