nat.data_models.optimizable#

Attributes#

T

Classes#

`SearchSpace`
`OptimizableMixin`

Functions#

OptimizableField([default, space, merge_conflict])

Module Contents#

T#

class SearchSpace(/, **data: Any)#

Bases: pydantic.BaseModel, Generic[T]

values: collections.abc.Sequence[T] | None = None#

low: T | None = None#

high: T | None = None#

log: bool = False#

step: float | None = None#

is_prompt: bool = False#

prompt: str | None = None#

prompt_purpose: str | None = None#

model_config#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

validate_search_space_parameters()#: Validate SearchSpace configuration.

suggest(trial: optuna.Trial, name: str)#

to_grid_values() → list[Any]#

Convert SearchSpace to a list of values for GridSampler.

Grid search requires explicit values. This can be provided in two ways: 1. Explicit values: SearchSpace(values=[0.1, 0.5, 0.9]) 2. Range with step: SearchSpace(low=0.1, high=0.9, step=0.2)

For ranges, step is required (no default will be applied) to avoid unintentional combinatorial explosion.

OptimizableField(

default: Any = PydanticUndefined,

*,

space: SearchSpace | None = None,

merge_conflict: str = 'overwrite',

**fld_kw,

)#

class OptimizableMixin(/, **data: Any)#

Bases: pydantic.BaseModel

optimizable_params: list[str] = None#

search_space: dict[str, SearchSpace] = None#