Delete exps

exps/v3_medusa_euclidean/checkpoints/best_model.pt

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exps/v3_medusa_euclidean/checkpoints/checkpoint_epoch_0001.pt

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exps/v3_medusa_euclidean/checkpoints/checkpoint_epoch_0016.pt

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exps/v3_medusa_euclidean/checkpoints/checkpoint_epoch_0152.pt

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exps/v3_medusa_euclidean/checkpoints/checkpoint_epoch_0175.pt

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exps/v3_medusa_euclidean/checkpoints/checkpoint_epoch_0200.pt

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exps/v3_medusa_euclidean/checkpoints/checkpoint_epoch_0225.pt

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exps/v3_medusa_euclidean/checkpoints/checkpoint_epoch_0250.pt

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exps/v3_medusa_euclidean/config.json

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-{
-  "data_path": "/home/jovyan/gnncp/datasets_all/medusagraph_enhanced_graphs.pt",
-  "axis": "euclidean",
-  "train_ratio": 0.7,
-  "val_ratio": 0.1,
-  "calib_ratio": 0.1,
-  "clip_quantile": 0.98,
-  "hidden_dim": 256,
-  "num_layers": 6,
-  "num_heads": 8,
-  "dropout": 0.1,
-  "lr": 0.0003,
-  "batch_size": 8,
-  "num_epochs": 400,
-  "patience": 100,
-  "min_epochs": 100,
-  "target_coverage": 0.9,
-  "lambda_width": 0.3,
-  "seed": 42,
-  "start_time": "2026-01-19T15:35:57.728106"
-}

exps/v3_medusa_euclidean/logs/metrics.csv

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exps/v3_medusa_euclidean/logs/train.log

