content: /*
   * 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.ml;
  
  // $example on$
  import org.apache.spark.ml.classification.BinaryLogisticRegressionSummary;
  import org.apache.spark.ml.classification.LogisticRegression;
  import org.apache.spark.ml.classification.LogisticRegressionModel;
  import org.apache.spark.ml.classification.LogisticRegressionTrainingSummary;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.SparkSession;
  import org.apache.spark.sql.functions;
  // $example off$
  
  public class JavaLogisticRegressionSummaryExample {
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaLogisticRegressionSummaryExample")
        .getOrCreate();
  
      // Load training data
      Dataset<Row> training = spark.read().format("libsvm")
        .load("data/mllib/sample_libsvm_data.txt");
  
      LogisticRegression lr = new LogisticRegression()
        .setMaxIter(10)
        .setRegParam(0.3)
        .setElasticNetParam(0.8);
  
      // Fit the model
      LogisticRegressionModel lrModel = lr.fit(training);
  
      // $example on$
      // Extract the summary from the returned LogisticRegressionModel instance trained in the earlier
      // example
      LogisticRegressionTrainingSummary trainingSummary = lrModel.summary();
  
      // Obtain the loss per iteration.
      double[] objectiveHistory = trainingSummary.objectiveHistory();
      for (double lossPerIteration : objectiveHistory) {
        System.out.println(lossPerIteration);
      }
  
      // 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.
      BinaryLogisticRegressionSummary binarySummary =
        (BinaryLogisticRegressionSummary) trainingSummary;
  
      // Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
      Dataset<Row> roc = binarySummary.roc();
      roc.show();
      roc.select("FPR").show();
      System.out.println(binarySummary.areaUnderROC());
  
      // Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with
      // this selected threshold.
      Dataset<Row> fMeasure = binarySummary.fMeasureByThreshold();
      double maxFMeasure = fMeasure.select(functions.max("F-Measure")).head().getDouble(0);
      double bestThreshold = fMeasure.where(fMeasure.col("F-Measure").equalTo(maxFMeasure))
        .select("threshold").head().getDouble(0);
      lrModel.setThreshold(bestThreshold);
      // $example off$
  
      spark.stop();
    }
  }
  