需求满足率与缺货成本全量化 — Fill Rate三层模型与OOS全链路损失计算

"""
需求满足率与缺货成本全量化系统
功能：Fill Rate三层计算 + OOS五层成本量化 + 动态FR预测 + 决策框架
"""
import numpy as np
import pandas as pd
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Tuple
from datetime import datetime, timedelta
import warnings
warnings.filterwarnings('ignore')


@dataclass
class OrderLine:
    """订单行"""
    order_id: str
    sku_id: str
    demanded_qty: int
    fulfilled_qty: int
    oos_occurred: bool = False

    @property
    def line_fulfilled(self) -> bool:
        return self.fulfilled_qty >= self.demanded_qty * 0.98

    @property
    def unit_fill_rate(self) -> float:
        return self.fulfilled_qty / max(self.demanded_qty, 1)


@dataclass
class OOSEvent:
    """缺货事件"""
    sku_id: str
    start_date: datetime
    end_date: datetime
    daily_demand_pre_oos: float     # OOS前日均销量
    unit_margin: float              # 单位毛利($)
    # 广告参数
    daily_ad_spend: float           # 日广告花费($)
    # 排名影响参数
    rank_recovery_days: int = 10    # 排名恢复天数
    rank_flow_decay: float = 0.55   # 排名恢复期流量衰减
    # 买家流失参数
    daily_visitors_pre_oos: int = 0 # OOS前日访客数
    permanent_loss_rate: float = 0.08  # 永久流失率
    customer_ltv: float = 150.0     # 客户生命周期价值($)

    @property
    def oos_days(self) -> int:
        return max((self.end_date - self.start_date).days, 0)


def compute_fill_rates(order_lines: List[OrderLine]) -> Dict:
    """计算三层Fill Rate"""
    if not order_lines:
        return {}

    # Line Fill Rate