Github user MLnick commented on a diff in the pull request: https://github.com/apache/spark/pull/11601#discussion_r104404339 --- 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 +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 { + + /** + * 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 (relative error less than 0.001). + * Default: mean + * + * @group param + */ + final val strategy: Param[String] = new Param(this, "strategy", s"strategy for imputation. " + + s"If ${Imputer.mean}, then replace missing values using the mean value of the feature. " + + s"If ${Imputer.median}, then replace missing values using the median value of the feature.", + ParamValidators.inArray[String](Array(Imputer.mean, Imputer.median))) + + /** @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 == $(inputCols).distinct.length, s"inputCols duplicates:" + + s" (${$(inputCols).mkString(", ")})") + require($(outputCols).length == $(outputCols).distinct.length, s"outputCols duplicates:" + + s" (${$(outputCols).mkString(", ")})") + require($(inputCols).length == $(outputCols).length, s"inputCols(${$(inputCols).length})" + + s" and outputCols(${$(outputCols).length}) should have the same length") + val outputFields = $(inputCols).zip($(outputCols)).map { case (inputCol, outputCol) => + val inputField = schema(inputCol) + SchemaUtils.checkColumnTypes(schema, inputCol, Seq(DoubleType, FloatType)) + StructField(outputCol, inputField.dataType, inputField.nullable) + } + StructType(schema ++ outputFields) + } +} + +/** + * :: 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("2.2.0")(override val uid: String) + extends Estimator[ImputerModel] with ImputerParams with DefaultParamsWritable { + + @Since("2.2.0") + def this() = this(Identifiable.randomUID("imputer")) + + /** @group setParam */ + @Since("2.2.0") + def setInputCols(value: Array[String]): this.type = set(inputCols, value) + + /** @group setParam */ + @Since("2.2.0") + def setOutputCols(value: Array[String]): this.type = set(outputCols, value) + + /** + * Imputation strategy. Available options are ["mean", "median"]. + * @group setParam + */ + @Since("2.2.0") + def setStrategy(value: String): this.type = set(strategy, value) + + /** @group setParam */ + @Since("2.2.0") + def setMissingValue(value: Double): this.type = set(missingValue, value) + + import org.apache.spark.ml.feature.Imputer._ --- End diff -- This import should probably be above with the others (or within `fit`)
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