混淆矩阵精度计算

原创已于 2024-03-07 15:57:02 修改 · 543 阅读

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于 2024-03-07 15:55:02 首次发布

本文介绍了如何使用Python的pymannkendall和numpy库处理示例数据，计算预测值（mod10a1f_predictions_sample）与实际值(true_values_sample)的混淆矩阵，并计算了精度、召回率、F1分数等统计指标。

import pymannkendall as mk
import numpy as np


# 定义示例数据
true_values_sample = np.array([1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
])
mod10a1f_predictions_sample = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
])

# 计算混淆矩阵的组成部分
tp = np.sum((true_values_sample == 1) & (mod10a1f_predictions_sample == 1))
tn = np.sum((true_values_sample == 0) & (mod10a1f_predictions_sample == 0))
fp = np.sum((true_values_sample == 0) & (mod10a1f_predictions_sample == 1))
fn = np.sum((true_values_sample == 1) & (mod10a1f_predictions_sample == 0))

# 计算统计指标
accuracy = (tp + tn) / (tp + tn + fp + fn)
precision = tp / (tp + fp) if tp + fp > 0 else 0
recall = tp / (tp + fn) if tp + fn > 0 else 0
f1_score = 2 *tp/(2*tp+fn+fp) if (precision + recall) > 0 else 0
OA = fp / (fp + tn) if fp + tn > 0 else 0
UA = fn / (fn + tp) if fn + tp > 0 else 0

print(tp, tn, fp, fn, accuracy, precision, recall, f1_score, OA,UA)

混淆矩阵精度计算