[GitHub] [spark] zhengruifeng commented on a change in pull request #24793: [SPARK-27944][ML] Unify the behavior of checking empty output column names
zhengruifeng commented on a change in pull request #24793: [SPARK-27944][ML]
Unify the behavior of checking empty output column names
URL: https://github.com/apache/spark/pull/24793#discussion_r303695757
##
File path:
mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
##
@@ -33,7 +33,7 @@ import org.apache.spark.ml.util.Instrumentation.instrumented
import org.apache.spark.mllib.linalg.{Matrices => OldMatrices, Matrix =>
OldMatrix,
Vector => OldVector, Vectors => OldVectors}
import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
+import org.apache.spark.sql._
Review comment:
ok, I will revert these place.
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[GitHub] [spark] zhengruifeng commented on a change in pull request #24793: [SPARK-27944][ML] Unify the behavior of checking empty output column names
zhengruifeng commented on a change in pull request #24793: [SPARK-27944][ML]
Unify the behavior of checking empty output column names
URL: https://github.com/apache/spark/pull/24793#discussion_r302814838
##
File path:
mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
##
@@ -107,9 +107,15 @@ class BisectingKMeansModel private[ml] (
@Since("2.0.0")
override def transform(dataset: Dataset[_]): DataFrame = {
transformSchema(dataset.schema, logging = true)
-val predictUDF = udf((vector: Vector) => predict(vector))
-dataset.withColumn($(predictionCol),
- predictUDF(DatasetUtils.columnToVector(dataset, getFeaturesCol)))
+if ($(predictionCol).nonEmpty) {
Review comment:
I changed here to keep in line with other algs like
[LDA.transform](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala#L461)
Or leave alone algs with only one output column, and remove the check in
algs like LDA?
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