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| author | Yanbo Liang <ybliang8@gmail.com> | 2015-05-10 00:57:14 -0700 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2015-05-10 00:57:14 -0700 |
| commit | bf7e81a51cd81706570615cd67362c86602dec88 (patch) | |
| tree | dc3d55d57d58606fe4c10f8bb2ec0be428ec24b6 /mllib | |
| parent | b13162b364aeff35e3bdeea9c9a31e5ce66f8c9a (diff) | |
| download | spark-bf7e81a51cd81706570615cd67362c86602dec88.tar.gz spark-bf7e81a51cd81706570615cd67362c86602dec88.tar.bz2 spark-bf7e81a51cd81706570615cd67362c86602dec88.zip | |
[SPARK-6091] [MLLIB] Add MulticlassMetrics in PySpark/MLlib
https://issues.apache.org/jira/browse/SPARK-6091
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #6011 from yanboliang/spark-6091 and squashes the following commits:
bb3e4ba [Yanbo Liang] trigger jenkins
53c045d [Yanbo Liang] keep compatibility for python 2.6
972d5ac [Yanbo Liang] Add MulticlassMetrics in PySpark/MLlib
Diffstat (limited to 'mllib')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala | 8 |
1 files changed, 8 insertions, 0 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala index 666362ae67..4628dc5690 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala @@ -23,6 +23,7 @@ import org.apache.spark.SparkContext._ import org.apache.spark.annotation.Experimental import org.apache.spark.mllib.linalg.{Matrices, Matrix} import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame /** * ::Experimental:: @@ -33,6 +34,13 @@ import org.apache.spark.rdd.RDD @Experimental class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) { + /** + * An auxiliary constructor taking a DataFrame. + * @param predictionAndLabels a DataFrame with two double columns: prediction and label + */ + private[mllib] def this(predictionAndLabels: DataFrame) = + this(predictionAndLabels.map(r => (r.getDouble(0), r.getDouble(1)))) + private lazy val labelCountByClass: Map[Double, Long] = predictionAndLabels.values.countByValue() private lazy val labelCount: Long = labelCountByClass.values.sum private lazy val tpByClass: Map[Double, Int] = predictionAndLabels |