[ANN] EvoTrees.jl v0.17 - API update (and breaking changes)

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Below are key changes with upcoming v0.17 release.
It’s yet to be registered in General, and for now available through main branch, so changes could still be brought if some design choices appear problematic.

The general objective with the API changes was to get improved alignement with the MLJ (and potentially LearnAPI) scope for model constructor/learner, and fit method. Also, improve consistency with NeuroTreeModels

Breaking changes:

Model constructors (EvoTreeRegressor, EvoTreeClassifier…) now include the following arguments:

  • metric: the evaluation metric to be tracked
  • early_stopping_rounds
  • device: either :cpu or :gpu

Example:

config = EvoTreeRegressor(; loss=:mse, metric=:mae, early_stopping_rounds=10, device=:gpu)

Deprecation of fit_evotree in favor of import of MLJModelInterface fit :

Note that fit_evotree results in a call to fit.
The following legacy kwargs of fit_evotree will be ignored:

  • metric
  • return_logger
  • early_stopping_rounds
  • device
m = fit_evotree(config, dtrain; target_name="y", feature_names=["x1", "x2"]) #old 
m = fit(config, dtrain; target_name="y", feature_names=["x1", "x2"]) #new

Changes in the naming of variables identity in the Tables / DataFrames based internal API, which were previously kwargs of fit_evotree:

  • fnames => feature_names
  • w_name => weight_names
m = fit_evotree(config, dtrain; target_name="y", feature_names=["x1", "x2"])

The logger, which tracks metrics on eval data through the iterations, is now automatically included in a fitted model info field

m = fit(config, dtrain; target_name="y", feature_names=["x1", "x2"], deval)
logger = m.info[:logger]

Changes related to losses:

  • L1 / l1 loss is no longer supported. Use loss=:mae in EvoTreeRegressor instead.

Constructors are not longer parametrics: EvoTreeRegressor{L<:ModelType} => EvoTreeRegressor

This one shouldn’t affect user’s experience.

Fixes and improvements to GPU:

  • Models trained through MLJ now support the :gpu argument (passed through the constructor like EvoTreeRegressor as showned above).
  • Inference is now properly dispatch to :gpu when using: m(dtrain; device=:gpu)
  • Both :mae and :quantile losses are now now suported on GPU (device = :gpu)
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