[GitHub] spark pull request: [SPARK-13568] [ML] Create feature transformer ...

[hhbyyh <gi...@git.apache.org>]

Github user hhbyyh commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11601#discussion_r58474384
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Imputer.scala ---
    @@ -0,0 +1,300 @@
    +/*
    + * 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 org.apache.hadoop.fs.Path
    +
    +import org.apache.spark.SparkException
    +import org.apache.spark.annotation.{Experimental, Since}
    +import org.apache.spark.ml.{Estimator, Model}
    +import org.apache.spark.ml.param._
    +import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
    +import org.apache.spark.ml.util._
    +import org.apache.spark.mllib.linalg._
    +import org.apache.spark.rdd.RDD
    +import org.apache.spark.sql.{DataFrame, Row}
    +import org.apache.spark.sql.functions.{col, udf}
    +import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
    +
    +/**
    +  * Params for [[Imputer]] and [[ImputerModel]].
    +  */
    +private[feature] trait ImputerParams extends Params with HasInputCol with HasOutputCol {
    +
    +  /**
    +    * The imputation strategy.
    +    * If "mean", then replace missing values using the mean value of the feature.
    +    * If "median", then replace missing values using the median value of the feature.
    +    * If "most", then replace missing using the most frequent value of the feature.
    +    * Default: mean
    +    *
    +    * @group param
    +    */
    +  val strategy: Param[String] = new Param(this, "strategy", "strategy for imputation. " +
    +    "If mean, then replace missing values using the mean value of the feature." +
    +    "If median, then replace missing values using the median value of the feature." +
    +    "If most, then replace missing using the most frequent value of the feature.",
    +    ParamValidators.inArray[String](Imputer.supportedStrategyNames.toArray))
    +
    +  /** @group getParam */
    +  def getStrategy: String = $(strategy)
    +
    +  /**
    +    * The placeholder for the missing values. All occurrences of missingValue will be imputed.
    +    * Default: Double.NaN
    +    *
    +    * @group param
    +    */
    +  val missingValue: DoubleParam = new DoubleParam(this, "missingValue",
    +    "The placeholder for the missing values. All occurrences of missingValue will be imputed")
    +
    +  /** @group getParam */
    +  def getMissingValue: Double = $(missingValue)
    +
    +  private[feature] def isMissingValue(value: Double): Boolean = {
    +    val miss = $(missingValue)
    +    value == miss || (value.isNaN && miss.isNaN)
    +  }
    +
    +  /** Validates and transforms the input schema. */
    +  protected def validateAndTransformSchema(schema: StructType): StructType = {
    +    val inputType = schema($(inputCol)).dataType
    +    require(inputType.isInstanceOf[VectorUDT] || inputType.isInstanceOf[DoubleType],
    +      s"Input column ${$(inputCol)} must of type Vector or Double")
    +    require(!schema.fieldNames.contains($(outputCol)),
    +      s"Output column ${$(outputCol)} already exists.")
    +    val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false)
    +    StructType(outputFields)
    +  }
    +
    +}
    +
    +/**
    + * :: Experimental ::
    + * Imputation estimator for completing missing values, either using the mean, the median or
    + * the most frequent value of the column in which the missing values are located. This class
    + * also allows for different missing values.
    + */
    +@Experimental
    +class Imputer @Since("2.0.0")(override val uid: String)
    +  extends Estimator[ImputerModel] with ImputerParams with DefaultParamsWritable {
    +
    +  @Since("2.0.0")
    +  def this() = this(Identifiable.randomUID("imputer"))
    +
    +  /** @group setParam */
    +  def setInputCol(value: String): this.type = set(inputCol, value)
    +
    +  /** @group setParam */
    +  def setOutputCol(value: String): this.type = set(outputCol, value)
    +
    +  /**
    +   * Imputation strategy. Available options are "mean", "median" and "most".
    +   * @group setParam
    +   */
    +  def setStrategy(value: String): this.type = set(strategy, value)
    +
    +  /** @group setParam */
    +  def setMissingValue(value: Double): this.type = set(missingValue, value)
    +
    +  setDefault(strategy -> "mean", missingValue -> Double.NaN)
    +
    +  override def fit(dataset: DataFrame): ImputerModel = {
    +    val alternate = dataset.select($(inputCol)).schema.fields(0).dataType match {
    +      case DoubleType =>
    +        val doubleRDD = dataset.select($(inputCol)).rdd.map(_.getDouble(0))
    +        Vectors.dense(getColStatistics(doubleRDD))
    +      case _: VectorUDT =>
    +        val filteredDF = dataset.where(s"${$(inputCol)} IS NOT NULL").select($(inputCol))
    +        val vectorRDD = filteredDF.rdd.map(_.getAs[Vector](0)).cache()
    +        val vl = dataset.first().getAs[Vector]($(inputCol)).size
    +        $(strategy) match {
    +          case "mean" =>
    +            val summary = vectorRDD.treeAggregate((new Array[Double](vl), new Array[Int](vl)))(
    +              (prev, data) => (prev, data) match { case ((mean, count), data) =>
    +                  var i = 0
    +                  while (i < mean.length) {
    +                    if (data(i) != 0 && !data(i).isNaN) {
    +                      count(i) += 1
    +                      mean(i) = mean(i) + (data(i) - mean(i)) / count(i)
    +                    }
    +                    i += 1
    +                  }
    +                  (mean, count)
    +              }, (aggregator1, aggregator2) => (aggregator1, aggregator2) match {
    +                case ((mean1, c1), (mean2, c2)) =>
    +                  (0 until mean1.length).foreach{ i =>
    +                    mean1(i) = mean1(i) + (mean2(i) - mean1(i)) * c2(i) / (c1(i) + c2(i))
    +                    c1(i) += c2(i)
    +                  }
    +                  (mean1, c1)
    +              })
    +            Vectors.dense(summary._1)
    +          case _ =>
    +            val statisticsArray = new Array[Double](vl)
    +            (0 until vl).foreach(i => {
    +              statisticsArray(i) = getColStatistics(vectorRDD.map(v => v(i)))
    +            })
    +            Vectors.dense(statisticsArray)
    +        }
    +    }
    +    copyValues(new ImputerModel(uid, alternate).setParent(this))
    +  }
    +
    +  /** Extract the statistics info from a Double column according to the strategy */
    +  private def getColStatistics(data: RDD[Double]): Double = {
    +    val filteredRDD = data.filter(!isMissingValue(_))
    +    val colStatistics = $(strategy) match {
    +      case "mean" => filteredRDD.mean()
    +      case "median" => filteredRDD.sortBy(d => d).zipWithIndex()
    --- End diff --
    
    @MLnick Sorry I cannot find out how approxQuantile supports multiple columns or vector type...


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