shangfangjian
cb36dc3ef9
P0-05: confidence → subjective_confidence 改名(15 文件)
LLM 主观置信度与概率分离
P0-02: predictions 增加 cutoff_at + input_hash(轻量快照)
MatchContext 暴露 match_dt
单/多 Agent 路径均记录快照元数据
P2-05: 数据库 CHECK 约束
- pred_home_goals >= 0
- pred_away_goals >= 0
- subjective_confidence 0~1
- pred_1x2 IN (1,X,2)
- mode IN (single,multi)
P2-06: limit 分页约束(ge=1, le=200)
P2-01: 新增 /health/ready 就绪检查
113 lines
4.0 KiB
Python
113 lines
4.0 KiB
Python
"""测试核心路径。"""
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import pytest
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from datetime import datetime, timezone
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from src.data.normalize import NormalizedMatch, normalize_bzzoiro, _parse_date, _to_int, _to_float, derive_season_label
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class TestNormalize:
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def test_parse_date_iso(self):
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assert _parse_date("2026-09-15T15:00:00Z") is not None
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def test_parse_date_bare(self):
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assert _parse_date("2026-09-15") is not None
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def test_parse_date_none(self):
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assert _parse_date(None) is None
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assert _parse_date("") is None
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def test_to_int_strict(self):
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assert _to_int("2") == 2
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assert _to_int("2.0") == 2
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assert _to_int("2.8") is None # 拒绝非整数值
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assert _to_int(None) is None
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assert _to_int("-") is None
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def test_to_float(self):
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assert _to_float("1.5") == 1.5
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assert _to_float(None) is None
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def test_normalize_bzzoiro_finished(self):
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raw = {
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"event_date": "2026-09-15T15:00:00Z",
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"status": "finished",
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"home_team": "Man City",
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"away_team": "Man United",
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"home_score": 2,
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"away_score": 1,
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"round_number": 5,
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}
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m = normalize_bzzoiro(raw, "E0")
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assert m is not None
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assert m.home_team == "Manchester City"
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assert m.away_team == "Manchester United"
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assert m.home_goals == 2
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assert m.away_goals == 1
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assert m.match_status == "finished"
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assert m.match_stage == "第 5 轮"
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def test_normalize_bzzoiro_unknown_status(self):
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raw = {"event_date": "2026-09-15", "status": "weird", "home_team": "A", "away_team": "B"}
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assert normalize_bzzoiro(raw, "E0") is None
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def test_normalized_match_validate_finished_no_score(self):
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m = NormalizedMatch(
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league_type="E0", date=_parse_date("2026-09-15"),
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home_team="A", away_team="B", match_status="finished",
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)
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with pytest.raises(ValueError, match="must have score"):
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m.validate()
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def test_normalized_match_validate_goals_range(self):
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m = NormalizedMatch(
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league_type="E0", date=_parse_date("2026-09-15"),
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home_team="A", away_team="B", match_status="finished",
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home_goals=50, away_goals=0,
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)
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with pytest.raises(ValueError, match="out of range"):
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m.validate()
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class TestSeasonLabel:
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def test_august_is_new_season(self):
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# 8 月属于新赛季
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d = datetime(2026, 8, 15, tzinfo=timezone.utc)
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assert derive_season_label(d) == "2026-2027"
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def test_july_is_old_season(self):
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# 7 月属于上一赛季
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d = datetime(2026, 7, 15, tzinfo=timezone.utc)
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assert derive_season_label(d) == "2025-2026"
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def test_january_is_old_season(self):
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d = datetime(2026, 1, 15, tzinfo=timezone.utc)
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assert derive_season_label(d) == "2025-2026"
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class TestProviderMock:
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"""用 mock 测试 LLM provider 的解析逻辑。"""
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@pytest.mark.asyncio
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async def test_provider_parses_json(self, monkeypatch):
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from src.llm.provider import LLMProvider
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async def fake_post(*args, **kwargs):
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class FakeResp:
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status_code = 200
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def raise_for_status(self): pass
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def json(self):
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return {
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"choices": [{"message": {"content": '{"pred_home_goals": 1.5, "pred_away_goals": 1.0, "1x2": "1", "subjective_confidence": 0.7, "reasoning": "test"}'}}],
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"usage": {"prompt_tokens": 100, "completion_tokens": 50},
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}
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return FakeResp()
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import httpx
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monkeypatch.setattr(httpx.AsyncClient, "post", fake_post)
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p = LLMProvider(api_key="test", model="gpt-4o")
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resp = await p.chat("sys", "user", json_mode=True)
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assert resp.error is None
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assert resp.parsed is not None
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assert resp.parsed["pred_home_goals"] == 1.5
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assert resp.parsed["1x2"] == "1"
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