2025-05-22 09:09:47,331 [INFO ] [logger.py:dumpArgs:73]	 Starting with arguments
{"name": "celeba", "dataset": {"path": "../../datasets/CelebAMask/256/img", "lists_root": "datasets/celeba", "attributes_path": "../../datasets/CelebAMask/1024/CelebAMask-HQ-attribute-anno.txt", "return_index": true, "targets": ["Smiling"]}, "cache_directory": "../../datasets/CelebAMask/cache/256/bottom_test", "output": "./results/mvce-25/Smiling/bottom/", "classifier": "./models/celeba/Smiling/celeba-20250508-133956.pklz", "classifier_feature": null, "mask": {"name": "bottom"}, "drop": null, "threads": 4, "generator_samples": [3, 30], "decision_samples": 1000, "cuda_index": 1, "zero_imputation": true, "zero_variance": true, "beta_variance_scales": [0.5, 0.99], "action_spaces": [{"name": "zero-one"}], "class_count": 1, "batch_size": 100, "batch_mean_imputation": [1], "buckets": 10, "trials": 4, "seed": 1337, "verbose": 1}
2025-05-22 09:09:47,363 [INFO ] [util.py:selectDevice:88]	 Using cuda:1 for tensor calculations
2025-05-22 09:09:47,521 [INFO ] [mvce_dataset.py:<module>:90]	 Running on cuda:1
2025-05-22 09:09:47,852 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 09:09:54,882 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Smiling']
2025-05-22 09:09:54,958 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 09:09:54,958 [INFO ] [mvce_dataset.py:<module>:102]	 Using dataset celeba with 2000 test samples
2025-05-22 09:09:54,958 [INFO ] [mvce_dataset.py:<module>:111]	 Loading mask bottom
2025-05-22 09:09:54,973 [INFO ] [mvce_dataset.py:<module>:128]	 Setting classifier activation function to sigmoid for single class
2025-05-22 09:09:54,973 [INFO ] [mvce_dataset.py:<module>:143]	 Creating generator using cache at ../../datasets/CelebAMask/cache/256/bottom_test
2025-05-22 09:09:54,981 [INFO ] [mvce_dataset.py:<module>:156]	 Including baseline imputators with zero imputation.
2025-05-22 09:09:54,981 [INFO ] [mvce_dataset.py:<module>:161]	 Including batch mean imputators with sizes [1].
2025-05-22 09:09:54,981 [INFO ] [mvce_dataset.py:<module>:166]	 Running all baseline imputators zero variance.
2025-05-22 09:09:54,981 [INFO ] [mvce_dataset.py:<module>:168]	 Running all baseline imputators with [0.5, 0.99] scaled beta max variance.
2025-05-22 09:09:54,981 [INFO ] [mvce_dataset.py:<module>:184]	 Adding 8 methods.
2025-05-22 09:09:54,981 [INFO ] [mvce_dataset.py:<module>:192]	 Using masked dataset with mask {'name': 'bottom'}
2025-05-22 09:09:54,982 [INFO ] [mvce_dataset.py:<module>:208]	 Considering action space zero-one
2025-05-22 09:09:54,982 [INFO ] [threading_utils.py:distributeTasks:95]	 Running 8 tasks on 4 threads
2025-05-22 09:09:54,983 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one
2025-05-22 09:09:54,996 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one
2025-05-22 09:09:54,998 [INFO ] [calibration.py:__call__:223]	 Started running Basic Imputation - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one
2025-05-22 09:09:54,998 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.99 - Zero Imputation missing bottom in zero-one
2025-05-22 09:10:26,086 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.05233095586299896 for Beta Mean * 0.99 - Zero Imputation in 31.07638627756387 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1513,  222,   69,   83,  113], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5686, 0.6190, 0.7475, 0.8540, 0.9675])
        * Final bucket consistencies: tensor([0.5373, 0.6216, 0.6232, 0.6627, 0.6814])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 435, 1565], dtype=torch.int32)
        * Average consistency: 0.5630000233650208
    
2025-05-22 09:10:54,861 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.052701979875564575 for Beta Mean * 0.99 - Zero Imputation in 28.76455121766776 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1518,  215,   71,   82,  114], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5685, 0.6190, 0.7477, 0.8541, 0.9670])
        * Final bucket consistencies: tensor([0.5375, 0.6140, 0.6197, 0.6341, 0.7018])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 429, 1571], dtype=torch.int32)
        * Average consistency: 0.5619999766349792
    
