"""场景二 · 市场业务真实性：养卡骗补检测（R9）。

检测"脉冲式增长 + 规律性衰减"的周期性造假：渠道每月新增大量用户订购，
固定周期后这些用户集中退订（骗补后弃养）。结合佣金与业务质量匹配度。
"""

from __future__ import annotations

from dataclasses import dataclass

from app.scenarios.base import BaseScenario, ClueDraft, ScanOutcome
from app.scenarios.registry import register_scenario


@dataclass
class CohortPoint:
    """某新增批次（cohort）在第 N 个月的留存率。"""

    month_index: int
    retention: float  # 0-1


@dataclass
class ChurnFinding:
    cliff_month: int | None
    max_drop: float
    pulse_then_decay: bool


def detect_pulse_decay(
    retention_curve: list[CohortPoint],
    cliff_drop: float = 0.5,
) -> ChurnFinding:
    """识别留存曲线中的"断崖式集中退订"。

    若某月留存相对上月骤降超过 cliff_drop（默认 50%），判为规律性衰减。
    """
    ordered = sorted(retention_curve, key=lambda p: p.month_index)
    max_drop = 0.0
    cliff_month: int | None = None
    for prev, cur in zip(ordered, ordered[1:], strict=False):
        drop = prev.retention - cur.retention
        if drop > max_drop:
            max_drop = drop
            if drop >= cliff_drop:
                cliff_month = cur.month_index
    return ChurnFinding(
        cliff_month=cliff_month,
        max_drop=round(max_drop, 3),
        pulse_then_decay=cliff_month is not None,
    )


def commission_quality_mismatch(
    commission_paid: float,
    active_ratio: float,
    zero_usage_ratio: float,
) -> float:
    """佣金与业务质量不匹配度（0-1）。

    active_ratio：仍活跃用户占比；zero_usage_ratio：零通话/零流量用户占比。
    佣金已发但活跃低、零使用高 → 不匹配度高。
    """
    if commission_paid <= 0:
        return 0.0
    mismatch = 0.6 * zero_usage_ratio + 0.4 * (1 - active_ratio)
    return round(min(max(mismatch, 0.0), 1.0), 3)


def churn_risk_score(finding: ChurnFinding, mismatch: float) -> float:
    """综合评分：断崖退订 + 佣金质量不匹配。"""
    if not finding.pulse_then_decay:
        return round(0.3 * mismatch, 3)
    base = 0.4 + 0.4 * finding.max_drop + 0.2 * mismatch
    return round(min(base, 1.0), 3)


def build_rationale(finding: ChurnFinding, mismatch: float) -> str:
    if finding.pulse_then_decay:
        return (
            f"渠道新增用户在第 {finding.cliff_month} 个月出现断崖式集中退订"
            f"（最大单月留存骤降 {finding.max_drop:.0%}），呈"
            f"'脉冲式增长 + 规律性衰减'特征；佣金与业务质量不匹配度 {mismatch:.0%}，"
            f"高度疑似养卡骗补（骗补后弃养）。"
        )
    return (
        f"未见明显断崖退订，但佣金与业务质量不匹配度为 {mismatch:.0%}，建议关注。"
    )


@register_scenario
class ChurnFraudScenario(BaseScenario):
    """场景二养卡骗补检测的插件封装：时序断崖 + 佣金质量不匹配 → 线索草稿。"""

    code = "R9"
    title = "疑似养卡骗补（脉冲增长+规律退订）"
    risk_domain = "成本"
    label = "养卡骗补"
    score_threshold = 0.5  # 与原 run_churn_scan 的 score>=0.5 一致

    def __init__(
        self,
        retention_curve: list[CohortPoint],
        commission_paid: float,
        active_ratio: float,
        zero_usage_ratio: float,
        channel_key: str,
    ) -> None:
        self.retention_curve = retention_curve
        self.commission_paid = commission_paid
        self.active_ratio = active_ratio
        self.zero_usage_ratio = zero_usage_ratio
        self.channel_key = channel_key

    def scan(self, session, *, data_version_id=None) -> ScanOutcome:
        finding = detect_pulse_decay(self.retention_curve)
        mismatch = commission_quality_mismatch(
            self.commission_paid, self.active_ratio, self.zero_usage_ratio
        )
        score = churn_risk_score(finding, mismatch)
        draft = ClueDraft(
            score=score,
            rationale=build_rationale(finding, mismatch),
            evidence={
                "cliff_month": finding.cliff_month,
                "max_drop": finding.max_drop,
                "commission_paid": self.commission_paid,
                "active_ratio": self.active_ratio,
                "zero_usage_ratio": self.zero_usage_ratio,
                "mismatch": mismatch,
            },
            subjects={"channel": self.channel_key},
            amount_involved=self.commission_paid,
        )
        return ScanOutcome(scanned_count=len(self.retention_curve), drafts=[draft])