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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.
*/
// scalastyle:off println
package org.apache.spark.examples.mllib
import org.apache.spark.{SparkConf, SparkContext}
// $example on$
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
// $example off$
object BinaryClassificationMetricsExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("BinaryClassificationMetricsExample")
val sc = new SparkContext(conf)
// $example on$
// Load training data in LIBSVM format
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_binary_classification_data.txt")
// Split data into training (60%) and test (40%)
val Array(training, test) = data.randomSplit(Array(0.6, 0.4), seed = 11L)
training.cache()
// Run training algorithm to build the model
val model = new LogisticRegressionWithLBFGS()
.setNumClasses(2)
.run(training)
// Clear the prediction threshold so the model will return probabilities
model.clearThreshold
// Compute raw scores on the test set
val predictionAndLabels = test.map { case LabeledPoint(label, features) =>
val prediction = model.predict(features)
(prediction, label)
}
// Instantiate metrics object
val metrics = new BinaryClassificationMetrics(predictionAndLabels)
// Precision by threshold
val precision = metrics.precisionByThreshold
precision.foreach { case (t, p) =>
println(s"Threshold: $t, Precision: $p")
}
// Recall by threshold
val recall = metrics.recallByThreshold
recall.foreach { case (t, r) =>
println(s"Threshold: $t, Recall: $r")
}
// Precision-Recall Curve
val PRC = metrics.pr
// F-measure
val f1Score = metrics.fMeasureByThreshold
f1Score.foreach { case (t, f) =>
println(s"Threshold: $t, F-score: $f, Beta = 1")
}
val beta = 0.5
val fScore = metrics.fMeasureByThreshold(beta)
f1Score.foreach { case (t, f) =>
println(s"Threshold: $t, F-score: $f, Beta = 0.5")
}
// AUPRC
val auPRC = metrics.areaUnderPR
println("Area under precision-recall curve = " + auPRC)
// Compute thresholds used in ROC and PR curves
val thresholds = precision.map(_._1)
// ROC Curve
val roc = metrics.roc
// AUROC
val auROC = metrics.areaUnderROC
println("Area under ROC = " + auROC)
// $example off$
sc.stop()
}
}
// scalastyle:on println
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