Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/11601#discussion_r103886487
--- 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)
--- End diff --
`.head`
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