diff --git a/artifacts/models/lgbm_daily_high.txt b/artifacts/models/lgbm_daily_high.txt index 7f687ca9..255f926f 100644 --- a/artifacts/models/lgbm_daily_high.txt +++ b/artifacts/models/lgbm_daily_high.txt @@ -6,1430 +6,385 @@ label_index=0 max_feature_idx=26 objective=regression feature_names=actual_high_lag_1 actual_high_lag_2 actual_high_lag_3 actual_high_lag_7 actual_high_mean_7 actual_high_mean_14 actual_high_trend_3 open_meteo ecmwf gfs gem jma icon mgm nws deb_prediction model_median model_spread current_temp max_so_far humidity wind_speed_kt visibility_mi local_hour month weekday peak_status_code -feature_infos=[-4.4000000953674316:34.400001525878906] [-4.4000000953674316:34.400001525878906] [-2.5:34.400001525878906] [0.40000000596046448:35.799999237060547] [2.8714284896850586:34.128570556640625] [2.9642856121063232:33.392856597900391] [-15.100000381469727:11] [-0.20000000298023224:74.400001525878906] [-1.6000000238418579:74.900001525878906] [-0.20000000298023224:74.400001525878906] [-1.1000000238418579:74.400001525878906] [-1.5:76.400001525878906] [-1.7999999523162842:74.599998474121094] none [37:88] [-0.60000002384185791:75.199996948242188] [-1.1000000238418579:74.400001525878906] [0.60000002384185791:18.600000381469727] none [10:28.899999618530273] none none none none none [0:6] [0:2] -tree_sizes=895 1007 1028 967 1084 1049 871 1120 1105 930 1029 1185 1046 1161 1046 1082 1136 935 1029 879 949 1068 1068 1073 973 1053 972 1137 1051 1135 864 1072 1135 976 1059 1044 1236 1140 980 965 1058 1059 965 1061 1042 1037 1065 1056 1024 1143 1128 1013 1074 1016 1068 1147 987 934 1013 1073 989 1045 1115 1096 1038 943 1069 970 1139 1095 965 1052 988 1151 1221 +feature_infos=[2:33] [5.5999999046325684:28] [5.5999999046325684:28] [5.4000000953674316:27.100000381469727] [7.5714287757873535:27.871429443359375] [8.9142856597900391:26.335714340209961] [-14:11] [7.5:30.899999618530273] [7.5:30.899999618530273] [8.1999998092651367:32.900001525878906] [8.1999998092651367:30.700000762939453] [8.3000001907348633:30.799999237060547] [7.5:30.899999618530273] none none [7.9000000953674316:30.899999618530273] [8.1999998092651367:30.899999618530273] [0.30000001192092896:7.5999999046325684] [0:29.200000762939453] [2:32.099998474121094] [46.900001525878906:81] [1:17] none [5:20] none [0:6] [0:2] +tree_sizes=411 401 404 404 426 314 427 422 423 424 430 427 519 431 427 518 427 429 500 427 Tree=0 -num_leaves=9 +num_leaves=3 num_cat=0 -split_feature=7 14 8 17 2 25 3 10 -split_gain=1660.02 227.601 192.575 165.486 98.2909 39.52 6.48 4.54422 -threshold=20.10000038146973 56.500000000000007 16.099999427795414 4.2500000000000009 23.649999618530277 2.5000000000000004 8.7500000000000018 16.19999980926514 -decision_type=2 10 2 2 2 2 2 2 -left_child=2 3 5 4 -2 -1 -7 -4 -right_child=1 -3 7 -5 -6 6 -8 -9 -leaf_value=19.148928607077828 19.581500047842663 19.441250036160152 19.315000025431317 20.071666705608369 19.881666704018912 18.941250042120618 19.031250039736431 19.386500021802885 -leaf_weight=7.0000000000000009 5.0000000000000053 3.9999999999999991 3.9999999999999982 5.9999999999999991 6 4 4 5 -leaf_count=7 5 4 4 6 6 4 4 5 -internal_value=19.4559 19.7806 19.1719 19.8604 19.7452 19.0622 18.9863 19.3547 -internal_weight=45 21 24 17 11 15 8 9 -internal_count=45 21 24 17 11 15 8 9 +split_feature=10 2 +split_gain=1001.8 123.037 +threshold=20.04999923706055 19.94999980926514 +decision_type=10 10 +left_child=-1 -2 +right_child=1 -3 +leaf_value=19.431389017899832 20.051389029050867 20.38722237745921 +leaf_weight=6.0000000000000027 4.9999999999999982 6 +leaf_count=6 5 6 +internal_value=19.9511 20.2346 +internal_weight=17 11 +internal_count=17 11 is_linear=0 shrinkage=1 Tree=1 -num_leaves=10 +num_leaves=3 num_cat=0 -split_feature=7 0 8 10 17 2 17 7 1 -split_gain=1659.21 