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Custom transformers for configuring continuous and categorical feature information #14

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vruusmann opened this issue Apr 5, 2017 · 1 comment
Open

Custom transformers for configuring continuous and categorical feature information #14

vruusmann opened this issue Apr 5, 2017 · 1 comment

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@vruusmann
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vruusmann commented Apr 5, 2017 •
edited

As requested in jpmml/jpmml-evaluator#56

The JPMML-SkLearn project defines two custom transformation types sklearn2pmml.decoration.CategoricalDomain and sklearn2pmml.decoration.ContinuousDomain, which provide the ability to configure missing value, invalid value etc. treatments. For example:

mapper = DataFrameMapper([
  ("Sepal.Length", ContinuousDomain(missing_value_treatment = "as_is", invalid_value_treatment = "as_is"))
])

The JPMML-SparkML should provide identical functionality.
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