Document visualization workflow for lab 5 report

2025-11-12 15:06:53 +03:00
parent ca1095671e
commit f213bc3fb5
9 changed files with 1077 additions and 1 deletions
--- a/lab5/experiments.py
+++ b/lab5/experiments.py
@@ -0,0 +1,129 @@
+"""Parameter sweep experiments for the evolution strategy."""
+
+from __future__ import annotations
+
+import statistics
+from pathlib import Path
+from typing import Iterable
+
+import numpy as np
+from prettytable import PrettyTable
+
+from es import EvolutionStrategyConfig, run_evolution_strategy
+from functions import axis_parallel_hyperellipsoid, default_bounds
+
+POPULATION_SIZES = [5, 10, 20, 40]
+MUTATION_PROBABILITIES = [0.3, 0.5, 0.7, 0.9, 1.0]
+NUM_RUNS = 5
+LAMBDA_FACTOR = 5
+RESULTS_DIR = Path("lab5_experiments")
+
+
+def build_config(dimension: int, mu: int, mutation_probability: float) -> EvolutionStrategyConfig:
+    x_min, x_max = default_bounds(dimension)
+    search_range = x_max - x_min
+    initial_sigma = np.full(dimension, 0.15 * search_range[0], dtype=np.float64)
+    return EvolutionStrategyConfig(
+        fitness_func=axis_parallel_hyperellipsoid,
+        dimension=dimension,
+        x_min=x_min,
+        x_max=x_max,
+        mu=mu,
+        lambda_=mu * LAMBDA_FACTOR,
+        mutation_probability=mutation_probability,
+        initial_sigma=initial_sigma,
+        max_generations=300,
+        selection="comma",
+        recombination="intermediate",
+        parents_per_offspring=2,
+        success_rule_window=5,
+        success_rule_target=0.2,
+        sigma_increase=1.22,
+        sigma_decrease=0.82,
+        sigma_scale_min=1e-3,
+        sigma_scale_max=50.0,
+        sigma_min=1e-5,
+        sigma_max=2.0,
+        best_value_threshold=1e-6,
+        max_stagnation_generations=80,
+        save_generations=None,
+        results_dir=str(RESULTS_DIR / "tmp"),
+        log_every_generation=False,
+        seed=None,
+    )
+
+
+def run_single_experiment(config: EvolutionStrategyConfig) -> tuple[float, int, float]:
+    result = run_evolution_strategy(config)
+    return result.time_ms, result.generations_count, result.best_generation.best.fitness
+
+
+def summarize(values: Iterable[float]) -> tuple[float, float]:
+    values = list(values)
+    if not values:
+        return 0.0, 0.0
+    if len(values) == 1:
+        return values[0], 0.0
+    return statistics.mean(values), statistics.stdev(values)
+
+
+def run_grid_for_dimension(dimension: int) -> PrettyTable:
+    table = PrettyTable()
+    table.field_names = ["mu \\ p_mut"] + [f"{pm:.2f}" for pm in MUTATION_PROBABILITIES]
+
+    for mu in POPULATION_SIZES:
+        row = [str(mu)]
+        for pm in MUTATION_PROBABILITIES:
+            times: list[float] = []
+            generations: list[int] = []
+            best_values: list[float] = []
+
+            for run_idx in range(NUM_RUNS):
+                config = build_config(dimension, mu, pm)
+                # Для воспроизводимости меняем seed для каждого запуска
+                config.seed = np.random.randint(0, 1_000_000)
+                time_ms, gens, best = run_single_experiment(config)
+                times.append(time_ms)
+                generations.append(gens)
+                best_values.append(best)
+
+            avg_time, std_time = summarize(times)
+            avg_gen, std_gen = summarize(generations)
+            avg_best, std_best = summarize(best_values)
+
+            cell = f"{avg_time:.1f}±{std_time:.1f} ({avg_gen:.0f}±{std_gen:.0f}) {avg_best:.4f}"
+            row.append(cell)
+        table.add_row(row)
+
+    return table
+
+
+def save_table(table: PrettyTable, path: Path) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", encoding="utf-8") as f:
+        f.write(table.get_csv_string())
+
+
+def main() -> None:
+    if RESULTS_DIR.exists():
+        for child in RESULTS_DIR.iterdir():
+            if child.is_file():
+                child.unlink()
+
+    print("=" * 80)
+    print("Исследование параметров эволюционной стратегии")
+    print("Популяции:", POPULATION_SIZES)
+    print("Вероятности мутации:", MUTATION_PROBABILITIES)
+    print(f"Каждая конфигурация запускается {NUM_RUNS} раз")
+    print("=" * 80)
+
+    for dimension in (2, 3):
+        print(f"\nРезультаты для размерности n={dimension}")
+        table = run_grid_for_dimension(dimension)
+        print(table)
+        save_table(table, RESULTS_DIR / f"dimension_{dimension}.csv")
+        print(f"Таблица сохранена в {RESULTS_DIR / f'dimension_{dimension}.csv'}")
+
+
+if __name__ == "__main__":
+    main()