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