AllenNLPExecutor(trial: optuna.trial._trial.Trial, config_file: str, serialization_dir: str, metrics: str = 'best_validation_accuracy', *, include_package: Union[str, List[str], None] = None)¶
AllenNLP extension to use optuna with Jsonnet config file.
This feature is experimental since AllenNLP major release will come soon. The interface may change without prior notice to correspond to the update.
See the examples of objective function.
From Optuna v2.1.0, users have to cast their parameters by using methods in Jsonnet. Call
std.parseIntfor integer, or
std.parseJsonfor floating point. Please see the example configuration.
AllenNLPExecutor, you can pass parameters to AllenNLP by either defining a search space using Optuna suggest methods or setting environment variables just like AllenNLP CLI. If a value is set in both a search space in Optuna and the environment variables, the executor will use the value specified in the search space in Optuna.
trial – A
Trialcorresponding to the current evaluation of the objective function.
config_file – Config file for AllenNLP. Hyperparameters should be masked with
std.extVar. Please refer to the config example.
serialization_dir – A path which model weights and logs are saved.
metrics – An evaluation metric for the result of
include_package – Additional packages to include. For more information, please see AllenNLP documentation.
Added in v1.4.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v1.4.0.
__init__(trial: optuna.trial._trial.Trial, config_file: str, serialization_dir: str, metrics: str = 'best_validation_accuracy', *, include_package: Union[str, List[str], None] = None)¶
Initialize self. See help(type(self)) for accurate signature.
__init__(trial, config_file, serialization_dir)
Train a model using AllenNLP.