Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/11601#discussion_r61321225
--- 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.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.{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("mean") or the
+ * median("median") of the column in which the missing values are located.
--- End diff --
Another subtlety I noticed is that, regardless of the `missingValue` param,
we filter out `NaN` values before computing the mean. I think this is the
correct behavior, since otherwise you'd just replace missing values with `NaN`.
I'm not sure if we even need to document it, it might make things more
confusing. We could note that the "mean" strategy is nan-safe maybe?
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