2025-05-22 09:11:21,077 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.05526898056268692 for Beta Mean * 0.99 - Zero Imputation in 26.1958279768005 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1511,  226,   66,   78,  119], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5683, 0.6197, 0.7499, 0.8521, 0.9645])
        * Final bucket consistencies: tensor([0.5328, 0.6150, 0.6515, 0.6410, 0.6891])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 430, 1570], dtype=torch.int32)
        * Average consistency: 0.559499979019165
    
2025-05-22 09:11:47,766 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.059528980404138565 for Beta Mean * 0.99 - Zero Imputation in 26.683357778005302 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1505,  233,   64,   79,  119], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5691, 0.6196, 0.7503, 0.8496, 0.9646])
        * Final bucket consistencies: tensor([0.5316, 0.6481, 0.6875, 0.6076, 0.6891])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 442, 1558], dtype=torch.int32)
        * Average consistency: 0.5625
    
2025-05-22 09:11:47,767 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.99 - Zero Imputation missing bottom in zero-one in 112.76077420543879 seconds
2025-05-22 09:11:47,768 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.5 - Zero Imputation missing bottom in zero-one
2025-05-22 09:12:18,358 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.40864962339401245 for Beta Mean * 0.5 - Zero Imputation in 30.584112964570522 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  72,   58,   82,  140, 1648], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5549, 0.6513, 0.7514, 0.8549, 0.9880])
        * Final bucket consistencies: tensor([0.5000, 0.4483, 0.4756, 0.4714, 0.5479])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 259, 1741], dtype=torch.int32)
        * Average consistency: 0.5350000262260437
    
2025-05-22 09:12:48,384 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.4104579985141754 for Beta Mean * 0.5 - Zero Imputation in 30.016293196938932 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  69,   57,   85,  143, 1646], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5486, 0.6422, 0.7502, 0.8551, 0.9881])
        * Final bucket consistencies: tensor([0.4783, 0.4035, 0.4941, 0.4615, 0.5480])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 261, 1739], dtype=torch.int32)
        * Average consistency: 0.5329999923706055
    
2025-05-22 09:13:14,466 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.4084659814834595 for Beta Mean * 0.5 - Zero Imputation in 26.077804238535464 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  69,   58,   92,  132, 1649], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5488, 0.6465, 0.7530, 0.8576, 0.9879])
        * Final bucket consistencies: tensor([0.5362, 0.4483, 0.4130, 0.5076, 0.5470])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 257, 1743], dtype=torch.int32)
        * Average consistency: 0.5350000262260437
    
2025-05-22 09:13:40,448 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.40906253457069397 for Beta Mean * 0.5 - Zero Imputation in 25.973720490932465 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  68,   60,   84,  142, 1646], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5520, 0.6503, 0.7545, 0.8553, 0.9883])
        * Final bucket consistencies: tensor([0.5147, 0.4667, 0.4643, 0.4718, 0.5474])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 257, 1743], dtype=torch.int32)
        * Average consistency: 0.5350000262260437
    
2025-05-22 09:13:40,449 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.5 - Zero Imputation missing bottom in zero-one in 112.681166915223 seconds
2025-05-22 09:13:40,449 [INFO ] [calibration.py:__call__:223]	 Started running Basic Imputation - Zero Imputation missing bottom in zero-one
2025-05-22 09:14:03,200 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.4660000205039978 for Basic Imputation - Zero Imputation in 22.745663557201624 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([2000], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.5340])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 259, 1741], dtype=torch.int32)
        * Average consistency: 0.5339999794960022
    
2025-05-22 09:14:23,380 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.4660000205039978 for Basic Imputation - Zero Imputation in 20.175989822484553 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([2000], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.5340])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 259, 1741], dtype=torch.int32)
        * Average consistency: 0.5339999794960022
    
2025-05-22 09:14:41,489 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.4660000205039978 for Basic Imputation - Zero Imputation in 18.107644693925977 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([2000], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.5340])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 259, 1741], dtype=torch.int32)
        * Average consistency: 0.5339999794960022
    
2025-05-22 09:14:57,507 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.4660000205039978 for Basic Imputation - Zero Imputation in 16.01282795611769 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([2000], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.5340])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 259, 1741], dtype=torch.int32)
        * Average consistency: 0.5339999794960022
    
2025-05-22 09:14:57,508 [INFO ] [calibration.py:__call__:249]	 Finished running Basic Imputation - Zero Imputation missing bottom in zero-one in 77.0585174318403 seconds
2025-05-22 09:14:57,508 [INFO ] [calibration.py:__call__:223]	 Started running Monte Carlo - Caching Generator - 30 samples missing bottom in zero-one
2025-05-22 09:24:53,722 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.23250001668930054 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 898.6662784423679 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([2000], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.7675])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 966, 1034], dtype=torch.int32)
        * Average consistency: 0.7674999833106995
    
