viirya commented on a change in pull request #25442: [SPARK-28722][ML] Change
sequential label sorting in StringIndexer fit to parallel
URL: https://github.com/apache/spark/pull/25442#discussion_r314084861
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
##########
@@ -190,59 +189,69 @@ class StringIndexer @Since("1.4.0") (
new Column(If(col.isNaN.expr, Literal(null),
col.expr)).cast(StringType)
}
}
+ }
+ private def countByValue(
+ dataset: Dataset[_],
+ inputCols: Array[String]): Array[OpenHashMap[String, Long]] = {
+
+ val aggregator = new StringIndexerAggregator(inputCols.length)
+ implicit val encoder = Encoders.kryo[Array[OpenHashMap[String, Long]]]
+
+ val selectedCols = getSelectedCols(dataset, inputCols)
dataset.select(selectedCols: _*)
.toDF
.groupBy().agg(aggregator.toColumn)
.as[Array[OpenHashMap[String, Long]]]
.collect()(0)
}
- @Since("2.0.0")
- override def fit(dataset: Dataset[_]): StringIndexerModel = {
- transformSchema(dataset.schema, logging = true)
+ private def sortByFreq(dataset: Dataset[_], ascending: Boolean):
Array[Array[String]] = {
+ val (inputCols, _) = getInOutCols()
+ val sortFunc = StringIndexer.getSortFunc(ascending = ascending)
+ val orgStrings = countByValue(dataset, inputCols).toSeq
+ ThreadUtils.parmap(orgStrings, "sortingStringLabels", 8) { counts =>
+ counts.toSeq.sortWith(sortFunc).map(_._1).toArray
+ }.toArray
+ }
+
+ private def sortByAlphabet(dataset: Dataset[_], ascending: Boolean):
Array[Array[String]] = {
val (inputCols, _) = getInOutCols()
// If input dataset is not originally cached, we need to unpersist it
// once we persist it later.
val needUnpersist = dataset.storageLevel == StorageLevel.NONE
+ dataset.persist()
Review comment:
This persist, I think, was because there was many same operations per
column. As we finish Spark job once for all columns, maybe we don't need
persist now?
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