218.294 213.267 81.4644 78.4537 66.244 46.4881 9.56367 5.05143 -threshold=20.10000038146973 31.399999618530277 16.099999427795414 8.7000002861022967 3.8999999761581425 21.649999618530277 1.4499999880790713 14.150000095367433 16.250000000000004 -decision_type=2 2 2 2 2 2 2 2 2 -left_child=2 4 3 -1 5 -2 -5 -8 -4 -right_child=1 -3 8 6 -6 -7 7 -9 -10 -leaf_value=-0.60475444793701072 -0.011071863025426858 0.64966664314270028 -0.13289376646280288 -0.48337945938110355 0.37400348186492921 0.2619199991226196 -0.32928347587585444 -0.21994643211364742 -0.053431242960505179 -leaf_weight=4.0000000000000062 4.0000000000000018 3.9999999999999991 4 3.9999999999999991 5.9999999999999991 5 4 4 4 -leaf_count=4 4 4 4 4 6 5 4 4 4 -internal_value=-0.027599 0.321474 -0.303948 -0.409341 0.233956 0.14059 -0.344203 -0.274615 -0.0931625 -internal_weight=43 19 24 16 15 9 12 8 8 -internal_count=43 19 24 16 15 9 12 8 8 +split_feature=7 0 +split_gain=858.905 67.8176 +threshold=15.950000286102297 25.85000038146973 +decision_type=8 2 +left_child=-1 -2 +right_child=1 -3 +leaf_value=-0.50156944513320922 0.1882326394319534 0.47938890457153316 +leaf_weight=4.9999999999999991 4 4 +leaf_count=5 4 4 +internal_value=0.0125107 0.333811 +internal_weight=13 8 +internal_count=13 8 is_linear=0 shrinkage=0.05 Tree=2 -num_leaves=10 +num_leaves=3 num_cat=0 -split_feature=7 14 8 7 17 5 25 8 12 -split_gain=1916.05 245.192 237.559 163.298 79.2897 76.2971 20.2967 16.4425 4.05023 -threshold=20.10000038146973 56.500000000000007 16.099999427795414 28.599999427795414 3.1499999761581425 9.3392858505249041 2.5000000000000004 24.750000000000004 17.149999618530277 -decision_type=2 10 2 2 2 2 2 2 2 -left_child=2 3 5 7 -5 -1 -7 -2 -4 -right_child=1 -3 8 4 -6 6 -8 -9 -10 -leaf_value=-0.59283512830734153 0.19743309140205359 0.0070880746841430675 -0.12313608974218371 0.38919467329978952 0.68785985946655259 -0.28502313409532826 -0.40542930534907751 0.32566149234771724 -0.055634195730090132 -leaf_weight=4.0000000000000062 5.0000000000000053 4.9999999999999991 3.9999999999999982 3.9999999999999982 5 6.9999999999999982 7 5 5 -leaf_count=4 5 5 4 4 5 7 7 5 5 -internal_value=-0.00643591 0.318625 -0.295379 0.400608 0.55512 -0.40025 -0.345226 0.261547 -0.085635 -internal_weight=51 24 27 19 9 18 14 10 9 -internal_count=51 24 27 19 9 18 14 10 9 +split_feature=10 0 +split_gain=763.646 100.764 +threshold=20.04999923706055 25.85000038146973 +decision_type=10 2 +left_child=-1 -2 +right_child=1 -3 +leaf_value=-0.43713240964072081 0.10051891766488551 0.45541944503784171 +leaf_weight=7.0000000000000009 4 4 +leaf_count=7 4 4 +internal_value=-0.0557449 0.277969 +internal_weight=15 8 +internal_count=15 8 is_linear=0 shrinkage=0.05 Tree=3 -num_leaves=9 +num_leaves=3 num_cat=0 -split_feature=7 14 12 11 9 17 12 7 -split_gain=1733.95 247.163 298.65 144.447 68.7075 34.0132 24.3917 10.3114 -threshold=20.10000038146973 56.500000000000007 30.04999923706055 15.750000000000002 10.600000381469728 1.4499999880790713 23.60000038146973 14.150000095367433 -decision_type=2 10 2 2 2 2 2 2 -left_child=3 2 6 4 -1 -6 -2 -7 -right_child=1 -3 -4 -5 5 7 -8 -9 -leaf_value=-0.49268550475438383 0.14314907789230324 0.0067336666584014897 0.6594188077109201 -0.050687082931399341 -0.40944919586181644 -0.29405292272567757 0.29792678356170649 -0.18634806394577025 -leaf_weight=6.0000000000000062 4.0000000000000053 4.9999999999999991 6.9999999999999991 4.9999999999999991 3.9999999999999991 3.9999999999999982 7 5 -leaf_count=6 4 5 7 5 4 4 7 5 -internal_value=0.00749759 0.317725 0.404112 -0.289804 -0.35273 -0.288135 0.241644 -0.234217 -internal_weight=47 23 18 24 19 13 11 9 -internal_count=47 23 18 24 19 13 11 9 +split_feature=10 0 +split_gain=466.421 90.9391 +threshold=20.04999923706055 25.85000038146973 +decision_type=10 2 +left_child=-1 -2 +right_child=1 -3 +leaf_value=-0.35153238058090203 0.095492970943450911 0.43264847993850702 +leaf_weight=5.0000000000000009 4 4 +leaf_count=5 4 4 +internal_value=0.0273003 0.264071 +internal_weight=13 8 +internal_count=13 8 is_linear=0 shrinkage=0.05 Tree=4 -num_leaves=11 +num_leaves=3 num_cat=0 -split_feature=7 14 8 7 17 17 3 8 8 10 -split_gain=1383.2 171.542 167.603 127.297 65.2496 52.7069 55.4447 20.3157 18.9273 3.69813 -threshold=20.10000038146973 56.500000000000007 16.099999427795414 28.599999427795414 4.2500000000000009 1.6999999880790713 12.750000000000002 12.150000095367433 24.750000000000004 16.19999980926514 -decision_type=2 10 2 2 2 2 2 2 2 2 -left_child=2 3 5 8 -5 6 -1 -7 -2 -4 -right_child=1 -3 9 4 -6 7 -8 -9 -10 -11 -leaf_value=-0.56991612911224276 0.16804212331771834 0.0063969886302948009 -0.11389921978116038 0.34553068876266474 0.63112121224403372 -0.3458895444869996 -0.30665597915649406 -0.19471013069152832 0.29363174835840855 -0.049398089498281478 -leaf_weight=4.0000000000000053 6.0000000000000053 4.9999999999999991 3.9999999999999982 4 4 3.9999999999999982 4 5 6 5 -leaf_count=4 6 5 4 4 4 4 4 5 6 5 -internal_value=0.00279708 0.268345 -0.252538 0.333833 0.488326 -0.344906 -0.438286 -0.261901 0.230837 -0.0780653 -internal_weight=51 25 26 20 8 17 8 9 12 9 -internal_count=51 25 26 20 8 17 8 9 12 9 +split_feature=8 20 +split_gain=680.381 113.467 +threshold=16.19999980926514 inf +decision_type=2 8 +left_child=-1 -2 +right_child=1 -3 +leaf_value=-0.44872442086537651 0.38909818649292011 0.066590625792741762 +leaf_weight=6.0000000000000027 4.9999999999999973 6.0000000000000009 +leaf_count=6 5 6 +internal_value=-0.0204301 0.213185 +internal_weight=17 11 +internal_count=17 11 is_linear=0 shrinkage=0.05 Tree=5 -num_leaves=10 +num_leaves=2 num_cat=0 -split_feature=0 8 0 1 10 12 0 25 6 -split_gain=1263.66 258.088 116.005 67.815 45.3947 28.6922 27.5819 11.3736 7.3524 -threshold=21.69999980926514 16.099999427795414 31.399999618530277 12.84999990463257 8.7000002861022967 23.60000038146973 15.000000000000002 2.5000000000000004 -1.0000000180025095e-35 -decision_type=2 2 2 2 2 2 2 2 2 -left_child=1 4 5 -3 -1 -2 -5 -6 -7 -right_child=2 3 -4 6 7 8 -8 -9 -10 -leaf_value=-0.49968813061714085 0.12601964771747579 0.11414172500371933 0.54374278783798224 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3.5499999523162846 36.350000381469734 24.750000000000004 2.5000000000000004 17.149999618530277 4.2500000000000009 16.250000000000004 2.4500000476837163 -decision_type=2 2 2 2 2 2 2 2 2 -left_child=1 -1 3 4 7 -6 8 -2 -5 -right_child=2 -3 -4 6 5 -7 -8 -9 -10 -leaf_value=-0.10529112871736286 0.011787899707754452 -0.017905994877219198 -0.043826798014342784 0.018469447921961549 -0.021801466246445973 0.0010143940647443135 0.0022764256689697501 -0.0044204517186153675 0.029041258618235585 -leaf_weight=4 6 4 4.9999999999999991 3.9999999999999982 6 6 4 5 4 -leaf_count=4 6 4 5 4 6 6 4 5 4 -internal_value=-0.0122682 -0.0615986 -0.00240207 0.00351575 -0.00330858 -0.0103935 0.0165957 0.00442047 0.0237554 -internal_weight=48 8 40 35 23 12 12 11 8 -internal_count=48 8 40 35 23 12 12 11 8 -is_linear=0 -shrinkage=0.05 - - -Tree=73 -num_leaves=11 -num_cat=0 -split_feature=6 0 2 5 1 17 0 17 12 4 -split_gain=16.2815 10.6055 3.33677 1.08116 1.0485 0.706907 0.358389 0.320935 0.253859 0.185781 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-Tree=74 -num_leaves=12 -num_cat=0 -split_feature=2 0 2 3 25 17 2 3 3 0 17 -split_gain=19.9083 9.24093 2.00627 0.906871 0.538062 0.389537 0.764903 0.264204 0.296006 0.123535 0.110182 -threshold=6.6000001430511483 10.050000190734865 11.050000190734865 23.000000000000004 2.5000000000000004 3.1499999761581425 18.500000000000004 16.35000038146973 18.750000000000004 16.599999427795414 4.2500000000000009 -decision_type=2 2 2 2 2 2 2 2 2 2 2 -left_child=-1 -2 -3 4 7 6 -6 9 -9 -4 -5 -right_child=1 2 3 10 5 -7 -8 8 -10 -11 -12 -leaf_value=0.10757777839899063 -0.072860270133242019 -0.034766422252869238 -0.0085607353597879406 0.019685196628173196 -0.03168413508683441 0.0033655822277069095 -0.0023495866358280177 0.020120526989921922 0.00088494652882218348 0.0038658092729747292 0.0089719646421144699 -leaf_weight=3.9999999999999991 4.9999999999999991 3.9999999999999991 4 5.9999999999999982 4.0000000000000018 3.9999999999999991 5 4 4 4 4 -leaf_count=4 5 4 4 6 4 4 5 4 4 4 4 -internal_value=0.000406984 -0.00852392 -0.00104294 0.00241587 -0.00206138 -0.00961709 -0.0153872 0.00407764 0.0105027 -0.00234746 0.0153999 -internal_weight=52 48 43 39 29 13 9 16 8 8 10 -internal_count=52 48 43 39 29 13 9 16 8 8 10 +split_feature=10 10 +split_gain=180.679 24.1787 +threshold=20.04999923706055 27.150000572204593 +decision_type=8 10 +left_child=-1 -2 +right_child=1 -3 +leaf_value=-0.22275811135768878 0.053300471178122935 0.19726094961166379 +leaf_weight=6.0000000000000027 6.9999999999999982 5 +leaf_count=6 7 5 +internal_value=0.00126997 0.113284 +internal_weight=18 12 +internal_count=18 12 is_linear=0 shrinkage=0.05 @@ -1437,24 +392,19 @@ shrinkage=0.05 end of trees feature_importances: -actual_high_lag_1=80 -model_spread=77 -open_meteo=68 -actual_high_lag_2=60 -actual_high_lag_3=51 -actual_high_trend_3=44 -weekday=39 -actual_high_lag_7=38 -ecmwf=38 -icon=35 -actual_high_mean_14=34 -actual_high_mean_7=32 -gem=25 -jma=19 -gfs=13 -nws=11 -deb_prediction=11 -model_median=1 +gem=9 +open_meteo=5 +current_temp=5 +actual_high_lag_1=4 +actual_high_lag_3=4 +wind_speed_kt=4 +max_so_far=3 +actual_high_trend_3=2 +humidity=2 +actual_high_lag_7=1 +ecmwf=1 +jma=1 +icon=1 parameters: [boosting: gbdt] @@ -1465,7 +415,7 @@ parameters: [data_sample_strategy: bagging] [data: ] [valid: ] -[num_iterations: 75] +[num_iterations: 20] [learning_rate: 0.05] [num_leaves: 15] [num_threads: 0] diff --git a/artifacts/models/lgbm_daily_high_schema.json b/artifacts/models/lgbm_daily_high_schema.json index 59ead30a..f4638a29 100644 --- a/artifacts/models/lgbm_daily_high_schema.json +++ b/artifacts/models/lgbm_daily_high_schema.json @@ -42,25 +42,25 @@ "nws" ], "model_path": "artifacts\\models\\lgbm_daily_high.txt", - "sample_count": 54, - "train_count": 42, + "sample_count": 18, + "train_count": 6, "validation_count": 12, "metrics": { "validation": { "sample_count": 12, - "lgbm_mae": 1.349, - "deb_mae": 0.875, - "best_single_mae": 0.325, - "median_mae": 0.758 + "lgbm_mae": 7.992, + "deb_mae": 2.375, + "best_single_mae": 1.05, + "median_mae": 2.292 }, "full_sample": { - "sample_count": 54, - "lgbm_mae": 0.691, - "deb_mae": 6.287, - "best_single_mae": 5.431, - "median_mae": 6.265 + "sample_count": 18, + "lgbm_mae": 3.034, + "deb_mae": 1.689, + "best_single_mae": 0.822, + "median_mae": 1.711 } }, - "generated_at": "2026-04-02T16:27:44.816882Z", - "trained_at": "2026-04-02T16:27:44.816882Z" + "generated_at": "2026-04-15T16:41:56.814421Z", + "trained_at": "2026-04-15T16:41:56.814421Z" } \ No newline at end of file diff --git a/artifacts/probability_calibration/default.json b/artifacts/probability_calibration/default.json index 4ee955e5..138bb4a9 100644 --- a/artifacts/probability_calibration/default.json +++ b/artifacts/probability_calibration/default.json @@ -1,20 +1,20 @@ { - "version": "emos-20260402162744", - "trained_at": "2026-04-02T16:27:44.114836+00:00", + "version": "emos-20260415164136", + "trained_at": "2026-04-15T16:41:36.704829+00:00", "global": { "mu": { - "intercept": 1.54512641, - "raw_mu_coef": 2.96105052, - "deb_coef": -1.53260815, - "ens_median_coef": -0.72849343, - "max_so_far_gap_coef": 9.52557689 + "intercept": -0.39094186, + "raw_mu_coef": 0.27476248, + "deb_coef": 