[ENH] LightGBMLSS interface

[yuvimittal]

Reference Issues/PRs

Fixes #589

What does this implement/fix? Explain your changes.

Adds interface for lightgbmlss probabilistic regressor

Does your contribution introduce a new dependency? If yes, which one?

What should a reviewer concentrate their feedback on?

Did you add any tests for the change?

Any other comments?

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[fkiraly]

code quality checks are failing, see https://www.sktime.net/en/stable/developer_guide/coding_standards.html

[yuvimittal]

code quality checks are failing, see https://www.sktime.net/en/stable/developer_guide/coding_standards.html

@fkiraly , i will look into it. Thank you

[fkiraly]

_hyper_opt is actually being used inside _fit when n_trials > 0:

Ah, I see - you are right.

In this case, I believe this feature requires optuna?

In the __init__, then, set_tag should be used to set python_dependencies including optuna, if n_trials>0.

[fkiraly]

Nice! Looks like it should work.

Two comments:

• the estimator seems to be failing tests - is this stochastic? FYI @StatMixedML
• this looks largely identical to the xgboostlss interface - good that you recognized this! Of course copy-pasting is a good starting idea, but I think we should not leave it with duplicate code, due to the DRY principle https://en.wikipedia.org/wiki/Don%27t_repeat_yourself. I would move methods or pieces of code that are common to both estimators into a new mixin class in a new module regression.adapters._statmixedml (if this looks reasonably modularizable)

[yuvimittal]

Nice! Looks like it should work.

Two comments:

• the estimator seems to be failing tests - is this stochastic? FYI @StatMixedML
• this looks largely identical to the xgboostlss interface - good that you recognized this! Of course copy-pasting is a good starting idea, but I think we should not leave it with duplicate code, due to the DRY principle https://en.wikipedia.org/wiki/Don%27t_repeat_yourself. I would move methods or pieces of code that are common to both estimators into a new mixin class in a new module regression.adapters._statmixedml (if this looks reasonably modularizable)

Thanks for the review!

• I’ve introduced a new shared module regression.adapters._statmixedml and refactored the overlapping logic from both XGBoostLSS and LightGBMLSS into it.

•Regarding the failing CI tests, I'm trying to reproduce them locally. The CI output shows that test_fit_does_not_overwrite_hyper_params is failing for LightGBMLSS, but this test is being skipped locally because LightGBMLSS is not picked up during local test discovery. Could you please let me know the recommended way to run the CI-equivalent test suite locally so I can reproduce the same failure before pushing further commits?

[fkiraly]

but this test is being skipped locally because LightGBMLSS is not picked up during local test discovery.

It should get picked up, can you share how you are trying to run, or how you are discovering tests?

Could you please let me know the recommended way to run the CI-equivalent test suite locally so I can reproduce the same failure before pushing further commits?

The easiest is to run check_estimator from skpro.utils on your new estimator.

[yuvimittal]

but this test is being skipped locally because LightGBMLSS is not picked up during local test discovery.

It should get picked up, can you share how you are trying to run, or how you are discovering tests?

Could you please let me know the recommended way to run the CI-equivalent test suite locally so I can reproduce the same failure before pushing further commits?

The easiest is to run check_estimator from skpro.utils on your new estimator.

Initially i was looking at the failed tests from CI and running them, but now i ran check_estimator on both xgboostlss and lightgbmlss, xgboostlss is running perfectly but i am unable to install lightgbm

this is the error i am getting :

[fkiraly]

I think the package is available only for up to python 3.11 - so you nay want to set the python_dependencies tag to exclude the failing versions.

[StatMixedML]

The original LightGBMLSS is now aligned with @fkiraly's implementation for XGBoostLSS. just wanted to inform you.

[fkiraly]

Thanks!

Please restore deleted content, e.g., the entire docstring of XGBoostLSS, or the _hyperopt method, etc.

If you use AI, please check what it does and do not allow it to delete random stuff.