1 configs.optuna_config

configs.optuna_config

Optuna study construction and config-tree search-space application.

This module owns the optional Optuna boundary for ARIA-NBV experiments. It maps :class:Optimizable field metadata onto trials while experiment execution and metric reporting remain owned by aria_nbv.lightning.

1.1 Classes

Name Description
OptunaConfig Configure an Optuna study used by aria_nbv.lightning.AriaNBVExperimentConfig.

1.1.1 OptunaConfig

configs.optuna_config.OptunaConfig()

Configure an Optuna study used by aria_nbv.lightning.AriaNBVExperimentConfig.

1.1.1.1 Attributes

Name Description
study_name Study name (used for Optuna storage and W&B grouping).
direction Optuna optimization direction.
n_trials Number of Optuna trials to run.
monitor Metric key to optimize (must be present in the Lightning trainer metrics).
load_if_exists Re-use an existing study with the same name when True.
sampler Optuna sampler choice.
pruner Optuna pruner choice.
suggested_params Latest trial suggestions applied to the config tree (W&B friendly).

1.1.1.2 Methods

Name Description
setup_target Create or resume the configured study in the project SQLite store.
setup_optimizables Apply Optimizable hints embedded in the config tree.
log_to_wandb Send the most recent suggestions to W&B.
get_pruning_callback Return a PyTorch Lightning pruning callback for the configured monitor.
1.1.1.2.1 setup_target
configs.optuna_config.OptunaConfig.setup_target()

Create or resume the configured study in the project SQLite store.

1.1.1.2.2 setup_optimizables
configs.optuna_config.OptunaConfig.setup_optimizables(
    experiment_config,
    trial,
    *,
    console=None,
)

Apply Optimizable hints embedded in the config tree.

1.1.1.2.3 log_to_wandb
configs.optuna_config.OptunaConfig.log_to_wandb()

Send the most recent suggestions to W&B.

1.1.1.2.4 get_pruning_callback
configs.optuna_config.OptunaConfig.get_pruning_callback(trial)

Return a PyTorch Lightning pruning callback for the configured monitor.

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