I have a class `FancyEstimator(BaseEstimator, MetaEstimatorMixin): ...`
around an arbitrary sklearn estimator to add some functionality I am
This class contains an attribute `self.estimator` that contains the wrapped
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
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
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
refer to the base estimator. Is there an easy way of doing that?
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