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/*
* 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<double[], double[]>(new double[]{0.0, 1.0}, new double[]{0.0, 2.0}),
new Tuple2<double[], double[]>(new double[]{0.0, 2.0}, new double[]{0.0, 1.0}),
new Tuple2<double[], double[]>(new double[]{}, new double[]{0.0}),
new Tuple2<double[], double[]>(new double[]{2.0}, new double[]{2.0}),
new Tuple2<double[], double[]>(new double[]{2.0, 0.0}, new double[]{2.0, 0.0}),
new Tuple2<double[], double[]>(new double[]{0.0, 1.0, 2.0}, new double[]{0.0, 1.0}),
new Tuple2<double[], double[]>(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();
}
}
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