Joseph K. Bradley created SPARK-4081:
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Summary: 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
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[Int, Int] =
datasetIndexer.getCategoricalFeaturesInfo()
{code}
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