"""场景检测器单元测试（纯逻辑，无需数据库）。"""

from app.scenarios.churn_fraud import (
    CohortPoint,
    churn_risk_score,
    commission_quality_mismatch,
    detect_pulse_decay,
)
from app.scenarios.split_contract import (
    ContractRecord,
    detect_threshold_edge,
    split_risk_score,
)

# ---------- 场景一：政企拆单 (R8) ----------

def test_threshold_edge_detects_split():
    # 阈值 100 万，8 份合同集中在 79万-99万
    contracts = [ContractRecord(f"C{i}", f"CUST{i}", 810000 + i * 20000) for i in range(8)]
    finding = detect_threshold_edge(contracts, approval_threshold=1_000_000)
    assert finding.hit
    assert len(finding.near_threshold) == 8


def test_threshold_edge_no_split_when_amounts_spread():
    contracts = [
        ContractRecord("C1", "A", 100000),
        ContractRecord("C2", "B", 2_000_000),
    ]
    finding = detect_threshold_edge(contracts, approval_threshold=1_000_000)
    assert not finding.hit


def test_split_score_higher_with_shared_controller():
    contracts = [ContractRecord(f"C{i}", f"CUST{i}", 850000) for i in range(8)]
    finding = detect_threshold_edge(contracts, 1_000_000)
    s_no = split_risk_score(finding, shared_controller=False)
    s_yes = split_risk_score(finding, shared_controller=True)
    assert s_yes > s_no
    assert s_yes <= 1.0


def test_threshold_must_be_positive():
    import pytest

    with pytest.raises(ValueError):
        detect_threshold_edge([], approval_threshold=0)


# ---------- 场景二：养卡骗补 (R9) ----------

def test_pulse_decay_detects_cliff():
    curve = [
        CohortPoint(0, 1.0),
        CohortPoint(1, 0.95),
        CohortPoint(2, 0.92),
        CohortPoint(3, 0.10),  # 第3个月断崖
    ]
    finding = detect_pulse_decay(curve)
    assert finding.pulse_then_decay
    assert finding.cliff_month == 3


def test_no_cliff_for_smooth_curve():
    curve = [CohortPoint(i, 1.0 - 0.05 * i) for i in range(5)]
    finding = detect_pulse_decay(curve)
    assert not finding.pulse_then_decay


def test_commission_mismatch_high_for_zero_usage():
    m = commission_quality_mismatch(commission_paid=100000, active_ratio=0.05, zero_usage_ratio=0.9)
    assert m > 0.7


def test_churn_score_combines_signals():
    curve = [CohortPoint(0, 1.0), CohortPoint(1, 0.2)]
    finding = detect_pulse_decay(curve)
    score = churn_risk_score(finding, mismatch=0.8)
    assert 0.0 < score <= 1.0