Adriano Dal Pastro
b6539802e0
- mv src/multi_swarm → src/multi_swarm_core/multi_swarm_core (member layout) - sed-replace globale degli import: from/import multi_swarm.* → multi_swarm_core.* - 115 occorrenze aggiornate in src/ scripts/ tests/ - multi_swarm_coevolutive (nome repo) preservato dal word boundary Pre-condizione per il setup uv workspace della Fase 3. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
import random
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from multi_swarm_core.ga.selection import elite_select, tournament_select
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from multi_swarm_core.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_tournament_picks_best_in_sample() -> None:
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population = [make(i) for i in range(10)]
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fitnesses = {g.id: float(i) for i, g in enumerate(population)}
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rng = random.Random(0)
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winner = tournament_select(population, fitnesses, k=5, rng=rng)
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assert isinstance(winner, HypothesisAgentGenome)
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assert fitnesses[winner.id] >= 0.0
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def test_tournament_size_one_is_random() -> None:
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population = [make(i) for i in range(10)]
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fitnesses = {g.id: float(i) for i, g in enumerate(population)}
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rng = random.Random(0)
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picks = [tournament_select(population, fitnesses, k=1, rng=rng) for _ in range(50)]
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distinct = {p.id for p in picks}
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assert len(distinct) > 1
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def test_elite_select_returns_top_k() -> None:
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population = [make(i) for i in range(10)]
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fitnesses = {g.id: float(i) for i, g in enumerate(population)}
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elites = elite_select(population, fitnesses, k=3)
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elite_fitnesses = sorted([fitnesses[g.id] for g in elites], reverse=True)
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assert elite_fitnesses == [9.0, 8.0, 7.0]
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