Github user Krimit commented on a diff in the pull request:
https://github.com/apache/spark/pull/16607#discussion_r96328450
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
---
@@ -320,14 +341,29 @@ object Word2VecModel extends
MLReadable[Word2VecModel] {
private val className = classOf[Word2VecModel].getName
override def load(path: String): Word2VecModel = {
+ val spark = sparkSession
+ import spark.implicits._
+
val metadata = DefaultParamsReader.loadMetadata(path, sc, className)
+
val dataPath = new Path(path, "data").toString
- val data = sparkSession.read.parquet(dataPath)
- .select("wordIndex", "wordVectors")
- .head()
- val wordIndex = data.getAs[Map[String, Int]](0)
- val wordVectors = data.getAs[Seq[Float]](1).toArray
- val oldModel = new feature.Word2VecModel(wordIndex, wordVectors)
+ val rawData = spark.read.parquet(dataPath)
+
+ val oldModel = if (rawData.columns.contains("wordIndex")) {
--- End diff --
I'd only ever seen ``SaveLoadV1_0`` used in MLlib, is it still the
preferred way to mark versions? In ml land I've seen things like relying on the
spark version:
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala#L981,
https://github.com/apache/spark/blob/f830bb9170f6b853565d9dd30ca7418b93a54fe3/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala#L210,
https://github.com/apache/spark/blob/7db09abb0168b77697064c69126ee82ca89609a0/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala#L234
which I don't really like in this case since it relies on something
extraneous and makes it difficult to backport.
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