org.apache.spark.examples.ml.LogisticRegressionSummaryExample.scala Maven / Gradle / Ivy

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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.ml

// $example on$
import org.apache.spark.ml.classification.{BinaryLogisticRegressionSummary, LogisticRegression}
// $example off$
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.functions.max
import org.apache.spark.{SparkConf, SparkContext}

object LogisticRegressionSummaryExample {

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("LogisticRegressionSummaryExample")
    val sc = new SparkContext(conf)
    val sqlCtx = new SQLContext(sc)
    import sqlCtx.implicits._

    // Load training data
    val training = sqlCtx.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.summary

    // Obtain the objective per iteration.
    val objectiveHistory = trainingSummary.objectiveHistory
    objectiveHistory.foreach(loss => println(loss))

    // Obtain the metrics useful to judge performance on test data.
    // We cast the summary to a BinaryLogisticRegressionSummary since the problem is a
    // binary classification problem.
    val binarySummary = trainingSummary.asInstanceOf[BinaryLogisticRegressionSummary]

    // Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
    val roc = binarySummary.roc
    roc.show()
    println(binarySummary.areaUnderROC)

    // Set the model threshold to maximize F-Measure
    val fMeasure = binarySummary.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)
    // $example off$

    sc.stop()
  }
}
// scalastyle:on println