water_turbidity_det/accuracy.py

import json
import argparse
import numpy as np

def argsparser():
    parser = argparse.ArgumentParser(prog=__file__)
    parser.add_argument('--input', '-i',type=str, default='./prediction_result.json', help='path of prediction json')
    args = parser.parse_args()
    return args


def calculate_metrics(y_true, y_pred):
    """
    使用numpy手动计算分类评估指标 - 修正版
    """
    y_pred = y_pred.astype(np.int32)
    y_true = y_true.astype(np.int32)

    # 获取唯一类别并排序
    classes = np.unique(np.concatenate([y_true, y_pred])).astype(np.int32)
    n_classes = len(classes)

    # 创建混淆矩阵
    #  TP  FN
    #  FP  TN
    confusion_matrix = np.zeros((n_classes, n_classes), dtype=np.float32)

    # 手动填充混淆矩阵
    for true_val, pred_val in zip(y_true, y_pred):
        if true_val == pred_val:  # 预测正确的样本
            if true_val == 1:  # 真实为正例
                confusion_matrix[0, 0] += 1
            else:
                confusion_matrix[1, 1] += 1
        else:  # 预测错误的样本
            if true_val == 0:  # 真实为负例
                confusion_matrix[1, 0] += 1
            else:  # 真实为正例
                confusion_matrix[0, 1] += 1
    tp = confusion_matrix[0, 0]
    tn = confusion_matrix[1, 1]
    fn = confusion_matrix[0, 1]
    fp = confusion_matrix[1, 0]
    # 计算精度
    accuracy = (tp + tn) / (tp + fp + fn + tn + 1e-8)
    # 计算准确率
    precision = tp / (tp + fp + 1e-8)
    # 计算召回率
    recall = tp / (tp + fn + 1e-8)
    # 计算F1分数
    f1 = 2 * precision * recall / (precision + recall + 1e-8)
    return {
        'confusion_matrix': confusion_matrix,
        'classes': classes,
        'tp': tp,
        'tn': tn,
        'fp': fp,
        'fn': fn,
        'accuracy': accuracy,
        'precision': precision,
        'recall': recall,
        'f1': f1
    }

def main(args):
    # 读取json文件
    data = None
    with open(args.input, 'r') as f:
        data = json.load(f)
    if data is None:
        return 0
    # 开始计算精度 格式：真值，预测值
    data = [[i['y'], i['x']] for i in data.values()]
    data = np.array(data, dtype=np.float32)
    # 分离真值和预测值
    y_true = data[:, 0]  # 第一列是真值
    y_pred = data[:, 1]  # 第二列是预测值
    # # 计算评估指标
    # metrics = calculate_metrics(y_true, y_pred)
    #
    # # 打印结果
    # print("=== 分类评估结果 ===")
    # print(f"总体精度: {metrics['accuracy']:.4f}")
    # print(f"混淆矩阵：TP FN")
    # print(f"        FP TN")
    # print(f"{metrics['confusion_matrix']}")
    # print(f"查准率: {metrics['precision']:.4f}")
    # print(f"查全率: {metrics['recall']:.4f}")
    # print(f"F1分数: {metrics['f1']:.4f}")
    from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
    # 计算各项指标
    report = classification_report(y_true, y_pred)
    cm = confusion_matrix(y_true, y_pred)
    accuracy = accuracy_score(y_true, y_pred)
    print(f"=== sklearn 分类评估结果 ===")
    print(f"分类报告:\n{report}")
    print(f"混淆矩阵:\n{cm}")
    print(f"准确率: {accuracy:.4f}")
    return 1

if __name__ == '__main__':
    args = argsparser()
    main(args)