imatiach-msft commented on a change in pull request #21632:
[SPARK-19591][ML][MLlib] Add sample weights to decision trees
URL: https://github.com/apache/spark/pull/21632#discussion_r247767225
##########
File path: mllib/src/test/scala/org/apache/spark/ml/tree/impl/TreeTests.scala
##########
@@ -18,37 +18,46 @@
package org.apache.spark.ml.tree.impl
import scala.collection.JavaConverters._
+import scala.util.Random
import org.apache.spark.{SparkContext, SparkFunSuite}
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.ml.attribute.{AttributeGroup, NominalAttribute,
NumericAttribute}
-import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.feature.{Instance, LabeledPoint}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.tree._
+import org.apache.spark.mllib.util.TestingUtils._
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, SparkSession}
private[ml] object TreeTests extends SparkFunSuite {
/**
* Convert the given data to a DataFrame, and set the features and label
metadata.
+ *
* @param data Dataset. Categorical features and labels must already have
0-based indices.
* This must be non-empty.
* @param categoricalFeatures Map: categorical feature index to number of
distinct values
* @param numClasses Number of classes label can take. If 0, mark as
continuous.
* @return DataFrame with metadata
*/
def setMetadata(
- data: RDD[LabeledPoint],
+ data: RDD[_],
Review comment:
I tried to create two methods with different RDDs, one for LabeledPoint and
the other for Instance, but I got an error complaining about duplicate methods
(probably due to type erasure). Maybe I misunderstood, is there a way to make
it work?
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