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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.MulticlassMetrics
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
// $example off$
object MulticlassMetricsExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("MulticlassMetricsExample")
val sc = new SparkContext(conf)
// $example on$
// Load training data in LIBSVM format
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_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(3)
.run(training)
// 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 MulticlassMetrics(predictionAndLabels)
// Confusion matrix
println("Confusion matrix:")
println(metrics.confusionMatrix)
// Overall Statistics
val accuracy = metrics.accuracy
println("Summary Statistics")
println(s"Accuracy = $accuracy")
// Precision by label
val labels = metrics.labels
labels.foreach { l =>
println(s"Precision($l) = " + metrics.precision(l))
}
// Recall by label
labels.foreach { l =>
println(s"Recall($l) = " + metrics.recall(l))
}
// False positive rate by label
labels.foreach { l =>
println(s"FPR($l) = " + metrics.falsePositiveRate(l))
}
// F-measure by label
labels.foreach { l =>
println(s"F1-Score($l) = " + metrics.fMeasure(l))
}
// Weighted stats
println(s"Weighted precision: ${metrics.weightedPrecision}")
println(s"Weighted recall: ${metrics.weightedRecall}")
println(s"Weighted F1 score: ${metrics.weightedFMeasure}")
println(s"Weighted false positive rate: ${metrics.weightedFalsePositiveRate}")
// $example off$
sc.stop()
}
}
// scalastyle:on println
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