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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 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");
// $example off$
sc.stop();
}
}
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