Enable L0 hyperparameter tuning

[juaristi22]

Fix #77
Fix #76

This enables tuning the L0 parameters to achieve a combination of loss reduction, estimates being within 10% of targets, and sparsity (proportion of weights that are 0). The objective function combines the three, and the parameter "objectives_contribution" allows changing how much we want to prioritize each objective, making it flexible to tune for different needs.

It also supports target holdout. n_holdout_sets can be set to 1 with a holdout_fraction of 0 if we dont want to holdout targets.

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Project	Deployment	Preview	Comments	Updated (UTC)
microcalibrate	Ready	Preview	Comment	Aug 22, 2025 11:01am

[baogorek]

Hi Maria, I just ran the test and it ran very smoothly and I saw the sparse output. I see that you were able to leverage an open-source parameter optimizer (Optuna), which was smart. One thing I would like to see is a bit of information about the holdouts once tuning is one. There should be an error for each of the holdout sets and a way to see which targets were held out. I'm probably not going to review every line of code here, but if I could see that, I would feel very good about putting my checkmark here.

[baogorek]

There's a lot in here, and I don't want to block useful functionality from going in. I am worried about data leakage from the training folds into the holdout folds, which is virtually guaranteed to happen given the way we define targets. All I would ask is that there be some warning added so the user can see it and we will be reminded to make it more robust in the future.