1 files changed, 4 insertions, 2 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
index 7de5a4d5d3..e9df600c8a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MaxAbsScaler.scala
@@ -66,7 +66,8 @@ class MaxAbsScaler @Since("2.0.0") (override val uid: String)
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  override def fit(dataset: DataFrame): MaxAbsScalerModel = {
+  @Since("2.0.0")
+  override def fit(dataset: Dataset[_]): MaxAbsScalerModel = {
     transformSchema(dataset.schema, logging = true)
     val input = dataset.select($(inputCol)).rdd.map { case Row(v: Vector) => v }
     val summary = Statistics.colStats(input)
@@ -111,7 +112,8 @@ class MaxAbsScalerModel private[ml] (
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  override def transform(dataset: DataFrame): DataFrame = {
+  @Since("2.0.0")
+  override def transform(dataset: Dataset[_]): DataFrame = {
     transformSchema(dataset.schema, logging = true)
     // TODO: this looks hack, we may have to handle sparse and dense vectors separately.
     val maxAbsUnzero = Vectors.dense(maxAbs.toArray.map(x => if (x == 0) 1 else x))