2025-05-22 09:08:59,949 [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": ["Blond_Hair"]}, "cache_directory": "../../datasets/CelebAMask/cache/256/top_test", "output": "./results/mvce-25/Blond_Hair/top/", "classifier": "./models/celeba/Blond_Hair/celeba-20250508-133714.pklz", "classifier_feature": null, "mask": {"name": "top"}, "drop": null, "threads": 4, "generator_samples": [3, 30], "decision_samples": 1000, "cuda_index": 0, "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 09:08:59,978 [INFO ] [util.py:selectDevice:88]	 Using cuda:0 for tensor calculations
2025-05-22 09:09:00,146 [INFO ] [mvce_dataset.py:<module>:90]	 Running on cuda:0
2025-05-22 09:09:00,387 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 09:09:05,325 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Blond_Hair']
2025-05-22 09:09:05,378 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 09:09:05,378 [INFO ] [mvce_dataset.py:<module>:102]	 Using dataset celeba with 2000 test samples
2025-05-22 09:09:05,378 [INFO ] [mvce_dataset.py:<module>:111]	 Loading mask top
2025-05-22 09:09:05,378 [INFO ] [mvce_dataset.py:<module>:128]	 Setting classifier activation function to sigmoid for single class
2025-05-22 09:09:05,378 [INFO ] [mvce_dataset.py:<module>:143]	 Creating generator using cache at ../../datasets/CelebAMask/cache/256/top_test
2025-05-22 09:09:05,379 [INFO ] [mvce_dataset.py:<module>:156]	 Including baseline imputators with zero imputation.
2025-05-22 09:09:05,379 [INFO ] [mvce_dataset.py:<module>:161]	 Including batch mean imputators with sizes [1].
2025-05-22 09:09:05,379 [INFO ] [mvce_dataset.py:<module>:166]	 Running all baseline imputators zero variance.
2025-05-22 09:09:05,379 [INFO ] [mvce_dataset.py:<module>:168]	 Running all baseline imputators with [0.5, 0.99] scaled beta max variance.
2025-05-22 09:09:05,379 [INFO ] [mvce_dataset.py:<module>:184]	 Adding 8 methods.
2025-05-22 09:09:05,379 [INFO ] [mvce_dataset.py:<module>:192]	 Using masked dataset with mask {'name': 'top'}
2025-05-22 09:09:05,380 [INFO ] [mvce_dataset.py:<module>:208]	 Considering action space zero-one
2025-05-22 09:09:05,380 [INFO ] [threading_utils.py:distributeTasks:95]	 Running 8 tasks on 4 threads
2025-05-22 09:09:05,380 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation missing top in zero-one
2025-05-22 09:09:05,380 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing top in zero-one
2025-05-22 09:09:05,380 [INFO ] [calibration.py:__call__:223]	 Started running Basic Imputation - 1 Sample Mean Caching Generator Imputation missing top in zero-one
2025-05-22 09:09:05,380 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.99 - Zero Imputation missing top in zero-one
2025-05-22 09:09:23,807 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.107294961810112 for Beta Mean * 0.99 - Zero Imputation in 18.354654277674854 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  25,   11,   12,   22, 1930], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5639, 0.6521, 0.7580, 0.8662, 0.9981])
        * Final bucket consistencies: tensor([0.7200, 0.1818, 0.0833, 0.0455, 0.9052])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1981,   19], dtype=torch.int32)
        * Average consistency: 0.8845000267028809
    
2025-05-22 09:09:39,452 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.10544511675834656 for Beta Mean * 0.99 - Zero Imputation in 15.636944864876568 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  23,   12,   11,   23, 1931], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5579, 0.6358, 0.7489, 0.8591, 0.9980])
        * Final bucket consistencies: tensor([0.6087, 0.3333, 0.0909, 0.0435, 0.9047])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1983,   17], dtype=torch.int32)
        * Average consistency: 0.8834999799728394
    
2025-05-22 09:10:01,857 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.10725400596857071 for Beta Mean * 0.99 - Zero Imputation in 22.40273114759475 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  26,    8,   13,   22, 1931], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5581, 0.6394, 0.7470, 0.8622, 0.9980])
        * Final bucket consistencies: tensor([0.6923, 0.1250, 0.0769, 0.0455, 0.9047])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1982,   18], dtype=torch.int32)
        * Average consistency: 0.8840000033378601
    
2025-05-22 09:10:31,202 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.10737094283103943 for Beta Mean * 0.99 - Zero Imputation in 29.339503398165107 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  24,   10,   12,   24, 1930], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5578, 0.6279, 0.7441, 0.8598, 0.9981])
        * Final bucket consistencies: tensor([0.7083, 0.2000, 0.0833, 0.0417, 0.9052])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1982,   18], dtype=torch.int32)
        * Average consistency: 0.8840000033378601
    
2025-05-22 09:10:31,207 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.99 - Zero Imputation missing top in zero-one in 85.82578046247363 seconds
2025-05-22 09:10:31,207 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.5 - Zero Imputation missing top in zero-one
2025-05-22 09:11:00,891 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.09833496063947678 for Beta Mean * 0.5 - Zero Imputation in 29.681777919642627 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   9,   13,   11,   29, 1938], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5331, 0.6586, 0.7445, 0.8513, 0.9980])
        * Final bucket consistencies: tensor([0.6667, 0.3846, 0.4545, 0.2414, 0.9097])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1962,   38], dtype=torch.int32)
        * Average consistency: 0.8930000066757202
    