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-2026-01-18 20:11:55,774 - INFO - 输出目录: ./experiments/v3_medusa_euclidean
-2026-01-18 20:11:55,774 - INFO - 配置: {
-  "data_path": "/home/jovyan/gnncp/datasets_all/medusagraph_enhanced_graphs.pt",
-  "axis": "euclidean",
-  "train_ratio": 0.7,
-  "val_ratio": 0.1,
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-  "hidden_dim": 256,
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-  "lr": 0.0003,
-  "batch_size": 8,
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-  "seed": 42,
-  "start_time": "2026-01-18T20:11:55.773948"
-}
-2026-01-18 20:11:55,775 - INFO - 设备: cuda
-2026-01-18 20:11:55,775 - INFO - 加载数据: /home/jovyan/gnncp/datasets_all/medusagraph_enhanced_graphs.pt
-2026-01-18 20:35:51,916 - INFO - 总图数: 4860
-2026-01-18 20:35:51,922 - INFO - 节点特征维度: 82
-2026-01-18 20:35:51,922 - INFO - 边特征维度: 4
-2026-01-18 20:35:51,925 - INFO - 数据划分: Train=3402, Val=486, Calib=486, Test=486
-2026-01-18 20:35:52,443 - INFO - 误差分布: median=17.097, q90=35.229, q95=45.789
-2026-01-18 20:35:52,546 - INFO - 裁剪阈值 (98%): 57.7338
-2026-01-18 20:35:52,642 - INFO - Baseline (标准 CP): radius=35.2293, width=70.4586
-2026-01-18 20:35:53,053 - INFO - 模型参数: 8,682,023
-2026-01-18 20:35:53,817 - INFO - ======================================================================
-2026-01-18 20:35:53,817 - INFO - CQR-GNN Ultra 训练 (v2)
-2026-01-18 20:35:53,817 - INFO - 轴: euclidean | 目标覆盖率: [88%, 93%]
-2026-01-18 20:35:53,817 - INFO - min_epochs: 100 | patience: 100
-2026-01-18 20:35:53,817 - INFO - ======================================================================
-2026-01-18 20:42:58,161 - INFO - [Epoch 001] Train: loss=12.2193, cov=75.41%, width=27.37 | Val: cov=90.33% (达标✓), width=34.11 (L=0.01, U=34.10) | best_w=34.11 | no_imp=0 | time=423.3s
-2026-01-18 23:33:59,463 - INFO - [Epoch 025] Train: loss=6.2118, cov=83.96%, width=22.27 | Val: cov=76.12% (欠覆盖), width=22.72 (L=0.01, U=22.71) | best_w=24.19 | no_imp=9 | time=419.6s
-2026-01-19 02:30:25,283 - INFO - [Epoch 050] Train: loss=4.9478, cov=85.16%, width=21.75 | Val: cov=78.84% (欠覆盖), width=22.39 (L=0.01, U=22.38) | best_w=24.19 | no_imp=34 | time=436.4s
-2026-01-19 15:35:58,040 - INFO - 输出目录: ./experiments/v3_medusa_euclidean
-2026-01-19 15:35:58,054 - INFO - 配置: {
-  "data_path": "/home/jovyan/gnncp/datasets_all/medusagraph_enhanced_graphs.pt",
-  "axis": "euclidean",
-  "train_ratio": 0.7,
-  "val_ratio": 0.1,
-  "calib_ratio": 0.1,
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-  "hidden_dim": 256,
-  "num_layers": 6,
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-  "dropout": 0.1,
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-  "batch_size": 8,
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-  "patience": 100,
-  "min_epochs": 100,
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-  "lambda_width": 0.3,
-  "seed": 42,
-  "start_time": "2026-01-19T15:35:57.728106"
-}
-2026-01-19 15:35:58,565 - INFO - 设备: cuda
-2026-01-19 15:35:58,565 - INFO - 加载数据: /home/jovyan/gnncp/datasets_all/medusagraph_enhanced_graphs.pt
-2026-01-19 16:04:25,055 - INFO - 总图数: 4860
-2026-01-19 16:04:25,056 - INFO - 节点特征维度: 82
-2026-01-19 16:04:25,056 - INFO - 边特征维度: 4
-2026-01-19 16:04:25,058 - INFO - 数据划分: Train=3402, Val=486, Calib=486, Test=486
-2026-01-19 16:04:26,793 - INFO - 误差分布: median=17.097, q90=35.229, q95=45.789
-2026-01-19 16:04:26,856 - INFO - 裁剪阈值 (98%): 57.7338
-2026-01-19 16:04:26,913 - INFO - Baseline (标准 CP): radius=35.2293, width=70.4586
-2026-01-19 16:04:27,754 - INFO - 模型参数: 8,682,023
-2026-01-19 16:04:29,183 - INFO - ======================================================================
-2026-01-19 16:04:29,183 - INFO - CQR-GNN Ultra 训练 (v2)
-2026-01-19 16:04:29,183 - INFO - 轴: euclidean | 目标覆盖率: [88%, 93%]
-2026-01-19 16:04:29,183 - INFO - min_epochs: 100 | patience: 100
-2026-01-19 16:04:29,183 - INFO - ======================================================================
-2026-01-19 16:08:08,828 - INFO - [Epoch 001] Train: loss=13.2179, cov=72.69%, width=26.72 | Val: cov=85.32% (欠覆盖), width=32.34 (L=0.01, U=32.33) | best_w=inf | no_imp=1 | time=219.6s
