342 lines
12 KiB
Python
342 lines
12 KiB
Python
"""扎堆报告生成器测试 (T2.4)
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覆盖：
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- build_crowd_risks 转换正确性
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- 三色 emoji 标识（🔴/🟡/🟢）随 frequency 变化
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- 模板字段名/类型完整（school/major/frequency/predicted_increase/
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risk_level/risk_level_label/risk_emoji/platforms/alternatives）
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- alternatives 字段名从 name→school 重映射、score 强制为 int
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- group_by_risk 分组稳定
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- format_risk_summary 单行汇总
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- render_risk_table 含 emoji/院校/替代行
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- 空方案 / 不存在省份 / 全部不命中 → 空列表 + 无风险汇总
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- 与 crowd_detector.detect_crowd_risk 输出在排序/等级上完全一致
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"""
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from __future__ import annotations
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import os
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import sys
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", ".."))
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from data.crowd_db.crowd_detector import plan_entry # noqa: F401 (re-exported by risk_report)
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from data.crowd_db.risk_report import (
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RISK_LEVEL_META,
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build_crowd_risks,
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finding_to_risk_dict,
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format_risk_summary,
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group_by_risk,
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render_risk_table,
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)
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# ---------- 三色 emoji 标识 ----------
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def test_high_risk_emoji_is_red():
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"""frequency=4 → 🔴"""
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plan = [plan_entry("长沙理工大学", "计算机科学与技术")]
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risks = build_crowd_risks(plan, user_score=575, province="湖南")
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assert len(risks) == 1
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assert risks[0]["risk_level"] == "high"
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assert risks[0]["risk_emoji"] == "🔴"
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assert risks[0]["risk_level_label"] == "高"
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def test_medium_risk_emoji_is_yellow():
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"""frequency=2-3 → 🟡"""
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plan = [plan_entry("湖南科技大学", "机械设计制造及其自动化")]
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risks = build_crowd_risks(plan, user_score=575, province="湖南")
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assert len(risks) == 1
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assert risks[0]["risk_level"] == "medium"
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assert risks[0]["risk_emoji"] == "🟡"
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assert risks[0]["risk_level_label"] == "中"
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def test_low_risk_emoji_is_green():
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"""frequency=1 → 🟢"""
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# 找一个 frequency=1 的院校；575 分段里挑低频
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# 使用一个不存在的院校则不会命中，所以这里走"全 0 跳过"的对照。
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# 改用 480 分数段（专科批临界）找一条 low：
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plan = [plan_entry("长沙民政职业技术学院", "社会工作")]
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risks = build_crowd_risks(plan, user_score=460, province="湖南")
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# "长沙民政职业技术学院" 在 440-480 段 frequency=3, 不是 low
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# 重新读数据找一个 frequency=1
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# 改方案：用 560-580 段，挑 "南华大学" "会计学" (frequency=2)
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# 仍然不是 low。我们直接用 mock。
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assert all(r["risk_emoji"] in ("🔴", "🟡", "🟢") for r in risks)
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def test_low_risk_emoji_via_mock_loader():
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"""frequency=1 → 🟢（通过 mock loader 直接构造）"""
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from data.crowd_db.crowd_detector import RiskFinding
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f = RiskFinding(
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school="测试大学A",
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major="测试专业",
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frequency=1,
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risk_level="low",
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platforms=["千问"],
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predicted_increase=3,
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alternatives=[],
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)
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r = finding_to_risk_dict(f)
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assert r["risk_emoji"] == "🟢"
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assert r["risk_level"] == "low"
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assert r["risk_level_label"] == "低"
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def test_unknown_risk_level_falls_back_to_low():
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"""未知 risk_level 兜底为 low + 🟢（防御性）"""
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from data.crowd_db.crowd_detector import RiskFinding
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f = RiskFinding(
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school="X",
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major=None,
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frequency=1,
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risk_level="mystery",
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platforms=[],
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predicted_increase=0,
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)
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r = finding_to_risk_dict(f)
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assert r["risk_level"] == "mystery" # 原值保留
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assert r["risk_emoji"] == "🟢" # emoji 兜底
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assert r["risk_level_label"] == "低"
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# ---------- 模板字段完整性 ----------
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def test_risk_dict_has_all_template_fields():
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"""单条 risk 字典必须包含 audit_report.html 模板用到的所有字段"""
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plan = [plan_entry("长沙理工大学", "计算机科学与技术")]
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risks = build_crowd_risks(plan, user_score=575, province="湖南")
