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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.
*/
package org.apache.spark.examples.mllib;
// $example on$
import java.util.Arrays;
import scala.Tuple2;
import org.apache.spark.api.java.*;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.mllib.classification.LogisticRegressionModel;
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.optimization.*;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;
// $example off$
public class JavaLBFGSExample {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName("L-BFGS Example");
SparkContext sc = new SparkContext(conf);
// $example on$
String path = "data/mllib/sample_libsvm_data.txt";
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
int numFeatures = data.take(1).get(0).features().size();
// Split initial RDD into two... [60% training data, 40% testing data].
JavaRDD<LabeledPoint> trainingInit = data.sample(false, 0.6, 11L);
JavaRDD<LabeledPoint> test = data.subtract(trainingInit);
// Append 1 into the training data as intercept.
JavaRDD<Tuple2<Object, Vector>> training = data.map(
new Function<LabeledPoint, Tuple2<Object, Vector>>() {
public Tuple2<Object, Vector> call(LabeledPoint p) {
return new Tuple2<Object, Vector>(p.label(), MLUtils.appendBias(p.features()));
}
});
training.cache();
// Run training algorithm to build the model.
int numCorrections = 10;
double convergenceTol = 1e-4;
int maxNumIterations = 20;
double regParam = 0.1;
Vector initialWeightsWithIntercept = Vectors.dense(new double[numFeatures + 1]);
Tuple2<Vector, double[]> result = LBFGS.runLBFGS(
training.rdd(),
new LogisticGradient(),
new SquaredL2Updater(),
numCorrections,
convergenceTol,
maxNumIterations,
regParam,
initialWeightsWithIntercept);
Vector weightsWithIntercept = result._1();
double[] loss = result._2();
final LogisticRegressionModel model = new LogisticRegressionModel(
Vectors.dense(Arrays.copyOf(weightsWithIntercept.toArray(), weightsWithIntercept.size() - 1)),
(weightsWithIntercept.toArray())[weightsWithIntercept.size() - 1]);
// Clear the default threshold.
model.clearThreshold();
// Compute raw scores on the test set.
JavaRDD<Tuple2<Object, Object>> scoreAndLabels = test.map(
new Function<LabeledPoint, Tuple2<Object, Object>>() {
public Tuple2<Object, Object> call(LabeledPoint p) {
Double score = model.predict(p.features());
return new Tuple2<Object, Object>(score, p.label());
}
});
// Get evaluation metrics.
BinaryClassificationMetrics metrics =
new BinaryClassificationMetrics(scoreAndLabels.rdd());
double auROC = metrics.areaUnderROC();
System.out.println("Loss of each step in training process");
for (double l : loss)
System.out.println(l);
System.out.println("Area under ROC = " + auROC);
// $example off$
sc.stop();
}
}
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