[jira] [Resolved] (SPARK-4081) Categorical feature indexing

["Xiangrui Meng (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-4081?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xiangrui Meng resolved SPARK-4081.
----------------------------------
       Resolution: Fixed
    Fix Version/s: 1.4.0

Issue resolved by pull request 3000
[https://github.com/apache/spark/pull/3000]

> Categorical feature indexing
> ----------------------------
>
>                 Key: SPARK-4081
>                 URL: https://issues.apache.org/jira/browse/SPARK-4081
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>    Affects Versions: 1.1.0
>            Reporter: Joseph K. Bradley
>            Assignee: Joseph K. Bradley
>            Priority: Minor
>             Fix For: 1.4.0
>
>
> DecisionTree and RandomForest require that categorical features and labels be indexed 0,1,2....  There is currently no code to aid with indexing a dataset.  This is a proposal for a helper class for computing indices (and also deciding which features to treat as categorical).
> Proposed functionality:
> * This helps process a dataset of unknown vectors into a dataset with some continuous features and some categorical features. The choice between continuous and categorical is based upon a maxCategories parameter.
> * This can also map categorical feature values to 0-based indices.
> Usage:
> {code}
> val myData1: RDD[Vector] = ...
> val myData2: RDD[Vector] = ...
> val datasetIndexer = new DatasetIndexer(maxCategories)
> datasetIndexer.fit(myData1)
> val indexedData1: RDD[Vector] = datasetIndexer.transform(myData1)
> datasetIndexer.fit(myData2)
> val indexedData2: RDD[Vector] = datasetIndexer.transform(myData2)
> val categoricalFeaturesInfo: Map[Double, Int] = datasetIndexer.getCategoricalFeatureIndexes()
> {code}



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