2569718930@qq.com
282 lines
9.0 KiB
Python
282 lines
9.0 KiB
Python
from src.analysis.deb_algorithm import (
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_collapse_forecasts_for_deb,
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calculate_deb_prediction,
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calculate_dynamic_weights,
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)
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def test_deb_collapses_regional_model_families_before_blending():
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collapsed = _collapse_forecasts_for_deb(
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{
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"ECMWF": 20.0,
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"ECMWF AIFS": 20.5,
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"GFS": 19.5,
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"ICON": 21.0,
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"ICON-EU": 21.4,
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"ICON-D2": 21.8,
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"GEM": 18.8,
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"GDPS": 19.0,
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"RDPS": 19.4,
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"HRDPS": 20.2,
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"JMA": 19.8,
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}
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)
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assert collapsed == {
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"ECMWF": 20.0,
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"ECMWF AIFS": 20.5,
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"GFS": 19.5,
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"ICON-D2": 21.8,
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"HRDPS": 20.2,
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"JMA": 19.8,
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}
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def test_deb_equal_weight_uses_deduped_family_values(monkeypatch):
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monkeypatch.setattr("src.analysis.deb_algorithm.load_history", lambda _: {})
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blended, info = calculate_dynamic_weights(
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"ankara",
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{
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"ECMWF": 20.0,
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"GFS": 20.0,
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"ICON": 30.0,
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"ICON-EU": 40.0,
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"ICON-D2": 50.0,
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},
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)
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assert blended == 30.0
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assert "家族去重" in info
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def test_deb_weighted_path_uses_deduped_family_values(monkeypatch):
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monkeypatch.setattr(
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"src.analysis.deb_algorithm.load_history",
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lambda _: {
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"ankara": {
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"2026-04-14": {
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"actual_high": 22.0,
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"forecasts": {
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"ECMWF": 22.0,
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"GFS": 21.0,
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"ICON-D2": 30.0,
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},
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},
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"2026-04-15": {
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"actual_high": 23.0,
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"forecasts": {
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"ECMWF": 23.0,
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"GFS": 22.0,
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"ICON-D2": 31.0,
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},
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},
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}
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},
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)
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blended, info = calculate_dynamic_weights(
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"ankara",
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{
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"ECMWF": 24.0,
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"GFS": 24.0,
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"ICON": 32.0,
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"ICON-EU": 34.0,
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"ICON-D2": 36.0,
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},
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lookback_days=5,
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)
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assert blended < 30.0
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assert "ICON-D2" in info
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assert "家族去重" in info
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def test_calculate_deb_prediction_keeps_raw_and_adds_versioned_bias_correction(monkeypatch):
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monkeypatch.setattr(
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"src.analysis.deb_algorithm.load_history",
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lambda _: {
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"ankara": {
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"2026-04-14": {
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"actual_high": 22.0,
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"deb_prediction": 20.0,
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"forecasts": {"ECMWF": 20.0, "GFS": 20.0},
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},
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"2026-04-15": {
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"actual_high": 23.0,
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"deb_prediction": 21.0,
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"forecasts": {"ECMWF": 21.0, "GFS": 21.0},
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},
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"2026-04-16": {
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"actual_high": 25.0,
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"deb_prediction": 24.0,
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"forecasts": {"ECMWF": 24.0, "GFS": 24.0},
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},
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}
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},
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)
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result = calculate_deb_prediction(
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"ankara",
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{"ECMWF": 24.0, "GFS": 24.0},
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)
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assert result["raw_prediction"] == 24.0
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assert result["prediction"] == 25.0
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assert result["version"] == "deb_v3_guarded_calibrated"
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assert result["selected_version"] == "deb_v1_recent_bias_corrected"
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assert result["guard_reason"] == "bucket_unavailable"
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assert result["bias_adjustment"] == 1.0
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assert result["bias_samples"] == 3
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def test_calculate_deb_prediction_prefers_bucket_calibration_when_enough_samples(monkeypatch):
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monkeypatch.setattr(
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"src.analysis.deb_algorithm.load_history",
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lambda _: {
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"ankara": {
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"2026-04-11": {
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"actual_high": 21.0,
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"deb_prediction": 20.4,
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"forecasts": {"ECMWF": 20.4, "GFS": 20.4},
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},
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"2026-04-12": {
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"actual_high": 22.0,
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"deb_prediction": 21.4,
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"forecasts": {"ECMWF": 21.4, "GFS": 21.4},
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},
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"2026-04-13": {
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"actual_high": 23.0,
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"deb_prediction": 22.4,
