nat.plugins.config_optimizer.prompts.ga_prompt_optimizer#

GA prompt optimizer: genetic-algorithm implementation for evolving prompts.

Attributes#

Genetic-algorithm prompt optimizer.

`_on_prompt_trial_end`(→ None)	Build TrialResults for each individual in a GA generation and fire on_trial_end.
`_on_prompt_study_end`(→ None)	Fire on_study_end for a completed prompt GA optimisation study.
`optimize_prompts`(→ None)	Entry point: run GA prompt optimizer (thin wrapper around GAPromptOptimizer).

_on_prompt_trial_end( callback_manager: nat.profiler.parameter_optimization.optimizer_callbacks.OptimizerCallbackManager | None, population: collections.abc.Sequence[nat.plugins.config_optimizer.prompts.ga_individual.Individual], eval_metrics: list[str], frozen_params: dict[str, Any] | None, prompt_format_map: dict[str, str | None], best: nat.plugins.config_optimizer.prompts.ga_individual.Individual, ) → None#: Build TrialResults for each individual in a GA generation and fire on_trial_end.

_on_prompt_study_end( callback_manager: nat.profiler.parameter_optimization.optimizer_callbacks.OptimizerCallbackManager | None, best: nat.plugins.config_optimizer.prompts.ga_individual.Individual, frozen_params: dict[str, Any] | None, prompt_format_map: dict[str, str | None], trial_number_offset: int, generations: int, pop_size: int, ) → None#: Fire on_study_end for a completed prompt GA optimisation study.

async optimize_prompts( *, base_cfg: nat.data_models.config.Config, full_space: dict[str, nat.data_models.optimizable.SearchSpace], optimizer_config: nat.data_models.optimizer.OptimizerConfig, opt_run_config: nat.data_models.optimizer.OptimizerRunConfig, callback_manager: nat.profiler.parameter_optimization.optimizer_callbacks.OptimizerCallbackManager | None = None, trial_number_offset: int = 0, frozen_params: dict[str, Any] | None = None, ) → None#: Entry point: run GA prompt optimizer (thin wrapper around GAPromptOptimizer).

class GAPromptOptimizer#

Genetic-algorithm prompt optimizer.

async _evaluate_single_given_trial( ind: nat.plugins.config_optimizer.prompts.ga_individual.Individual, cfg_trial: nat.data_models.config.Config, optimizer_config: nat.data_models.optimizer.OptimizerConfig, opt_run_config: nat.data_models.optimizer.OptimizerRunConfig, oracle_feedback_worst_n: int, ) → None#: Run EvaluationRun for an already-built trial config; fill ind.metrics.

async _post_evaluate_single( ind: nat.plugins.config_optimizer.prompts.ga_individual.Individual, all_results: list[list[tuple[str, Any]]], optimizer_config: nat.data_models.optimizer.OptimizerConfig, opt_run_config: nat.data_models.optimizer.OptimizerRunConfig, oracle_feedback_worst_n: int, ) → None#: Extract worst_items_reasoning for oracle feedback.

async _evaluate_population( population: list[nat.plugins.config_optimizer.prompts.ga_individual.Individual], base_cfg: nat.data_models.config.Config, optimizer_config: nat.data_models.optimizer.OptimizerConfig, opt_run_config: nat.data_models.optimizer.OptimizerRunConfig, max_concurrency: int = 8, callback_manager: nat.profiler.parameter_optimization.optimizer_callbacks.OptimizerCallbackManager | None = None, oracle_feedback_worst_n: int = 5, ) → None#: Evaluate all individuals (concurrently).

static _normalize_generation( individuals: collections.abc.Sequence[nat.plugins.config_optimizer.prompts.ga_individual.Individual], metric_names: collections.abc.Sequence[str], directions: collections.abc.Sequence[str], eps: float = 1e-12, ) → list[dict[str, float]]#: Return per-individual dict of normalised scores in [0,1] where higher is better.

static _scalarize( norm_scores: dict[str, float], *, mode: str, weights: collections.abc.Sequence[float] | None, ) → float#: Collapse normalised scores to a single scalar (higher is better).

static _apply_diversity_penalty( individuals: collections.abc.Sequence[nat.plugins.config_optimizer.prompts.ga_individual.Individual], diversity_lambda: float, ) → list[float]#: Per-individual diversity penalty (exact-string key).

_compute_fitness( population: list[nat.plugins.config_optimizer.prompts.ga_individual.Individual], optimizer_config: nat.data_models.optimizer.OptimizerConfig, diversity_lambda: float = 0.0, ) → None#: Set scalar_fitness on each individual (normalize, scalarize, diversity penalty).

static _persist_checkpoint( gen: int, best: nat.plugins.config_optimizer.prompts.ga_individual.Individual, prompt_space: dict[str, tuple[str, str]], out_dir: pathlib.Path, ) → None#: Write generation checkpoint JSON.

static _persist_final( best: nat.plugins.config_optimizer.prompts.ga_individual.Individual, history_rows: list[dict[str, Any]], prompt_space: dict[str, tuple[str, str]], out_dir: pathlib.Path, ) → None#: Write final optimized_prompts.json and ga_history_prompts.csv.

static _tournament_select( pop: collections.abc.Sequence[nat.plugins.config_optimizer.prompts.ga_individual.Individual], k: int, ) → nat.plugins.config_optimizer.prompts.ga_individual.Individual#: Select one individual by tournament (max fitness).

async run( *, base_cfg: nat.data_models.config.Config, full_space: dict[str, nat.data_models.optimizable.SearchSpace], optimizer_config: nat.data_models.optimizer.OptimizerConfig, opt_run_config: nat.data_models.optimizer.OptimizerRunConfig, callback_manager: nat.profiler.parameter_optimization.optimizer_callbacks.OptimizerCallbackManager | None = None, trial_number_offset: int = 0, frozen_params: dict[str, Any] | None = None, ) → None#