def test_get_all_and_random_organisms():
organisms = population.get_all_organisms()
assert isinstance(organisms, Iterable)
organisms = list(organisms)
for organism in organisms:
assert isinstance(organism, population.Organism)
assert len(list(organisms)) == 4
ids = [organism.id for organism in organisms]
org = population.get_random_organism()
assert org.id in ids
def _eval_exiting(self, portfolio: Portfolio) -> None:
"""Оценивает существующих."""
tickers = tuple(portfolio.index[:-2])
end = portfolio.date
count = population.count()
for step, parent in enumerate(population.get_all_organisms(), 1):
print(f"***{end.date()}: Шаг переоценки существующих организмов - {step}/{count}***")
population.print_stat()
print()
print("Родитель:")
parent_fitness = self._eval_and_print(parent, tickers, end)
if parent_fitness is None:
continue
def __init__(
self, tickers: Tuple[str, ...], date: pd.Timestamp,
):
self._tickers = tickers
self._date = date
self._forecasts = []
for organism in tqdm.tqdm(population.get_all_organisms(), desc="Forecasts"):
try:
forecast = organism.forecast(tickers, date)
except ForecastError:
continue
self._forecasts.append(forecast)
if not self._forecasts:
raise population.ForecastError("Отсутствуют прогнозы - необходимо обучить модели")
def evolve(self, portfolio: Portfolio) -> NoReturn:
"""Осуществляет одну эпоху эволюции.
При необходимости создается начальная популяция из организмов по умолчанию.
"""
tickers = tuple(portfolio.index[:-2])
end = portfolio.date
self._setup()
count = population.count()
for step, parent in enumerate(population.get_all_organisms(), 1):
print(f"***{end.date()}: Шаг эпохи - {step}/{count}***")
population.print_stat()
print()
print("Родитель:")
parent_fitness = self._eval_and_print(parent, tickers, end)
if parent_fitness is None:
continue
child = parent.make_child()
print("Потомок:")
child_fitness = self._eval_and_print(child, tickers, end)
if child_fitness is None:
continue
if population.count() <= self._max_population:
continue
weakest = parent
if parent_fitness > child_fitness:
weakest = child
weakest = weakest.find_weaker()
print("Более слабый и наиболее медленный - удаляю:")
self._eval_and_print(weakest, tickers, end)
weakest.die()
def evolve(self, portfolio: Portfolio) -> NoReturn:
"""Осуществляет одну эпоху эволюции.
При необходимости создается начальная популяция из организмов по умолчанию.
"""
tickers = tuple(portfolio.index[:-2])
end = portfolio.date
self._setup()
self._eval_exiting(portfolio)
count = population.count()
factor = MUTATION_FACTOR
for step, parent in enumerate(population.get_all_organisms(), 1):
print(f"***{end.date()}: Шаг размножения - {step}/{count}***")
population.print_stat()
print(f"Фактор - {factor:.2%}")
print()
child = parent.make_child(factor)
print("Потомок:")
child_fitness = self._eval_and_print(child, tickers, end)
if child_fitness is None:
factor *= MUTATION_FACTOR
continue
if population.count() <= self._max_population:
continue
weakest = child.find_weaker()
print("Наиболее слабый - удаляю:")
self._eval_and_print(weakest, tickers, end)
weakest.die()
