46 lines
1.8 KiB
Python
46 lines
1.8 KiB
Python
import random
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from multi_swarm.ga.loop import GAConfig, next_generation
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from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
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def make(idx: int) -> HypothesisAgentGenome:
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return HypothesisAgentGenome(
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system_prompt=f"p-{idx}",
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feature_access=["close"],
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temperature=0.9,
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top_p=0.95,
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model_tier=ModelTier.C,
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lookback_window=100,
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cognitive_style="x",
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)
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def test_next_generation_size_preserved() -> None:
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population = [make(i) for i in range(20)]
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fitnesses = {g.id: float(i) for i, g in enumerate(population)}
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cfg = GAConfig(population_size=20, elite_k=2, tournament_k=3, p_crossover=0.5)
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new_pop = next_generation(population, fitnesses, cfg, rng=random.Random(0))
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assert len(new_pop) == 20
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def test_next_generation_includes_elites() -> None:
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population = [make(i) for i in range(20)]
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fitnesses = {g.id: float(i) for i, g in enumerate(population)}
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cfg = GAConfig(population_size=20, elite_k=2, tournament_k=3, p_crossover=0.5)
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new_pop = next_generation(population, fitnesses, cfg, rng=random.Random(0))
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elite_ids = {
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g.id for g in sorted(population, key=lambda g: fitnesses[g.id], reverse=True)[:2]
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}
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new_ids = {g.id for g in new_pop}
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assert elite_ids.issubset(new_ids)
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def test_next_generation_increments_generation_for_offspring() -> None:
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population = [make(i) for i in range(20)]
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fitnesses = {g.id: float(i) for i, g in enumerate(population)}
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cfg = GAConfig(population_size=20, elite_k=2, tournament_k=3, p_crossover=0.5)
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new_pop = next_generation(population, fitnesses, cfg, rng=random.Random(0))
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new_offspring = [g for g in new_pop if g.id not in {p.id for p in population}]
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assert all(g.generation > 0 for g in new_offspring)
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