spark LogisticRegressionSummaryExample 源码
spark LogisticRegressionSummaryExample 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionSummaryExample.scala
/*
* 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.ml
// $example on$
import org.apache.spark.ml.classification.LogisticRegression
// $example off$
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.max
object LogisticRegressionSummaryExample {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder
.appName("LogisticRegressionSummaryExample")
.getOrCreate()
import spark.implicits._
// Load training data
val training = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
val lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.3)
.setElasticNetParam(0.8)
// Fit the model
val lrModel = lr.fit(training)
// $example on$
// Extract the summary from the returned LogisticRegressionModel instance trained in the earlier
// example
val trainingSummary = lrModel.binarySummary
// Obtain the objective per iteration.
val objectiveHistory = trainingSummary.objectiveHistory
println("objectiveHistory:")
objectiveHistory.foreach(loss => println(loss))
// Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
val roc = trainingSummary.roc
roc.show()
println(s"areaUnderROC: ${trainingSummary.areaUnderROC}")
// Set the model threshold to maximize F-Measure
val fMeasure = trainingSummary.fMeasureByThreshold
val maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0)
val bestThreshold = fMeasure.where($"F-Measure" === maxFMeasure)
.select("threshold").head().getDouble(0)
lrModel.setThreshold(bestThreshold)
// $example off$
spark.stop()
}
}
// scalastyle:on println
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