P1: first upload

analyze_visits.py

@@ -1,135 +0,0 @@
-"""
-分析：访问总次数是否由每次访问平均需求量决定
-使用相关性分析和回归分析
-"""
-import pandas as pd
-import numpy as np
-from scipy import stats
-import matplotlib.pyplot as plt
-
-# 读取数据
-df = pd.read_excel('prob/MFP Regular Sites 2019.xlsx')
-
-# 提取关键列
-visits = df['Number of Visits in 2019']
-avg_demand = df['Average Demand per Visit']
-std_demand = df['StDev(Demand per Visit)']
-
-print("=" * 60)
-print("数据基本统计")
-print("=" * 60)
-print(f"样本数量: {len(visits)}")
-print(f"\n访问总次数:")
-print(f"  均值: {visits.mean():.2f}, 标准差: {visits.std():.2f}")
-print(f"\n每次访问平均需求量:")
-print(f"  均值: {avg_demand.mean():.2f}, 标准差: {avg_demand.std():.2f}")
-
-# 1. 皮尔逊相关系数分析
-print("\n" + "=" * 60)
-print("1. 皮尔逊相关系数分析")
-print("=" * 60)
-r, p_value = stats.pearsonr(avg_demand, visits)
-print(f"相关系数 r = {r:.4f}")
-print(f"p值 = {p_value:.4e}")
-print(f"决定系数 R² = {r**2:.4f} (可解释{r**2*100:.1f}%的变异)")
-
-if p_value < 0.05:
-    print("结论: p < 0.05, 相关性显著")
-else:
-    print("结论: p >= 0.05, 相关性不显著")
-
-# 2. 线性回归分析
-print("\n" + "=" * 60)
-print("2. 线性回归分析 (访问次数 ~ 平均需求量)")
-print("=" * 60)
-slope, intercept, r_val, p_val, std_err = stats.linregress(avg_demand, visits)
-print(f"回归方程: 访问次数 = {slope:.4f} × 平均需求量 + {intercept:.4f}")
-print(f"斜率标准误: {std_err:.4f}")
-print(f"p值: {p_val:.4e}")
-
-# 3. 标准差作为辅助分析
-print("\n" + "=" * 60)
-print("3. 标准差辅助分析")
-print("=" * 60)
-# 变异系数 (CV) = 标准差/均值, 衡量相对离散程度
-cv = std_demand / avg_demand
-print(f"变异系数 (CV = 标准差/均值) 统计:")
-print(f"  均值: {cv.mean():.4f}")
-print(f"  范围: {cv.min():.4f} - {cv.max():.4f}")
-
-# 标准差与访问次数的相关性
-r_std, p_std = stats.pearsonr(std_demand.dropna(), visits[std_demand.notna()])
-print(f"\n标准差与访问次数的相关系数: r = {r_std:.4f}, p = {p_std:.4e}")
-
-# 4. 多元回归 (平均需求量 + 标准差 -> 访问次数)
-print("\n" + "=" * 60)
-print("4. 多元回归分析 (同时考虑平均需求量和标准差)")
-print("=" * 60)
-from sklearn.linear_model import LinearRegression
-from sklearn.preprocessing import StandardScaler
-
-# 准备数据 (去除缺失值)
-mask = std_demand.notna()
-X = np.column_stack([avg_demand[mask], std_demand[mask]])
-y = visits[mask]
-
-model = LinearRegression()
-model.fit(X, y)
-y_pred = model.predict(X)
-ss_res = np.sum((y - y_pred) ** 2)
-ss_tot = np.sum((y - y.mean()) ** 2)
-r2_multi = 1 - ss_res / ss_tot
-
-print(f"多元 R² = {r2_multi:.4f} (可解释{r2_multi*100:.1f}%的变异)")
-print(f"系数: 平均需求量 = {model.coef_[0]:.4f}, 标准差 = {model.coef_[1]:.4f}")
-print(f"截距: {model.intercept_:.4f}")
-
-# 5. 总结
-print("\n" + "=" * 60)
-print("综合结论")
-print("=" * 60)
-if abs(r) < 0.3:
-    strength = "弱"
-elif abs(r) < 0.7:
-    strength = "中等"
-else:
-    strength = "强"
-
-direction = "正" if r > 0 else "负"
-print(f"• 平均需求量与访问次数呈{strength}{direction}相关 (r={r:.3f})")
-print(f"• 平均需求量仅能解释访问次数{r**2*100:.1f}%的变异")
-print(f"• 加入标准差后可解释{r2_multi*100:.1f}%的变异")
-
-if r**2 < 0.25:
-    print("• 结论: 访问总次数主要不由每次访问平均需求量决定")
-else:
-    print("• 结论: 每次访问平均需求量对访问总次数有较大影响")
-
-# 绘图
-fig, axes = plt.subplots(1, 2, figsize=(12, 5))
-
-# 散点图 + 回归线
-ax1 = axes[0]
