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.List;
  
  import com.google.common.collect.Lists;
  
  import org.apache.spark.ml.attribute.Attribute;
  import org.apache.spark.ml.attribute.AttributeGroup;
  import org.apache.spark.ml.attribute.NumericAttribute;
  import org.apache.spark.ml.feature.VectorSlicer;
  import org.apache.spark.ml.linalg.Vectors;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.RowFactory;
  import org.apache.spark.sql.types.*;
  // $example off$
  
  public class JavaVectorSlicerExample {
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaVectorSlicerExample")
        .getOrCreate();
  
      // $example on$
      Attribute[] attrs = new Attribute[]{
        NumericAttribute.defaultAttr().withName("f1"),
        NumericAttribute.defaultAttr().withName("f2"),
        NumericAttribute.defaultAttr().withName("f3")
      };
      AttributeGroup group = new AttributeGroup("userFeatures", attrs);
  
      List<Row> data = Lists.newArrayList(
        RowFactory.create(Vectors.sparse(3, new int[]{0, 1}, new double[]{-2.0, 2.3})),
        RowFactory.create(Vectors.dense(-2.0, 2.3, 0.0))
      );
  
      Dataset<Row> dataset =
        spark.createDataFrame(data, (new StructType()).add(group.toStructField()));
  
      VectorSlicer vectorSlicer = new VectorSlicer()
        .setInputCol("userFeatures").setOutputCol("features");
  
      vectorSlicer.setIndices(new int[]{1}).setNames(new String[]{"f3"});
      // or slicer.setIndices(new int[]{1, 2}), or slicer.setNames(new String[]{"f2", "f3"})
  
      Dataset<Row> output = vectorSlicer.transform(dataset);
      output.show(false);
      // $example off$
  
      spark.stop();
    }
  }
  
  