342 lines
12 KiB
Python
342 lines
12 KiB
Python
import os
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import random
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import shutil
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import time
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from copy import deepcopy
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from dataclasses import asdict, dataclass
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from typing import Callable
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import numpy as np
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from matplotlib import pyplot as plt
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from numpy.typing import NDArray
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from .chromosome import Chromosome
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from .types import Fitnesses, Population
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type FitnessFn = Callable[[Chromosome], float]
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type InitializePopulationFn = Callable[[int], Population]
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type CrossoverFn = Callable[[Chromosome, Chromosome], tuple[Chromosome, Chromosome]]
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type MutationFn = Callable[[Chromosome], Chromosome]
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type SelectionFn = Callable[[Population, Fitnesses], Population]
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@dataclass(frozen=True)
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class GARunConfig:
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fitness_func: FitnessFn
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crossover_fn: CrossoverFn
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mutation_fn: MutationFn
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selection_fn: SelectionFn
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init_population: Population
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pc: float # вероятность кроссинговера
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pm: float # вероятность мутации
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max_generations: int # максимальное количество поколений
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elitism: int = (
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0 # сколько лучших особей перенести без изменения в следующее поколение
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)
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max_best_repetitions: int | None = (
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None # остановка при повторении лучшего результата
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)
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seed: int | None = None # seed для генератора случайных чисел
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minimize: bool = True # если True, ищем минимум вместо максимума
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save_generations: list[int] | None = (
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None # индексы поколений для сохранения графиков
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)
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results_dir: str = "results" # папка для сохранения графиков
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fitness_avg_threshold: float | None = (
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None # порог среднего значения фитнес функции для остановки
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)
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best_value_threshold: float | None = (
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None # остановка при достижении значения фитнеса лучше заданного
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)
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log_every_generation: bool = False # логировать каждое поколение
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def save(self, filename: str = "GARunConfig.txt"):
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"""Сохраняет конфиг в results_dir."""
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os.makedirs(self.results_dir, exist_ok=True)
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path = os.path.join(self.results_dir, filename)
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with open(path, "w", encoding="utf-8") as f:
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for k, v in asdict(self).items():
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f.write(f"{k}: {v}\n")
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@dataclass(frozen=True)
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class Generation:
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number: int
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best: Chromosome
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best_fitness: float
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population: Population
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fitnesses: Fitnesses
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@dataclass(frozen=True)
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class GARunResult:
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generations_count: int
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best_generation: Generation
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history: list[Generation]
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time_ms: float
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def save(self, path: str, filename: str = "GARunResult.txt"):
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"""Сохраняет конфиг в results_dir."""
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os.makedirs(path, exist_ok=True)
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path = os.path.join(path, filename)
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with open(path, "w", encoding="utf-8") as f:
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for k, v in asdict(self).items():
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if k == "history":
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continue
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if k == "best_generation":
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f.write(
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f"{k}: Number: {v['number']}, Best Fitness: {v['best_fitness']}, Best: {v['best']}\n"
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)
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else:
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f.write(f"{k}: {v}\n")
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def crossover(
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population: Population,
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pc: float,
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crossover_fn: CrossoverFn,
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) -> Population:
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"""Оператор кроссинговера (скрещивания) выполняется с заданной вероятностью pc.
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Две хромосомы (родители) выбираются случайно из промежуточной популяции.
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Если популяция нечетного размера, то последняя хромосома скрещивается со случайной
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другой хромосомой из популяции. В таком случае одна из хромосом может поучаствовать
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в кроссовере дважды.
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"""
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# Создаем копию популяции и перемешиваем её для случайного выбора пар
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shuffled_population = population.copy()
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random.shuffle(shuffled_population)
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next_population = []
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pop_size = len(shuffled_population)
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for i in range(0, pop_size, 2):
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p1 = shuffled_population[i]
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p2 = shuffled_population[(i + 1) % pop_size]
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if np.random.random() <= pc:
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p1, p2 = crossover_fn(p1, p2)
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next_population.append(p1)
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next_population.append(p2)
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return next_population[:pop_size]
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def mutation(
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population: Population, pm: float, gen_num: int, mutation_fn: MutationFn
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) -> Population:
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"""Мутация происходит с вероятностью pm."""
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next_population = []
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for chrom in population:
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next_population.append(
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mutation_fn(chrom) if np.random.random() <= pm else chrom
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)
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return next_population
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def clear_results_directory(results_dir: str) -> None:
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"""Очищает папку с результатами перед началом эксперимента."""
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if os.path.exists(results_dir):
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shutil.rmtree(results_dir)
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os.makedirs(results_dir, exist_ok=True)
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def eval_population(population: Population, fitness_func: FitnessFn) -> Fitnesses:
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return np.array([fitness_func(chrom) for chrom in population])
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def save_generation(
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generation: Generation, history: list[Generation], config: GARunConfig
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) -> None:
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os.makedirs(config.results_dir, exist_ok=True)
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fig = plt.figure(figsize=(7, 7))
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fig.suptitle(
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f"Поколение #{generation.number}. "
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f"Лучшая особь: {generation.best_fitness:.0f}. "
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f"Среднее значение: {np.mean(generation.fitnesses):.0f}",
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fontsize=14,
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y=0.95,
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)
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# Рисуем
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...
