ef83512601
evaluate_genomes_gpu в gpu/batch_sweep.py — общий раундовый симулятор (_simulate_states) для sweep и эволюции, та же формула фитнеса, что в objective.evaluate (КПД либо -1+доля пройденных ступеней). run_evolution получил use_gpu: поколение целиком уходит в батч-интегратор (на сервере cupy/GTX 1070), полировка остаётся точной на CPU. search_mode=evolve-gpu(...). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
import random
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from gausse.components.database import ComponentDatabase
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from gausse.optim.evolutionary import polish_best, run_evolution
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from gausse.optim.search_space import SearchBounds, sample_genome
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from gausse.storage.database import count_runs, fetch_runs, open_connection
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DB = ComponentDatabase.load()
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def test_run_evolution_writes_all_evaluations_and_returns_summary(tmp_path):
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db_path = tmp_path / "runs.sqlite3"
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bounds = SearchBounds(max_stages=2)
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summary = run_evolution(
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db_path,
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n_generations=2,
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population_size=6,
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bounds=bounds,
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n_workers=2,
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seed=7,
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polish=False,
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)
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assert summary["n_evaluated"] == 12 # 2 поколения x 6 особей
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assert "best_fitness" in summary
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conn = open_connection(db_path)
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assert count_runs(conn) == 12
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rows = fetch_runs(conn)
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assert all(r.search_mode == "evolve" for r in rows)
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conn.close()
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def test_evolution_is_reproducible_given_same_seed(tmp_path):
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bounds = SearchBounds(max_stages=2)
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summary_a = run_evolution(
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tmp_path / "a.sqlite3", n_generations=2, population_size=6, bounds=bounds,
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n_workers=1, seed=99, polish=False,
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)
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summary_b = run_evolution(
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tmp_path / "b.sqlite3", n_generations=2, population_size=6, bounds=bounds,
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n_workers=1, seed=99, polish=False,
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)
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assert summary_a["best_fitness"] == summary_b["best_fitness"]
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assert summary_a["best_efficiency"] == summary_b["best_efficiency"]
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def test_polish_does_not_make_the_best_genome_worse():
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rng = random.Random(15)
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bounds = SearchBounds(max_stages=2)
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# ищем реализуемый геном как стартовую точку доводки
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genome = None
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for _ in range(30):
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candidate = sample_genome(DB, bounds, rng)
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from gausse.optim.objective import evaluate
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result = evaluate(candidate, DB, bounds)
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if result.feasible:
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genome = candidate
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baseline_fitness = result.fitness
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break
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assert genome is not None, "не нашли реализуемый геном за 30 попыток"
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_, polished_result = polish_best(genome, DB, bounds)
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assert polished_result.fitness >= baseline_fitness - 1e-9
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def test_run_evolution_gpu_batch_mode(tmp_path):
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"""GPU-режим эволюции (батч-оценка поколения; здесь numpy-бэкенд):
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пишет прогоны в ту же БД и находит реализуемые конфигурации."""
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from gausse.storage.database import count_runs, fetch_runs, open_connection
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db_path = tmp_path / "evo_gpu.sqlite3"
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summary = run_evolution(
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db_path, n_generations=3, population_size=40, seed=5, polish=False, use_gpu=True
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)
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assert summary["n_evaluated"] == 120
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conn = open_connection(db_path)
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assert count_runs(conn) >= 120
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rows = fetch_runs(conn, limit=5)
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assert all(r.search_mode.startswith("evolve-gpu") for r in rows)
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