计算recall和precision以及map代码

以下是计算recall、precision和map的Python代码：

def recall(actual, predicted):
    true_positives = len(set(actual) & set(predicted))
    false_negatives = len(actual) - true_positives
    return true_positives / (true_positives + false_negatives)

def precision(actual, predicted):
    true_positives = len(set(actual) & set(predicted))
    false_positives = len(predicted) - true_positives
    return true_positives / (true_positives + false_positives)

def average_precision(actual, predicted):
    score = 0.0
    hits = 0.0
    for i, p in enumerate(predicted):
        if p in actual and p not in predicted[:i]:
            hits += 1.0
            score += hits / (i+1.0)
    if hits == 0.0:
        return 0.0
    return score / hits

def mean_average_precision(actual, predicted):
    return np.mean([average_precision(a, p) for a, p in zip(actual, predicted)])

其中，`actual`和`predicted`都是列表类型，分别表示实际值和预测值。`recall`和`precision`分别计算召回率和精确率，`average_precision`计算平均精度，`mean_average_precision`计算平均平均精度。