56c7ab9c1d
- optim/search_space.py: Genome encodes only indices into the real component database plus continuous/discrete geometry parameters; number of stages is itself mutable via add/remove/duplicate-stage operators. repair() clips everything back into bounds after mutation/crossover so decode() never sees an invalid genome. - optim/objective.py: evaluate() decodes a genome, computes real BOM cost from wire length/price and component prices, runs the full chain, and scores fitness = efficiency for feasible runs or a soft penalty scaled by how many stages it got through for infeasible ones (so the GA gets gradient instead of a wall). Tested against the real component database (not synthetic fixtures) — including an explicit infeasible case using the real inductive sensor's actual sensitivity/threshold values. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
import random
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from gausse.components.database import ComponentDatabase
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from gausse.optim.objective import compute_cost_rub, decoded_summary, evaluate
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from gausse.optim.search_space import SearchBounds, decode, sample_genome
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DB = ComponentDatabase.load()
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BOUNDS = SearchBounds()
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def test_evaluate_feasible_genome_has_fitness_equal_efficiency():
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rng = random.Random(2)
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found_feasible = False
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for _ in range(50):
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genome = sample_genome(DB, BOUNDS, rng)
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result = evaluate(genome, DB, BOUNDS)
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if result.feasible:
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found_feasible = True
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assert result.fitness == result.efficiency
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assert 0.0 <= result.efficiency <= 1.0
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assert result.cost_rub > 0
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break
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assert found_feasible, "ни один из 50 случайных геномов не оказался реализуемым"
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def test_evaluate_infeasible_genome_has_negative_fitness_and_reason():
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rng = random.Random(9)
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genome = sample_genome(DB, BOUNDS, rng)
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# индукционный датчик требует скорости >= порог/чувствительность = 5 м/с,
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# а старт по умолчанию 3 м/с -- первая ступень обязана провалиться
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inductive_idx = next(i for i, s in enumerate(DB.sensors) if s.kind == "inductive")
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genome.stages[0].sensor_idx = inductive_idx
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result = evaluate(genome, DB, BOUNDS)
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assert not result.feasible
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assert result.fitness < 0
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assert result.reason is not None
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assert result.failed_stage_index == 0
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def test_compute_cost_rub_is_positive_and_scales_with_stage_count():
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rng = random.Random(4)
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genome = sample_genome(DB, BOUNDS, rng)
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config, _, _ = decode(genome, DB, BOUNDS)
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cost = compute_cost_rub(config, DB)
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assert cost > 0
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genome.stages.append(genome.stages[0])
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genome.inter_stage_gaps_m.append(0.05)
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config2, _, _ = decode(genome, DB, BOUNDS)
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cost2 = compute_cost_rub(config2, DB)
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assert cost2 > cost
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def test_decoded_summary_uses_real_part_numbers():
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rng = random.Random(6)
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genome = sample_genome(DB, BOUNDS, rng)
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summary = decoded_summary(genome, DB)
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assert len(summary["stages"]) == len(genome.stages)
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real_wire_ids = {w.part_id for w in DB.wires}
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assert all(s["wire"] in real_wire_ids for s in summary["stages"])
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