Mechanism for registering custom Estimator and Transformer converter classes about sklearn2pmml

[vruusmann]

First, you need to introduce an appropriate converter class into the JPMML-SkLearn project:

1. Create a subclass of sklearn.Transformer, and implement the conversion "business logic" in the Transformer#encodeFeatures(List, List, FeatureMapper) method.
2. Register this class with org.jpmml.sklearn.PickleUtil.

There's not much documentation about it. Here's an example about implementing a converter class for Scikit-Learn's FunctionTransformer transformation type:
jpmml/jpmml-sklearn@5c4a181

Then, build your modified JPMML-SkLearn project with Apache Maven, and drop the resulting JAR file into the sklearn2pmml /resources/ directory. Currently, you would be replacing jpmml-sklearn-1.1.4.jar with your own jpmml-sklearn-1.1-SNAPSHOT.jar.

[vruusmann]

Edited the title of this issue to reflect the real user need.

Actually, one shouldn't be making custom converter classes part of the JPMML-SkLearn library. They could be isolated into a standalone (mini-)project, which depends on the JPMML-SkLearn library (and other Java libraries).

If this (mini-)project is built, then it should produce a JAR file, which is suitable for dropping into sklearn2pmml /resources/ directory. At the moment, the problem is that there is no way of informing org.jpmml.sklearn.PickleUtil about those newly dropped-in converter classes - the list of supported converters is hard-coded.

A solution would be to introduce some sort of "sklearn2pmml extension module metadata" mechanism. For example, the JAR file could contain a properties file META-INF/sklearn2pmml.properties, which lists the names of new converter classes.

[geoHeil]

Is there a simpler solution e.g. similar to http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html where an arbitrary function can be registered?

[vruusmann]

Class sklearn.preprocessing.FunctionTransformer supports a limited number of 1-parameter Numpy ufuncs: https://github.com/jpmml/jpmml-sklearn/blob/master/src/main/java/sklearn/preprocessing/FunctionTransformer.java#L79

As you can see, in order to support an ufunc, you still need to write conversion "business logic" in Java, and (re-)build a modified JPMML-SkLearn library.

[vruusmann]

Could you perhaps share your Python Transformer class? Maybe I can suggest a simple and easy way of automatically translating it to PMML then.

For example, the JPMML-R library now includes a general-purpose R-to-PMML expression conversion functionality:

iris.rf = randomForest(Species ~ . + I(log(Sepal.Length / Sepal.Width) + 1), data = iris)

Should be possible to build a similar Python-to-PMML expression converter. Of course, the trouble is that you cannot refer fields by name in Scikit-Learn; have to use field references something like $1, $2, .., $n instead.

[geoHeil]

Unfortunately sharing the code will not be possible. But I can explain you the actions which are performed.

• filtering some customer groups (no state involved)
• handling NaN values (state required for imputation)
• generating some features (no state involved)

The whole pipeline is a bit similar to http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html where the cleaning of the data is encapsulated in its own transformer.

[vruusmann]

Starting from the JPMML-SkLearn library version 1.2.0, the PickleUtil utility class will scan all the JAR files in the application classpath for the META-INF/sklearn2pmml.properties file, and if found, will automatically register all the listed converter classes with the runtime. For example, here is the list of built-in converter classes: https://github.com/jpmml/jpmml-sklearn/blob/master/src/main/resources/META-INF/sklearn2pmml.properties

You can implement your own Transformer, Selector and Estimator converter classes as appropriate. When done, create a corresponding META-INF/sklearn2pmml.properties file, and package everything as a regular JAR file.

During conversion, you can add a list of JAR files to the application classpath using the newly introduced user_classpath argument:

sklearn2pmml(estimator, mapper, "estimator_mapper.pmml", user_classpath = ["/path/to/extensions.jar"])