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[SPARK-18596][ML] add checking and caching to bisecting kmeans #16020

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hhbyyh wants to merge 5 commits into from
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[SPARK-18596][ML] add checking and caching to bisecting kmeans #16020

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a4c88ee
add checking and caching to bisecting kmeans
hhbyyh Nov 27, 2016
f38c81a
Merge remote-tracking branch 'upstream/master' into bikcache
hhbyyh Nov 28, 2016
0105220
change storage check
hhbyyh Nov 28, 2016
0fc34dc
Merge remote-tracking branch 'upstream/master' into bikcache
YY-OnCall Dec 2, 2016
678cd43
kmeans cache
YY-OnCall Dec 2, 2016
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13 changes: 13 additions & 0 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
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Expand Up @@ -33,6 +33,7 @@ import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Dataset, Row}
import org.apache.spark.sql.functions.{col, udf}
import org.apache.spark.sql.types.{IntegerType, StructType}
import org.apache.spark.storage.StorageLevel


/**
Expand Down Expand Up @@ -250,10 +251,19 @@ class BisectingKMeans @Since("2.0.0") (

@Since("2.0.0")
override def fit(dataset: Dataset[_]): BisectingKMeansModel = {
val handlePersistence = dataset.storageLevel == StorageLevel.NONE
fit(dataset, handlePersistence)
}

@Since("2.2.0")
protected def fit(dataset: Dataset[_], handlePersistence: Boolean): BisectingKMeansModel = {
transformSchema(dataset.schema, logging = true)
val rdd: RDD[OldVector] = dataset.select(col($(featuresCol))).rdd.map {
case Row(point: Vector) => OldVectors.fromML(point)
}
if (handlePersistence) {
rdd.persist(StorageLevel.MEMORY_AND_DISK)
}

val instr = Instrumentation.create(this, rdd)
instr.logParams(featuresCol, predictionCol, k, maxIter, seed, minDivisibleClusterSize)
Expand All @@ -268,6 +278,9 @@ class BisectingKMeans @Since("2.0.0") (
val summary = new BisectingKMeansSummary(
model.transform(dataset), $(predictionCol), $(featuresCol), $(k))
model.setSummary(Some(summary))
if (handlePersistence) {
rdd.unpersist()
}
instr.logSuccess(model)
model
}
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4 changes: 2 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
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Expand Up @@ -302,7 +302,7 @@ class KMeans @Since("1.5.0") (

@Since("2.0.0")
override def fit(dataset: Dataset[_]): KMeansModel = {
val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
val handlePersistence = dataset.storageLevel == StorageLevel.NONE
fit(dataset, handlePersistence)
}

Expand Down Expand Up @@ -330,10 +330,10 @@ class KMeans @Since("1.5.0") (
val summary = new KMeansSummary(
model.transform(dataset), $(predictionCol), $(featuresCol), $(k))
model.setSummary(Some(summary))
instr.logSuccess(model)
if (handlePersistence) {
instances.unpersist()
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@MLnick MLnick Nov 28, 2016

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prefer to keep this form according to style guide.

}
instr.logSuccess(model)
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@MLnick MLnick Dec 2, 2016

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The handlePersistence check in KMeans at L309 should also be updated to use dataset.storageLevel. Since we're touching KMeans here anyway we may as well do it now.

model
}

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9 changes: 9 additions & 0 deletions mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
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Expand Up @@ -24,6 +24,7 @@ import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
import org.apache.spark.mllib.clustering.{KMeans => MLlibKMeans}
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.{DataFrame, Dataset, SparkSession}
import org.apache.spark.storage.StorageLevel

private[clustering] case class TestRow(features: Vector)

Expand Down Expand Up @@ -143,6 +144,14 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultR
assert(model.getPredictionCol == predictionColName)
}

test("DataFrame storage level check") {
val df = KMeansSuite.generateKMeansData(spark, 5, 3, 2)
assert(df.storageLevel == StorageLevel.NONE)
df.persist(StorageLevel.MEMORY_AND_DISK)
assert(df.storageLevel != StorageLevel.NONE)
df.unpersist()
}

test("read/write") {
def checkModelData(model: KMeansModel, model2: KMeansModel): Unit = {
assert(model.clusterCenters === model2.clusterCenters)
Expand Down