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filename = f"generation_{generation.number:03d}.png"
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path_png = os.path.join(config.results_dir, filename)
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fig.savefig(path_png, dpi=150, bbox_inches="tight")
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plt.close(fig)
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def genetic_algorithm(config: GARunConfig) -> GARunResult:
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if config.seed is not None:
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random.seed(config.seed)
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np.random.seed(config.seed)
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if config.save_generations:
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clear_results_directory(config.results_dir)
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population = config.init_population
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start = time.perf_counter()
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history: list[Generation] = []
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best: Generation | None = None
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generation_number = 1
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best_repetitions = 0
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while True:
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# Вычисляем фитнес для всех особей в популяции
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fitnesses = eval_population(population, config.fitness_func)
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# Сохраняем лучших особей для переноса в следующее поколение
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elites: list[Chromosome] = []
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if config.elitism:
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elites = deepcopy(
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[
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population[i]
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for i in sorted(
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range(len(fitnesses)),
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key=lambda i: fitnesses[i],
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reverse=not config.minimize,
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)
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][: config.elitism]
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)
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# Находим лучшую особь в поколении
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best_index = (
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int(np.argmin(fitnesses)) if config.minimize else int(np.argmax(fitnesses))
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)
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# Добавляем эпоху в историю
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current = Generation(
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number=generation_number,
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best=population[best_index],
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best_fitness=fitnesses[best_index],
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# population=deepcopy(population),
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population=[],
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# fitnesses=deepcopy(fitnesses),
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fitnesses=np.array([]),
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)
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history.append(current)
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if config.log_every_generation:
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print(
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f"Generation #{generation_number} best: {current.best_fitness},"
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f" avg: {np.mean(fitnesses)}"
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)
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# Обновляем лучшую эпоху
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if (
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best is None
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or (config.minimize and current.best_fitness < best.best_fitness)
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or (not config.minimize and current.best_fitness > best.best_fitness)
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):
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best = current
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# Проверка критериев остановки
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stop_algorithm = False
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if generation_number >= config.max_generations:
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stop_algorithm = True
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if config.max_best_repetitions is not None and generation_number > 1:
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if history[-2].best_fitness == current.best_fitness:
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best_repetitions += 1
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if best_repetitions == config.max_best_repetitions:
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stop_algorithm = True
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else:
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best_repetitions = 0
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if config.best_value_threshold is not None:
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if (
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config.minimize and current.best_fitness < config.best_value_threshold
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) or (
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not config.minimize
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and current.best_fitness > config.best_value_threshold
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):
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stop_algorithm = True
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if config.fitness_avg_threshold is not None:
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mean_fitness = np.mean(fitnesses)
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if (config.minimize and mean_fitness < config.fitness_avg_threshold) or (
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not config.minimize and mean_fitness > config.fitness_avg_threshold
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):
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stop_algorithm = True
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# Сохраняем указанные поколения и последнее поколение
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if config.save_generations and (
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stop_algorithm or generation_number in config.save_generations
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):
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save_generation(current, history, config)
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if stop_algorithm:
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break
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# селекция (для минимума инвертируем знак)
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parents = config.selection_fn(
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population, fitnesses if not config.minimize else -fitnesses
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)
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# кроссинговер попарно
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next_population = crossover(parents, config.pc, config.crossover_fn)
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# мутация
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next_population = mutation(
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next_population,
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config.pm,
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generation_number,
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config.mutation_fn,
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)
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# Вставляем элиту в новую популяцию
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population = next_population[: len(population) - config.elitism] + elites
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generation_number += 1
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end = time.perf_counter()
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assert best is not None, "Best was never set"
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return GARunResult(
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len(history),
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best,
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history,
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(end - start) * 1000.0,
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)
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def plot_fitness_history(result: GARunResult, save_path: str | None = None) -> None:
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"""Рисует график изменения лучших и средних значений фитнеса по поколениям."""
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generations = [gen.number for gen in result.history]
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best_fitnesses = [gen.best_fitness for gen in result.history]
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avg_fitnesses = [np.mean(gen.fitnesses) for gen in result.history]
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fig, ax = plt.subplots(figsize=(10, 6))
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ax.plot(
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generations, best_fitnesses, label="Лучшее значение", linewidth=2, color="blue"
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)
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ax.plot(
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generations,
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avg_fitnesses,
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label="Среднее значение",
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linewidth=2,
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color="orange",
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)
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ax.set_xlabel("Поколение", fontsize=12)
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ax.set_ylabel("Значение фитнес-функции", fontsize=12)
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ax.legend(fontsize=11)
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ax.grid(True, alpha=0.3)
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if save_path:
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fig.savefig(save_path, dpi=150, bbox_inches="tight")
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print(f"График сохранен в {save_path}")
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else:
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plt.show()
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plt.close(fig)
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