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From: petro-rudenko <git@git.apache.org>
To: reviews@spark.apache.org
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Subject: [GitHub] spark pull request: [SPARK-5885][MLLIB] Add VectorAssembler
 as a f...
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Message-Id: <20150403164815.0AE3DE1105@git1-us-west.apache.org>
Date: Fri,  3 Apr 2015 16:48:15 +0000 (UTC)

Github user petro-rudenko commented on a diff in the pull request:

    https://github.com/apache/spark/pull/5196#discussion_r27739585
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala ---
    @@ -0,0 +1,101 @@
    +/*
    + * 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.ml.feature
    +
    +import scala.collection.mutable.ArrayBuilder
    +
    +import org.apache.spark.SparkException
    +import org.apache.spark.annotation.AlphaComponent
    +import org.apache.spark.ml.Transformer
    +import org.apache.spark.ml.param.{HasInputCols, HasOutputCol, ParamMap}
    +import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
    +import org.apache.spark.sql.{Column, DataFrame, Row}
    +import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
    +import org.apache.spark.sql.catalyst.expressions.CreateStruct
    +import org.apache.spark.sql.functions._
    +import org.apache.spark.sql.types._
    +
    +/**
    + * :: AlphaComponent ::
    + * A feature transformer than merge multiple columns into a vector column.
    + */
    +@AlphaComponent
    +class VectorAssembler extends Transformer with HasInputCols with HasOutputCol {
    +
    +  /** @group setParam */
    +  def setInputCols(value: Array[String]): this.type = set(inputCols, value)
    +
    +  /** @group setParam */
    +  def setOutputCol(value: String): this.type = set(outputCol, value)
    +
    +  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
    +    val map = this.paramMap ++ paramMap
    +    val assembleFunc = udf { r: Row =>
    +      VectorAssembler.assemble(r.toSeq: _*)
    +    }
    +    val args = map(inputCols).map(c => UnresolvedAttribute(c))
    +    dataset.select(col("*"), assembleFunc(new Column(CreateStruct(args))).as(map(outputCol)))
    +  }
    +
    +  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
    +    val map = this.paramMap ++ paramMap
    +    val inputColNames = map(inputCols)
    +    val outputColName = map(outputCol)
    +    val inputDataTypes = inputColNames.map(name => schema(name).dataType)
    +    for (dataType <- inputDataTypes) {
    +      if (!(dataType == DoubleType || dataType.isInstanceOf[VectorUDT])) {
    +        throw new IllegalArgumentException(s"Data type $dataType is not supported.")
    +      }
    +    }
    +    if (schema.fieldNames.contains(outputColName)) {
    +      throw new IllegalArgumentException(s"Output column $outputColName already exists.")
    +    }
    +    StructType(schema.fields :+ new StructField(outputColName, new VectorUDT, false))
    +  }
    +}
    +
    +@AlphaComponent
    +object VectorAssembler {
    +
    +  private[feature] def assemble(vv: Any*): Vector = {
    +    val indices = ArrayBuilder.make[Int]
    +    val values = ArrayBuilder.make[Double]
    +    var cur = 0
    +    vv.foreach {
    +      case v: Double =>
    --- End diff --
    
    Would be good to support Integers also and just convert them to double.


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