Comments (1)
I think we could do this much more easily by automatically adding an entry to criterion_kwargs
(e.g. "maximization": True
) in maximize
before we call minimize
in it. Then we can define the internal criterion function such that it always passes the scaled fitness to the queue instead of the "true" fitness. That way the dashboard code does not have to be touched at all.
The option to plot a transformed fitness might be more helpful in general. For example, we might want to plot the "true" likelihood instead of the log likelihood.
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Related Issues (20)
- Error in multistart with `ls` optimizers HOT 2
- Use modern pre-commit hooks and implement own
- Improve handling of notebooks in documentation
- Styling of yaml files HOT 4
- Logging fails with SQLAlchemy 2.0 HOT 1
- Continue running `check_constraints_are_satisfied` beyond first failure HOT 1
- Equality constraints involving params values not always caught correctly HOT 11
- Analyze code coverage of numba-jitted functions
- Estimation table: reconsider visualization of integers
- update `siunitx_warning` for estimation_table HOT 2
- logging for estimate_msm not working HOT 7
- Improve error messages for empty log files
- Replace `DateFrame.applymap` with `DataFrame.map` (in estimation_table) HOT 1
- `get_moments_cov` does not forward `bootstrap_kwargs` to `bootstrap`
- Nested pd.Series (and more) dont get unraveled properly some calls (i think it is numerical jacobeans) HOT 1
- Bug in `get_moments_cov` HOT 1
- Bootstrap with survey weights HOT 1
- DOCS: Some improvements
- full optimization results not saved in estimation HOT 1
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from estimagic.