content: /*
   * 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);
      // $example off$
    }
  }
  
  