Feature Selection framework based on Information Theory that includes: mRMR, InfoGain, JMI and other commonly used FS filters.
@sramirez / (8)
This package contains a generic implementation of greedy Information Theoretic Feature Selection (FS) methods. The implementation is based on the common theoretic framework presented by Gavin Brown. Implementations of mRMR, InfoGain, JMI and other commonly used FS filters are provided.
Include this package in your Spark Applications using:
spark-shell, pyspark, or spark-submit
> $SPARK_HOME/bin/spark-shell --packages sramirez:spark-infotheoretic-feature-selection:1.4.4
If you use the sbt-spark-package plugin, in your sbt build file, add:
spDependencies += "sramirez/spark-infotheoretic-feature-selection:1.4.4"
resolvers += "Spark Packages Repo" at "https://repos.spark-packages.org/" libraryDependencies += "sramirez" % "spark-infotheoretic-feature-selection" % "1.4.4"
MavenIn your pom.xml, add:
<dependencies> <!-- list of dependencies --> <dependency> <groupId>sramirez</groupId> <artifactId>spark-infotheoretic-feature-selection</artifactId> <version>1.4.4</version> </dependency> </dependencies> <repositories> <!-- list of other repositories --> <repository> <id>SparkPackagesRepo</id> <url>https://repos.spark-packages.org/</url> </repository> </repositories>