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[Ml] SPARK-5804 Explicitly manage cache in Crossvalidator k-fold loop #4595

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[Ml] SPARK-5804 Explicitly manage cache in Crossvalidator k-fold loop #4595

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[Ml] SPARK-5804 Explicitly manage cache in Crossvalidator k-fold loop
petro-rudenko Feb 13, 2015
66a7cfb
Move validationDataset cache to declaration
petro-rudenko Feb 13, 2015
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Move validationDataset cache to declaration
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@petro-rudenko
petro-rudenko committed Feb 13, 2015
commit 66a7cfb3e119739ada90c31cf12c2f38ddc8e02d
3 changes: 1 addition & 2 deletions mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
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Expand Up @@ -77,12 +77,11 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP
val splits = MLUtils.kFold(dataset.rdd, map(numFolds), 0)
splits.zipWithIndex.foreach { case ((training, validation), splitIndex) =>
val trainingDataset = sqlCtx.createDataFrame(training, schema).cache()
val validationDataset = sqlCtx.createDataFrame(validation, schema)
val validationDataset = sqlCtx.createDataFrame(validation, schema).cache()
// multi-model training
logDebug(s"Train split $splitIndex with multiple sets of parameters.")
val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]]
trainingDataset.unpersist()
validationDataset.cache()
var i = 0
while (i < numModels) {
val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map)
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