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.NaiveBayes;
  import org.apache.spark.ml.classification.NaiveBayesModel;
  import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.SparkSession;
  // $example off$
  
  /**
   * An example for Naive Bayes Classification.
   */
  public class JavaNaiveBayesExample {
  
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaNaiveBayesExample")
        .getOrCreate();
  
      // $example on$
      // Load training data
      Dataset<Row> dataFrame =
        spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
      // Split the data into train and test
      Dataset<Row>[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L);
      Dataset<Row> train = splits[0];
      Dataset<Row> test = splits[1];
  
      // create the trainer and set its parameters
      NaiveBayes nb = new NaiveBayes();
  
      // train the model
      NaiveBayesModel model = nb.fit(train);
  
      // Select example rows to display.
      Dataset<Row> predictions = model.transform(test);
      predictions.show();
  
      // compute accuracy on the test set
      MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
        .setLabelCol("label")
        .setPredictionCol("prediction")
        .setMetricName("accuracy");
      double accuracy = evaluator.evaluate(predictions);
      System.out.println("Test set accuracy = " + accuracy);
      // $example off$
  
      spark.stop();
    }
  }
  