[SPARK-20533][SPARKR] SparkR Wrappers Model should be private and val…

…ue should be lazy ## What changes were proposed in this pull request? MultilayerPerceptronClassifierWrapper model should be private. LogisticRegressionWrapper.scala rFeatures and rCoefficients should be lazy. ## How was this patch tested? Unit tests. Author: wangmiao1981 <[email protected]> Closes #17808 from wangmiao1981/lazy.
apache · Apr 29, 2017 · ee694cd · ee694cd
1 parent add9d1b
commit ee694cd
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diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/LogisticRegressionWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/LogisticRegressionWrapper.scala
@@ -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

diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/MultilayerPerceptronClassifierWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/MultilayerPerceptronClassifierWrapper.scala
@@ -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)