Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/18902#discussion_r133649896
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Imputer.scala ---
@@ -133,23 +133,45 @@ class Imputer @Since("2.2.0") (@Since("2.2.0")
override val uid: String)
override def fit(dataset: Dataset[_]): ImputerModel = {
transformSchema(dataset.schema, logging = true)
val spark = dataset.sparkSession
- import spark.implicits._
- val surrogates = $(inputCols).map { inputCol =>
- val ic = col(inputCol)
- val filtered = dataset.select(ic.cast(DoubleType))
- .filter(ic.isNotNull && ic =!= $(missingValue) && !ic.isNaN)
- if(filtered.take(1).length == 0) {
- 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 Imputer.mean =>
filtered.select(avg(inputCol)).as[Double].first()
- case Imputer.median => filtered.stat.approxQuantile(inputCol,
Array(0.5), 0.001).head
- }
- surrogate
+
+ val cols = $(inputCols).map { inputCol =>
+ when(col(inputCol).equalTo($(missingValue)), null)
+ .when(col(inputCol).isNaN, null)
+ .otherwise(col(inputCol))
+ .cast("double")
+ .as(inputCol)
+ }
+
+ val results = $(strategy) match {
+ case Imputer.mean =>
+ val row = dataset.select(cols.map(avg): _*).head()
+ Array.range(0, $(inputCols).length).map { i =>
+ if (row.isNullAt(i)) {
+ Double.NaN
+ } else {
+ row.getDouble(i)
+ }
+ }
+
+ case Imputer.median =>
+ dataset.select(cols: _*).stat.approxQuantile($(inputCols),
Array(0.5), 0.001)
--- End diff --
BTW, add annotation here to clarify _approxQuantile_ function will ignore
_null_ automatically.
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