/*
* 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.
*/
(function () {
var Predictor = require(EclairJS_Globals.NAMESPACE + '/ml/Predictor');
var Logger = require(EclairJS_Globals.NAMESPACE + '/Logger');
var Utils = require(EclairJS_Globals.NAMESPACE + '/Utils');
/**
* @classdesc
* [Gradient-Boosted Trees (GBTs)]{@link http://en.wikipedia.org/wiki/Gradient_boosting}
* 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]
*/
var GBTRegressor = function (uid) {
this.logger = Logger.getLogger("ml_regression_GBTRegressor_js");
var jvmObject;
if (uid) {
if (uid instanceof org.apache.spark.ml.regression.GBTRegressor) {
jvmObject = uid;
} else {
jvmObject = new org.apache.spark.ml.regression.GBTRegressor(uid);
}
} else {
jvmObject = new org.apache.spark.ml.regression.GBTRegressor();
}
Predictor.call(this, jvmObject);
};
GBTRegressor.prototype = Object.create(Predictor.prototype);
GBTRegressor.prototype.constructor = GBTRegressor;
/**
* An immutable unique ID for the object and its derivatives.
* @returns {string}
*/
GBTRegressor.prototype.uid = function () {
return this.getJavaObject().uid();
};
/**
* @param {integer} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setMaxDepth = function (value) {
var javaObject = this.getJavaObject().setMaxDepth(value);
return new GBTRegressor(javaObject);
};
/**
* @param {integer} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setMaxBins = function (value) {
var javaObject = this.getJavaObject().setMaxBins(value);
return new GBTRegressor(javaObject);
};
/**
* @param {integer} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setMinInstancesPerNode = function (value) {
var javaObject = this.getJavaObject().setMinInstancesPerNode(value);
return new GBTRegressor(javaObject);
};
/**
* @param {float} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setMinInfoGain = function (value) {
var javaObject = this.getJavaObject().setMinInfoGain(value);
return new GBTRegressor(javaObject);
};
/**
* @param {integer} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setMaxMemoryInMB = function (value) {
var javaObject = this.getJavaObject().setMaxMemoryInMB(value);
return new GBTRegressor(javaObject);
};
/**
* @param {boolean} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setCacheNodeIds = function (value) {
var javaObject = this.getJavaObject().setCacheNodeIds(value);
return new GBTRegressor(javaObject);
};
/**
* @param {integer} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setCheckpointInterval = function (value) {
var javaObject = this.getJavaObject().setCheckpointInterval(value);
return new GBTRegressor(javaObject);
};
/**
* 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 javaObject = this.getJavaObject().setImpurity(value);
return new GBTRegressor(javaObject);
};
/**
* @param {float} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setSubsamplingRate = function (value) {
var javaObject = this.getJavaObject().setSubsamplingRate(value);
return new GBTRegressor(javaObject);
};
/**
* @param {integer} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setSeed = function (value) {
var javaObject = this.getJavaObject().setSeed(value);
return new GBTRegressor(javaObject);
};
/**
* @param {integer} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setMaxIter = function (value) {
var javaObject = this.getJavaObject().setMaxIter(value);
return new GBTRegressor(javaObject);
};
/**
* @param {float} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setStepSize = function (value) {
var javaObject = this.getJavaObject().setStepSize(value);
return new GBTRegressor(javaObject);
};
/**
* @param {string} value
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.setLossType = function (value) {
var javaObject = this.getJavaObject().setLossType(value);
return new GBTRegressor(javaObject);
};
/**
* @returns {string}
*/
GBTRegressor.prototype.getLossType = function () {
return this.getJavaObject().getLossType();
};
/**
* @param {module:eclairjs/ml/param.ParamMap} extra
* @returns {module:eclairjs/ml/regression.GBTRegressor}
*/
GBTRegressor.prototype.copy = function (extra) {
var extra_uw = Utils.unwrapObject(extra);
var javaObject = this.getJavaObject().copy(extra_uw);
return new GBTRegressor(javaObject);
};
/*
Static methods
*/
/**
* @param {string} path
* @returns {module:eclairjs/ml/regression.GBTRegressor}
* @function
* @name module:eclairjs/ml/regression.GBTRegressor#load
* @static
*/
GBTRegressor.load = function(path) {
var javaObject = org.apache.spark.ml.regression.GBTRegressor.load(path);
return new GBTRegressor(javaObject);
};
module.exports = GBTRegressor;
})();