JuliaAI / MLJTuning.jl

Hyperparameter optimization algorithms for use in the MLJ machine learning framework
MIT License
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Improve the `Explicit` strategy #141

Closed ablaom closed 3 years ago

ablaom commented 3 years ago

This stategy (originally added for testing purposed only) is has not been publicised, but needs some improvements before doing so:

Problems:

Suggested resolution:

Implementation:

To get around the design issue requiring all models to have the same type, we can apply a thin wrapper to all models in the list and give a wrapped model the same setproperty!/getproperty interface as the original. The alternative (I'm guessing, from memory) is to remove the model type parameter M from MLJBase.Resampler{M} which could be painful, but worth checking as this would be less of a hack. Any loss of performance in dropping the type parameter is likely trivial in 99% of use cases.

rikhuijzer commented 3 years ago

The last part makes me conclude that this issue is a bit to tough for me. Let me know if I can help out (with documentation?).