kx_guo
1d58bb8913
- 统一供应商异常统计为近3年数据口径 - 以近3年异常记录数作为合作频次代理值并明确提示 - 实现 A/B 缺陷占比与 S/A/B/C 层级判定 - 按供应商层级输出正常合作、加强巡检、驻厂及备选供应商等管理建议 - 完善风险卡片和供应商卡片的频次、层级、建议展示 - 补充供应商画像接口、测试和历史会话卡片兼容处理
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
from app.domain.risk_engine import BaselineRiskEngine, IndicatorValue
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from app.main import app
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from fastapi.testclient import TestClient
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def test_preview_requires_authentication() -> None:
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response = TestClient(app).post(
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"/api/v1/risk-predictions/preview",
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json={
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"subjectId": "customer-001",
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"indicators": [
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{"name": "debtBurden", "value": 80, "weight": 3},
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{"name": "cashflowVolatility", "value": 20, "weight": 1},
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],
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},
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)
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assert response.status_code == 401
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def test_supplier_profile_requires_authentication() -> None:
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response = TestClient(app).get("/api/v1/suppliers")
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assert response.status_code == 401
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def test_baseline_engine_calculates_weighted_score() -> None:
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score = BaselineRiskEngine().predict(
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[
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IndicatorValue(name="debtBurden", value=80, weight=3),
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IndicatorValue(name="cashflowVolatility", value=20, weight=1),
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]
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)
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assert score.score == 35.0
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assert score.level.value == "high"
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assert [factor.contribution for factor in score.factors] == [15.0, 20.0]
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def test_risk_level_boundaries_follow_prd() -> None:
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engine = BaselineRiskEngine()
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cases = (
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(80, "low"),
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(60, "moderately_low"),
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(40, "medium"),
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(20, "high"),
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(19.99, "very_high"),
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)
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for score, expected_level in cases:
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result = engine.predict([IndicatorValue(name="risk", value=100 - score, weight=1)])
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assert result.score == score
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assert result.level.value == expected_level
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