运行 Python 函数 X 秒

Question

我有这段代码可以让 AutoKeras 运行 X 秒

def run_auto_keras(x_train, y_train, x_eval, y_eval):
    print('Starting AutoKeras')
    with timeout(TIME_IN_SEC):
        clf = ak.StructuredDataClassifier(
            max_trials=NUM_MODELS, directory=os.getcwd())
        clf.fit(x_train, y_train, epochs=NUM_EPOCHS)

        return clf.evaluate(x_eval, y_eval, batch_size=BATCH_SZ)

if not TRAIN_PATH.endswith('.csv'):
    raise Exception(f'{TRAIN_PATH} is not CSV')

train = pd.read_csv(TRAIN_PATH)

if TARGET_COL not in train.columns:
    raise Exception(f'{TARGET_COL} not in {TRAIN_PATH}')

mask = np.random.rand(len(train.index)) == SPLIT_P

y = train.pop(TARGET_COL)

x_train = train[mask]
y_train = y[mask]

x_eval = train[~mask]
y_eval = y.pop(TARGET_COL)[~mask]

x_train = x_train.to_numpy()
y_train = y_train.to_numpy()

x_eval = x_eval.to_numpy()
y_eval = y_eval.to_numpy()

try:
    eval_score = run_auto_keras(x_train, y_train, x_eval, y_eval)
    pd.DataFrame(data=[eval_score, NUM_MODELS, 'AutoKeras'], columns=[
                 'result', 'num_ensemble', 'ta2']).to_csv(LOSS_PATH, index=False)
except TimeoutError:
    pd.DataFrame(data=[0, NUM_MODELS, 'AutoKeras'], columns=[
                 'result', 'num_ensemble', 'ta2']).to_csv(LOSS_PATH, index=False)

我在博客中读到，这种让进程运行 X 秒的方法可能是最好的

@contextmanager
def timeout(time):
    signal.signal(signal.SIGALRM, raise_timeout)
    signal.alarm(time)

    try:
        yield
    except TimeoutError:
        pass
    finally:
        signal.signal(signal.SIGALRM, signal.SIG_IGN)

def raise_timeout(signum, frame):
    raise TimeoutError

这是在 X 秒内运行函数的正确方法吗？我想要的是如果它完成返回结果，如果没有，那么就抛出一个错误。

Answer 1

尝试使用：

import time

time.sleep(1)

这将暂停 x 秒。