把上述指标公式翻译为python版

以下是将上述指标公式转化为Python代码的示例：

import numpy as np

def accuracy(y_true, y_pred):
    """
    计算准确率
    :param y_true: 真实标签
    :param y_pred: 预测标签
    :return: 准确率
    """
    correct = np.sum(y_true == y_pred)
    total = len(y_true)
    acc = correct / total
    return acc

def precision(y_true, y_pred):
    """
    计算精确率
    :param y_true: 真实标签
    :param y_pred: 预测标签
    :return: 精确率
    """
    true_positive = np.sum(np.logical_and(y_true == 1, y_pred == 1))
    false_positive = np.sum(np.logical_and(y_true == 0, y_pred == 1))
    precision = true_positive / (true_positive + false_positive)
    return precision

def recall(y_true, y_pred):
    """
    计算召回率
    :param y_true: 真实标签
    :param y_pred: 预测标签
    :return: 召回率
    """
    true_positive = np.sum(np.logical_and(y_true == 1, y_pred == 1))
    false_negative = np.sum(np.logical_and(y_true == 1, y_pred == 0))
    recall = true_positive / (true_positive + false_negative)
    return recall

def f1_score(y_true, y_pred):
    """
    计算F1分数
    :param y_true: 真实标签
    :param y_pred: 预测标签
    :return: F1分数
    """
    p = precision(y_true, y_pred)
    r = recall(y_true, y_pred)
    f1 = 2 * p * r / (p + r)
    return f1

这些函数可以分别接受真实的标签和预测的标签，并计算相应的指标。