2569718930@qq.com
93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
from __future__ import annotations
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from src.analysis.weathernext2_calibration import (
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apply_quantile_calibration_to_payload,
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train_lightgbm_quantile_calibrator,
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)
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from src.data_collection.weathernext2_sources import build_weathernext2_city_probability
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def _synthetic_training_rows(count: int = 170):
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rows = []
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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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median = 30.0 + (idx % 7) * 0.2
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residual = 1.0 if city == "houston" else -0.5
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rows.append(
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{
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"city": city,
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"target_date": f"2026-05-{idx % 28 + 1:02d}",
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"actual_high_c": median + residual,
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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_c": 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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return rows
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def test_lightgbm_quantile_calibrator_trains_and_saves_ordered_quantiles(tmp_path):
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result = train_lightgbm_quantile_calibrator(
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_synthetic_training_rows(),
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model_dir=tmp_path,
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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["trained"] is True
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assert result["samples"] == 170
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assert (tmp_path / "metadata.json").is_file()
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assert (tmp_path / "q10.pkl").is_file()
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assert (tmp_path / "q50.pkl").is_file()
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assert (tmp_path / "q90.pkl").is_file()
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assert result["validation"]["ordered_quantiles"] is True
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def test_lightgbm_quantile_calibrator_skips_when_samples_are_insufficient(tmp_path):
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result = train_lightgbm_quantile_calibrator(
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_synthetic_training_rows(20),
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model_dir=tmp_path,
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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["trained"] is False
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assert result["reason"] == "insufficient_global_samples"
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def test_calibrated_distribution_rebuilds_market_buckets_from_shifted_members(tmp_path):
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train_lightgbm_quantile_calibrator(
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_synthetic_training_rows(),
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model_dir=tmp_path,
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min_global_samples=150,
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min_city_samples=5,
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)
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raw = build_weathernext2_city_probability(
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city="houston",
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member_highs=[30.0, 30.2, 30.4, 30.6],
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temp_symbol="°C",
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target_date="2026-06-29",
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)
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calibrated = apply_quantile_calibration_to_payload(raw, model_dir=tmp_path)
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assert calibrated["calibration"]["engine"] == "lightgbm_quantile"
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assert calibrated["calibration"]["samples"] == 170
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assert calibrated["calibration"]["raw_summary"]["median"] == raw["summary"]["median"]
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assert calibrated["calibration"]["calibrated_summary"]["median"] > raw["summary"]["median"]
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assert calibrated["buckets"]
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assert calibrated["top_bucket"]["label"].endswith("°C")
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