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>
91 lines
3.4 KiB
Python
91 lines
3.4 KiB
Python
import random
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import pytest
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from gausse.components.database import ComponentDatabase
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from gausse.optim.search_space import (
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TUBE_WALL_CLEARANCE_M,
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SearchBounds,
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crossover,
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decode,
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genome_from_dict,
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genome_to_dict,
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mutate,
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repair,
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sample_genome,
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)
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DB = ComponentDatabase.load()
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BOUNDS = SearchBounds()
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def test_sample_genome_respects_bounds():
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rng = random.Random(42)
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for _ in range(50):
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genome = sample_genome(DB, BOUNDS, rng)
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assert BOUNDS.min_stages <= len(genome.stages) <= BOUNDS.max_stages
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assert len(genome.inter_stage_gaps_m) == len(genome.stages) - 1
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assert BOUNDS.tube_od_m_min <= genome.tube_od_m <= BOUNDS.tube_od_m_max
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assert genome.projectile.diameter_m <= genome.tube_od_m - TUBE_WALL_CLEARANCE_M
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for stage in genome.stages:
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assert 0 <= stage.wire_idx < len(DB.wires)
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assert BOUNDS.turns_per_layer_min <= stage.turns_per_layer <= BOUNDS.turns_per_layer_max
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assert BOUNDS.layers_min <= stage.layers <= BOUNDS.layers_max
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def test_decode_produces_valid_config():
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rng = random.Random(1)
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genome = sample_genome(DB, BOUNDS, rng)
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config, initial_x_m, initial_v_mps = decode(genome, DB, BOUNDS)
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assert len(config.stages) == len(genome.stages)
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assert len(config.inter_stage_gaps_m) == len(genome.stages) - 1
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assert initial_x_m < -config.stages[0].sensor_to_coil_distance_m
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assert initial_v_mps == BOUNDS.initial_launch_velocity_mps
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for stage_config, gene in zip(config.stages, genome.stages):
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max_voltage = min(stage_config.capacitor.voltage_v, stage_config.switch.max_voltage_v)
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assert stage_config.charge_voltage_v <= max_voltage + 1e-9
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def test_mutate_keeps_genome_within_bounds():
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rng = random.Random(7)
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genome = sample_genome(DB, BOUNDS, rng)
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for _ in range(200):
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genome = mutate(genome, DB, BOUNDS, rng, rate=0.5)
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assert BOUNDS.min_stages <= len(genome.stages) <= BOUNDS.max_stages
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assert len(genome.inter_stage_gaps_m) == len(genome.stages) - 1
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assert BOUNDS.tube_od_m_min <= genome.tube_od_m <= BOUNDS.tube_od_m_max
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assert genome.projectile.diameter_m <= genome.tube_od_m - TUBE_WALL_CLEARANCE_M + 1e-9
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# decode должен всегда успевать без исключений после repair
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decode(genome, DB, BOUNDS)
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def test_mutate_can_change_stage_count():
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rng = random.Random(3)
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small_bounds = SearchBounds(min_stages=1, max_stages=3)
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genome = sample_genome(DB, small_bounds, rng)
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genome.stages = genome.stages[:1]
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genome.inter_stage_gaps_m = []
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counts = set()
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for _ in range(100):
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genome = mutate(genome, DB, small_bounds, rng, rate=0.6)
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counts.add(len(genome.stages))
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assert len(counts) > 1 # число ступеней реально меняется, не застряло
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def test_crossover_produces_decodable_child():
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rng = random.Random(11)
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a = sample_genome(DB, BOUNDS, rng)
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b = sample_genome(DB, BOUNDS, rng)
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for _ in range(20):
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child = crossover(a, b, rng)
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child = repair(child, DB, BOUNDS)
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config, _, _ = decode(child, DB, BOUNDS)
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assert len(config.stages) == len(child.stages)
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def test_genome_dict_roundtrip():
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rng = random.Random(5)
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genome = sample_genome(DB, BOUNDS, rng)
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restored = genome_from_dict(genome_to_dict(genome))
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assert restored == genome
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