Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/12066#discussion_r61474435
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala ---
@@ -37,8 +37,38 @@ private[spark] trait ClassifierParams
schema: StructType,
fitting: Boolean,
featuresDataType: DataType): StructType = {
- val parentSchema = super.validateAndTransformSchema(schema, fitting,
featuresDataType)
- SchemaUtils.appendColumn(parentSchema, $(rawPredictionCol), new
VectorUDT)
+ // TODO: Support casting Array[Double] and Array[Float] to Vector when
FeaturesType = Vector
+ SchemaUtils.checkColumnType(schema, $(featuresCol), featuresDataType)
+ if (fitting) {
+ SchemaUtils.checkNumericType(schema, $(labelCol))
+ }
+ val newSchema = SchemaUtils.appendColumn(schema, $(predictionCol),
DoubleType,
+ nullable = false, generatePredictionMetadata(schema))
+ SchemaUtils.appendColumn(newSchema, $(rawPredictionCol), new VectorUDT)
+ }
+
+ protected def generatePredictionMetadata(schema: StructType): Metadata =
{
+ // The label column for base binary classifier of OneVsRest will not
be retained during
+ // model transformation, so we should not handle label column metadata
as well.
+ if (schema.fieldNames.contains($(labelCol))) {
+ // determine number of classes either from metadata if provided.
+ val labelSchema = schema($(labelCol))
+ MetadataUtils.getNumClasses(labelSchema) match {
+ case Some(numClasses) =>
+ // extract label metadata from label column if present, or
create a nominal attribute
+ // to output the number of labels
+ val labelAttribute = Attribute.fromStructField(labelSchema)
match {
+ case _: NumericAttribute | UnresolvedAttribute =>
+
NominalAttribute.defaultAttr.withName($(predictionCol)).withNumValues(numClasses)
+ case attr: Attribute => attr
+ }
+ labelAttribute.toMetadata()
+ case None =>
+ Metadata.empty
--- End diff --
You could do ```NominalAttribute.defaultAttr.withName($(predictionCol))```
instead of Metadata.empty
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