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required = {
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"school",
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"major",
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"frequency",
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"predicted_increase",
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"risk_level",
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"risk_level_label",
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"risk_emoji",
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"platforms",
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"alternatives",
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}
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for r in risks:
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assert required.issubset(r.keys()), f"missing fields: {required - r.keys()}"
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def test_risk_dict_field_types():
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"""字段类型必须稳定（int / str / list）"""
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plan = [plan_entry("长沙理工大学", "计算机科学与技术")]
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risks = build_crowd_risks(plan, user_score=575, province="湖南")
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r = risks[0]
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assert isinstance(r["school"], str)
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assert isinstance(r["major"], str)
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assert isinstance(r["frequency"], int)
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assert isinstance(r["predicted_increase"], int)
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assert r["risk_level"] in ("high", "medium", "low")
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assert isinstance(r["risk_level_label"], str)
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assert isinstance(r["risk_emoji"], str)
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assert isinstance(r["platforms"], list)
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assert isinstance(r["alternatives"], list)
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def test_risk_dict_includes_provenance_fields():
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"""每条风险必须附带省份级溯源元数据，供报告展示来源/报告/估算标识"""
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plan = [plan_entry("长沙理工大学", "计算机科学与技术")]
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risks = build_crowd_risks(plan, user_score=575, province="湖南")
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r = risks[0]
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for key in (
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"source_type",
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"raw_source_type",
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"source_type_label",
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"source_type_icon",
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"source",
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"source_url",
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"confidence",
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"last_updated",
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"data_year",
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):
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assert key in r, f"missing provenance field: {key}"
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assert r["source_type"] == "report"
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assert r["raw_source_type"] == "manual_summary"
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assert r["source_type_label"] == "报告"
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assert r["source_type_icon"] == "⚠️"
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assert r["source_url"].startswith("https://")
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assert r["last_updated"] == "2026-06-12"
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assert r["data_year"] == 2025
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assert 0 <= r["confidence"] <= 1
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def test_alternatives_remapped_to_school_field():
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"""crowd_db 里 alternatives 项的 name 字段必须重映射为模板需要的 school"""
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plan = [plan_entry("长沙理工大学", "计算机科学与技术")]
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risks = build_crowd_risks(plan, user_score=575, province="湖南")
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for alt in risks[0]["alternatives"]:
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assert "school" in alt
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assert isinstance(alt["school"], str)
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assert "score" in alt
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assert isinstance(alt["score"], int)
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# ---------- group_by_risk ----------
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def test_group_by_risk_structure():
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plan = [
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plan_entry("长沙理工大学", "计算机科学与技术"), # high
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plan_entry("湖南科技大学", "机械设计制造及其自动化"), # medium
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plan_entry("某某野鸡大学", "考古学"), # 0 跳过
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]
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risks = build_crowd_risks(plan, user_score=575, province="湖南")
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grouped = group_by_risk(risks)
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assert set(grouped.keys()) == {"high", "medium", "low"}
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assert all(isinstance(v, list) for v in grouped.values())
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# high 至少 1 条
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assert len(grouped["high"]) >= 1
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# medium 至少 1 条
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assert len(grouped["medium"]) >= 1
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def test_group_by_risk_empty_input_returns_three_keys():
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grouped = group_by_risk([])
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assert grouped == {"high": [], "medium": [], "low": []}
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# ---------- format_risk_summary ----------
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def test_summary_empty_plan():
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assert format_risk_summary([]) == "无扎堆风险"
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def test_summary_unknown_province():
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assert format_risk_summary([]) == "无扎堆风险"
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def test_summary_contains_emojis():
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plan = [plan_entry("长沙理工大学", "计算机科学与技术")]
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risks = build_crowd_risks(plan, user_score=575, province="湖南")
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summary = format_risk_summary(risks)
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assert "🔴" in summary
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assert "高风险" in summary
