Closed mmccarty closed 5 years ago
It would be better to refactor certain parts of sklearn
API code in xgboost
to be more reusable here rather than duplicating the code. Once this settles out, I would like to clean that up.
@TomAugspurger Thanks for taking a look. If folks are good with this approach, I will proceed.
So to summarize the discussion from https://github.com/dask/dask-xgboost/pull/54/files/d10bb355a1d82a89233c58295c2b515d3e39baed#diff-66bc4b86e5e634f64f45b16394051674, we decided that eval_set
and sample_weight_eval_set
should be in-memory ndarrays, which are sent to each worker? That matches what we do with Hyperband in dask-ml.
Eventually we'll want to support sample_weight
, which IIUC will be a dask array the same length as X and y.
Overall, things are looking good here @mmccarty. I think just linting issues now.
Great! Thank you for checking. I’ll get the linting fixed ASAP.
On Tue, Oct 1, 2019 at 6:23 PM Tom Augspurger notifications@github.com wrote:
Overall, things are looking good here @mmccarty https://github.com/mmccarty. I think just linting issues now.
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@TomAugspurger CI is passing. I also want to add this to the Regressor. I'm working on that now.
@TomAugspurger @mrocklin All done here, unless there are further comments.
Alrightly, let's merge this. We can followup if xgboost decides to change their behavior.
Thanks @mmccarty!
Is there a time where a release would be good for you? I can do one sometime next week if that works.
Great! Thanks! Yeah, sometime next week works!
This change adds support for early stopping to the sklearn interface by passing arguments in the proper format.
fixes dask/dask-xgboost#38