/*
* 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 Predictor = require('../Predictor')();
var gKernelP = kernelP;
/**
* @classdesc
* [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]]
* learning algorithm for regression.
* It supports both continuous and categorical features.
*
* The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.
*
* Notes on Gradient Boosting vs. TreeBoost:
* - This implementation is for Stochastic Gradient Boosting, not for TreeBoost.
* - Both algorithms learn tree ensembles by minimizing loss functions.
* - TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes
* based on the loss function, whereas the original gradient boosting method does not.
* - When the loss is SquaredError, these methods give the same result, but they could differ
* for other loss functions.
* - We expect to implement TreeBoost in the future:
* [https://issues.apache.org/jira/browse/SPARK-4240]
* @class
* @extends module:eclairjs/ml.Predictor
* @memberof module:eclairjs/ml/regression
* @param {string} [uid]
*/
function GBTRegressor() {
Utils.handleConstructor(this, arguments, gKernelP);
}
GBTRegressor.prototype = Object.create(Predictor.prototype);
GBTRegressor.prototype.constructor = GBTRegressor;
/**
* An immutable unique ID for the object and its derivatives.
* @returns {Promise.<string>}
*/
GBTRegressor.prototype.uid = function () {
var args = {
target: this,
method: 'uid',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
* @param {number} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setMaxDepth = function(value) {
var args = {
target: this,
method: 'setMaxDepth',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressor
};
return Utils.generate(args);
};
/**
* @param {number} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setMaxBins = function(value) {
var args = {
target: this,
method: 'setMaxBins',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressor
};
return Utils.generate(args);
};
/**
* @param {number} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setMinInstancesPerNode = function(value) {
var args = {
target: this,
method: 'setMinInstancesPerNode',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressor
};
return Utils.generate(args);
};
/**
* @param {number} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setMinInfoGain = function(value) {
var args = {
target: this,
method: 'setMinInfoGain',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressor
};
return Utils.generate(args);
};
/**
* @param {number} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setMaxMemoryInMB = function(value) {
var args = {
target: this,
method: 'setMaxMemoryInMB',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressor
};
return Utils.generate(args);
};
/**
* @param {boolean} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setCacheNodeIds = function(value) {
var args = {
target: this,
method: 'setCacheNodeIds',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressor
};
return Utils.generate(args);
};
/**
* @param {number} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setCheckpointInterval = function(value) {
var args = {
target: this,
method: 'setCheckpointInterval',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressor
};
return Utils.generate(args);
};
/**
* The impurity setting is ignored for GBT models.
* Individual trees are built using impurity "Variance."
* @param {string} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setImpurity = function(value) {
var args = {
target: this,
method: 'setImpurity',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressor
};
return Utils.generate(args);
};
/**
* @param {number} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setSubsamplingRate = function(value) {
var args = {
target: this,
method: 'setSubsamplingRate',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressor
};
return Utils.generate(args);
};
/**
* @param {number} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setSeed = function(value) {
var args = {
target: this,
method: 'setSeed',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressor
};
return Utils.generate(args);
};
/**
* @param {number} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setMaxIter = function(value) {
var args = {
target: this,
method: 'setMaxIter',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressor
};
return Utils.generate(args);
};
/**
* @param {number} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setStepSize = function(value) {
var args = {
target: this,
method: 'setStepSize',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressor
};
return Utils.generate(args);
};
/**
* @param {string} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setLossType = function(value) {
var args = {
target: this,
method: 'setLossType',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressor
};
return Utils.generate(args);
};
/**
* @param {module:eclairjs/ml/param.ParamMap} extra
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.copy = function(extra) {
var args = {
target: this,
method: 'copy',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
* @param {module:eclairjs/sql.DataFrame} dataset
* @returns {module:eclairjs/ml/regression.GBTRegressionModel}
*/
GBTRegressor.prototype.fit = function(dataset) {
var AFTSurvivalRegressionModel = require('./GBTRegressionModel.js')();
var args = {
target: this,
method: 'fit',
args: Utils.wrapArguments(arguments),
returnType: GBTRegressionModel
};
return Utils.generate(args);
};
//
// static methods
//
/**
* @param {string} path
* @returns {GBTRegressor}
*/
GBTRegressor.load = function(path) {
var args ={
target: GBTRegressor,
method: 'load',
args: Utils.wrapArguments(arguments),
static: true,
returnType: GBTRegressor
};
return Utils.generate(args);
};
GBTRegressor.moduleLocation = '/ml/regression/GBTRegressor';
return GBTRegressor;
})();
};