-2026-01-19 17:22:52,752 - INFO - [Epoch 025] Train: loss=6.2277, cov=83.90%, width=22.38 | Val: cov=73.16% (欠覆盖), width=22.51 (L=0.01, U=22.50) | best_w=23.57 | no_imp=2 | time=187.5s
-2026-01-19 18:40:38,759 - INFO - [Epoch 050] Train: loss=5.1184, cov=85.37%, width=21.84 | Val: cov=82.29% (欠覆盖), width=22.65 (L=0.01, U=22.64) | best_w=23.50 | no_imp=16 | time=185.5s
-2026-01-19 19:57:57,058 - INFO - [Epoch 075] Train: loss=5.9828, cov=83.88%, width=21.62 | Val: cov=82.86% (欠覆盖), width=22.46 (L=0.01, U=22.45) | best_w=23.02 | no_imp=2 | time=189.6s
-2026-01-19 21:15:12,321 - INFO - [Epoch 100] Train: loss=5.4905, cov=84.39%, width=21.08 | Val: cov=80.97% (欠覆盖), width=22.12 (L=0.01, U=22.11) | best_w=23.02 | no_imp=27 | time=195.2s
-2026-01-19 22:32:06,356 - INFO - [Epoch 125] Train: loss=5.2248, cov=84.89%, width=20.76 | Val: cov=77.97% (欠覆盖), width=21.65 (L=0.01, U=21.64) | best_w=23.02 | no_imp=52 | time=184.3s
-2026-01-19 23:49:11,067 - INFO - [Epoch 150] Train: loss=4.8327, cov=85.97%, width=20.69 | Val: cov=73.92% (欠覆盖), width=21.44 (L=0.01, U=21.43) | best_w=23.02 | no_imp=77 | time=183.6s
-2026-01-20 01:08:06,951 - INFO - [Epoch 175] Train: loss=5.9186, cov=83.94%, width=20.79 | Val: cov=74.89% (欠覆盖), width=21.50 (L=0.01, U=21.49) | best_w=22.42 | no_imp=23 | time=184.1s
-2026-01-20 02:27:53,092 - INFO - [Epoch 200] Train: loss=5.9916, cov=83.95%, width=20.58 | Val: cov=75.72% (欠覆盖), width=21.42 (L=0.01, U=21.41) | best_w=22.42 | no_imp=48 | time=189.5s
-2026-01-20 03:46:32,091 - INFO - [Epoch 225] Train: loss=6.0471, cov=84.48%, width=20.47 | Val: cov=65.28% (欠覆盖), width=20.95 (L=0.01, U=20.94) | best_w=22.42 | no_imp=73 | time=186.4s
-2026-01-20 05:05:02,843 - INFO - [Epoch 250] Train: loss=6.2032, cov=85.19%, width=20.29 | Val: cov=76.55% (欠覆盖), width=21.29 (L=0.01, U=21.28) | best_w=22.42 | no_imp=98 | time=193.7s
-2026-01-20 05:11:19,514 - INFO - 早停于 epoch 252
-2026-01-20 05:11:19,532 - INFO - 恢复最佳模型 (best_width=22.42)
-2026-01-20 05:11:19,544 - INFO - 训练完成，总耗时: 47210.3s
-2026-01-20 05:11:19,544 - INFO - ======================================================================
-2026-01-20 05:11:19,544 - INFO - 校准阶段
-2026-01-20 05:11:19,544 - INFO - ======================================================================
-2026-01-20 05:11:31,789 - INFO - 全局 η_lower: 0.0000
-2026-01-20 05:11:31,789 - INFO - 全局 η_upper: 0.1315
-2026-01-20 05:11:31,789 - INFO - 局部 η_lower: ['0.000', '0.000', '0.000', '0.000', '0.000']
-2026-01-20 05:11:31,789 - INFO - 局部 η_upper: ['-0.094', '0.232', '0.425', '-0.214', '0.336']
-2026-01-20 05:11:31,789 - INFO - ======================================================================
-2026-01-20 05:11:31,789 - INFO - 测试阶段 (非对称区间)
-2026-01-20 05:11:31,789 - INFO - ======================================================================
-2026-01-20 05:11:53,965 - INFO - 
-[全局校准]
-2026-01-20 05:11:53,966 - INFO -   Coverage: 91.06%
-2026-01-20 05:11:53,966 - INFO -   Avg Width: 21.3108 Å (Lower: 0.0100, Upper: 21.3008)
-2026-01-20 05:11:53,966 - INFO - 
-[局部校准]
-2026-01-20 05:11:53,966 - INFO -   Coverage: 91.15%
-2026-01-20 05:11:53,966 - INFO -   Avg Width: 21.3241 Å (Lower: 0.0100, Upper: 21.3141)
-2026-01-20 05:11:53,966 - INFO - 
-======================================================================
-2026-01-20 05:11:53,966 - INFO - 最终结果
-2026-01-20 05:11:53,966 - INFO - ======================================================================
-2026-01-20 05:11:53,966 - INFO - 方法                             Coverage     Avg Width       说明
-2026-01-20 05:11:53,966 - INFO - ---------------------------------------------------------------------------
-2026-01-20 05:11:53,966 - INFO - Standard CP (Baseline)         91.03%       70.4586 Å       对称 ±35.23
-2026-01-20 05:11:53,966 - INFO - CQR-GNN Ultra (局部校准)           91.15%       21.3241 Å       非对称 [-0.01, +21.31]
-2026-01-20 05:11:53,966 - INFO - 
-区间宽度改进: 69.7%
-2026-01-20 05:11:54,023 - INFO - 
-所有结果已保存到: ./experiments/v3_medusa_euclidean

