Better function transformers in ML pipelines - 2018-11-21

A transformer factory using metaprogramming

One of the most convenient features in scikit-learn is the ability to build complex models by chaining transformers and estimators into pipelines.

Importantly, all (hyper-)parameters of each transformer remain accessible and tunable. The simplicity suffers somewhat once we need to add custom preprocessing functions into the pipeline. The “standard” approach using sklearn.preprocessing.FunctionTransformer felt decidedly unsatisfactory once I tried to define some parameter search spaces, so I looked into implementing a more usable alternative:

Beautiful is better than ugly!