Cuml: [FEA] Genetic Programming for Feature Engineering

[aerdem4]

Is your feature request related to a problem? Please describe.
Genetic Programming is very useful for feature engineering but main challenge is its time complexity. Luckily, they are easily parallelizable. Therefore, I believe it is a good fit for cuML.

Example: Let's assume you have 2 columns A and B, and a binary target. This target is 1 most of the time when A > B. It is very difficult to learn it with a tree based model but GP can engineer this feature for you.

Describe the solution you'd like
I would like to have the functionalities of gplearn accelerated on GPU. (https://gplearn.readthedocs.io/en/stable/)

[teju85]

@aerdem4 so, are you only looking for a gpu-accelerated SymbolicTransformer?

[aerdem4]

@teju85 I think all of them are the same except the metric. Multiple options for the metric would be nice but spearman is the most useful.

[JohnZed]

Alright, whose idea of a joke was it to tag this with Good First Issue? I'm looking at you @wxbn ! ;)