0.91159557, + "ens_median_coef": -0.16959018, + "max_so_far_gap_coef": -0.94232523 }, "sigma": { - "intercept": 0.67432479, - "raw_sigma_coef": 0.6936692, - "spread_coef": 0.08877484, - "peak_flag_coef": -0.58374835, - "max_so_far_gap_coef": -0.8172477 + "intercept": -1.56485068, + "raw_sigma_coef": 0.12930442, + "spread_coef": 0.15947819, + "peak_flag_coef": 0.19328621, + "max_so_far_gap_coef": -0.08469752 } }, "sigma_constraints": { @@ -29,60 +29,54 @@ "max_bucket_brier_increase": 0.05 }, "blending": { - "alpha_mu": 0.0, - "alpha_sigma": 0.05 + "alpha_mu": 1.0, + "alpha_sigma": 0.5 }, "cities": { - "ankara": { - "samples": 3, - "mu_bias": 1.271844, - "sigma_scale": 1.477644, - "confidence": 0.375 - }, - "hong kong": { + "tokyo": { "samples": 4, - "mu_bias": 1.32008, - "sigma_scale": 1.04023, + "mu_bias": 0.211244, + "sigma_scale": 0.5, "confidence": 0.5 }, - "milan": { - "samples": 3, - "mu_bias": -3.935178, - "sigma_scale": 2.0, - "confidence": 0.375 + "ankara": { + "samples": 11, + "mu_bias": 0.027471, + "sigma_scale": 0.5, + "confidence": 1.0 }, - "shanghai": { - "samples": 3, - "mu_bias": 1.810495, - "sigma_scale": 2.0, - "confidence": 0.375 + "istanbul": { + "samples": 12, + "mu_bias": 0.048913, + "sigma_scale": 0.5, + "confidence": 1.0 + }, + "hong kong": { + "samples": 12, + "mu_bias": -0.026823, + "sigma_scale": 1.020494, + "confidence": 1.0 }, "taipei": { - "samples": 3, - "mu_bias": 3.577828, - "sigma_scale": 2.0, - "confidence": 0.375 - }, - "warsaw": { - "samples": 3, - "mu_bias": -0.625333, - "sigma_scale": 1.25968, - "confidence": 0.375 + "samples": 5, + "mu_bias": 0.192719, + "sigma_scale": 0.5, + "confidence": 0.625 } }, "metrics": { - "sample_count": 54, - "mean_crps": 3.792563, - "legacy_mean_crps": 4.308029, - "legacy_mean_mae": 4.51037, - "legacy_bucket_hit_rate": 0.537037, - "legacy_bucket_brier": 0.833294, - "selected_mean_crps": 4.249828, - "selected_mean_mae": 4.51037, - "selected_bucket_hit_rate": 0.555556, - "selected_bucket_brier": 0.831872, - "selected_score": 5.991436, - "legacy_score": 6.078481, + "sample_count": 50, + "mean_crps": 0.278798, + "legacy_mean_crps": 1.26525, + "legacy_mean_mae": 1.4674, + "legacy_bucket_hit_rate": 0.54, + "legacy_bucket_brier": 0.744518, + "selected_mean_crps": 0.278798, + "selected_mean_mae": 0.248457, + "selected_bucket_hit_rate": 0.82, + "selected_bucket_brier": 0.369542, + "selected_score": 0.8508, + "legacy_score": 2.660378, "filled_actual_from_history": 0, "settlement_history_city_count": 30 }, diff --git a/artifacts/probability_calibration/evaluation_report.json b/artifacts/probability_calibration/evaluation_report.json index fe114bbb..46cb1df6 100644 --- a/artifacts/probability_calibration/evaluation_report.json +++ b/artifacts/probability_calibration/evaluation_report.json @@ -1,257 +1,95 @@ { "summary": { - "sample_count": 54, - "filled_actual_from_history": 2, + "sample_count": 17, + "filled_actual_from_history": 0, "legacy": { - "mean_crps": 4.300621, - "mean_mae": 4.502963, - "bucket_hit_rate": 0.537037 + "mean_crps": 1.095786, + "mean_mae": 1.243529, + "bucket_hit_rate": 0.588235 }, "emos": { - "mean_crps": 4.213889, - "mean_mae": 4.502963, - "bucket_hit_rate": 0.537037 + "mean_crps": 1.257578, + "mean_mae": 1.545328, + "bucket_hit_rate": 0.352941 }, "delta": { - "crps": -0.086732, - "mae": 0.0, - "bucket_hit_rate": 0.0 + "crps": 0.161791, + "mae": 0.301798, + "bucket_hit_rate": -0.235294 } }, "by_city": { "ankara": { "samples": 3, - "legacy_mean_crps": 0.327701, - "emos_mean_crps": 0.439705, - "legacy_mean_mae": 0.066667, - "emos_mean_mae": 0.066667, - "legacy_bucket_hit_rate": 1.0, - "emos_bucket_hit_rate": 1.0 - }, - "atlanta": { - "samples": 2, - "legacy_mean_crps": 30.449382, - 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"emos_bucket_hit_rate": 