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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2014/08/04 06:41:12 UTC
[jira] [Resolved] (SPARK-2272) Feature scaling which standardizes
the range of independent variables or features of data.
[ https://issues.apache.org/jira/browse/SPARK-2272?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiangrui Meng resolved SPARK-2272.
----------------------------------
Resolution: Fixed
Fix Version/s: 1.1.0
Issue resolved by pull request 1207
[https://github.com/apache/spark/pull/1207]
> Feature scaling which standardizes the range of independent variables or features of data.
> ------------------------------------------------------------------------------------------
>
> Key: SPARK-2272
> URL: https://issues.apache.org/jira/browse/SPARK-2272
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: DB Tsai
> Assignee: DB Tsai
> Fix For: 1.1.0
>
>
> Feature scaling is a method used to standardize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step.
> In this work, a trait called `VectorTransformer` is defined for generic transformation of a vector. It contains two methods, `apply` which applies transformation on a vector and `unapply` which applies inverse transformation on a vector.
> There are three concrete implementations of `VectorTransformer`, and they all can be easily extended with PMML transformation support.
> 1) `VectorStandardizer` - Standardises a vector given the mean and variance. Since the standardization will densify the output, the output is always in dense vector format.
>
> 2) `VectorRescaler` - Rescales a vector into target range specified by a tuple of two double values or two vectors as new target minimum and maximum. Since the rescaling will substrate the minimum of each column first, the output will always be in dense vector regardless of input vector type.
> 3) `VectorDivider` - Transforms a vector by dividing a constant or diving a vector with element by element basis. This transformation will preserve the type of input vector without densifying the result.
> Utility helper methods are implemented for taking an input of RDD[Vector], and then transformed RDD[Vector] and transformer are returned for dividing, rescaling, normalization, and standardization.
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