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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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.
*/
// scalastyle:off println
package org.apache.spark.examples.mllib
import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.classification.LogisticRegressionModel
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.optimization.{LBFGS, LogisticGradient, SquaredL2Updater}
import org.apache.spark.mllib.util.MLUtils
// $example off$
object LBFGSExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("LBFGSExample")
val sc = new SparkContext(conf)
// $example on$
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
val numFeatures = data.take(1)(0).features.size
// Split data into training (60%) and test (40%).
val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
// Append 1 into the training data as intercept.
val training = splits(0).map(x => (x.label, MLUtils.appendBias(x.features))).cache()
val test = splits(1)
// Run training algorithm to build the model
val numCorrections = 10
val convergenceTol = 1e-4
val maxNumIterations = 20
val regParam = 0.1
val initialWeightsWithIntercept = Vectors.dense(new Array[Double](numFeatures + 1))
val (weightsWithIntercept, loss) = LBFGS.runLBFGS(
training,
new LogisticGradient(),
new SquaredL2Updater(),
numCorrections,
convergenceTol,
maxNumIterations,
regParam,
initialWeightsWithIntercept)
val model = new LogisticRegressionModel(
Vectors.dense(weightsWithIntercept.toArray.slice(0, weightsWithIntercept.size - 1)),
weightsWithIntercept(weightsWithIntercept.size - 1))
// Clear the default threshold.
model.clearThreshold()
// Compute raw scores on the test set.
val scoreAndLabels = test.map { point =>
val score = model.predict(point.features)
(score, point.label)
}
// Get evaluation metrics.
val metrics = new BinaryClassificationMetrics(scoreAndLabels)
val auROC = metrics.areaUnderROC()
println("Loss of each step in training process")
loss.foreach(println)
println("Area under ROC = " + auROC)
// $example off$
}
}
// scalastyle:on println
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