PICP 的python代码

由于PICP是一种统计方法，具体的实现可能会因为数据和模型的不同而有所差异。以下是一种可能的Python代码实现：

import numpy as np
import pandas as pd
from scipy.stats import norm

def picp(y_true, y_pred, alpha):
    lower = np.percentile(y_pred, (1 - alpha) * 100)
    upper = np.percentile(y_pred, alpha * 100)
    return np.mean((y_true >= lower) & (y_true <= upper))

def pinew(y_true, y_pred, alpha):
    lower = np.percentile(y_pred, (1 - alpha) * 100)
    upper = np.percentile(y_pred, alpha * 100)
    coverage = (y_true >= lower) & (y_true <= upper)
    interval_width = np.abs(upper - lower)
    return np.mean(coverage * interval_width)

def pit(y_true, y_pred):
    lower = np.percentile(y_pred, 2.5)
    upper = np.percentile(y_pred, 97.5)
    return np.mean((y_true >= lower) & (y_true <= upper))

def picp_loss(y_true, y_pred):
    alpha = 0.95
    return 1 - picp(y_true, y_pred, alpha)

def pinew_loss(y_true, y_pred):
    alpha = 0.95
    return pinew(y_true, y_pred, alpha)

def picp_pinew_loss(y_true, y_pred):
    alpha = 0.95
    picp_loss = 1 - picp(y_true, y_pred, alpha)
    pinew_loss = pinew(y_true, y_pred, alpha)
    return picp_loss + pinew_loss

def pinball_loss(y_true, y_pred):
    tau = np.arange(0.01, 1, 0.01)
    pinball_losses = np.zeros(len(tau))
    for i, t in enumerate(tau):
        e = y_true - y_pred
        pinball_losses[i] = np.mean(np.maximum(t * e, (t - 1) * e))
    return np.min(pinball_losses)

def crps(y_true, y_pred):
    return np.mean((y_true - y_pred) ** 2) + 0.25 * np.mean((norm.cdf(y_true, y_pred, 1) - 0.5) ** 2)

def wls(y_true, y_pred, ws):
    return np.sum(ws * (y_true - y_pred) ** 2) / np.sum(ws)

def qloss(y_true, y_pred, quantile):
    e = y_true - y_pred
    return np.mean(np.maximum(quantile * e, (quantile - 1) * e))

其中，`picp`函数计算PICP，`pinew`函数计算PINew，`pit`函数计算PIT，`picp_loss`函数计算PICP Loss，`pinew_loss`函数计算PINew Loss，`picp_pinew_loss`函数同时计算PICP Loss和PINew Loss的加权和，`pinball_loss`函数计算Pinball Loss，`crps`函数计算CRPS，`wls`函数计算加权最小二乘损失，`qloss`函数计算分位数损失。这些函数可以根据具体需求进行选择和调用。