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 java.util.List;
  
  import scala.Tuple2;
  
  import org.apache.spark.api.java.*;
  import org.apache.spark.mllib.evaluation.MultilabelMetrics;
  import org.apache.spark.SparkConf;
  // $example off$
  
  public class JavaMultiLabelClassificationMetricsExample {
    public static void main(String[] args) {
      SparkConf conf = new SparkConf().setAppName("Multilabel Classification Metrics Example");
      JavaSparkContext sc = new JavaSparkContext(conf);
      // $example on$
      List<Tuple2<double[], double[]>> data = Arrays.asList(
        new Tuple2<>(new double[]{0.0, 1.0}, new double[]{0.0, 2.0}),
        new Tuple2<>(new double[]{0.0, 2.0}, new double[]{0.0, 1.0}),
        new Tuple2<>(new double[]{}, new double[]{0.0}),
        new Tuple2<>(new double[]{2.0}, new double[]{2.0}),
        new Tuple2<>(new double[]{2.0, 0.0}, new double[]{2.0, 0.0}),
        new Tuple2<>(new double[]{0.0, 1.0, 2.0}, new double[]{0.0, 1.0}),
        new Tuple2<>(new double[]{1.0}, new double[]{1.0, 2.0})
      );
      JavaRDD<Tuple2<double[], double[]>> scoreAndLabels = sc.parallelize(data);
  
      // Instantiate metrics object
      MultilabelMetrics metrics = new MultilabelMetrics(scoreAndLabels.rdd());
  
      // Summary stats
      System.out.format("Recall = %f\n", metrics.recall());
      System.out.format("Precision = %f\n", metrics.precision());
      System.out.format("F1 measure = %f\n", metrics.f1Measure());
      System.out.format("Accuracy = %f\n", metrics.accuracy());
  
      // Stats by labels
      for (int i = 0; i < metrics.labels().length - 1; i++) {
        System.out.format("Class %1.1f precision = %f\n", metrics.labels()[i], metrics.precision(
          metrics.labels()[i]));
        System.out.format("Class %1.1f recall = %f\n", metrics.labels()[i], metrics.recall(
          metrics.labels()[i]));
        System.out.format("Class %1.1f F1 score = %f\n", metrics.labels()[i], metrics.f1Measure(
          metrics.labels()[i]));
      }
  
      // Micro stats
      System.out.format("Micro recall = %f\n", metrics.microRecall());
      System.out.format("Micro precision = %f\n", metrics.microPrecision());
      System.out.format("Micro F1 measure = %f\n", metrics.microF1Measure());
  
      // Hamming loss
      System.out.format("Hamming loss = %f\n", metrics.hammingLoss());
  
      // Subset accuracy
      System.out.format("Subset accuracy = %f\n", metrics.subsetAccuracy());
      // $example off$
  
      sc.stop();
    }
  }
  