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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