2025-05-22 09:24:53,748 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.19603340327739716 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 898.7459962060675 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  34,   43,   54,   90, 1779], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5533, 0.6489, 0.7447, 0.8511, 0.9913])
        * Final bucket consistencies: tensor([0.4706, 0.5814, 0.5741, 0.5111, 0.7965])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 966, 1034], dtype=torch.int32)
        * Average consistency: 0.7674999833106995
    
2025-05-22 09:24:53,753 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.15913094580173492 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 898.7670566821471 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 231,   62,   36,   76, 1595], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5668, 0.6371, 0.7539, 0.8528, 0.9917])
        * Final bucket consistencies: tensor([0.6104, 0.4355, 0.5556, 0.6579, 0.8201])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1045,  955], dtype=torch.int32)
        * Average consistency: 0.7730000019073486
    
2025-05-22 09:31:55,835 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.045164503157138824 for Monte Carlo - Caching Generator - 30 samples in 1018.2638379922137 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([231, 278, 262, 293, 936], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5486, 0.6502, 0.7506, 0.8535, 0.9760])
        * Final bucket consistencies: tensor([0.5498, 0.6151, 0.7137, 0.8362, 0.9060])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1001,  999], dtype=torch.int32)
        * Average consistency: 0.7889999747276306
    
2025-05-22 09:42:55,301 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.15457101166248322 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1081.4087314447388 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 228,   62,   36,   77, 1597], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5665, 0.6334, 0.7474, 0.8496, 0.9917])
        * Final bucket consistencies: tensor([0.5877, 0.5000, 0.5833, 0.6494, 0.8197])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1046,  954], dtype=torch.int32)
        * Average consistency: 0.7724999785423279
    
2025-05-22 09:42:55,330 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.23250001668930054 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1081.339426547289 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([2000], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.7675])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 966, 1034], dtype=torch.int32)
        * Average consistency: 0.7674999833106995
    
2025-05-22 09:42:55,331 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.19560793042182922 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1081.4141906099394 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  37,   38,   57,   85, 1783], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5542, 0.6481, 0.7466, 0.8517, 0.9911])
        * Final bucket consistencies: tensor([0.5135, 0.5526, 0.6140, 0.4824, 0.7964])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 969, 1031], dtype=torch.int32)
        * Average consistency: 0.7680000066757202
    
2025-05-22 09:48:25,738 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.04618650674819946 for Monte Carlo - Caching Generator - 30 samples in 989.8588553089648 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([231, 268, 257, 302, 942], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5480, 0.6496, 0.7471, 0.8512, 0.9754])
        * Final bucket consistencies: tensor([0.5671, 0.6231, 0.7043, 0.8311, 0.9076])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1004,  996], dtype=torch.int32)
        * Average consistency: 0.7925000190734863
    
2025-05-22 09:59:59,241 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.23250001668930054 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1023.8752396870404 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([2000], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.7675])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 966, 1034], dtype=torch.int32)
        * Average consistency: 0.7674999833106995
    
2025-05-22 09:59:59,254 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.19592705368995667 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1023.8887736462057 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  34,   43,   60,   82, 1781], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5451, 0.6478, 0.7500, 0.8552, 0.9912])
        * Final bucket consistencies: tensor([0.5588, 0.4884, 0.5667, 0.5122, 0.7973])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 967, 1033], dtype=torch.int32)
        * Average consistency: 0.7680000066757202
    
2025-05-22 09:59:59,259 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.15458856523036957 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1023.8937829770148 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 227,   65,   38,   73, 1597], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5663, 0.6332, 0.7574, 0.8547, 0.9915])
        * Final bucket consistencies: tensor([0.5903, 0.5385, 0.5263, 0.6575, 0.8197])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1045,  955], dtype=torch.int32)
        * Average consistency: 0.7730000019073486
    
2025-05-22 10:05:12,092 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.04631047695875168 for Monte Carlo - Caching Generator - 30 samples in 1006.3270594570786 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([238, 260, 278, 285, 939], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5502, 0.6517, 0.7493, 0.8546, 0.9758])
        * Final bucket consistencies: tensor([0.5168, 0.6538, 0.6871, 0.8526, 0.9052])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1004,  996], dtype=torch.int32)
        * Average consistency: 0.7885000109672546
    
2025-05-22 10:17:08,862 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.23250001668930054 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1029.557503823191 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([2000], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.7675])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 966, 1034], dtype=torch.int32)
        * Average consistency: 0.7674999833106995
    
