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Rolling-origin harness gains rise_rain_quantile, rise_rain_quantile_uw, rise_rain_qsigma (L2 point + quantile sigma) and rise_rain_fc48, all opt-in, plus --from-cache for reproducible offline reruns. Results in models/eval_2026-09-12*.json, write-up in docs/FLOOD_FORECASTING.md: - quantile point prediction: better MAE, worse first-alert lead at 5 of 11 events (P.103 2022-08-14 +6h -> +1h) -> rejected - quantile sigma only: Brier within noise (0.0031 -> 0.0029) -> not worth 3x heads - rain_fc48: neutral everywhere except 2024-10-03 P.1 (+21h -> +72h), n=1 -> deferred to after the 2026 season src/ml/hii_rain.py: catchment-mean hourly rain from the ~130 HII gauges in the upper-Ping box and a 24h-sum comparison against Open-Meteo. Not a training feature (table exists only since 2026-08-11, no archive); exposed at GET /api/hii/rainfall/catchment so the two sources' agreement is on record by the time a fold can test it. data._read_cache now skips non-station files in models/cache/ (the shared dir also holds rain_openmeteo / dam_* caches, which crashed the reader). scripts/summarize_eval.py prints per-variant lead/peak-error tables.
48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
"""HII gauge-rain aggregate: pure-function tests (no DB)."""
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import numpy as np
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import pandas as pd
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from src.ml import hii_rain
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def _hourly(start, n):
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return pd.date_range(start, periods=n, freq="h")
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def test_compare_identical_series_has_zero_bias():
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idx = _hourly("2026-08-12", 200)
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rng = np.random.default_rng(1)
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rain = pd.Series(rng.exponential(0.5, len(idx)), index=idx)
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out = hii_rain.compare_with_openmeteo(rain, rain.copy(), window_h=24)
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assert out["overlap_hours"] == 200
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assert out["bias_mm"] == 0.0
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assert out["mae_mm"] == 0.0
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assert out["corr"] > 0.999
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def test_compare_reports_constant_bias():
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idx = _hourly("2026-08-12", 100)
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gauge = pd.Series(1.0, index=idx)
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model = pd.Series(1.5, index=idx) # model wetter by 0.5 mm/h
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out = hii_rain.compare_with_openmeteo(gauge, model, window_h=24)
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assert abs(out["bias_mm"] - 12.0) < 1e-9 # 0.5 mm/h x 24 h
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def test_compare_uses_overlap_only():
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gauge = pd.Series(1.0, index=_hourly("2026-08-12", 100))
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model = pd.Series(1.0, index=_hourly("2026-08-14", 100)) # 52 h overlap
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out = hii_rain.compare_with_openmeteo(gauge, model, window_h=24)
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assert out["overlap_hours"] == 52
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def test_compare_no_overlap():
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gauge = pd.Series(1.0, index=_hourly("2026-01-01", 10))
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model = pd.Series(1.0, index=_hourly("2026-06-01", 10))
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assert hii_rain.compare_with_openmeteo(gauge, model) == {"overlap_hours": 0}
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def test_load_gauge_mean_without_db_returns_none(monkeypatch):
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monkeypatch.setattr(hii_rain, "resolve_db_url", lambda *a, **k: None)
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assert hii_rain.load_gauge_mean() is None
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