nat.data_models.optimizer

Classes

OptimizerMetric	Parameters used by the workflow optimizer to define a metric to optimize.
SamplerType	str(object='') -> str
NumericOptimizationConfig	Configuration for numeric/enum optimization (Optuna).
PromptGAOptimizationConfig	Configuration for prompt optimization using a Genetic Algorithm.
OptimizerConfig	Parameters used by the workflow optimizer.
OptimizerRunConfig	Parameters used for an Optimizer R=run

Module Contents

class OptimizerMetric(/, **data: Any)

Bases: pydantic.BaseModel

Parameters used by the workflow optimizer to define a metric to optimize.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

evaluator_name: str = None

direction: str = None

weight: float = None

class SamplerType

Bases: str, enum.Enum

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.__str__() (if defined) or repr(object). encoding defaults to ‘utf-8’. errors defaults to ‘strict’.

Initialize self. See help(type(self)) for accurate signature.

BAYESIAN = 'bayesian'

GRID = 'grid'

class NumericOptimizationConfig(/, **data: Any)

Bases: pydantic.BaseModel

Configuration for numeric/enum optimization (Optuna).

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

enabled: bool = None

n_trials: int = None

sampler: SamplerType | None = None

class PromptGAOptimizationConfig(/, **data: Any)

Bases: pydantic.BaseModel

Configuration for prompt optimization using a Genetic Algorithm.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

enabled: bool = None

prompt_population_init_function: str | None = None

prompt_recombination_function: str | None = None

ga_population_size: int = None

ga_generations: int = None

ga_offspring_size: int | None = None

ga_crossover_rate: float = None

ga_mutation_rate: float = None

ga_elitism: int = None

ga_selection_method: str = None

ga_tournament_size: int = None

ga_parallel_evaluations: int = None

ga_diversity_lambda: float = None

class OptimizerConfig(/, **data: Any)

Bases: pydantic.BaseModel

Parameters used by the workflow optimizer.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

output_path: pathlib.Path | None = None

eval_metrics: dict[str, OptimizerMetric] | None = None

reps_per_param_set: int = None

target: float | None = None

multi_objective_combination_mode: str = None

numeric: NumericOptimizationConfig

prompt: PromptGAOptimizationConfig

class OptimizerRunConfig(/, **data: Any)

Bases: pydantic.BaseModel

Parameters used for an Optimizer R=run

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

config_file: pathlib.Path | pydantic.BaseModel

dataset: str | pathlib.Path | None

result_json_path: str = '$'

endpoint: str | None = None

endpoint_timeout: int = 300

override: tuple[tuple[str, str], Ellipsis] = ()