[jira] [Commented] (SPARK-9312) The OneVsRest model does not provide rawPrediction

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-9312?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16722608#comment-16722608 ] 

ASF GitHub Bot commented on SPARK-9312:
---------------------------------------

srowen closed pull request #7652: [SPARK-9312] [ML] Added max confidence factor to OneVsRestModel
URL: https://github.com/apache/spark/pull/7652
 
 
   

This is a PR merged from a forked repository.
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the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
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diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index ea757c5e40c76..0e0126c112ab6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -24,18 +24,19 @@ import scala.language.existentials
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml._
 import org.apache.spark.ml.attribute._
+import org.apache.spark.ml.param.shared.HasRawPredictionCol
 import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
-import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.storage.StorageLevel
 
 /**
  * Params for [[OneVsRest]].
  */
-private[ml] trait OneVsRestParams extends PredictorParams {
+private[ml] trait OneVsRestParams extends ClassifierParams {
 
   // scalastyle:off structural.type
   type ClassifierType = Classifier[F, E, M] forSome {
@@ -127,10 +128,16 @@ final class OneVsRestModel private[ml] (
       predictions.maxBy(_._2)._1.toDouble
     }
 
-    // output label and label metadata as prediction
+    // output the highest confidence as rawPredictionCol
+    val probabilityUDF = udf { (predictions: Map[Int, Double]) =>
+      predictions.maxBy(_._2)._2.toDouble
+    }
+
+    // output label, confidence factor and label metadata as prediction
     aggregatedDataset
       .withColumn($(predictionCol), labelUDF(col(accColName)).as($(predictionCol), labelMetadata))
-      .drop(accColName)
+      .withColumn($(rawPredictionCol), probabilityUDF(col(accColName)).as($(rawPredictionCol),
+      labelMetadata)).drop(accColName)
   }
 
   override def copy(extra: ParamMap): OneVsRestModel = {
@@ -220,4 +227,5 @@ final class OneVsRest(override val uid: String)
     }
     copied
   }
+  
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
index 75cf5bd4ead4f..c416c9080f6f9 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
@@ -110,7 +110,8 @@ class OneVsRestSuite extends SparkFunSuite with MLlibTestSparkContext {
     val ovr = new OneVsRest()
       .setClassifier(logReg)
     val output = ovr.fit(dataset).transform(dataset)
-    assert(output.schema.fieldNames.toSet === Set("label", "features", "prediction"))
+    assert(output.schema.fieldNames.toSet === Set("label", "features", "prediction",
+      "rawPrediction"))
   }
 
   test("OneVsRest.copy and OneVsRestModel.copy") {


 

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> The OneVsRest model does not provide rawPrediction
> --------------------------------------------------
>
>                 Key: SPARK-9312
>                 URL: https://issues.apache.org/jira/browse/SPARK-9312
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, MLlib
>    Affects Versions: 1.4.0, 1.4.1
>            Reporter: Badari Madhav
>            Assignee: Lu Wang
>            Priority: Major
>              Labels: features
>             Fix For: 2.4.0
>
>   Original Estimate: 72h
>  Remaining Estimate: 72h
>




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