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[SPARK-24489][ML]Check for invalid input type of weight data in ml.PowerIterationClustering #21509

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[SPARK-24489][ML]Check for invalid input type of weight data in ml.PowerIterationClustering #21509

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1 change: 1 addition & 0 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/PowerIterationClustering.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -166,6 +166,7 @@ class PowerIterationClustering private[clustering] (
val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) {
lit(1.0)
} else {
SchemaUtils.checkNumericType(dataset.schema, $(weightCol))
col($(weightCol)).cast(DoubleType)
}

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15 changes: 15 additions & 0 deletions mllib/src/test/scala/org/apache/spark/ml/clustering/PowerIterationClusteringSuite.scala
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Expand Up @@ -145,6 +145,21 @@ class PowerIterationClusteringSuite extends SparkFunSuite
assert(msg.contains("Similarity must be nonnegative"))
}

test("check for invalid input types of weight") {
val invalidWeightData = spark.createDataFrame(Seq(
(0L, 1L, "a"),
(2L, 3L, "b")
)).toDF("src", "dst", "weight")

val msg = intercept[IllegalArgumentException] {
new PowerIterationClustering()
.setWeightCol("weight")
.assignClusters(invalidWeightData)
}.getMessage
assert(msg.contains("requirement failed: Column weight must be of type numeric" +
" but was actually of type string."))
}

test("test default weight") {
val dataWithoutWeight = data.sample(0.5, 1L).select('src, 'dst)

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