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c38311e5fa
Implementazione completa Phase 2.5 (LLM prompt mutator) seguendo il piano in docs/superpowers/plans/2026-05-11-mutate-prompt-llm-phase-2-5.md. Nuovo modulo src/multi_swarm/genome/mutation_prompt_llm.py: - 6 mutation instructions (tighten_threshold, swap_comparator, add_condition, remove_condition, change_timeframe, add_temporal_gate) - mutate_prompt_llm(g, llm, rng, mutator_tier=ModelTier.B): clona genome con tier B per la call mutator, costruisce system+user prompt con istruzione scelta random, estrae prompt da tag <prompt>...</prompt>, valida - is_valid_prompt(): 3 check (lunghezza >= 50, keyword tecnica, diff > 5% Levenshtein-like via difflib.SequenceMatcher) - Fallback random_mutate su qualsiasi validation fail O LLM exception Esteso src/multi_swarm/genome/mutation.py con weighted_random_mutate dispatcher: con probabilità prompt_mutation_weight invoca mutate_prompt_llm, altrimenti random_mutate. Backward compat: llm=None oppure weight=0 → solo scalare. Integrazione GA loop + RunConfig: - GAConfig.prompt_mutation_weight: float = 0.0 (default off) - next_generation(llm=...) propagato all'invocazione mutator - RunConfig.prompt_mutation_weight con stesso default - run_phase1: passa llm a next_generation solo se weight > 0 - scripts/run_phase1.py: flag CLI --prompt-mutation-weight Tests (+18, 175 totale): - tests/unit/test_mutation_prompt_llm.py (12): extract_prompt, is_valid_prompt 3 check, operator success + fallback su 3 modi (invalid/identical/exception), tier B per LLM call, istruzione scelta dal pool - tests/unit/test_mutation_dispatcher.py (4): weight 0/1/None + distribuzione 30/70 su 1000 estrazioni con tolleranza ±5% - tests/integration/test_ga_loop_with_prompt_mutator.py (2): loop con weight=1.0 produce prompt evoluti; backward compat weight=0 invariato Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
from __future__ import annotations
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import random
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from dataclasses import dataclass
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from typing import Any
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from ..genome.crossover import uniform_crossover
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from ..genome.hypothesis import HypothesisAgentGenome
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from ..genome.mutation import weighted_random_mutate
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from .selection import elite_select, tournament_select
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@dataclass(frozen=True)
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class GAConfig:
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population_size: int
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elite_k: int
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tournament_k: int
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p_crossover: float
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prompt_mutation_weight: float = 0.0 # Phase 2.5: opt-in LLM mutator
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def next_generation(
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population: list[HypothesisAgentGenome],
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fitnesses: dict[str, float],
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cfg: GAConfig,
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rng: random.Random,
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llm: Any | None = None,
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) -> list[HypothesisAgentGenome]:
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"""Costruisce la prossima generazione: elitismo + tournament + crossover/mutate.
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Quando ``cfg.prompt_mutation_weight > 0`` e ``llm`` è fornito, la mutazione
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invoca ``weighted_random_mutate`` che con quella probabilità usa
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``mutate_prompt_llm`` (Phase 2.5).
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"""
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new_pop: list[HypothesisAgentGenome] = list(
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elite_select(population, fitnesses, cfg.elite_k)
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)
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while len(new_pop) < cfg.population_size:
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if rng.random() < cfg.p_crossover and len(population) >= 2:
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p1 = tournament_select(population, fitnesses, cfg.tournament_k, rng)
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p2 = tournament_select(population, fitnesses, cfg.tournament_k, rng)
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child = uniform_crossover(p1, p2, rng)
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else:
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parent = tournament_select(population, fitnesses, cfg.tournament_k, rng)
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child = weighted_random_mutate(
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parent, rng, llm=llm, prompt_mutation_weight=cfg.prompt_mutation_weight
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)
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new_pop.append(child)
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return new_pop[: cfg.population_size]
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