2569718930@qq.com
109 lines
3.9 KiB
Python
109 lines
3.9 KiB
Python
from __future__ import annotations
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import json
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from datetime import date, timedelta
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from src.database.runtime_state import (
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RuntimeStateDB,
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TrainingFeatureRecordRepository,
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TruthRecordRepository,
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)
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from web.weathernext2_worker_service import run_weathernext2_cycle
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def _seed_training_rows(feature_repo, truth_repo, count: int = 170):
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for idx in range(count):
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city = "houston" if idx % 2 == 0 else "shanghai"
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target_date = (date(2026, 1, 1) + timedelta(days=idx)).isoformat()
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median = 30.0 + (idx % 7) * 0.2
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residual = 1.0 if city == "houston" else -0.5
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feature_repo.upsert_record(
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city,
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target_date,
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{
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"weathernext2": {
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"summary": {
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"mean": median,
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"median": median,
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"p10": median - 1.0,
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"p25": median - 0.5,
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"p75": median + 0.5,
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"p90": median + 1.0,
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"spread": 2.0,
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}
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},
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"deb_prediction": median + 0.2,
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"model_median_c": median + 0.1,
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"model_spread": 1.4,
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"current_max_so_far_c": median - 2.0,
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"local_hour": 12,
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},
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)
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truth_repo.upsert_truth(
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city=city,
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target_date=target_date,
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actual_high=median + residual,
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settlement_source="metar",
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settlement_station_code="TEST",
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settlement_station_label="TEST",
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truth_version="test",
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updated_by="test",
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is_final=True,
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)
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def test_weathernext2_worker_writes_city_artifact_and_trains_calibrator(tmp_path):
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db = RuntimeStateDB(str(tmp_path / "polyweather.db"))
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feature_repo = TrainingFeatureRecordRepository(db)
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truth_repo = TruthRecordRepository(db)
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_seed_training_rows(feature_repo, truth_repo)
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def fake_fetcher(city, meta, target_date):
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return {
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"city": city,
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"target_date": target_date,
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"source_run": "2026-06-29T00:00:00Z",
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"member_highs": [30.0, 30.2, 30.4, 30.6],
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}
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output_path = tmp_path / "weathernext2_city_highs.json"
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model_dir = tmp_path / "model"
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result = run_weathernext2_cycle(
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city_registry={
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"houston": {"name": "Houston", "lat": 29.7, "lon": -95.3, "use_fahrenheit": False, "tz_offset": -5 * 3600},
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},
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output_path=str(output_path),
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model_dir=str(model_dir),
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fetcher=fake_fetcher,
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feature_repo=feature_repo,
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truth_repo=truth_repo,
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min_global_samples=150,
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min_city_samples=5,
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)
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assert result["status"] == "written"
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assert result["city_count"] == 1
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assert result["calibration"]["trained"] is True
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assert (model_dir / "metadata.json").is_file()
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payload = json.loads(output_path.read_text(encoding="utf-8"))
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assert payload["cities"]["houston"]["member_highs"] == [30.0, 30.2, 30.4, 30.6]
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assert payload["calibration_training"]["samples"] == 170
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def test_weathernext2_worker_does_not_overwrite_artifact_when_no_city_payloads(tmp_path):
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db = RuntimeStateDB(str(tmp_path / "polyweather.db"))
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output_path = tmp_path / "weathernext2_city_highs.json"
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output_path.write_text('{"cities":{"old":{"member_highs":[1]}}}', encoding="utf-8")
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result = run_weathernext2_cycle(
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city_registry={"houston": {"name": "Houston", "lat": 29.7, "lon": -95.3}},
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output_path=str(output_path),
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model_dir=str(tmp_path / "model"),
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fetcher=lambda _city, _meta, _target_date: None,
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feature_repo=TrainingFeatureRecordRepository(db),
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truth_repo=TruthRecordRepository(db),
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)
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assert result["status"] == "no_city_payloads"
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assert json.loads(output_path.read_text(encoding="utf-8"))["cities"]["old"]["member_highs"] == [1]
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