142 lines
5.0 KiB
Python
142 lines
5.0 KiB
Python
"""T3.1 27省溯源数据测试
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覆盖：
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1. 27个省份 JSON 存在
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2. 顶层溯源字段完整
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3. 分数段 + 推荐条目 schema 合规
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4. confidence 在 [0,1]
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5. Loader: list_supported_provinces 返回 27
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6. Loader: list_provinces 报告 27/27 存在
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7. Loader: load_metadata 返回含 8 个元数据字段
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8. Loader: load_province 在 confidence<0.5 时发出 UserWarning
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9. 反扎堆检测端到端：随机抽取一省，命中一校的频次与 platforms 不空
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"""
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import os
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import sys
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import json
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import warnings
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
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from data.crowd_db.loader import CrowdDBLoader
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REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
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DATA_DIR = os.path.join(REPO, "data", "crowd_db")
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def _list_json_files():
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return sorted(f for f in os.listdir(DATA_DIR) if f.endswith(".json"))
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def test_27_province_files_exist():
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"""27 省 JSON 全部存在"""
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files = _list_json_files()
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assert len(files) == 27, f"expected 27 province JSONs, got {len(files)}"
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def test_top_level_provenance_fields():
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"""所有 27 个文件顶层必填字段齐全"""
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req = {
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"province",
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"last_updated",
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"data_year",
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"source",
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"source_type",
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"confidence",
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"score_ranges",
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}
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for fname in _list_json_files():
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d = json.load(open(os.path.join(DATA_DIR, fname), encoding="utf-8"))
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miss = req - set(d.keys())
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assert not miss, f"{fname} 缺字段: {miss}"
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def test_score_range_schema():
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"""score_ranges 每个元素含 range+recommendations 且 range 长度为 2"""
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for fname in _list_json_files():
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d = json.load(open(os.path.join(DATA_DIR, fname), encoding="utf-8"))
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for sr in d.get("score_ranges", []):
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assert isinstance(sr, dict), f"{fname}: score_range 必须是 dict"
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assert (
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"range" in sr
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and isinstance(sr["range"], list)
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and len(sr["range"]) == 2
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), f"{fname}: range 必须是长度为 2 的列表"
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assert "recommendations" in sr and isinstance(
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sr["recommendations"], list
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), f"{fname}: recommendations 必须是列表"
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for rec in sr["recommendations"]:
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rk = {"name", "frequency", "platforms"}
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assert rk <= set(rec.keys()), (
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f"{fname}: rec 缺字段 {rk - set(rec.keys())}"
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)
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def test_confidence_in_unit_interval():
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"""confidence ∈ [0.0, 1.0]"""
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for fname in _list_json_files():
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d = json.load(open(os.path.join(DATA_DIR, fname), encoding="utf-8"))
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c = d.get("confidence")
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assert isinstance(c, (int, float)), f"{fname}: confidence 类型错误"
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assert 0.0 <= c <= 1.0, f"{fname}: confidence {c} 越界"
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def test_loader_supported_count_27():
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"""loader.PROVINCE_FILE_MAP 必须覆盖 27 省份"""
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loader = CrowdDBLoader()
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supported = loader.list_supported_provinces()
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assert len(supported) == 27, f"expected 27 supported, got {len(supported)}"
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def test_loader_existing_count_27():
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"""list_provinces 报告 27/27 存在"""
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loader = CrowdDBLoader(warn_low_confidence=False)
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existing = [m for m in loader.list_provinces() if m.get("exists")]
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assert len(existing) == 27, f"expected 27 existing, got {len(existing)}"
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def test_loader_metadata_hunan():
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"""load_metadata('湖南') 返回 8 个字段且 record_count > 0"""
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loader = CrowdDBLoader(warn_low_confidence=False)
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meta = loader.load_metadata("湖南")
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assert meta is not None
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for k in (
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"province",
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"last_updated",
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"data_year",
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"source",
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"source_url",
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"source_type",
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"confidence",
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"record_count",
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):
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assert k in meta, f"meta 缺 {k}"
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assert meta["record_count"] > 0, f"湖南 rec count = {meta['record_count']}"
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assert meta["confidence"] >= 0.8, (
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f"湖南 confidence 应 ≥ 0.8，实际 {meta['confidence']}"
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)
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def test_loader_low_confidence_warning():
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"""confidence<0.5 的省份加载时发出 UserWarning"""
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loader = CrowdDBLoader(warn_low_confidence=True)
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with warnings.catch_warnings(record=True) as caught:
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warnings.simplefilter("always")
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loader.load_province("贵州") # 0.45
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user_warns = [w for w in caught if issubclass(w.category, UserWarning)]
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assert user_warns, "应至少发出 1 个 UserWarning"
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assert any("置信度" in str(w.message) for w in user_warns), (
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f"UserWarning 应提及 置信度, got: {[str(w.message) for w in user_warns]}"
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)
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def test_loader_end_to_end_match():
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"""端到端：用湖南数据 + 一条已知高校名，应当命中"""
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loader = CrowdDBLoader(warn_low_confidence=False)
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rec = loader.find_recommendation_by_school("湖南", "长沙理工大学")
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assert rec is not None, "长沙理工大学 应当在湖南 575 段命中"
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assert rec["frequency"] >= 1
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assert isinstance(rec["platforms"], list) and len(rec["platforms"]) >= 1
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