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;
  
  import org.apache.spark.sql.SparkSession;
  
  // $example on$
  import java.util.Map;
  
  import org.apache.spark.ml.feature.VectorIndexer;
  import org.apache.spark.ml.feature.VectorIndexerModel;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  // $example off$
  
  public class JavaVectorIndexerExample {
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaVectorIndexerExample")
        .getOrCreate();
  
      // $example on$
      Dataset<Row> data = spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
  
      VectorIndexer indexer = new VectorIndexer()
        .setInputCol("features")
        .setOutputCol("indexed")
        .setMaxCategories(10);
      VectorIndexerModel indexerModel = indexer.fit(data);
  
      Map<Integer, Map<Double, Integer>> categoryMaps = indexerModel.javaCategoryMaps();
      System.out.print("Chose " + categoryMaps.size() + " categorical features:");
  
      for (Integer feature : categoryMaps.keySet()) {
        System.out.print(" " + feature);
      }
      System.out.println();
  
      // Create new column "indexed" with categorical values transformed to indices
      Dataset<Row> indexedData = indexerModel.transform(data);
      indexedData.show();
      // $example off$
      spark.stop();
    }
  }
  