/*
* 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');
var LogisticRegressionModel = require(EclairJS_Globals.NAMESPACE + '/mllib/classification/LogisticRegressionModel');
/**
* Train a classification model for Binary Logistic Regression
* using Stochastic Gradient Descent. By default L2 regularization is used,
* which can be changed via {@link optimizer}.
* NOTE: Labels used in Logistic Regression should be {0, 1, ..., k - 1}
* for k classes multi-label classification problem.
* Using {@link module:eclairjs/mllib/regression.LogisticRegressionWithLBFGS} is recommended over this.
*
* @classdesc
*/
/**
* Construct a LogisticRegression object with default parameters: {stepSize: 1.0,
* numIterations: 100, regParm: 0.01, miniBatchFraction: 1.0}.
* @class
* @memberof module:eclairjs/mllib/classification
*/
var LogisticRegressionWithSGD = function (jvmObject) {
this.logger = Logger.getLogger("LogisticRegressionWithSGD_js");
JavaWrapper.call(this, jvmObject);
};
LogisticRegressionWithSGD.prototype = Object.create(JavaWrapper.prototype);
LogisticRegressionWithSGD.prototype.constructor = LogisticRegressionWithSGD;
/**
* Train a logistic regression model given an RDD of (label, features) pairs. We run a fixed
* number of iterations of gradient descent using the specified step size. Each iteration uses
* `miniBatchFraction` fraction of the data to calculate the gradient. The weights used in
* gradient descent are initialized using the initial weights provided.
* NOTE: Labels used in Logistic Regression should be {0, 1}
*
* @param {module:eclairjs.RDD} input RDD of (label, array of features) pairs.
* @param {number} numIterations Number of iterations of gradient descent to run.
* @param {number} [stepSize] step size to be used for each iteration of gradient descent, defaults to 1.0.
* @param {number} [miniBatchFraction] fraction of data to be used per iteration.
* @param {module:eclairjs/mllib/linalg.Vector} [initialWeights] initial set of weights to be used. Array should be equal in size to
* the number of features in the data.
* @returns {module:eclairjs/mllib/classification.LogisticRegressionModel}
*/
LogisticRegressionWithSGD.train = function (input, numIterations, stepSize, miniBatchFraction, initialWeights) {
var lrdd = input.getJavaObject().rdd();
//var lrdd = org.apache.spark.api.java.JavaRDD.toRDD(jo);
var model;
if (arguments.length === 5) {
model = org.apache.spark.mllib.classification.LogisticRegressionWithSGD.train(lrdd, numIterations, stepSize, miniBatchFraction, Utils.unwrapObject(initialWeights));
} else if (arguments.length === 4) {
model = org.apache.spark.mllib.classification.LogisticRegressionWithSGD.train(lrdd, numIterations, stepSize, miniBatchFraction);
} else if (arguments.length === 3) {
model = org.apache.spark.mllib.classification.LogisticRegressionWithSGD.train(lrdd, numIterations, stepSize);
} else if (arguments.length === 2) {
model = org.apache.spark.mllib.classification.LogisticRegressionWithSGD.train(lrdd, numIterations);
} else {
throw "LogisticRegressionWithSGD.train invalid arguments"
}
return new LogisticRegressionModel(model);
};
module.exports = LogisticRegressionWithSGD;
})();