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.MulticlassMetrics;
  import org.apache.spark.mllib.regression.LabeledPoint;
  import org.apache.spark.mllib.util.MLUtils;
  import org.apache.spark.mllib.linalg.Matrix;
  // $example off$
  import org.apache.spark.SparkConf;
  import org.apache.spark.SparkContext;
  
  public class JavaMulticlassClassificationMetricsExample {
    public static void main(String[] args) {
      SparkConf conf = new SparkConf().setAppName("Multi class Classification Metrics Example");
      SparkContext sc = new SparkContext(conf);
      // $example on$
      String path = "data/mllib/sample_multiclass_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(3)
        .run(training.rdd());
  
      // Compute raw scores on the test set.
      JavaRDD<Tuple2<Object, Object>> predictionAndLabels = test.map(
        new Function<LabeledPoint, Tuple2<Object, Object>>() {
          public Tuple2<Object, Object> call(LabeledPoint p) {
            Double prediction = model.predict(p.features());
            return new Tuple2<Object, Object>(prediction, p.label());
          }
        }
      );
  
      // Get evaluation metrics.
      MulticlassMetrics metrics = new MulticlassMetrics(predictionAndLabels.rdd());
  
      // Confusion matrix
      Matrix confusion = metrics.confusionMatrix();
      System.out.println("Confusion matrix: \n" + confusion);
  
      // Overall statistics
      System.out.println("Accuracy = " + metrics.accuracy());
  
      // Stats by labels
      for (int i = 0; i < metrics.labels().length; i++) {
        System.out.format("Class %f precision = %f\n", metrics.labels()[i],metrics.precision(
          metrics.labels()[i]));
        System.out.format("Class %f recall = %f\n", metrics.labels()[i], metrics.recall(
          metrics.labels()[i]));
        System.out.format("Class %f F1 score = %f\n", metrics.labels()[i], metrics.fMeasure(
          metrics.labels()[i]));
      }
  
      //Weighted stats
      System.out.format("Weighted precision = %f\n", metrics.weightedPrecision());
      System.out.format("Weighted recall = %f\n", metrics.weightedRecall());
      System.out.format("Weighted F1 score = %f\n", metrics.weightedFMeasure());
      System.out.format("Weighted false positive rate = %f\n", metrics.weightedFalsePositiveRate());
  
      // Save and load model
      model.save(sc, "target/tmp/LogisticRegressionModel");
      LogisticRegressionModel sameModel = LogisticRegressionModel.load(sc,
        "target/tmp/LogisticRegressionModel");
      // $example off$
    }
  }
  