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exps/v3_vina_euclidean/logs/train.log

@@ -1,77 +0,0 @@
-2026-01-18 15:02:36,841 - INFO - 输出目录: ./experiments/v3_vina_euclidean
-2026-01-18 15:02:36,842 - INFO - 配置: {
-  "data_path": "/home/jovyan/gnncp/datasets_all/autodock_vina_enhanced_graphs.pt",
-  "axis": "euclidean",
-  "train_ratio": 0.7,
-  "val_ratio": 0.1,
-  "calib_ratio": 0.1,
-  "clip_quantile": 0.98,
-  "hidden_dim": 256,
-  "num_layers": 6,
-  "num_heads": 8,
-  "dropout": 0.1,
-  "lr": 0.0003,
-  "batch_size": 8,
-  "num_epochs": 400,
-  "patience": 100,
-  "min_epochs": 100,
-  "target_coverage": 0.9,
-  "lambda_width": 0.3,
-  "seed": 42,
-  "start_time": "2026-01-18T15:02:36.841859"
-}
-2026-01-18 15:02:37,200 - INFO - 设备: cuda
-2026-01-18 15:02:37,200 - INFO - 加载数据: /home/jovyan/gnncp/datasets_all/autodock_vina_enhanced_graphs.pt
-2026-01-18 15:16:44,925 - INFO - 总图数: 4860
-2026-01-18 15:16:44,926 - INFO - 节点特征维度: 82
-2026-01-18 15:16:44,926 - INFO - 边特征维度: 4
-2026-01-18 15:16:44,928 - INFO - 数据划分: Train=3402, Val=486, Calib=486, Test=486
-2026-01-18 15:16:46,698 - INFO - 误差分布: median=12.006, q90=29.923, q95=33.432
-2026-01-18 15:16:46,707 - INFO - 裁剪阈值 (98%): 36.0029
-2026-01-18 15:16:46,716 - INFO - Baseline (标准 CP): radius=29.9225, width=59.8451
-2026-01-18 15:16:47,288 - INFO - 模型参数: 8,682,023
-2026-01-18 15:16:48,224 - INFO - ======================================================================
-2026-01-18 15:16:48,224 - INFO - CQR-GNN Ultra 训练 (v2)
-2026-01-18 15:16:48,224 - INFO - 轴: euclidean | 目标覆盖率: [88%, 93%]
-2026-01-18 15:16:48,224 - INFO - min_epochs: 100 | patience: 100
-2026-01-18 15:16:48,224 - INFO - ======================================================================
-2026-01-18 15:20:17,261 - INFO - [Epoch 001] Train: loss=11.5138, cov=78.29%, width=24.08 | Val: cov=74.13% (欠覆盖), width=19.52 (L=0.01, U=19.51) | best_w=inf | no_imp=1 | time=209.0s
-2026-01-18 16:33:22,450 - INFO - [Epoch 025] Train: loss=4.5400, cov=83.84%, width=16.76 | Val: cov=89.27% (达标✓), width=16.80 (L=0.01, U=16.79) | best_w=16.80 | no_imp=0 | time=181.1s
-2026-01-18 17:48:57,228 - INFO - [Epoch 050] Train: loss=3.5654, cov=85.22%, width=16.11 | Val: cov=77.40% (欠覆盖), width=15.30 (L=0.01, U=15.29) | best_w=16.80 | no_imp=25 | time=179.7s
-2026-01-18 19:04:28,005 - INFO - [Epoch 075] Train: loss=4.4632, cov=83.25%, width=15.78 | Val: cov=77.02% (欠覆盖), width=14.95 (L=0.01, U=14.94) | best_w=15.81 | no_imp=16 | time=179.3s
-2026-01-18 20:20:32,783 - INFO - [Epoch 100] Train: loss=4.0530, cov=84.31%, width=15.37 | Val: cov=82.59% (欠覆盖), width=14.74 (L=0.01, U=14.73) | best_w=15.81 | no_imp=41 | time=183.5s