-ax1.scatter(avg_demand, visits, alpha=0.6, edgecolors='black', linewidth=0.5)
-x_line = np.linspace(avg_demand.min(), avg_demand.max(), 100)
-y_line = slope * x_line + intercept
-ax1.plot(x_line, y_line, 'r-', linewidth=2, label=f'回归线 (R²={r**2:.3f})')
-ax1.set_xlabel('Average Demand per Visit (每次访问平均需求量)')
-ax1.set_ylabel('Number of Visits (访问总次数)')
-ax1.set_title('访问次数 vs 平均需求量')
-ax1.legend()
-ax1.grid(True, alpha=0.3)
-
-# 残差图
-ax2 = axes[1]
-residuals = visits - (slope * avg_demand + intercept)
-ax2.scatter(avg_demand, residuals, alpha=0.6, edgecolors='black', linewidth=0.5)
-ax2.axhline(y=0, color='r', linestyle='--', linewidth=2)
-ax2.set_xlabel('Average Demand per Visit (每次访问平均需求量)')
-ax2.set_ylabel('Residuals (残差)')
-ax2.set_title('残差分析')
-ax2.grid(True, alpha=0.3)
-
-plt.tight_layout()
-plt.savefig('analysis_result.png', dpi=150, bbox_inches='tight')
-print("\n图表已保存至 analysis_result.png")

kmin_effectiveness.py

@@ -0,0 +1,263 @@
+"""
+K_min-有效性分析
+k_min为实数：如2.7表示70%站点最低2次，30%站点最低3次
+"""
+
+import numpy as np
+import pandas as pd
+
+try:
+    import matplotlib
+
+    # 允许在无GUI环境生成图片
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    _HAS_MPL = True
+except ModuleNotFoundError:
+    plt = None
+    _HAS_MPL = False
+
+# 参数
+C_OPT = 250
+N_TOTAL = 722
+ALPHA = 0.4
+BETA = 0.2
+
+
+def load_data():
+    """加载数据"""
+    df = pd.read_excel('prob/MFP Regular Sites 2019.xlsx')
+    df = df.drop(columns=['Unnamed: 10', 'Demand per Visit == the number of clients serviced on that visit'])
+    df['TotalDemand'] = df['Average Demand per Visit'] * df['Number of Visits in 2019']
+    return df.sort_values('TotalDemand').reset_index(drop=True)
+
+
+def allocate_visits(df, k_min_real, n_total, c_opt):
+    """
+    根据实数k_min分配访问次数
+
+    k_min=2.7: floor=2, ceil=3, 70%站点得2次, 30%站点得3次
+    低需求站点优先获得较低的k_min（已按TotalDemand排序）
+    """
+    df = df.copy()
+    n = len(df)
+
+    k_floor = int(np.floor(k_min_real))
+    k_ceil = int(np.ceil(k_min_real))
+    frac = k_min_real - k_floor  # 获得ceil的比例
+
+    # 分配最低次数：低需求站点得floor，高需求站点得ceil
+    n_ceil = int(round(n * frac))
+    n_floor = n - n_ceil
+
+    k_base = np.array([k_floor] * n_floor + [k_ceil] * n_ceil)
+    df['K_base'] = k_base
+
+    # 计算剩余运力
+    n_reserved = k_base.sum()
+    n_free = n_total - n_reserved
+
+    if n_free < 0:
+        return None
+
+    # 按需求权重分配剩余运力
+    df['Weight'] = df['TotalDemand'] / df['TotalDemand'].sum()
+    df['AllocatedVisits'] = (k_base + n_free * df['Weight'].values).round().astype(int)
+    df['AllocatedVisits'] = np.maximum(df['AllocatedVisits'], k_base)
+
+    # 调整总数
+    diff = n_total - df['AllocatedVisits'].sum()
+    if diff != 0:
+        sorted_idx = df['Weight'].sort_values(ascending=(diff < 0)).index.tolist()
+        for idx in sorted_idx[:abs(diff)]:
+            df.loc[idx, 'AllocatedVisits'] += int(np.sign(diff))
+
+    return df
+
+
+def calc_effectiveness(df, c_opt=C_OPT, alpha=ALPHA, beta=BETA):
+    """计算有效性指标"""
+    d = df['Average Demand per Visit'].values
