fix mlr3 integration, add light rcmdcheck and fix its complaints#29
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mmyrte
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May 6, 2026
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| # TODO put in validator that the learner has predict_type = "prob" set |
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@copilot fix this, but instead of erroring out straight away, i want both fit_partial_models and fit_full_models to coerce the learner to mlr3's predict_type "prob"; if that fails, then we should error out. we should also verify at this point that the learner supports twoclass classifications.
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Done in 552ceac. Added a coerce_learner_for_classif() internal helper that is called at the top of both fit_partial_models and fit_full_models (direct-learner branch). It:
- Checks the learner is an mlr3
Learner/AutoTuner(errors if not). - Checks
"twoclass" %in% learner$properties(errors if not). - Coerces
predict_typeto"prob"if it isn't already — wrapping the assignment intryCatchso that learners which don't support probabilistic output get a descriptive error instead of mlr3's raw param-set message.
Tests added: auto-coercion from "response" to "prob" (verifying the side-effect on the passed-in R6 object), and an error when twoclass support is absent.
Agent-Logs-Url: https://github.com/ethzplus/evoland-plus/sessions/5b22b07c-415d-4ace-8427-bb48fe16b121 Co-authored-by: mmyrte <24587121+mmyrte@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
if two models have the same score, but only one model has been trained, we get strange errors. this avoids it, but ideally the error should already crop up in fit_full_models
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There were issues I had not thought of in #24