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/*
 * 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.
 */

// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute}
import org.apache.spark.ml.feature.VectorSlicer
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.sql.Row
import org.apache.spark.sql.types.StructType
// $example off$
import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}

object VectorSlicerExample {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("VectorSlicerExample")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)

    // $example on$
    val data = Array(Row(Vectors.dense(-2.0, 2.3, 0.0)))

    val defaultAttr = NumericAttribute.defaultAttr
    val attrs = Array("f1", "f2", "f3").map(defaultAttr.withName)
    val attrGroup = new AttributeGroup("userFeatures", attrs.asInstanceOf[Array[Attribute]])

    val dataRDD = sc.parallelize(data)
    val dataset = sqlContext.createDataFrame(dataRDD, StructType(Array(attrGroup.toStructField())))

    val slicer = new VectorSlicer().setInputCol("userFeatures").setOutputCol("features")

    slicer.setIndices(Array(1)).setNames(Array("f3"))
    // or slicer.setIndices(Array(1, 2)), or slicer.setNames(Array("f2", "f3"))

    val output = slicer.transform(dataset)
    println(output.select("userFeatures", "features").first())
    // $example off$
    sc.stop()
  }
}
// scalastyle:on println