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 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.classification.LogisticRegressionWithLBFGS;
  import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics;
  import org.apache.spark.mllib.regression.LabeledPoint;
  import org.apache.spark.mllib.util.MLUtils;
  // $example off$
  import org.apache.spark.SparkConf;
  import org.apache.spark.SparkContext;
  
  public class JavaBinaryClassificationMetricsExample {
    public static void main(String[] args) {
      SparkConf conf = new SparkConf().setAppName("Java Binary Classification Metrics Example");
      SparkContext sc = new SparkContext(conf);
      // $example on$
      String path = "data/mllib/sample_binary_classification_data.txt";
      JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
  
      // Split initial RDD into two... [60% training data, 40% testing data].
      JavaRDD<LabeledPoint>[] splits =
        data.randomSplit(new double[]{0.6, 0.4}, 11L);
      JavaRDD<LabeledPoint> training = splits[0].cache();
      JavaRDD<LabeledPoint> test = splits[1];
  
      // Run training algorithm to build the model.
      final LogisticRegressionModel model = new LogisticRegressionWithLBFGS()
        .setNumClasses(2)
        .run(training.rdd());
  
      // Clear the prediction threshold so the model will return probabilities
      model.clearThreshold();
  
      // Compute raw scores on the test set.
      JavaRDD<Tuple2<Object, Object>> predictionAndLabels = test.map(
        new Function<LabeledPoint, Tuple2<Object, Object>>() {
          @Override
          public Tuple2<Object, Object> call(LabeledPoint p) {
            Double prediction = model.predict(p.features());
            return new Tuple2<Object, Object>(prediction, p.label());
          }
        }
      );
  
      // Get evaluation metrics.
      BinaryClassificationMetrics metrics =
        new BinaryClassificationMetrics(predictionAndLabels.rdd());
  
      // Precision by threshold
      JavaRDD<Tuple2<Object, Object>> precision = metrics.precisionByThreshold().toJavaRDD();
      System.out.println("Precision by threshold: " + precision.collect());
  
      // Recall by threshold
      JavaRDD<Tuple2<Object, Object>> recall = metrics.recallByThreshold().toJavaRDD();
      System.out.println("Recall by threshold: " + recall.collect());
  
      // F Score by threshold
      JavaRDD<Tuple2<Object, Object>> f1Score = metrics.fMeasureByThreshold().toJavaRDD();
      System.out.println("F1 Score by threshold: " + f1Score.collect());
  
      JavaRDD<Tuple2<Object, Object>> f2Score = metrics.fMeasureByThreshold(2.0).toJavaRDD();
      System.out.println("F2 Score by threshold: " + f2Score.collect());
  
      // Precision-recall curve
      JavaRDD<Tuple2<Object, Object>> prc = metrics.pr().toJavaRDD();
      System.out.println("Precision-recall curve: " + prc.collect());
  
      // Thresholds
      JavaRDD<Double> thresholds = precision.map(
        new Function<Tuple2<Object, Object>, Double>() {
          @Override
          public Double call(Tuple2<Object, Object> t) {
            return new Double(t._1().toString());
          }
        }
      );
  
      // ROC Curve
      JavaRDD<Tuple2<Object, Object>> roc = metrics.roc().toJavaRDD();
      System.out.println("ROC curve: " + roc.collect());
  
      // AUPRC
      System.out.println("Area under precision-recall curve = " + metrics.areaUnderPR());
  
      // AUROC
      System.out.println("Area under ROC = " + metrics.areaUnderROC());
  
      // Save and load model
      model.save(sc, "target/tmp/LogisticRegressionModel");
      LogisticRegressionModel.load(sc, "target/tmp/LogisticRegressionModel");
      // $example off$
    }
  }
  