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From hhbyyh <...@git.apache.org>
Subject [GitHub] spark pull request #11601: [SPARK-13568] [ML] Create feature transformer to ...
Date Fri, 03 Mar 2017 22:45:38 GMT
Github user hhbyyh commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11601#discussion_r104257956
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Imputer.scala ---
    @@ -0,0 +1,260 @@
    +/*
    + * 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.{HasInputCols, HasOutputCol}
    +import org.apache.spark.ml.util._
    +import org.apache.spark.sql.{DataFrame, Dataset, Row}
    +import org.apache.spark.sql.functions._
    +import org.apache.spark.sql.types._
    +
    +/**
    + * Params for [[Imputer]] and [[ImputerModel]].
    + */
    +private[feature] trait ImputerParams extends Params with HasInputCols 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 approximate median value of the
feature.
    +   * Default: mean
    +   *
    +   * @group param
    +   */
    +  final 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.",
    +    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.
    +   * Note that null values are always treated as missing.
    +   * Default: Double.NaN
    +   *
    +   * @group param
    +   */
    +  final 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)
    +
    +    /**
    +   * Param for output column names.
    +   * @group param
    +   */
    +  final val outputCols: StringArrayParam = new StringArrayParam(this, "outputCols",
    +    "output column names")
    +
    +  /** @group getParam */
    +  final def getOutputCols: Array[String] = $(outputCols)
    +
    +  /** Validates and transforms the input schema. */
    +  protected def validateAndTransformSchema(schema: StructType): StructType = {
    +    require($(inputCols).length == $(outputCols).length, "inputCols and outputCols should
have" +
    +      "the same length")
    +    val localInputCols = $(inputCols)
    +    val localOutputCols = $(outputCols)
    +    var outputSchema = schema
    +
    +    $(inputCols).indices.foreach { i =>
    +      val inputCol = localInputCols(i)
    +      val outputCol = localOutputCols(i)
    +      val inputType = schema(inputCol).dataType
    +      SchemaUtils.checkColumnTypes(schema, inputCol, Seq(DoubleType, FloatType))
    +      outputSchema = SchemaUtils.appendColumn(outputSchema, outputCol, inputType)
    +    }
    +    outputSchema
    +  }
    +}
    +
    +/**
    + * :: Experimental ::
    + * Imputation estimator for completing missing values, either using the mean or the median
    + * of the column in which the missing values are located. The input column should be
of
    + * DoubleType or FloatType. Currently Imputer does not support categorical features yet
    + * (SPARK-15041) and possibly creates incorrect values for a categorical feature.
    + *
    + * Note that the mean/median value is computed after filtering out missing values.
    + * All Null values in the input column are treated as missing, and so are also imputed.
    + */
    +@Experimental
    +class Imputer @Since("2.1.0")(override val uid: String)
    +  extends Estimator[ImputerModel] with ImputerParams with DefaultParamsWritable {
    +
    +  @Since("2.1.0")
    +  def this() = this(Identifiable.randomUID("imputer"))
    +
    +  /** @group setParam */
    +  @Since("2.1.0")
    +  def setInputCols(value: Array[String]): this.type = set(inputCols, value)
    +
    +  /** @group setParam */
    +  @Since("2.1.0")
    +  def setOutputCols(value: Array[String]): this.type = set(outputCols, value)
    +
    +  /**
    +   * Imputation strategy. Available options are ["mean", "median"].
    +   * @group setParam
    +   */
    +  @Since("2.1.0")
    +  def setStrategy(value: String): this.type = set(strategy, value)
    +
    +  /** @group setParam */
    +  @Since("2.1.0")
    +  def setMissingValue(value: Double): this.type = set(missingValue, value)
    +
    +  setDefault(strategy -> "mean", missingValue -> Double.NaN)
    +
    +  override def fit(dataset: Dataset[_]): ImputerModel = {
    +    transformSchema(dataset.schema, logging = true)
    +    val surrogates = $(inputCols).map { inputCol =>
    +      val ic = col(inputCol)
    +      val filtered = dataset.select(ic.cast(DoubleType))
    +        .filter(ic.isNotNull && ic =!= $(missingValue))
    +        .filter(!ic.isNaN)
    +      if(filtered.rdd.isEmpty()) {
    +        throw new SparkException(s"surrogate cannot be computed. " +
    +          s"All the values in ${inputCol} are Null, Nan or missingValue ($missingValue)")
    +      }
    +      val surrogate = $(strategy) match {
    +        case "mean" => filtered.select(avg(inputCol)).first().getDouble(0)
    +        case "median" => filtered.stat.approxQuantile(inputCol, Array(0.5), 0.001)(0)
    +      }
    +      surrogate.asInstanceOf[Double]
    +    }
    +
    +    import dataset.sparkSession.implicits._
    +    val surrogateDF = Seq(surrogates).toDF("surrogates")
    +    copyValues(new ImputerModel(uid, surrogateDF).setParent(this))
    +  }
    +
    +  override def transformSchema(schema: StructType): StructType = {
    +    validateAndTransformSchema(schema)
    +  }
    +
    +  override def copy(extra: ParamMap): Imputer = defaultCopy(extra)
    +}
    +
    +@Since("2.1.0")
    +object Imputer extends DefaultParamsReadable[Imputer] {
    +
    +  /** Set of strategy names that Imputer currently supports. */
    +  private[ml] val supportedStrategyNames = Set("mean", "median")
    --- End diff --
    
    Yes, that's better.


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