precision recall map

Precision（精确率）是指在所有被识别为正样本的样本中，真正为正样本的比例。它的计算公式是：precision = tp / (tp + fp)，其中tp表示真正为正样本的数量，fp表示误识别为正样本的数量。在给定的例子中，precision = 0.75，意味着识别为飞机的图片中，真正为飞机的图片占比为0.75。

Recall（召回率）是指在所有真正的正样本中，被正确识别为正样本的比例。它的计算公式是：recall = tp / (tp + fn)，其中tp表示真正为正样本的数量，fn表示未被识别为正样本的数量。在给定的例子中，recall = 0.6，即所有飞机图片中，有0.6的飞机被正确识别为飞机。

mAP（平均精度均值）是目标检测中常用的评估指标，用于衡量模型的性能。mAP是Precision-Recall曲线下的面积，表示所有可能的阈值下的平均精度值。mAP的范围在0到1之间，数值越高表示模型性能越好。

相关问题

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。

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。