-
Notifications
You must be signed in to change notification settings - Fork 1.1k
[timeseries] add persist logic to TimeSeriesPredictor #4005
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Conversation
|
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
LGTM, just a couple of nitpicks. Also need to check what's happening with the tests
timeseries/src/autogluon/timeseries/models/abstract/abstract_timeseries_model.py
Outdated
Show resolved
Hide resolved
timeseries/src/autogluon/timeseries/trainer/abstract_trainer.py
Outdated
Show resolved
Hide resolved
timeseries/src/autogluon/timeseries/trainer/abstract_trainer.py
Outdated
Show resolved
Hide resolved
2b0388a to
5cfc886
Compare
|
5cfc886 to
8d853eb
Compare
shchur
left a comment
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
LGTM, thank you!
| assert len(predictor._learner.load_trainer().models) == 0 | ||
|
|
||
|
|
||
| def _add_ensemble_to_predictor(predictor, hyperparameters, make_best_model=True): |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Very nice!
|
Issue #, if available:
Description of changes:
Adds methods to
persistpredictor, learner, trainer and model objects, bringing the ability to persist models in memory like inTabularPredictorBy submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.