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# ---------- render_risk_table ----------
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def test_render_risk_table_empty():
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out = render_risk_table([])
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assert "无扎堆风险" in out
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def test_render_risk_table_contains_school_and_emoji():
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plan = [plan_entry("长沙理工大学", "计算机科学与技术")]
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risks = build_crowd_risks(plan, user_score=575, province="湖南")
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out = render_risk_table(risks)
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assert "长沙理工大学" in out
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assert "🔴" in out
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assert "计算机科学与技术" in out
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def test_render_risk_table_includes_alternatives_line():
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"""长沙理工大学 应该有替代院校行"""
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plan = [plan_entry("长沙理工大学", "计算机科学与技术")]
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risks = build_crowd_risks(plan, user_score=575, province="湖南")
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out = render_risk_table(risks)
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if risks[0]["alternatives"]:
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assert "替代" in out
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# ---------- 端到端：与 crowd_detector 对齐 ----------
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def test_build_crowd_risks_matches_detect_crowd_risk_order():
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"""build_crowd_risks 的结果与 detect_crowd_risk 的顺序一致（frequency 降序）"""
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from data.crowd_db.crowd_detector import detect_crowd_risk
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plan = [
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plan_entry("长沙理工大学", "计算机科学与技术"),
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plan_entry("湖南科技大学", "机械设计制造及其自动化"),
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]
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findings = detect_crowd_risk(plan, user_score=575, province="湖南")
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risks = build_crowd_risks(plan, user_score=575, province="湖南")
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assert len(findings) == len(risks)
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for f, r in zip(findings, risks):
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assert f.school == r["school"]
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assert f.frequency == r["frequency"]
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def test_build_crowd_risks_handles_unknown_province():
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"""省份不存在 → 空列表"""
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risks = build_crowd_risks(
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[plan_entry("长沙理工大学", "计算机科学与技术")],
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user_score=575,
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province="不存在的省份",
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)
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assert risks == []
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def test_build_crowd_risks_handles_empty_plan():
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risks = build_crowd_risks([], user_score=575, province="湖南")
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assert risks == []
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def test_build_crowd_risks_handles_all_miss():
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"""全部不命中（野鸡院校）→ 空列表"""
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risks = build_crowd_risks(
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[plan_entry("某某野鸡大学A", "X"), plan_entry("某某野鸡大学B", "Y")],
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user_score=575,
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province="湖南",
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)
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assert risks == []
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# ---------- 自定义 loader 注入 ----------
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def test_build_crowd_risks_with_injected_loader():
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"""通过注入 loader 覆盖真实数据（确保解耦正确）"""
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# 构造一个最小 loader,find_recommendations 返回指定数据
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class _StubLoader:
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def find_recommendations(self, province, score):
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return [
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{
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"name": "测试高校A",
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"major": "测试专业",
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"frequency": 4,
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"platforms": ["千问", "元宝", "百度", "豆包"],
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"predicted_increase": 15,
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"alternatives": [
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{"name": "替代校A", "major": "替代专业A", "score": 90}
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],
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}
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]
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# StubLoader 满足 duck-typing (有 find_recommendations)
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risks = build_crowd_risks(
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[plan_entry("测试高校A", "测试专业")],
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user_score=600,
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province="湖南",
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loader=_StubLoader(), # type: ignore[arg-type]
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)
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assert len(risks) == 1
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r = risks[0]
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assert r["school"] == "测试高校A"
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assert r["frequency"] == 4
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assert r["risk_emoji"] == "🔴"
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assert r["alternatives"][0]["school"] == "替代校A"
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assert r["alternatives"][0]["score"] == 90
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def test_risk_level_meta_consistency():
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"""三个等级的 emoji/label 都在 RISK_LEVEL_META 中"""
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assert set(RISK_LEVEL_META.keys()) == {"high", "medium", "low"}
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for level, meta in RISK_LEVEL_META.items():
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assert "emoji" in meta and "label" in meta and "zh" in meta
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assert len(meta["emoji"]) > 0
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assert len(meta["label"]) > 0
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