Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/13937#discussion_r68697383
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala ---
@@ -206,24 +206,21 @@ object PCAModel extends MLReadable[PCAModel] {
override def load(path: String): PCAModel = {
val metadata = DefaultParamsReader.loadMetadata(path, sc, className)
- // explainedVariance field is not present in Spark <= 1.6
- val versionRegex = "([0-9]+)\\.([0-9]+).*".r
- val hasExplainedVariance = metadata.sparkVersion match {
- case versionRegex(major, minor) =>
- major.toInt >= 2 || (major.toInt == 1 && minor.toInt > 6)
- case _ => false
- }
+ val versionRegex = "([0-9]+)\\.(.+)".r
+ val versionRegex(major, _) = metadata.sparkVersion
val dataPath = new Path(path, "data").toString
- val model = if (hasExplainedVariance) {
+ val model = if (major.toInt >= 2) {
val Row(pc: DenseMatrix, explainedVariance: DenseVector) =
sparkSession.read.parquet(dataPath)
.select("pc", "explainedVariance")
.head()
new PCAModel(metadata.uid, pc, explainedVariance)
} else {
- val Row(pc: DenseMatrix) =
sparkSession.read.parquet(dataPath).select("pc").head()
- new PCAModel(metadata.uid, pc,
Vectors.dense(Array.empty[Double]).asInstanceOf[DenseVector])
+ // explainedVariance field is not present and we use the old
matrix in Spark <= 2.0
+ val Row(pc: OldDenseMatrix) =
sparkSession.read.parquet(dataPath).select("pc").head()
+ new PCAModel(metadata.uid, pc.asML,
+ Vectors.dense(Array.empty[Double]).asInstanceOf[DenseVector])
--- End diff --
Here we combine the ```explainedVariance``` field issue and the old matrix
issue together to handle backward compatibility.
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