P1: rebuild

task1/02_demand_correction.py

@@ -0,0 +1,140 @@
+"""
+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
+C = 400          # 有效容量上限 (基于 μ_max = 396.6)
+P_TRUNC_THRESHOLD = 0.02  # 截断概率阈值 (调低以捕获更多潜在截断站点)
+
+
+def truncation_correction(mu: float, sigma: float, C: float = 400) -> 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 + 0.4 * 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()