-2026-01-18 22:59:50,661 - INFO - [Epoch 125] Train: loss=3.9127, cov=85.64%, width=15.15 | Val: cov=78.09% (欠覆盖), width=14.48 (L=0.01, U=14.47) | best_w=15.81 | no_imp=66 | time=428.3s
-2026-01-19 01:56:10,884 - INFO - [Epoch 150] Train: loss=3.8179, cov=86.49%, width=15.09 | Val: cov=73.44% (欠覆盖), width=14.36 (L=0.01, U=14.35) | best_w=15.81 | no_imp=91 | time=434.6s
-2026-01-19 03:01:28,533 - INFO - 早停于 epoch 159
-2026-01-19 03:01:28,552 - INFO - 恢复最佳模型 (best_width=15.81)
-2026-01-19 03:01:28,558 - INFO - 训练完成，总耗时: 42280.3s
-2026-01-19 03:01:28,558 - INFO - ======================================================================
-2026-01-19 03:01:28,558 - INFO - 校准阶段
-2026-01-19 03:01:28,558 - INFO - ======================================================================
-2026-01-19 03:02:12,446 - INFO - 全局 η_lower: 0.0000
-2026-01-19 03:02:12,446 - INFO - 全局 η_upper: 0.3207
-2026-01-19 03:02:12,446 - INFO - 局部 η_lower: ['0.000', '0.000', '0.000', '-0.000', '-0.000']
-2026-01-19 03:02:12,446 - INFO - 局部 η_upper: ['0.160', '0.549', '0.812', '0.131', '-0.125']
-2026-01-19 03:02:12,446 - INFO - ======================================================================
-2026-01-19 03:02:12,446 - INFO - 测试阶段 (非对称区间)
-2026-01-19 03:02:12,446 - INFO - ======================================================================
-2026-01-19 03:03:43,384 - INFO - 
-[全局校准]
-2026-01-19 03:03:43,385 - INFO -   Coverage: 89.46%
-2026-01-19 03:03:43,385 - INFO -   Avg Width: 16.3535 Å (Lower: 0.0100, Upper: 16.3434)
-2026-01-19 03:03:43,385 - INFO - 
-[局部校准]
-2026-01-19 03:03:43,385 - INFO -   Coverage: 89.60%
-2026-01-19 03:03:43,385 - INFO -   Avg Width: 16.3389 Å (Lower: 0.0100, Upper: 16.3289)
-2026-01-19 03:03:43,385 - INFO - 
-======================================================================
-2026-01-19 03:03:43,386 - INFO - 最终结果
-2026-01-19 03:03:43,386 - INFO - ======================================================================
-2026-01-19 03:03:43,386 - INFO - 方法                             Coverage     Avg Width       说明
-2026-01-19 03:03:43,386 - INFO - ---------------------------------------------------------------------------
-2026-01-19 03:03:43,386 - INFO - Standard CP (Baseline)         90.15%       59.8451 Å       对称 ±29.92
-2026-01-19 03:03:43,386 - INFO - CQR-GNN Ultra (局部校准)           89.60%       16.3389 Å       非对称 [-0.01, +16.33]
-2026-01-19 03:03:43,386 - INFO - 
-区间宽度改进: 72.7%
-2026-01-19 03:03:43,450 - INFO - 
-所有结果已保存到: ./experiments/v3_vina_euclidean

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