I have a class `FancyEstimator(BaseEstimator, MetaEstimatorMixin): ...` that wraps around an arbitrary sklearn estimator to add some functionality I am interested about. This class contains an attribute `self.estimator` that contains the wrapped estimator. Delegation of the main methods, such as `fit`, `transform` works just fine, but I am having some issues with `get_params` and `set_params`.
The main idea is, I would like to use my wrapped class as a drop-in replacement for the original estimator, but this raises some issues with some functions that try using the `get_params` and `set_params` straight in my class, as the original parameters now have prefixed names (for instance `estimator__verbose` instead of `verbose`) I would like to delegate calls of set_params and get_params in a smart way so that if a parameter is unknown for my wrapper class, then it automatically goes looking for it in the wrapped estimator. I am not concerned about my class parameter names as there are only a couple of very specific names on it, so it is safe to assume that any unknown parameter name should refer to the base estimator. Is there an easy way of doing that? Cheers, J
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