✨ Add BalancedModel #1
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test/BalancedModel.jl
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| y_train, y_test = y[train_inds], y[test_inds] | ||
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| # Load models and balancers | ||
| SVC = @load SVC pkg = LIBSVM |
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I tend to avoid the C models and use pure Julia models in testing where possible, because it leads to less maintenance hangups. A simple Julia deterministic classifier is OneRuleClassifier (You can also turn a probabilistic model into a deterministic one using pipeline syntax probabilistic_model |> (y |> mode.(y)).)
Also, keep in mind that there is a known issue which prevents using composite models (from exported learning networks) in multithreading mode for some non-Julia models: JuliaAI/MLJBase.jl#783
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To test if a model in pure julia use you can do MLJBase.is_pure_julia(model).
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@ablaom I don't see how probabilistic_model |> (y |> mode.(y)) would change the model type itself from Probabilistic to Deterministic, will it? OneRuleClassifier only works with categorical data. I ended up ditching the SVM and using DeterministicConstantClassifier, afterall all I want to assure here is that it works with the Deterministic type with no problems.
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Got it. This is a good resolution.
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I think that Julia 1.6 (LTS) and Julia 1 (latest) should be in the CI matrix, and that suffices. |
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I believe the Windows fail is due to a known Julia issue with SVMLIB and XGBoost particular to Windows (actually a Julia bug) which was corrected at about Julia 1.9.2 or 1.9.3. It appeared around Julia 1.8.0. So this should disappear when you skip 1.8 testing. Another argument for avoiding non-Julia models in testing 😉 |
Co-authored-by: Anthony Blaom, PhD <[email protected]>
Co-authored-by: Anthony Blaom, PhD <[email protected]>
Co-authored-by: Anthony Blaom, PhD <[email protected]>
Co-authored-by: Anthony Blaom, PhD <[email protected]>
Co-authored-by: Anthony Blaom, PhD <[email protected]>
Co-authored-by: Anthony Blaom, PhD <[email protected]>
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@ablaom Ready to merge this once the final comments are addressed. Thank you so much for your time during the review. |
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Okay. There is only one comment to address (there was another but have found the answer). note that Github checks fail only due to daily limits. |
Description
This PR adds
BalancedModelwhich allows sequential composition of resampling models fromImbalance.jl(or any transforms that operate on(X, y)data.@ablaom could you please review the additions when you get the chance. The interface implemented is very similar to that of
StackinginMLJBase.