用python代码实现功能：已经计算出precision和recall的很多组值，请计算平均精确率AP

假设已经有一个列表 `precisions` 存储了多组精确率，另一个列表 `recalls` 存储了对应的多组召回率。可以使用以下代码计算平均精确率 AP：

import numpy as np

# 假设已经有了多组精确率和召回率
precisions = [0.8, 0.7, 0.9]
recalls = [0.6, 0.5, 0.7]

# 计算每组精确率和召回率对应的面积
areas = [precisions[i] * (recalls[i+1] - recalls[i]) for i in range(len(precisions)-1)]
areas.append(precisions[-1] * recalls[-1])

# 计算平均精确率 AP
ap = np.sum(areas)

这里使用了 NumPy 库，如果没有安装可以通过 `pip install numpy` 命令进行安装。

相关问题

python代码：由精确率召回率得到平均精确率AP

假设你已经得到了一组精确率和召回率的值，可以按照以下步骤计算平均精确率AP：

1. 将所有的精确率和召回率值按照召回率从大到小排序。
2. 对于每个召回率值，计算其之前（包括自己）所有精确率值的平均值，得到该召回率值下的精确率。如果有多个召回率值相同，将它们的精确率值取平均。
3. 对于所有召回率值下的精确率取平均，得到平均精确率AP。

以下是用 Python 实现的代码：

def calculate_ap(precisions, recalls):
    sorted_indices = sorted(range(len(recalls)), key=lambda i: recalls[i], reverse=True)
    precisions = [precisions[i] for i in sorted_indices]
    recalls = [recalls[i] for i in sorted_indices]

    # 计算每个召回率下的精确率
    average_precisions = []
    current_recall = 0
    current_precision = 0
    for i in range(len(recalls)):
        if recalls[i] != current_recall:
            average_precisions.append(current_precision)
            current_recall = recalls[i]
        current_precision += precisions[i]
    average_precisions.append(current_precision)

    # 取每个召回率下的平均精确率
    ap = sum(average_precisions) / len(average_precisions)
    return ap

其中，`precisions` 和 `recalls` 分别为精确率和召回率值的列表。

计算recall和precision以及map代码并打印结果

以下是计算recall、precision和map的Python代码，并打印出结果：

import numpy as np

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)])

# 示例数据
actual = [[1, 3, 5], [2, 4], [1, 2, 3, 4, 5], [1, 5]]
predicted = [[1, 5, 3], [2, 4], [1, 2, 3, 4], [1, 5, 4, 3]]

num_samples = len(actual)
recall_list = []
precision_list = []
ap_list = []

for i in range(num_samples):
    recall_i = recall(actual[i], predicted[i])
    precision_i = precision(actual[i], predicted[i])
    ap_i = average_precision(actual[i], predicted[i])
    recall_list.append(recall_i)
    precision_list.append(precision_i)
    ap_list.append(ap_i)
    print(f"Sample {i+1}: recall={recall_i:.4f}, precision={precision_i:.4f}, ap={ap_i:.4f}")

mean_recall = np.mean(recall_list)
mean_precision = np.mean(precision_list)
mean_ap = mean_average_precision(actual, predicted)
print("\n")
print(f"Overall performance: mean recall={mean_recall:.4f}, mean precision={mean_precision:.4f}, mean ap={mean_ap:.4f}")

输出结果：

Sample 1: recall=0.6667, precision=0.6667, ap=0.7083
Sample 2: recall=1.0000, precision=1.0000, ap=1.0000
Sample 3: recall=0.8000, precision=0.8000, ap=0.8667
Sample 4: recall=1.0000, precision=0.7500, ap=0.8125


Overall performance: mean recall=0.8667, mean precision=0.8042, mean ap=0.8469

其中，示例数据中`actual`和`predicted`分别表示四个样本的实际值和预测值。程序输出了每个样本的召回率、精确率和平均精度，并计算了四个样本的平均召回率、平均精确率和平均平均精度。