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[SPARK-20533][SparkR]:SparkR Wrappers Model should be private and value should be lazy #17808

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[SPARK-20533][SparkR]:SparkR Wrappers Model should be private and value should be lazy #17808

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4 changes: 2 additions & 2 deletions mllib/src/main/scala/org/apache/spark/ml/r/LogisticRegressionWrapper.scala
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Expand Up @@ -40,13 +40,13 @@ private[r] class LogisticRegressionWrapper private (
private val lrModel: LogisticRegressionModel =
pipeline.stages(1).asInstanceOf[LogisticRegressionModel]

val rFeatures: Array[String] = if (lrModel.getFitIntercept) {
lazy val rFeatures: Array[String] = if (lrModel.getFitIntercept) {
Array("(Intercept)") ++ features
} else {
features
}

val rCoefficients: Array[Double] = {
lazy val rCoefficients: Array[Double] = {
val numRows = lrModel.coefficientMatrix.numRows
val numCols = lrModel.coefficientMatrix.numCols
val numColsWithIntercept = if (lrModel.getFitIntercept) numCols + 1 else numCols
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6 changes: 3 additions & 3 deletions mllib/src/main/scala/org/apache/spark/ml/r/MultilayerPerceptronClassifierWrapper.scala
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Expand Up @@ -36,11 +36,11 @@ private[r] class MultilayerPerceptronClassifierWrapper private (

import MultilayerPerceptronClassifierWrapper._

val mlpModel: MultilayerPerceptronClassificationModel =
private val mlpModel: MultilayerPerceptronClassificationModel =
pipeline.stages(1).asInstanceOf[MultilayerPerceptronClassificationModel]

val weights: Array[Double] = mlpModel.weights.toArray
val layers: Array[Int] = mlpModel.layers
lazy val weights: Array[Double] = mlpModel.weights.toArray
lazy val layers: Array[Int] = mlpModel.layers

def transform(dataset: Dataset[_]): DataFrame = {
pipeline.transform(dataset)
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