[
https://issues.apache.org/jira/browse/SPARK-4081?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14392055#comment-14392055
]
Joseph K. Bradley commented on SPARK-4081:
------------------------------------------
Note: I am adding this to the spark.ml API since it is friendlier for feature
transformers; I will not add it to the spark.mllib API.
> 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
>
> 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}
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
