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[SPARK-18608][ML] Fix double caching #19197

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[SPARK-18608][ML] Fix double caching #19197

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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
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Expand Up @@ -484,7 +484,7 @@ class LogisticRegression @Since("1.2.0") (
}

override protected[spark] def train(dataset: Dataset[_]): LogisticRegressionModel = {
val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
val handlePersistence = dataset.storageLevel == StorageLevel.NONE
train(dataset, handlePersistence)
}

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4 changes: 2 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
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Expand Up @@ -164,7 +164,7 @@ final class OneVsRestModel private[ml] (
val newDataset = dataset.withColumn(accColName, initUDF())

// persist if underlying dataset is not persistent.
val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
val handlePersistence = dataset.storageLevel == StorageLevel.NONE
if (handlePersistence) {
newDataset.persist(StorageLevel.MEMORY_AND_DISK)
}
Expand Down Expand Up @@ -347,7 +347,7 @@ final class OneVsRest @Since("1.4.0") (
}

// persist if underlying dataset is not persistent.
val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
val handlePersistence = dataset.storageLevel == StorageLevel.NONE
if (handlePersistence) {
multiclassLabeled.persist(StorageLevel.MEMORY_AND_DISK)
}
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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
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Expand Up @@ -304,7 +304,7 @@ class KMeans @Since("1.5.0") (
override def fit(dataset: Dataset[_]): KMeansModel = {
transformSchema(dataset.schema, logging = true)

val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
val handlePersistence = dataset.storageLevel == StorageLevel.NONE
val instances: RDD[OldVector] = dataset.select(col($(featuresCol))).rdd.map {
case Row(point: Vector) => OldVectors.fromML(point)
}
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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
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Expand Up @@ -213,7 +213,7 @@ class AFTSurvivalRegression @Since("1.6.0") (@Since("1.6.0") override val uid: S
override def fit(dataset: Dataset[_]): AFTSurvivalRegressionModel = {
transformSchema(dataset.schema, logging = true)
val instances = extractAFTPoints(dataset)
val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
val handlePersistence = dataset.storageLevel == StorageLevel.NONE
if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)

val featuresSummarizer = {
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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
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Expand Up @@ -165,7 +165,7 @@ class IsotonicRegression @Since("1.5.0") (@Since("1.5.0") override val uid: Stri
transformSchema(dataset.schema, logging = true)
// Extract columns from data. If dataset is persisted, do not persist oldDataset.
val instances = extractWeightedLabeledPoints(dataset)
val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
val handlePersistence = dataset.storageLevel == StorageLevel.NONE
if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)

val instr = Instrumentation.create(this, dataset)
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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
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Expand Up @@ -251,7 +251,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
return lrModel
}

val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
val handlePersistence = dataset.storageLevel == StorageLevel.NONE
if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)

val (featuresSummarizer, ySummarizer) = {
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