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Posted to issues@spark.apache.org by "Sean R. Owen (Jira)" <ji...@apache.org> on 2019/11/10 19:21:00 UTC
[jira] [Resolved] (SPARK-29811) Missing persist on oldDataset in
ml.RandomForestRegressor.train()
[ https://issues.apache.org/jira/browse/SPARK-29811?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean R. Owen resolved SPARK-29811.
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Resolution: Duplicate
> Missing persist on oldDataset in ml.RandomForestRegressor.train()
> -----------------------------------------------------------------
>
> Key: SPARK-29811
> URL: https://issues.apache.org/jira/browse/SPARK-29811
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Affects Versions: 2.4.3
> Reporter: Dong Wang
> Priority: Major
>
> The rdd oldDataset in ml.regression.RandomForestRegressor.train() needs to be persisted, because it used in two actions in RandomForest.run() and oldDataset.first().
> {code:scala}
> override protected def train(
> dataset: Dataset[_]): RandomForestRegressionModel = instrumented { instr =>
> val categoricalFeatures: Map[Int, Int] =
> MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
> val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) // Needs to persist
> val strategy =
> super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity)
> instr.logPipelineStage(this)
> instr.logDataset(dataset)
> instr.logParams(this, labelCol, featuresCol, predictionCol, impurity, numTrees,
> featureSubsetStrategy, maxDepth, maxBins, maxMemoryInMB, minInfoGain,
> minInstancesPerNode, seed, subsamplingRate, cacheNodeIds, checkpointInterval)
> // First use oldDataset
> val trees = RandomForest
> .run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed, Some(instr))
> .map(_.asInstanceOf[DecisionTreeRegressionModel])
> // Second use oldDataset
> val numFeatures = oldDataset.first().features.size
> instr.logNamedValue(Instrumentation.loggerTags.numFeatures, numFeatures)
> new RandomForestRegressionModel(uid, trees, numFeatures)
> }
> {code}
> The same situation exits in ml.classification.RandomForestClassifier.train.
> This issue is reported by our tool CacheCheck, which is used to dynamically detecting persist()/unpersist() api misuses.
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