Comments (2)
Thank you for the reference. I will look into that.
from optuna.
Thank you for your feature request.
We've understood that some users including Kagglers need features to help form ensembles using optimization results, and we'd like to implement such features in the future.
For the meanwhile, please obtain trials using study.trials
or study.trials_dataframe()
and find trials suitable for ensemble models manually. In addition to that, this FAQ page may be useful to save/restore the models trained in trials.
If you already have your implementation, we will welcome your pull request.
from optuna.
Related Issues (20)
- Artifact in Google Colaboratory
- `JSONDecodeError` in `JournalStorage` HOT 3
- Grid search reevaluates the same parameter combination in parallelization scenario HOT 1
- GridSampler after_trial logic breaks ask/tell workflow HOT 3
- Clarify `GridSampler` and ask-and-tell interface problem
- Parent run missing in MLFlow when using nested trials HOT 3
- add domain aware BO algorithm
- Remove `study optimize` from CLI tutorial
- Same hypermeters produced different results.
- Stops with only 1 trial and reports it as the best trial!
- When the hyperparameters importance plot is generated it does not label the parameters by its actual name HOT 2
- NSGAIISampler AssertionError()
- NSGAIIISampler multiple errors
- import optuna have question:TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers'
- Passing float-castable str to `study.tell` raises an error
- Add documentation for customizing visualization function output
- ArangoDB as an optional DB. HOT 5
- user attribute keep best HOT 5
- Support best value with constraint HOT 2
- Train the model using PyTorch's DDP mode, and when OOM occurs, the next trial will not proceed.
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