v0.6

New functionality:

• Similar to Spark's StringIndexer, we have a ValueIndexer that can
  be used for indexing any type of values instead of only strings. Not
  only can it index these values, we also provide a reverse mapping via
  IndexToValue, similar to Spark's IndexToString transform.

• A new "clean missing" data estimator, example:

  val cmd = new CleanMissingData()
    .setInputCols(Array("some-column"))
    .setOutputCols(Array("some-column"))
    .setCleaningMode(CleanMissingData.customOpt)
    .setCustomValue(someCustomValue)
  val cmdModel = cmd.fit(dataset)
  val result = cmdModel.transform(dataset)
  

• New default featurization for date and timestamp spark types and our
  internal image type. For featurization of date columns, convert
  column to double features: year, day of week, month, day of month.
  For featurization of timestamp columns, same as date and in addition:
  hour of day, minute of hour, second of minute. For featurization of
  image columns, use image data converted to double with width and
  height info.

• Starting the docker image without an ACCEPT_EULA variable setting
  would throw an error. Instead, we now start a tiny web server that
  shows the EULA and replaces itself with the Jupyter interface when you
  click the AGREE button.

Breaking changes:

• Renamed ImageTransform to ImageTransformer.

Notable bug fixes and other changes:

• Improved sample notebooks, and a new one: "303 - Transfer Learning by
  DNN Featurization - Airplane or Automobile".

• Fix serialization bugs in generated python PipelineStages.

Acknowledgments

Thanks to Ali Zaidi for some notebook beautifications.