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[SPARK-9929][SQL] support metadata in withColumn #8159

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cloud-fan wants to merge 2 commits into from
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[SPARK-9929][SQL] support metadata in withColumn #8159

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4698d05
support metadata in withColumn
cloud-fan Aug 13, 2015
39ce9c7
make it private
cloud-fan Aug 14, 2015
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6 changes: 3 additions & 3 deletions mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
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Expand Up @@ -131,7 +131,7 @@ final class OneVsRestModel private[ml] (

// output label and label metadata as prediction
aggregatedDataset
.withColumn($(predictionCol), labelUDF(col(accColName)).as($(predictionCol), labelMetadata))
.withColumn($(predictionCol), labelUDF(col(accColName)), labelMetadata)
.drop(accColName)
}

Expand Down Expand Up @@ -203,8 +203,8 @@ final class OneVsRest(override val uid: String)
// TODO: use when ... otherwise after SPARK-7321 is merged
val newLabelMeta = BinaryAttribute.defaultAttr.withName("label").toMetadata()
val labelColName = "mc2b$" + index
val labelUDFWithNewMeta = labelUDF(col($(labelCol))).as(labelColName, newLabelMeta)
val trainingDataset = multiclassLabeled.withColumn(labelColName, labelUDFWithNewMeta)
val trainingDataset =
multiclassLabeled.withColumn(labelColName, labelUDF(col($(labelCol))), newLabelMeta)
val classifier = getClassifier
val paramMap = new ParamMap()
paramMap.put(classifier.labelCol -> labelColName)
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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
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Expand Up @@ -75,7 +75,7 @@ final class Bucketizer(override val uid: String)
}
val newCol = bucketizer(dataset($(inputCol)))
val newField = prepOutputField(dataset.schema)
dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata))
dataset.withColumn($(outputCol), newCol, newField.metadata)
}

private def prepOutputField(schema: StructType): StructField = {
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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
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Expand Up @@ -341,7 +341,7 @@ class VectorIndexerModel private[ml] (
val newField = prepOutputField(dataset.schema)
val transformUDF = udf { (vector: Vector) => transformFunc(vector) }
val newCol = transformUDF(dataset($(inputCol)))
dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata))
dataset.withColumn($(outputCol), newCol, newField.metadata)
}

override def transformSchema(schema: StructType): StructType = {
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3 changes: 1 addition & 2 deletions mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
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Expand Up @@ -119,8 +119,7 @@ final class VectorSlicer(override val uid: String)
case features: SparseVector => features.slice(inds)
}
}
dataset.withColumn($(outputCol),
slicer(dataset($(inputCol))).as($(outputCol), outputAttr.toMetadata()))
dataset.withColumn($(outputCol), slicer(dataset($(inputCol))), outputAttr.toMetadata())
}

/** Get the feature indices in order: indices, names */
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17 changes: 17 additions & 0 deletions sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
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Expand Up @@ -1149,6 +1149,23 @@ class DataFrame private[sql](
}
}

/**
* Returns a new [[DataFrame]] by adding a column with metadata.
*/
private[spark] def withColumn(colName: String, col: Column, metadata: Metadata): DataFrame = {
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@cloud-fan cloud-fan Aug 14, 2015

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should we make Column.as(alias: String, metadata: Metadata) also private? As it expose the Metadata too.

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@rxin rxin Aug 14, 2015

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Don't think we can change that since it's been public since 1.3.

val resolver = sqlContext.analyzer.resolver
val replaced = schema.exists(f => resolver(f.name, colName))
if (replaced) {
val colNames = schema.map { field =>
val name = field.name
if (resolver(name, colName)) col.as(colName, metadata) else Column(name)
}
select(colNames : _*)
} else {
select(Column("*"), col.as(colName, metadata))
}
}

/**
* Returns a new [[DataFrame]] with a column renamed.
* This is a no-op if schema doesn't contain existingName.
Expand Down