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https://issues.apache.org/jira/browse/SPARK-4081?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley updated SPARK-4081:
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Description:
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}
was:
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}
> 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}
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