+    k = df['AllocatedVisits'].values
+    D = df['TotalDemand'].values
+
+    # 截断：单次有效服务 = min(需求, 容量)
+    eff_per_visit = np.minimum(d, c_opt)
+    annual_eff = k * eff_per_visit
+
+    # 缺货率、浪费率
+    unmet = np.maximum(0, D - annual_eff) / np.maximum(D, 1)
+    waste = np.maximum(0, k * c_opt - D) / np.maximum(k * c_opt, 1)
+
+    # 有效性得分
+    base = annual_eff / np.maximum(D, 1)
+    score = np.clip(base - alpha * unmet - beta * waste, 0, 1)
+
+    n = score.size
+    bottom_n = max(1, int(np.ceil(n * 0.10)))
+    bottom10_mean = float(np.sort(score)[:bottom_n].mean())
+
+    # 总服务客户数 = Σ min(供给能力, 需求)
+    total_served = np.minimum(k * c_opt, D).sum()
+    total_demand = D.sum()
+
+    return {
+        'mean': score.mean(),
+        'min': score.min(),
+        'bottom10_mean': bottom10_mean,
+        'std': score.std(),
+        'total_unmet': (D - annual_eff).clip(min=0).sum(),
+        'total_waste': (k * c_opt - D).clip(min=0).sum(),
+        'total_served': total_served,
+        'total_demand': total_demand,
+        'serve_ratio': total_served / total_demand
+    }
+
+
+def analyze_kmin_range(df, k_range=np.arange(1.0, 10.1, 0.1)):
+    """扫描k_min范围，计算有效性"""
+    results = []
+
+    for k_min in k_range:
+        df_alloc = allocate_visits(df, k_min, N_TOTAL, C_OPT)
+        if df_alloc is None:
+            continue
+
+        metrics = calc_effectiveness(df_alloc)
+        results.append({
+            'k_min': k_min,
+            'effectiveness': metrics['mean'],
+            'min_eff': metrics['min'],
+            'bottom10_eff': metrics['bottom10_mean'],
+            'std_eff': metrics['std'],
+            'unmet': metrics['total_unmet'],
+            'waste': metrics['total_waste'],
+            'total_served': metrics['total_served'],
+            'total_demand': metrics['total_demand'],
+            'serve_ratio': metrics['serve_ratio']
+        })
+
+    return pd.DataFrame(results)
+
+
+def plot_results(results):
+    """绘制k_min-有效性曲线"""
+    if not _HAS_MPL:
+        raise RuntimeError("缺少依赖: matplotlib（无法绘图）。请先安装 matplotlib 再运行绘图部分。")
+
+    fig, axes = plt.subplots(3, 2, figsize=(12, 10))
+
+    # 找最优点
+    best_idx = results['effectiveness'].idxmax()
+    best_k = results.loc[best_idx, 'k_min']
+    best_eff = results.loc[best_idx, 'effectiveness']
+
+    # 1. 有效性均值
+    ax = axes[0, 0]
+    ax.plot(results['k_min'], results['effectiveness'], 'b-', lw=2)
+    ax.axvline(best_k, color='r', ls='--', label=f'Best k_min={best_k:.1f}')
+    ax.scatter([best_k], [best_eff], c='r', s=100, zorder=5)
+    ax.set_xlabel('k_min')
+    ax.set_ylabel('Mean Effectiveness')
+    ax.set_title('Mean Effectiveness vs k_min')
+    ax.legend()
+    ax.grid(True, alpha=0.3)
+
+    # 2. 最低10%有效性均值
+    ax = axes[0, 1]
+    ax.plot(results['k_min'], results['bottom10_eff'], 'm-', lw=2)
+    ax.axvline(best_k, color='r', ls='--')
+    ax.set_xlabel('k_min')
+    ax.set_ylabel('Bottom 10% Mean Effectiveness')
+    ax.set_title('Bottom 10% Mean Effectiveness vs k_min')
