rov-offline/celibration/test.py

import numpy as np
import pandas as pd
from scipy.optimize import minimize
from sklearn.metrics import mean_squared_error

# 1. 读取数据
# 假设文件名为 "data.csv"，有两列：预测值 (pred) 和真实值 (label)
file_path = "a.txt"
y_pred = []
y_true = []
with open(file_path, 'r') as f:
    for line in f.readlines():
        lines = line.strip().split("\t")
        y_pred.append(float(lines[0]))
        y_true.append(float(lines[1]))
y_pred = np.array(y_pred)
y_true = np.array(y_true)
# 2. 定义对数-线性缩放函数
def logistic_scaling(pred, a, b):
    """
    对数-线性缩放函数
    :param pred: 原始预测值 (范围 0 到 1)
    :param a: 缩放参数
    :param b: 偏移参数
    :return: 校准后的预测值
    """
    # logit_pred = np.log(pred / (1 - pred))  # 计算 logit
    # calibrated_logit = a * logit_pred + b  # 线性变换
    # return 1 / (1 + np.exp(-calibrated_logit))  # 通过 Sigmoid 归一化
    return np.power(pred + a, b)

# 3. 定义优化目标函数
def objective(params, y_pred, y_true):
    """
    目标函数，用于优化 a 和 b
    :param params: 待优化的参数 (a, b)
    :param y_pred: 原始预测值
    :param y_true: 真实值 (连续值)
    :return: MSE 损失
    """
    a, b = params
    # 使用 Logistic Scaling 校准预测值
    calibrated_pred = logistic_scaling(y_pred, a, b)
    # 计算均方误差 (MSE)
    return mean_squared_error(y_true, calibrated_pred)

# 4. 优化参数
# 初始化参数 a=1, b=0
initial_params = [0.0, 1.0]

# 使用 scipy.optimize.minimize 进行优化
result = minimize(objective, initial_params, args=(y_pred, y_true), method='L-BFGS-B')

# 获取优化后的参数 a 和 b
optimized_a, optimized_b = result.x
print(f"Optimized a: {optimized_a}, Optimized b: {optimized_b}")

# 5. 应用校准模型
calibrated_preds = logistic_scaling(y_pred, optimized_a, optimized_b)


# 7. 验证效果
# 计算校准前后的 MSE
original_mse = mean_squared_error(y_true, y_pred)
calibrated_mse = mean_squared_error(y_true, calibrated_preds)

print(f"Original MSE: {original_mse:.6f}")
print(f"Calibrated MSE: {calibrated_mse:.6f}")

def calibration_function(p, x0=0.07, k1=15, k2=15, p_max=0.13):
    # Sigmoid 平滑校正
    sigmoid_part = 1 / (1 + np.exp(-k1 * (p - x0)))
    # 线性与非线性的平滑过渡
    calibrated = p + sigmoid_part / (1 + np.exp(-k2 * (p - x0))) * (p_max - p)
    return calibrated

print(calibration_function(0.00001))