mcm-mfp/task1/02_demand_correction.py

"""
Step 02: 截断回归修正 - 估计真实需求

输入: 01_clean.xlsx
输出: 02_demand.xlsx

功能:
1. 识别被容量截断的高需求站点
2. 使用截断正态模型修正，估计真实需求 μ̃
3. 输出修正前后的对比

核心假设:
- 观测到的 μ_i 是真实需求在容量 C 下的截断观测
- 真实需求服从正态分布 N(μ̃_i, σ̃_i²)
"""

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

# 路径配置
INPUT_PATH = Path(__file__).parent / "01_clean.xlsx"
OUTPUT_PATH = Path(__file__).parent / "02_demand.xlsx"

# 模型参数
# 任务参数（按需可调）
C = 350          # 有效容量上限
P_TRUNC_THRESHOLD = 0.10  # 截断概率阈值 p_thresh


def truncation_correction(mu: float, sigma: float, C: float = 350) -> tuple:
    """
    截断回归修正

    Args:
        mu: 观测均值
        sigma: 观测标准差
        C: 有效容量上限

    Returns:
        (mu_tilde, p_trunc, is_corrected)
        - mu_tilde: 修正后的真实需求估计
        - p_trunc: 截断概率
        - is_corrected: 是否进行了修正
    """
    if sigma <= 0 or np.isnan(sigma):
        return mu, 0.0, False

    # 计算截断概率: P(D > C)
    z = (C - mu) / sigma
    p_trunc = 1 - norm.cdf(z)

    if p_trunc < P_TRUNC_THRESHOLD:
        # 截断概率低于阈值，视为未截断
        return mu, p_trunc, False
    else:
        # 截断修正: 使用 Mills ratio 近似
        # E[D | D > C] = μ + σ * φ(z) / (1 - Φ(z))
        # 修正后: μ̃ ≈ μ * (1 + α * p_trunc)
        # 这里使用简化的线性修正
        correction_factor = 1 + 0.1 * p_trunc
        mu_tilde = mu * correction_factor
        return mu_tilde, p_trunc, True


def main():
    print("=" * 60)
    print("Step 02: 截断回归修正 - 估计真实需求")
    print("=" * 60)

    # 1. 读取清洗后的数据
    print(f"\n[1] 读取输入: {INPUT_PATH}")
    df = pd.read_excel(INPUT_PATH)
    print(f"    读取 {len(df)} 条记录")

    # 2. 显示参数
    print(f"\n[2] 模型参数:")
    print(f"    有效容量上限 C = {C}")
    print(f"    截断概率阈值 = {P_TRUNC_THRESHOLD}")

    # 3. 应用截断修正
    print(f"\n[3] 应用截断修正...")
    results = []
    for _, row in df.iterrows():
        mu_tilde, p_trunc, is_corrected = truncation_correction(
            row['mu'], row['sigma'], C
        )
        results.append({
            'mu_tilde': mu_tilde,
            'p_trunc': p_trunc,
            'is_corrected': is_corrected
        })

    df_result = pd.DataFrame(results)
    df['mu_tilde'] = df_result['mu_tilde']
    df['p_trunc'] = df_result['p_trunc']
    df['is_corrected'] = df_result['is_corrected']

    # 4. 修正统计
    n_corrected = df['is_corrected'].sum()
    print(f"\n[4] 修正统计:")
    print(f"    被修正的站点数: {n_corrected} / {len(df)}")

    if n_corrected > 0:
        corrected_sites = df[df['is_corrected']]
        print(f"\n    被修正站点详情:")
        print(f"    {'site_id':<8} {'site_name':<35} {'μ':>8} {'σ':>8} {'p_trunc':>8} {'μ̃':>10} {'修正幅度':>10}")
        print(f"    {'-'*8} {'-'*35} {'-'*8} {'-'*8} {'-'*8} {'-'*10} {'-'*10}")
        for _, row in corrected_sites.iterrows():
            correction_pct = (row['mu_tilde'] - row['mu']) / row['mu'] * 100
            print(f"    {row['site_id']:<8} {row['site_name'][:35]:<35} {row['mu']:>8.1f} {row['sigma']:>8.1f} {row['p_trunc']:>8.3f} {row['mu_tilde']:>10.1f} {correction_pct:>9.1f}%")

    # 5. 修正前后对比
    print(f"\n[5] 修正前后对比:")
    print(f"    修正前 μ 总和: {df['mu'].sum():.1f}")
    print(f"    修正后 μ̃ 总和: {df['mu_tilde'].sum():.1f}")
    print(f"    增幅: {(df['mu_tilde'].sum() / df['mu'].sum() - 1) * 100:.2f}%")

    # 6. 保存输出
    print(f"\n[6] 保存输出: {OUTPUT_PATH}")
    # 选择输出列
    output_cols = ['site_id', 'site_name', 'lat', 'lon', 'visits_2019',
                   'mu', 'sigma', 'mu_tilde', 'p_trunc', 'is_corrected']
    df[output_cols].to_excel(OUTPUT_PATH, index=False)
    print(f"    已保存 {len(df)} 条记录")

    # 7. 输出预览
    print(f"\n[7] 输出数据预览 (μ 最高的10个站点):")
    top10 = df.nlargest(10, 'mu')[['site_id', 'site_name', 'mu', 'sigma', 'p_trunc', 'mu_tilde', 'is_corrected']]
    print(top10.to_string(index=False))

    print("\n" + "=" * 60)
    print("Step 02 完成")
    print("=" * 60)

    return df


if __name__ == "__main__":
    main()