2025-05-22 10:17:08,879 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.19550158083438873 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1029.57459524367 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  34,   44,   55,   81, 1786], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5526, 0.6496, 0.7467, 0.8510, 0.9908])
        * Final bucket consistencies: tensor([0.5294, 0.5455, 0.5818, 0.4815, 0.7968])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 965, 1035], dtype=torch.int32)
        * Average consistency: 0.7680000066757202
    
2025-05-22 10:17:08,880 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.15058650076389313 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1029.576131197624 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 235,   55,   37,   78, 1595], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5692, 0.6366, 0.7539, 0.8507, 0.9916])
        * Final bucket consistencies: tensor([0.5702, 0.5818, 0.4865, 0.6795, 0.8194])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1043,  957], dtype=torch.int32)
        * Average consistency: 0.7720000147819519
    
2025-05-22 10:17:08,885 [INFO ] [calibration.py:__call__:249]	 Finished running Basic Imputation - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one in 4033.886786237359 seconds
2025-05-22 10:17:08,885 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one in 4033.901862346567 seconds
2025-05-22 10:17:08,885 [INFO ] [calibration.py:__call__:223]	 Started running Monte Carlo - Caching Generator - 3 samples missing bottom in zero-one
2025-05-22 10:17:08,885 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one in 4033.888475854881 seconds
2025-05-22 10:17:08,885 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 1 stopping
2025-05-22 10:17:08,885 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 2 stopping
2025-05-22 10:17:08,891 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.04547303542494774 for Monte Carlo - Caching Generator - 30 samples in 716.7793502537534 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([232, 271, 258, 297, 942], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5497, 0.6500, 0.7485, 0.8529, 0.9754])
        * Final bucket consistencies: tensor([0.5345, 0.6236, 0.7132, 0.8316, 0.9066])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1005,  995], dtype=torch.int32)
        * Average consistency: 0.7889999747276306
    
2025-05-22 10:17:08,891 [INFO ] [calibration.py:__call__:249]	 Finished running Monte Carlo - Caching Generator - 30 samples missing bottom in zero-one in 3731.3831346044317 seconds
2025-05-22 10:17:08,891 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 4 stopping
2025-05-22 10:31:10,666 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.15029698610305786 for Monte Carlo - Caching Generator - 3 samples in 841.7793092671782 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 101,  406,  153,   75, 1265], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5520, 0.6606, 0.7431, 0.8494, 0.9974])
        * Final bucket consistencies: tensor([0.5842, 0.4261, 0.4902, 0.6933, 0.8775])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 988, 1012], dtype=torch.int32)
        * Average consistency: 0.734499990940094
    
2025-05-22 10:42:56,741 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.14900252223014832 for Monte Carlo - Caching Generator - 3 samples in 706.0599068049341 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 102,  410,  146,   75, 1267], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5533, 0.6603, 0.7439, 0.8474, 0.9973])
        * Final bucket consistencies: tensor([0.5686, 0.4293, 0.4726, 0.7067, 0.8777])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 989, 1011], dtype=torch.int32)
        * Average consistency: 0.734000027179718
    
2025-05-22 10:58:50,704 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.15104402601718903 for Monte Carlo - Caching Generator - 3 samples in 953.9336710097268 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 106,  397,  149,   81, 1267], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5542, 0.6616, 0.7388, 0.8466, 0.9972])
        * Final bucket consistencies: tensor([0.5849, 0.4131, 0.5101, 0.6790, 0.8769])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 993, 1007], dtype=torch.int32)
        * Average consistency: 0.734000027179718
    
2025-05-22 11:11:23,477 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.14799445867538452 for Monte Carlo - Caching Generator - 3 samples in 752.7438443843275 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  92,  416,  146,   80, 1266], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5492, 0.6602, 0.7422, 0.8477, 0.9974])
        * Final bucket consistencies: tensor([0.5543, 0.4303, 0.5000, 0.6875, 0.8776])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 988, 1012], dtype=torch.int32)
        * Average consistency: 0.734499990940094
    
2025-05-22 11:11:23,494 [INFO ] [calibration.py:__call__:249]	 Finished running Monte Carlo - Caching Generator - 3 samples missing bottom in zero-one in 3254.6092467345297 seconds
2025-05-22 11:11:23,495 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 3 stopping
2025-05-22 11:11:23,495 [INFO ] [mvce_dataset.py:<module>:222]	 Finished running 8/8 experiments.
2025-05-22 11:11:23,495 [INFO ] [mvce_dataset.py:<module>:227]	 Saving results to ./results/mvce-25/Smiling/bottom/celeba-20250522-090947.csv
2025-05-22 11:11:24,080 [INFO ] [mvce_dataset.py:<module>:237]	 Finished saving results
