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[ML-185]Select label and features columns and cache data #196

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Apr 1, 2022
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[ML-185]Select label and features columns and cache data #196

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7da5a2d
update
minmingzhu Mar 24, 2022
e891d7d
Update NaiveBayes.scala
minmingzhu Mar 29, 2022
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18 changes: 11 additions & 7 deletions mllib-dal/src/main/scala/org/apache/spark/ml/classification/spark320/NaiveBayes.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -133,10 +133,14 @@ class NaiveBayes @Since("1.5.0") (

val sc = spark.sparkContext

val executor_num = Utils.sparkExecutorNum(sc)
val executor_cores = Utils.sparkExecutorCores()
// select label and features columns and cache data.
val naiveBayesData = dataset.select($(labelCol), $(featuresCol)).cache()
naiveBayesData.count()

logInfo(s"NaiveBayesDAL fit using $executor_num Executors")
val executorNum = Utils.sparkExecutorNum(sc)
val executorCores = Utils.sparkExecutorCores()

logInfo(s"NaiveBayesDAL fit using $executorNum Executors")

// DAL only support [0..numClasses) as labels, should map original labels using StringIndexer
// Todo: optimize getting num of classes
Expand All @@ -146,17 +150,17 @@ class NaiveBayes @Since("1.5.0") (
// numClasses should be explicitly included in the parquet metadata
// This can be done by applying StringIndexer to the label column
val numClasses = confClasses match {
case -1 => getNumClasses(dataset)
case -1 => getNumClasses(naiveBayesData)
case _ => confClasses
}

instr.logNumClasses(numClasses)

val labeledPointsDS = dataset
val labeledPointsDS = naiveBayesData
.select(col(getLabelCol), DatasetUtils.columnToVector(dataset, getFeaturesCol))

val dalModel = new NaiveBayesDALImpl(uid, numClasses,
executor_num, executor_cores).train(labeledPointsDS, ${labelCol}, ${featuresCol})
executorNum, executorCores).train(labeledPointsDS, ${labelCol}, ${featuresCol})

val model = copyValues(new NaiveBayesModel(
dalModel.uid, dalModel.pi, dalModel.theta, dalModel.sigma))
Expand Down Expand Up @@ -332,4 +336,4 @@ class NaiveBayes @Since("1.5.0") (
val sigma = new DenseMatrix(numLabels, numFeatures, sigmaArray, true)
new NaiveBayesModel(uid, pi.compressed, theta.compressed, sigma.compressed)
}
}
}