Convenience method for transforming existing estimator objects to `PMMLPipeline` objects about sklearn2pmml

[vruusmann]

Simply wrap your pre-trained estimator object into a sklearn.PMMLPipeline object:

estimator = pickle.load(...)
pipeline = PMMLPipeline([
  ("pretrained-estimator", estimator)
])

sklearn2pmml(pipeline, "pretrained-estimator.pmml")

The main problem with using pre-trained estimators is that the resulting PMML document will be devoid of any supporting (meta-)information such as column names or data types. All input fields will be called x{index}, and they will be of some numeric data type (typically, float for decision tree-based model types, and double for all other model types).

If you have some knowledge about the "schema" of your pre-trained models, then it's possible to enhance the sklearn2pmml.PMMLPipeline object accordingly:

dataframemapper = pickle.load(...)
pipeline = PMMLPipeline([
  ("pretrained-dataframemapper", dataframemapper),
  ("pretrained-estimator", estimator)
])

or

pipeline = PMMLPipeline([
  ("pretrained-estimator", estimator)
])
pipeline.target_field = "Species"
pipeline.active_fields = numpy.array(["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"])

[rossmeissl]

Thanks @vruusmann!

[vruusmann]

It would be nice to have utility method(s) for doing this kind of wrapping work. Furthermore, it could happen silently inside the sklearn2pmml function itself.

The underlying JPMML-SkLearn library has no problem accepting "raw" estimator objects:
jpmml/jpmml-sklearn@7b258cd

So, this limitation exists only in the sklearn2pmml package, in the form of the following isinstance check:
https://github.com/jpmml/sklearn2pmml/blob/master/sklearn2pmml/__init__.py#L128