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.LogisticRegression;
  import org.apache.spark.ml.classification.OneVsRest;
  import org.apache.spark.ml.classification.OneVsRestModel;
  import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  // $example off$
  import org.apache.spark.sql.SparkSession;
  
  
  /**
   * An example of Multiclass to Binary Reduction with One Vs Rest,
   * using Logistic Regression as the base classifier.
   * Run with
   * <pre>
   * bin/run-example ml.JavaOneVsRestExample
   * </pre>
   */
  public class JavaOneVsRestExample {
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaOneVsRestExample")
        .getOrCreate();
  
      // $example on$
      // load data file.
      Dataset<Row> inputData = spark.read().format("libsvm")
        .load("data/mllib/sample_multiclass_classification_data.txt");
  
      // generate the train/test split.
      Dataset<Row>[] tmp = inputData.randomSplit(new double[]{0.8, 0.2});
      Dataset<Row> train = tmp[0];
      Dataset<Row> test = tmp[1];
  
      // configure the base classifier.
      LogisticRegression classifier = new LogisticRegression()
        .setMaxIter(10)
        .setTol(1E-6)
        .setFitIntercept(true);
  
      // instantiate the One Vs Rest Classifier.
      OneVsRest ovr = new OneVsRest().setClassifier(classifier);
  
      // train the multiclass model.
      OneVsRestModel ovrModel = ovr.fit(train);
  
      // score the model on test data.
      Dataset<Row> predictions = ovrModel.transform(test)
        .select("prediction", "label");
  
      // obtain evaluator.
      MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
              .setMetricName("accuracy");
  
      // compute the classification error on test data.
      double accuracy = evaluator.evaluate(predictions);
      System.out.println("Test Error = " + (1 - accuracy));
      // $example off$
  
      spark.stop();
    }
  
  }
  