spark-aucmu (homepage)

multi-calss performance matrix aucmu for Apache Spark

@poweihuang / (1)

AUCmu is a performance matric for multi-class classification models and it is an extension of the standard two-class area under the receiver operating characteristic curve (AUC-ROC). This matric comes from an accepted paper in ICML2019 written by Ross Kleiman. The repo produced the matric on Apache Spark. To use this Spark Package, please follow the instructions in the README.

Tags

  • 1|machine learning
  • 1|pyspark

How to

Include this package in your Spark Applications using:

spark-shell, pyspark, or spark-submit

> $SPARK_HOME/bin/spark-shell --packages poweihuang:spark-aucmu:1.0.0

sbt

If you use the sbt-spark-package plugin, in your sbt build file, add:

spDependencies += "poweihuang/spark-aucmu:1.0.0"

Otherwise,

resolvers += "Spark Packages Repo" at "https://repos.spark-packages.org/"

libraryDependencies += "poweihuang" % "spark-aucmu" % "1.0.0"

Maven

In your pom.xml, add: 
<dependencies>
  <!-- list of dependencies -->
  <dependency>
    <groupId>poweihuang</groupId>
    <artifactId>spark-aucmu</artifactId>
    <version>1.0.0</version>
  </dependency>
</dependencies>
<repositories>
  <!-- list of other repositories -->
  <repository>
    <id>SparkPackagesRepo</id>
    <url>https://repos.spark-packages.org/</url>
  </repository>
</repositories>

Releases

Version: 1.0.0 ( 113d22 | zip | jar ) / Date: 2019-10-21 / License: MIT