1.0 - }, - "taipei": { - "samples": 3, - "legacy_mean_crps": 0.356996, - "emos_mean_crps": 0.472738, - "legacy_mean_mae": 0.1, - "emos_mean_mae": 0.1, - "legacy_bucket_hit_rate": 1.0, - "emos_bucket_hit_rate": 1.0 - }, - "tel aviv": { - "samples": 2, - "legacy_mean_crps": 0.446758, - "emos_mean_crps": 0.578006, - "legacy_mean_mae": 0.3, - "emos_mean_mae": 0.3, - "legacy_bucket_hit_rate": 1.0, - "emos_bucket_hit_rate": 1.0 - }, - "tokyo": { - "samples": 2, - "legacy_mean_crps": 0.450128, - "emos_mean_crps": 0.582151, - "legacy_mean_mae": 0.25, - "emos_mean_mae": 0.25, - "legacy_bucket_hit_rate": 0.5, - "emos_bucket_hit_rate": 1.0 - }, - "toronto": { - "samples": 2, - "legacy_mean_crps": 5.497916, - "emos_mean_crps": 5.240552, - "legacy_mean_mae": 6.33, - "emos_mean_mae": 6.33, - "legacy_bucket_hit_rate": 0.0, - "emos_bucket_hit_rate": 0.0 - }, - "warsaw": { - "samples": 3, - "legacy_mean_crps": 1.618875, - "emos_mean_crps": 1.553232, - "legacy_mean_mae": 2.056667, - "emos_mean_mae": 2.056667, + "legacy_mean_crps": 2.707176, + "emos_mean_crps": 2.632737, + "legacy_mean_mae": 3.0, + "emos_mean_mae": 2.90439, "legacy_bucket_hit_rate": 0.333333, "emos_bucket_hit_rate": 0.333333 }, - "wellington": { - "samples": 2, - "legacy_mean_crps": 0.364919, - "emos_mean_crps": 0.475875, - "legacy_mean_mae": 0.15, - "emos_mean_mae": 0.15, + "busan": { + "samples": 1, + "legacy_mean_crps": 0.264698, + "emos_mean_crps": 0.797946, + "legacy_mean_mae": 0.3, + "emos_mean_mae": 1.213185, "legacy_bucket_hit_rate": 1.0, - "emos_bucket_hit_rate": 1.0 + "emos_bucket_hit_rate": 0.0 + }, + "chengdu": { + "samples": 1, + "legacy_mean_crps": 0.218827, + "emos_mean_crps": 0.321502, + "legacy_mean_mae": 0.3, + "emos_mean_mae": 0.539971, + "legacy_bucket_hit_rate": 1.0, + "emos_bucket_hit_rate": 0.0 + }, + "hong kong": { + "samples": 3, + "legacy_mean_crps": 0.321946, + "emos_mean_crps": 0.419198, + "legacy_mean_mae": 0.27, + "emos_mean_mae": 0.673605, + "legacy_bucket_hit_rate": 1.0, + "emos_bucket_hit_rate": 0.666667 + }, + "istanbul": { + "samples": 3, + "legacy_mean_crps": 1.308328, + "emos_mean_crps": 1.231189, + "legacy_mean_mae": 1.38, + "emos_mean_mae": 1.211693, + "legacy_bucket_hit_rate": 0.666667, + "emos_bucket_hit_rate": 0.666667 + }, + "seoul": { + "samples": 1, + "legacy_mean_crps": 0.208763, + "emos_mean_crps": 2.708973, + "legacy_mean_mae": 0.3, + "emos_mean_mae": 3.02868, + "legacy_bucket_hit_rate": 1.0, + "emos_bucket_hit_rate": 0.0 + }, + "taipei": { + "samples": 2, + "legacy_mean_crps": 0.767971, + "emos_mean_crps": 1.205469, + "legacy_mean_mae": 1.195, + "emos_mean_mae": 1.937374, + "legacy_bucket_hit_rate": 0.0, + "emos_bucket_hit_rate": 0.0 + }, + "tokyo": { + "samples": 3, + "legacy_mean_crps": 1.129263, + "emos_mean_crps": 0.763363, + "legacy_mean_mae": 1.3, + "emos_mean_mae": 1.081641, + "legacy_bucket_hit_rate": 0.333333, + "emos_bucket_hit_rate": 0.333333 } } } \ No newline at end of file diff --git a/src/analysis/deb_algorithm.py b/src/analysis/deb_algorithm.py index 5b177ac5..5faf7555 100644 --- a/src/analysis/deb_algorithm.py +++ b/src/analysis/deb_algorithm.py @@ -7,7 +7,6 @@ from src.analysis.settlement_rounding import apply_city_settlement from loguru import logger from src.database.runtime_state import ( DailyRecordRepository, - STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE, TrainingFeatureRecordRepository, TruthRecordRepository, @@ -79,7 +78,7 @@ def load_history(filepath): logger.error(f"Error loading daily records from sqlite, fallback to file: {e}") if not os.path.exists(filepath): - if mode == STATE_STORAGE_DUAL: + if mode == STATE_STORAGE_SQLITE: try: data = _daily_record_repo.load_all() _history_cache = data @@ -113,13 +112,12 @@ def save_history(filepath, data): _history_cache = data mode = get_state_storage_mode() - if mode in {STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE}: + if mode == STATE_STORAGE_SQLITE: try: _daily_record_repo.replace_all(data) except Exception as e: logger.error(f"Error saving daily records to sqlite: {e}") - if mode == STATE_STORAGE_SQLITE: - return + return if mode == STATE_STORAGE_SQLITE: return @@ -891,15 +889,14 @@ def update_daily_record( for d in old_dates: del data[city][d] - if mode in {STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE}: + if mode == STATE_STORAGE_SQLITE: try: _daily_record_repo.upsert_record(city_name, date_str, existing) cutoff = (datetime.now() - timedelta(days=14)).strftime("%Y-%m-%d") _daily_record_repo.delete_older_than(cutoff) except Exception as e: logger.error(f"Error upserting daily record to sqlite city={city_name} date={date_str}: {e}") - if mode == STATE_STORAGE_SQLITE: - raise + raise if mode != STATE_STORAGE_SQLITE: save_history(history_file, data) diff --git a/src/analysis/probability_snapshot_archive.py b/src/analysis/probability_snapshot_archive.py index 332ccf96..9231ef99 100644 --- a/src/analysis/probability_snapshot_archive.py +++ b/src/analysis/probability_snapshot_archive.py @@ -7,7 +7,6 @@ from typing import Any, Dict, List, Optional from src.database.runtime_state import ( ProbabilitySnapshotRepository, - STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE, TrainingFeatureRecordRepository, get_state_storage_mode, @@ -105,7 +104,7 @@ def load_snapshot_rows_for_day( root_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) path = archive_path or os.path.join(root_dir, "data", "probability_training_snapshots.jsonl") if not os.path.exists(path): - if mode == STATE_STORAGE_DUAL: + if mode == STATE_STORAGE_SQLITE: return _snapshot_repo.load_rows_by_city_date(city_key, date_key) return [] @@ -260,7 +259,7 @@ def append_probability_snapshot( return mode = get_state_storage_mode() - if mode in {STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE}: + if mode == STATE_STORAGE_SQLITE: _snapshot_repo.append_snapshot(payload) _training_feature_repo.upsert_record( city_key, diff --git a/src/data_collection/open_meteo_cache.py b/src/data_collection/open_meteo_cache.py index 667c8f6b..643e2ee5 100644 --- a/src/data_collection/open_meteo_cache.py +++ b/src/data_collection/open_meteo_cache.py @@ -7,7 +7,6 @@ import time from loguru import logger from src.database.runtime_state import ( OpenMeteoCacheRepository, - STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE, get_state_storage_mode, ) @@ -77,7 +76,7 @@ class OpenMeteoCacheMixin: self._disk_cache_last_mtime = current_mtime if loaded: logger.info(f"✅ 从磁盘加载 Open-Meteo 缓存 {loaded} 条 ({self._disk_cache_path})") - if mode == STATE_STORAGE_DUAL: + if mode == STATE_STORAGE_SQLITE: try: _open_meteo_cache_repo.replace_payload(saved, self._disk_cache_max_age_sec) except Exception as exc: @@ -124,7 +123,7 @@ class OpenMeteoCacheMixin: "multi_model": multi_model_snapshot, "saved_at": time.time(), } - if mode in {STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE}: + if mode == STATE_STORAGE_SQLITE: _open_meteo_cache_repo.replace_payload(payload, self._disk_cache_max_age_sec) self._disk_cache_last_mtime = _open_meteo_cache_repo.latest_updated_at() if mode != STATE_STORAGE_SQLITE: diff --git a/src/data_collection/settlement_sources.py b/src/data_collection/settlement_sources.py index 04d50de8..e521dc0d 100644 --- a/src/data_collection/settlement_sources.py +++ b/src/data_collection/settlement_sources.py @@ -12,7 +12,6 @@ from loguru import logger from src.database.runtime_state import ( OfficialIntradayObservationRepository, - STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE, get_state_storage_mode, ) @@ -183,7 +182,7 @@ class SettlementSourceMixin: