Github user MLnick commented on a diff in the pull request: https://github.com/apache/spark/pull/11601#discussion_r80644112 --- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Imputer.scala --- @@ -0,0 +1,219 @@ +/* + * 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.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.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 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 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. + * 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 = $(missingValue) + + /** Validates and transforms the input schema. */ + protected def validateAndTransformSchema(schema: StructType): StructType = { + val inputType = schema($(inputCol)).dataType + SchemaUtils.checkColumnTypes(schema, $(inputCol), Seq(DoubleType, FloatType)) + require(!schema.fieldNames.contains($(outputCol)), + s"Output column ${$(outputCol)} already exists.") + SchemaUtils.appendColumn(schema, $(outputCol), inputType) + } +} + +/** + * :: Experimental :: + * Imputation estimator for completing missing values, either using the mean or the median + * of the column in which the missing values are located. The input column should be of + * DoubleType or FloatType. + * + * Note that the mean/median value is computed after filtering out missing values. + * All Null values in the input column are treated as missing, and so are also imputed. + */ +@Experimental +class Imputer @Since("2.1.0")(override val uid: String) + extends Estimator[ImputerModel] with ImputerParams with DefaultParamsWritable { + + @Since("2.1.0") + def this() = this(Identifiable.randomUID("imputer")) + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + /** + * Imputation strategy. Available options are ["mean", "median"]. + * @group setParam + */ + def setStrategy(value: String): this.type = set(strategy, value) + + /** @group setParam */ + def setMissingValue(value: Double): this.type = set(missingValue, value) + + setDefault(strategy -> "mean", missingValue -> Double.NaN) + + override def fit(dataset: Dataset[_]): ImputerModel = { + transformSchema(dataset.schema, logging = true) + val ic = col($(inputCol)) + val filtered = dataset.select(ic.cast(DoubleType)) + .filter(ic.isNotNull && ic =!= $(missingValue)) + val surrogate = $(strategy) match { + case "mean" => filtered.filter(!ic.isNaN).select(avg($(inputCol))).first().getDouble(0) + case "median" => filtered.stat.approxQuantile($(inputCol), Array(0.5), 0.001)(0) + } + copyValues(new ImputerModel(uid, surrogate).setParent(this)) + } + + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) + } + + override def copy(extra: ParamMap): Imputer = { + val copied = new Imputer(uid) + copyValues(copied, extra) + } +} + +@Since("2.1.0") +object Imputer extends DefaultParamsReadable[Imputer] { + + /** Set of strategy names that Imputer currently supports. */ + private[ml] val supportedStrategyNames = Set("mean", "median") + + @Since("2.1.0") + override def load(path: String): Imputer = super.load(path) +} + +/** + * :: Experimental :: + * Model fitted by [[Imputer]]. + * + * @param surrogate Value by which missing values in the input column will be replaced. + */ +@Experimental +class ImputerModel private[ml]( + override val uid: String, + val surrogate: Double) + extends Model[ImputerModel] with ImputerParams with MLWritable { + + import ImputerModel._ + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transform(dataset: Dataset[_]): DataFrame = { + transformSchema(dataset.schema, logging = true) + val inputType = dataset.select($(inputCol)).schema.fields(0).dataType + val ic = col($(inputCol)) + dataset.withColumn($(outputCol), when(ic.isNull, surrogate) + .when(ic === $(missingValue), surrogate) + .otherwise(ic) + .cast(inputType)) + } + + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) + } + + override def copy(extra: ParamMap): ImputerModel = { + val copied = new ImputerModel(uid, surrogate) + copyValues(copied, extra).setParent(parent) + } + + @Since("2.1.0") + override def write: MLWriter = new ImputerModelWriter(this) +} + + +@Since("2.1.0") +object ImputerModel extends MLReadable[ImputerModel] { + + private[ImputerModel] class ImputerModelWriter(instance: ImputerModel) extends MLWriter { + + private case class Data(surrogate: Double) --- End diff -- I would think that if we support multiple columns, we need to match up the column name to the surrogate, correct? So I'd think we would want to save a DF with the same columns as `inputCol(s)` and then yes either double or vector type. Is this what you mean here?
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