Closed bbzzzz closed 6 years ago
AFAIK, the LightGBM framework only deals with models, and does not define its own pipeline concept/formalization.
All JPMML conversion libraries have "pluggable" architecture - you can do feature engineering using pipelines in one ML framework, and then train the model using some other ML framework. For example, putting JPMML-SkLearn and JPMML-LightGBM libraries together:
Standard Scikit-Learn pipeline: https://github.com/jpmml/jpmml-sklearn/blob/master/src/test/resources/main.py#L121-L155 https://github.com/jpmml/jpmml-sklearn/blob/master/src/test/resources/main.py#L162
Non-standard, LightGBM-style categorical feature encoding pipeline: https://github.com/jpmml/jpmml-sklearn/blob/master/src/test/resources/main.py#L177-L194 https://github.com/jpmml/jpmml-sklearn/blob/master/src/test/resources/main.py#L196
Hi Vilu,
Does this package support pipeline like sklearn2pmml?
Thanks, Bohan