[dask] Support custom objective functions

[jameslamb]

Summary

The Dask estimators in lightgbm.dask should support the use of a custom objective function.

Motivation

This feature would bring Dask estimators closer to parity with the sklearn estimators.

Description

I haven't thought through this much yet, just writing up a placeholder issue for discussion. If you're reading this and have ideas, please comment and the issue can be re-opened.

References

See

LightGBM/python-package/lightgbm/sklearn.py

Lines 430 to 453 in 15916a9

	A custom objective function can be provided for the ``objective`` parameter.
	In this case, it should have the signature
	``objective(y_true, y_pred) -> grad, hess`` or
	``objective(y_true, y_pred, group) -> grad, hess``:
	y_true : array-like of shape = [n_samples]
	The target values.
	y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
	The predicted values.
	group : array-like
	Group/query data.
	Only used in the learning-to-rank task.
	sum(group) = n_samples.
	For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
	where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
	grad : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
	The value of the first order derivative (gradient) for each sample point.
	hess : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
	The value of the second order derivative (Hessian) for each sample point.
	For binary task, the y_pred is margin.
	For multi-class task, the y_pred is group by class_id first, then group by row_id.
	If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i]
	and you should group grad and hess in this way as well.

for an explanation of how this works in the sklearn estimators.

Can look at how xgboost.dask handles this for inspiration:

• https://github.com/dmlc/xgboost/blob/9b267a435e0aa1dfdff211d1a5b668538c51cd35/python-package/xgboost/dask.py#L1552-L1555
• https://github.com/dmlc/xgboost/blob/9b267a435e0aa1dfdff211d1a5b668538c51cd35/python-package/xgboost/dask.py#L849

[jameslamb]

Closing this in favor of putting it in #2302 with other feature requests. Anyone is welcome to pick up this feature! Please comment if interested and the issue can be re-opened.

[jameslamb]

Re-opening this as I'm working on this one right now.

[github-actions]

This issue has been automatically locked since there has not been any recent activity since it was closed.
To start a new related discussion, open a new issue at https://github.com/microsoft/LightGBM/issues
including a reference to this.