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"forecasts": {"ECMWF": 22.4, "GFS": 22.4},
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},
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"2026-04-14": {
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"actual_high": 24.0,
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"deb_prediction": 23.4,
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"forecasts": {"ECMWF": 23.4, "GFS": 23.4},
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},
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"2026-04-15": {
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"actual_high": 25.0,
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"deb_prediction": 24.4,
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"forecasts": {"ECMWF": 24.4, "GFS": 24.4},
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},
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}
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},
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)
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result = calculate_deb_prediction(
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"ankara",
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{"ECMWF": 25.4, "GFS": 25.4},
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)
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assert result["raw_prediction"] == 25.4
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assert result["prediction"] == 26.0
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assert result["version"] == "deb_v3_guarded_calibrated"
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assert result["selected_version"] == "deb_v2_bucket_calibrated"
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assert result["guard_reason"] == "bucket_same_adjustment"
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assert result["bias_adjustment"] == 0.6
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assert result["bias_samples"] == 5
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def test_calculate_deb_prediction_reports_recent_quality_for_effective_deb(monkeypatch):
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monkeypatch.setattr(
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"src.analysis.deb_algorithm.load_history",
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lambda _: {
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"high": {
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"2026-04-11": {"actual_high": 21.0, "deb_prediction": 21.0},
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"2026-04-12": {"actual_high": 22.0, "deb_prediction": 22.0},
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"2026-04-13": {"actual_high": 23.0, "deb_prediction": 23.0},
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"2026-04-14": {"actual_high": 24.0, "deb_prediction": 24.0},
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"2026-04-15": {"actual_high": 25.0, "deb_prediction": 25.0},
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},
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"low": {
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"2026-04-11": {"actual_high": 20.0, "deb_prediction": 16.0},
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"2026-04-12": {"actual_high": 21.0, "deb_prediction": 26.0},
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"2026-04-13": {"actual_high": 22.0, "deb_prediction": 18.0},
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"2026-04-14": {"actual_high": 23.0, "deb_prediction": 28.0},
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"2026-04-15": {"actual_high": 24.0, "deb_prediction": 20.0},
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},
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"thin": {
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"2026-04-14": {"actual_high": 23.0, "deb_prediction": 23.0},
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"2026-04-15": {"actual_high": 24.0, "deb_prediction": 24.0},
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},
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},
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)
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def raw(_city, _forecasts, **_kwargs):
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return 25.0, "raw"
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high = calculate_deb_prediction("high", {"ECMWF": 25.0}, raw_calculator=raw)
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low = calculate_deb_prediction("low", {"ECMWF": 25.0}, raw_calculator=raw)
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thin = calculate_deb_prediction("thin", {"ECMWF": 25.0}, raw_calculator=raw)
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assert high["quality_tier"] == "high"
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assert high["recommendation"] == "primary"
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assert high["recent_hit_rate"] == 100.0
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assert low["quality_tier"] == "low"
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assert low["recommendation"] == "context_only"
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assert low["recent_hit_rate"] == 0.0
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assert "quality:low" in low["weights_info"]
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assert thin["quality_tier"] == "insufficient"
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assert thin["recommendation"] == "insufficient"
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assert thin["recent_samples"] == 2
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def test_compute_hourly_model_errors_basic():
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from src.analysis.deb_algorithm import compute_hourly_model_errors
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hourly_forecasts = {
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"ECMWF": [15.0, 15.5, 16.0, 16.5, 17.0, 17.5],
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"GFS": [14.5, 15.0, 15.5, 16.0, 16.5, 17.0],
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}
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hourly_actuals = [15.2, 15.8, 16.3, 16.8, 17.4, 18.0]
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result = compute_hourly_model_errors(hourly_forecasts, hourly_actuals)
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assert "ECMWF" in result
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assert "GFS" in result
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assert result["ECMWF"]["samples"] == 6
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assert result["GFS"]["samples"] == 6
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# ECMWF predicted lower, should have smaller MAE
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assert result["ECMWF"]["mae"] < result["GFS"]["mae"]
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assert result["ECMWF"]["rmse"] > 0
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assert result["GFS"]["rmse"] > 0
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def test_compute_hourly_model_errors_too_few_samples():
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from src.analysis.deb_algorithm import compute_hourly_model_errors
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hourly_forecasts = {"ECMWF": [15.0, 15.5]}
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hourly_actuals = [15.0, 15.5]
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result = compute_hourly_model_errors(hourly_forecasts, hourly_actuals)
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assert result == {} # < 6 samples, returns empty
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def test_blend_mae_with_hourly():
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from src.analysis.deb_algorithm import _blend_mae
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# Full 24 hourly samples → 0.7 hourly weight
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h_err = {"mae": 0.8, "rmse": 1.0, "samples": 24}
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blended = _blend_mae(1.5, h_err)
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# 1.5 * 0.3 + 0.8 * 0.7 = 0.45 + 0.56 = 1.01
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assert abs(blended - 1.01) < 0.01
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# 12 hourly samples → ~0.35 hourly weight
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h_err2 = {"mae": 1.0, "rmse": 1.2, "samples": 12}
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blended2 = _blend_mae(2.0, h_err2)
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# 12/24 * 0.7 = 0.35 hourly weight
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expected = 2.0 * (1 - 0.35) + 1.0 * 0.35 # 1.30 + 0.35 = 1.65
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assert abs(blended2 - expected) < 0.01
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# No hourly error → returns daily MAE unchanged
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blended3 = _blend_mae(2.5, None)
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assert blended3 == 2.5
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# Too few samples (< 6) → returns daily MAE unchanged
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h_err4 = {"mae": 0.5, "samples": 3}
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blended4 = _blend_mae(3.0, h_err4)
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assert blended4 == 3.0
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