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| author | Yanbo Liang <ybliang8@gmail.com> | 2015-05-20 07:55:51 -0700 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2015-05-20 07:55:51 -0700 |
| commit | 98a46f9dffec294386f6c39acafa7f11adb87a8f (patch) | |
| tree | a53b1011e52f440e97780de1bf1adc43624cc276 /mllib | |
| parent | 589b12f8e62ec5d10713ce057756ebc791e7ddc6 (diff) | |
| download | spark-98a46f9dffec294386f6c39acafa7f11adb87a8f.tar.gz spark-98a46f9dffec294386f6c39acafa7f11adb87a8f.tar.bz2 spark-98a46f9dffec294386f6c39acafa7f11adb87a8f.zip | |
[SPARK-6094] [MLLIB] Add MultilabelMetrics in PySpark/MLlib
Add MultilabelMetrics in PySpark/MLlib
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #6276 from yanboliang/spark-6094 and squashes the following commits:
b8e3343 [Yanbo Liang] Add MultilabelMetrics in PySpark/MLlib
Diffstat (limited to 'mllib')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala | 8 |
1 files changed, 8 insertions, 0 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala index a8378a76d2..bf6eb1d5bd 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala @@ -19,6 +19,7 @@ package org.apache.spark.mllib.evaluation import org.apache.spark.rdd.RDD import org.apache.spark.SparkContext._ +import org.apache.spark.sql.DataFrame /** * Evaluator for multilabel classification. @@ -27,6 +28,13 @@ import org.apache.spark.SparkContext._ */ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]) { + /** + * An auxiliary constructor taking a DataFrame. + * @param predictionAndLabels a DataFrame with two double array columns: prediction and label + */ + private[mllib] def this(predictionAndLabels: DataFrame) = + this(predictionAndLabels.map(r => (r.getSeq[Double](0).toArray, r.getSeq[Double](1).toArray))) + private lazy val numDocs: Long = predictionAndLabels.count() private lazy val numLabels: Long = predictionAndLabels.flatMap { case (_, labels) => |