Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/20146#discussion_r183254078
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala ---
@@ -130,21 +161,53 @@ class StringIndexer @Since("1.4.0") (
@Since("1.4.0")
def setOutputCol(value: String): this.type = set(outputCol, value)
+ /** @group setParam */
+ @Since("2.4.0")
+ def setInputCols(value: Array[String]): this.type = set(inputCols, value)
+
+ /** @group setParam */
+ @Since("2.4.0")
+ def setOutputCols(value: Array[String]): this.type = set(outputCols,
value)
+
@Since("2.0.0")
override def fit(dataset: Dataset[_]): StringIndexerModel = {
transformSchema(dataset.schema, logging = true)
- val values = dataset.na.drop(Array($(inputCol)))
- .select(col($(inputCol)).cast(StringType))
- .rdd.map(_.getString(0))
- val labels = $(stringOrderType) match {
- case StringIndexer.frequencyDesc =>
values.countByValue().toSeq.sortBy(-_._2)
- .map(_._1).toArray
- case StringIndexer.frequencyAsc =>
values.countByValue().toSeq.sortBy(_._2)
- .map(_._1).toArray
- case StringIndexer.alphabetDesc =>
values.distinct.collect.sortWith(_ > _)
- case StringIndexer.alphabetAsc => values.distinct.collect.sortWith(_
< _)
- }
- copyValues(new StringIndexerModel(uid, labels).setParent(this))
+
+ val (inputCols, _) = getInOutCols()
+ val zeroState = Array.fill(inputCols.length)(new OpenHashMap[String,
Long]())
+
+ // Counts by the string values in the dataset.
+ val countByValueArray = dataset.na.drop(inputCols)
+ .select(inputCols.map(col(_).cast(StringType)): _*)
+ .rdd.treeAggregate(zeroState)(
--- End diff --
Possible to aggregate natively with SQL? I don't think we will compromise
the performance with SQL aggregation like `groupBy` and `agg` and
`countDistinct` without using tree aggregation since the states will be very
small in this use-case.
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