/**
* Created by billreed on 4/11/16.
*//*
* 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 JavaWrapper = require(EclairJS_Globals.NAMESPACE + '/JavaWrapper');
var Logger = require(EclairJS_Globals.NAMESPACE + '/Logger');
var Utils = require(EclairJS_Globals.NAMESPACE + '/Utils');
/**
* Represents a gradient boosted trees model.
*
* @param algo algorithm for the ensemble model, either Classification or Regression
* @param trees tree ensembles
* @param treeWeights tree ensemble weights
* @classdesc
*/
/**
* @param {Algo} algo
* @param {module:eclairjs/mllib/tree/model.DecisionTreeModel[]} trees
* @param {number[]} treeWeights
* @returns {??}
* @class
* @memberof module:eclairjs/mllib/tree/model
*/
var GradientBoostedTreesModel = function (algo, trees, treeWeights) {
this.logger = Logger.getLogger("GradientBoostedTreesModel_js");
var jvmObject;
if (arguments[0] instanceof org.apache.spark.mllib.tree.model.GradientBoostedTreesModel) {
jvmObject = arguments[0];
} else {
jvmObject = new org.apache.spark.mllib.tree.model.GradientBoostedTreesModel(Utils.unwrapObject(arguments[0]),
Utils.unwrapObject(trees),
treeWeights
);
}
JavaWrapper.call(this, jvmObject);
};
GradientBoostedTreesModel.prototype = Object.create(JavaWrapper.prototype);
GradientBoostedTreesModel.prototype.constructor = GradientBoostedTreesModel;
/**
* @param {module:eclairjs.SparkContext} sc Spark context used to save model data.
* @param {string} path Path specifying the directory in which to save this model.
* If the directory already exists, this method throws an exception.
*/
GradientBoostedTreesModel.prototype.save = function (sc, path) {
var sc_uw = Utils.unwrapObject(sc);
this.getJavaObject().save(sc_uw.sc(), path);
};
/**
* Method to compute error or loss for every iteration of gradient boosting.
* @param {module:eclairjs.RDD} data RDD of {@link LabeledPoint}
* @param {module:eclairjs/mllib/tree/loss.Loss} loss evaluation metric.
* containing the first i+1 trees
* @returns {number[]} an array with index i having the losses or errors for the ensemble
*/
GradientBoostedTreesModel.prototype.evaluateEachIteration = function (data, loss) {
throw "not implemented by ElairJS";
// var data_uw = Utils.unwrapObject(data);
// var loss_uw = Utils.unwrapObject(loss);
// return this.getJavaObject().evaluateEachIteration(data_uw,loss_uw);
};
GradientBoostedTreesModel.prototype.predict = function (features) {
var features_uw = Utils.unwrapObject(features);
return this.getJavaObject().predict(features_uw);
};
/**
* Print the full model to a string.
* @returns {string}
*/
GradientBoostedTreesModel.prototype.toDebugString = function () {
return this.getJavaObject().toDebugString();
};
/**
* Print a summary of the model.
* @returns {string}
*/
GradientBoostedTreesModel.prototype.toString = function () {
return this.getJavaObject().toString();
};
//
// static methods
//
/**
* @param {module:eclairjs.SparkContext} sc Spark context used for loading model files.
* @param {string} path Path specifying the directory to which the model was saved.
* @returns {module:eclairjs/mllib/tree/model.GradientBoostedTreesModel} Model instance
*/
GradientBoostedTreesModel.load = function (sc, path) {
var sc_uw = Utils.unwrapObject(sc);
var javaObject = org.apache.spark.mllib.tree.model.GradientBoostedTreesModel.load(sc_uw.sc(), path);
return new GradientBoostedTreesModel(javaObject);
};
module.exports = GradientBoostedTreesModel;
})();