Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/6339#discussion_r35808042
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala ---
@@ -151,4 +153,77 @@ class StringIndexerModel private[ml] (
val copied = new StringIndexerModel(uid, labels)
copyValues(copied, extra)
}
+
+ /**
+ * Return a model to perform the inverse transformation.
+ * Note: By default we keep the original columns during this
transformation, so the inverse
+ * should only be used on new columns such as predicted labels.
+ */
+ def invert(inputCol: String, outputCol: String):
StringIndexerInverseTransformer = {
+ val labelsCol: String = $(this.outputCol)
+ new StringIndexerInverseTransformer(labelsCol)
+ .setInputCol(inputCol)
+ .setOutputCol(outputCol)
+ }
+}
+
+/**
+ * :: Experimental ::
+ * Transform a provided column back to the original input types using the
metadata on the
+ * labelsCol. Note: By default we keep the original columns during this
transformation,
+ * so the inverse should only be used on new columns such as predicted
labels.
+ */
+@Experimental
+class StringIndexerInverseTransformer private[ml] (
+ override val uid: String,
+ val labelsCol: String) extends Transformer
--- End diff --
We could do that, or we could have labelsCol be optional and if its set use
it for metadata and if it isn't use the inputColumn for metadata, whats your
thoughts?
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