date_str = local_dt.strftime("%Y-%m-%d") time_str = local_dt.strftime("%H:%M") mode = get_state_storage_mode() - if mode not in {STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE}: + if mode != STATE_STORAGE_SQLITE: return [{"time": time_str, "temp": round(float(current_temp), 1)}] lock = self._get_settlement_series_lock() diff --git a/src/database/runtime_state.py b/src/database/runtime_state.py index d9eeafff..88d6f2c3 100644 --- a/src/database/runtime_state.py +++ b/src/database/runtime_state.py @@ -16,9 +16,9 @@ from src.database.db_manager import DBManager STATE_STORAGE_FILE = "file" STATE_STORAGE_DUAL = "dual" STATE_STORAGE_SQLITE = "sqlite" +DEFAULT_STATE_STORAGE_MODE = STATE_STORAGE_SQLITE VALID_STATE_STORAGE_MODES = { STATE_STORAGE_FILE, - STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE, } @@ -26,12 +26,17 @@ _LOGGED_MODES: set[str] = set() def get_state_storage_mode() -> str: - raw = str(os.getenv("POLYWEATHER_STATE_STORAGE_MODE") or STATE_STORAGE_DUAL).strip().lower() + raw = str(os.getenv("POLYWEATHER_STATE_STORAGE_MODE") or DEFAULT_STATE_STORAGE_MODE).strip().lower() + if raw == STATE_STORAGE_DUAL: + logger.warning( + f"POLYWEATHER_STATE_STORAGE_MODE={STATE_STORAGE_DUAL!r} is deprecated, normalize to {STATE_STORAGE_SQLITE}" + ) + raw = STATE_STORAGE_SQLITE if raw not in VALID_STATE_STORAGE_MODES: logger.warning( - f"invalid POLYWEATHER_STATE_STORAGE_MODE={raw!r}, fallback to {STATE_STORAGE_DUAL}" + f"invalid POLYWEATHER_STATE_STORAGE_MODE={raw!r}, fallback to {DEFAULT_STATE_STORAGE_MODE}" ) - raw = STATE_STORAGE_DUAL + raw = DEFAULT_STATE_STORAGE_MODE if raw not in _LOGGED_MODES: logger.info(f"runtime state storage mode={raw}") _LOGGED_MODES.add(raw) diff --git a/src/models/lgbm_features.py b/src/models/lgbm_features.py index cba5fd46..12a33beb 100644 --- a/src/models/lgbm_features.py +++ b/src/models/lgbm_features.py @@ -289,13 +289,14 @@ def build_training_samples( history_data: Optional[Dict[str, Any]] = None, snapshot_index: Optional[Dict[Tuple[str, str], Dict[str, Any]]] = None, ) -> List[Dict[str, Any]]: + mode = get_state_storage_mode() if isinstance(history_data, dict): runtime_history = history_data - elif get_state_storage_mode() == STATE_STORAGE_SQLITE: + elif mode == STATE_STORAGE_SQLITE: runtime_history = DailyRecordRepository().load_all() else: runtime_history = load_history(_history_file_path()) - if get_state_storage_mode() == STATE_STORAGE_SQLITE: + if mode == STATE_STORAGE_SQLITE: truth_history = TruthRecordRepository().load_all() training_feature_history = TrainingFeatureRecordRepository().load_all() else: diff --git a/src/utils/telegram_push.py b/src/utils/telegram_push.py index 334f7de7..17a5e565 100644 --- a/src/utils/telegram_push.py +++ b/src/utils/telegram_push.py @@ -10,7 +10,6 @@ from typing import Any, Dict, List, Optional, Tuple from loguru import logger from src.database.runtime_state import ( - STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE, TelegramAlertStateRepository, get_state_storage_mode, @@ -208,11 +207,6 @@ def _load_state(path: str) -> Dict[str, Any]: except Exception as exc: logger.error(f"failed to load telegram push state from sqlite: {exc}") if not os.path.exists(path): - if mode == STATE_STORAGE_DUAL: - try: - return _telegram_state_repo.load_state() - except Exception: - return {"last_by_city": {}, "by_signature": {}} return {"last_by_city": {}, "by_signature": {}} try: with open(path, "r", encoding="utf-8") as fh: @@ -228,7 +222,7 @@ def _load_state(path: str) -> Dict[str, Any]: def _save_state(path: str, state: Dict[str, Any]) -> None: mode = get_state_storage_mode() - if mode in {STATE_STORAGE_DUAL, STATE_STORAGE_SQLITE}: + if mode == STATE_STORAGE_SQLITE: _telegram_state_repo.save_state(state) if mode == STATE_STORAGE_SQLITE: return