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;
  
  import org.apache.spark.SparkConf;
  import org.apache.spark.SparkContext;
  
  // $example on$
  import scala.Tuple2;
  
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.api.java.function.Function;
  import org.apache.spark.mllib.classification.SVMModel;
  import org.apache.spark.mllib.classification.SVMWithSGD;
  import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics;
  import org.apache.spark.mllib.regression.LabeledPoint;
  import org.apache.spark.mllib.util.MLUtils;
  // $example off$
  
  /**
   * Example for SVMWithSGD.
   */
  public class JavaSVMWithSGDExample {
    public static void main(String[] args) {
      SparkConf conf = new SparkConf().setAppName("JavaSVMWithSGDExample");
      SparkContext sc = new SparkContext(conf);
      // $example on$
      String path = "data/mllib/sample_libsvm_data.txt";
      JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
  
      // Split initial RDD into two... [60% training data, 40% testing data].
      JavaRDD<LabeledPoint> training = data.sample(false, 0.6, 11L);
      training.cache();
      JavaRDD<LabeledPoint> test = data.subtract(training);
  
      // Run training algorithm to build the model.
      int numIterations = 100;
      final SVMModel model = SVMWithSGD.train(training.rdd(), numIterations);
  
      // 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(JavaRDD.toRDD(scoreAndLabels));
      double auROC = metrics.areaUnderROC();
  
      System.out.println("Area under ROC = " + auROC);
  
      // Save and load model
      model.save(sc, "target/tmp/javaSVMWithSGDModel");
      SVMModel sameModel = SVMModel.load(sc, "target/tmp/javaSVMWithSGDModel");
      // $example off$
  
      sc.stop();
    }
  }
  