+    ax.grid(True, alpha=0.3)
+
+    # 3. 总服务客户数
+    ax = axes[1, 0]
+    ax.plot(results['k_min'], results['total_served'] / 1000, 'c-', lw=2)
+    ax.axhline(results['total_demand'].iloc[0] / 1000, color='gray', ls=':', label='Total Demand')
+    ax.axvline(best_k, color='r', ls='--')
+    ax.set_xlabel('k_min')
+    ax.set_ylabel('Served Families (×1000)')
+    ax.set_title('Total Served vs k_min')
+    ax.legend()
+    ax.grid(True, alpha=0.3)
+
+    # 4. 最小有效性
+    ax = axes[1, 1]
+    ax.plot(results['k_min'], results['min_eff'], 'g-', lw=2)
+    ax.axvline(best_k, color='r', ls='--')
+    ax.set_xlabel('k_min')
+    ax.set_ylabel('Min Effectiveness')
+    ax.set_title('Worst Site Effectiveness vs k_min')
+    ax.grid(True, alpha=0.3)
+
+    # 5. 未满足需求 vs 浪费
+    ax = axes[2, 0]
+    ax.plot(results['k_min'], results['unmet'] / 1000, 'r-', lw=2, label='Unmet')
+    ax.plot(results['k_min'], results['waste'] / 1000, 'b-', lw=2, label='Waste')
+    ax.axvline(best_k, color='gray', ls='--')
+    ax.set_xlabel('k_min')
+    ax.set_ylabel('Families (×1000)')
+    ax.set_title('Unmet Demand vs Wasted Capacity')
+    ax.legend()
+    ax.grid(True, alpha=0.3)
+
+    # 6. 有效性标准差
+    ax = axes[2, 1]
+    ax.plot(results['k_min'], results['std_eff'], color='tab:orange', lw=2)
+    ax.axvline(best_k, color='gray', ls='--')
+    ax.set_xlabel('k_min')
+    ax.set_ylabel('Std Effectiveness')
+    ax.set_title('Effectiveness Std vs k_min')
+    ax.grid(True, alpha=0.3)
+
+    plt.tight_layout()
+    plt.savefig('kmin_effectiveness.png', dpi=150)
+    plt.close(fig)
+
+    return best_k, best_eff
+
+
+def main():
+    # 1. 加载数据
+    df = load_data()
+    print(f"站点数: {len(df)}, 总运力: {N_TOTAL}")
+    print(f"总需求: {df['TotalDemand'].sum():,.0f} 家庭次")
+
+    # 2. 扫描k_min
+    print("\n扫描 k_min ∈ [1.0, 10.0]...")
+    results = analyze_kmin_range(df)
+
+    # 3. 绘图
+    best_idx = results['effectiveness'].idxmax()
+    best_k = results.loc[best_idx, 'k_min']
+    best_eff = results.loc[best_idx, 'effectiveness']
+    if _HAS_MPL:
+        plot_results(results)
+    else:
+        print("\n未检测到 matplotlib，跳过绘图（仍会保存CSV结果）。")
+
+    # 4. 输出最优结果
+    print(f"\n最优 k_min = {best_k:.1f}")
+    print(f"最优有效性 = {best_eff:.4f}")
+
+    # 5. 生成最优方案
+    df_opt = allocate_visits(df, best_k, N_TOTAL, C_OPT)
+    metrics = calc_effectiveness(df_opt)
+
+    print(f"\n最优方案统计:")
+    print(f"  有效性: {metrics['mean']:.4f} (min={metrics['min']:.4f})")
+    print(f"  总服务: {metrics['total_served']:,.0f} / {metrics['total_demand']:,.0f} ({metrics['serve_ratio']:.1%})")
+    print(f"  未满足: {metrics['total_unmet']:,.0f} 家庭次")
+    print(f"  浪费: {metrics['total_waste']:,.0f} 家庭次")
+    print(f"  访问次数: [{df_opt['AllocatedVisits'].min()}, {df_opt['AllocatedVisits'].max()}]")
+
+    # 6. 保存
+    results.to_csv('kmin_effectiveness_data.csv', index=False)
+    print("\n已保存: kmin_effectiveness.png, kmin_effectiveness_data.csv")
+
+
+if __name__ == '__main__':
+    main()