2025-05-22 09:11:29,024 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.09776998311281204 for Beta Mean * 0.5 - Zero Imputation in 28.130897898226976 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   9,   13,   13,   24, 1941], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5398, 0.6629, 0.7494, 0.8481, 0.9978])
        * Final bucket consistencies: tensor([0.5556, 0.3846, 0.3846, 0.2917, 0.9083])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1963,   37], dtype=torch.int32)
        * Average consistency: 0.8924999833106995
    
2025-05-22 09:12:00,222 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.09808950871229172 for Beta Mean * 0.5 - Zero Imputation in 31.191976034082472 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   9,   15,   10,   27, 1939], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5232, 0.6671, 0.7531, 0.8511, 0.9979])
        * Final bucket consistencies: tensor([0.4444, 0.3333, 0.5000, 0.2593, 0.9092])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1964,   36], dtype=torch.int32)
        * Average consistency: 0.8920000195503235
    
2025-05-22 09:12:30,946 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.09790655970573425 for Beta Mean * 0.5 - Zero Imputation in 30.71501356177032 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   9,   12,   12,   27, 1940], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5306, 0.6617, 0.7462, 0.8487, 0.9978])
        * Final bucket consistencies: tensor([0.5556, 0.3333, 0.4167, 0.2963, 0.9088])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1963,   37], dtype=torch.int32)
        * Average consistency: 0.8924999833106995
    
2025-05-22 09:12:30,947 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.5 - Zero Imputation missing top in zero-one in 119.74006093852222 seconds
2025-05-22 09:12:30,947 [INFO ] [calibration.py:__call__:223]	 Started running Basic Imputation - Zero Imputation missing top in zero-one
2025-05-22 09:12:58,191 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.10699999332427979 for Basic Imputation - Zero Imputation in 27.23938240017742 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([0.8930])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1962,   38], dtype=torch.int32)
        * Average consistency: 0.8930000066757202
    
2025-05-22 09:13:23,405 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.10699999332427979 for Basic Imputation - Zero Imputation in 25.20920463744551 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([0.8930])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1962,   38], dtype=torch.int32)
        * Average consistency: 0.8930000066757202
    
2025-05-22 09:13:49,247 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.10699999332427979 for Basic Imputation - Zero Imputation in 25.839072480797768 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([0.8930])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1962,   38], dtype=torch.int32)
        * Average consistency: 0.8930000066757202
    
2025-05-22 09:14:13,001 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.10699999332427979 for Basic Imputation - Zero Imputation in 23.750724422745407 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([0.8930])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1962,   38], dtype=torch.int32)
        * Average consistency: 0.8930000066757202
    
2025-05-22 09:14:13,001 [INFO ] [calibration.py:__call__:249]	 Finished running Basic Imputation - Zero Imputation missing top in zero-one in 102.05323460046202 seconds
2025-05-22 09:14:13,001 [INFO ] [calibration.py:__call__:223]	 Started running Monte Carlo - Caching Generator - 30 samples missing top in zero-one
2025-05-22 09:25:09,096 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.06650000810623169 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 963.7096150889993 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([0.9335])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1769,  231], dtype=torch.int32)
        * Average consistency: 0.9334999918937683
    
2025-05-22 09:25:09,233 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.050571098923683167 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 963.8454802865162 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 185,   31,   16,   41, 1727], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5672, 0.6342, 0.7468, 0.8519, 0.9959])
        * Final bucket consistencies: tensor([0.7676, 0.7419, 0.5625, 0.5854, 0.9687])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1815,  185], dtype=torch.int32)
        * Average consistency: 0.9355000257492065
    
2025-05-22 09:25:09,234 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.04830950126051903 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 963.8456082409248 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  12,   27,   32,   46, 1883], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5405, 0.6529, 0.7542, 0.8573, 0.9952])
        * Final bucket consistencies: tensor([0.6667, 0.4444, 0.5625, 0.4565, 0.9607])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1770,  230], dtype=torch.int32)
        * Average consistency: 0.9340000152587891
    
2025-05-22 09:33:57,374 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.011078459210693836 for Monte Carlo - Caching Generator - 30 samples in 1184.3719801222906 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  49,   46,   71,   75, 1759], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5493, 0.6486, 0.7488, 0.8535, 0.9930])
        * Final bucket consistencies: tensor([0.7347, 0.6522, 0.6197, 0.8267, 0.9920])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1773,  227], dtype=torch.int32)
        * Average consistency: 0.9585000276565552
    
2025-05-22 09:45:08,773 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.06650000810623169 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1199.5473003257066 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([0.9335])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1769,  231], dtype=torch.int32)
        * Average consistency: 0.9334999918937683
    
2025-05-22 09:45:08,867 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.049635954201221466 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1199.6313345180824 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 172,   44,   15,   42, 1727], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5655, 0.6235, 0.7437, 0.8531, 0.9959])
        * Final bucket consistencies: tensor([0.7616, 0.7500, 0.5333, 0.6429, 0.9682])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1815,  185], dtype=torch.int32)
        * Average consistency: 0.9355000257492065
    
2025-05-22 09:45:08,872 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.04714950546622276 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1199.6281840298325 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  13,   27,   30,   46, 1884], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5414, 0.6536, 0.7557, 0.8533, 0.9951])
        * Final bucket consistencies: tensor([0.4615, 0.4815, 0.5667, 0.4565, 0.9607])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1771,  229], dtype=torch.int32)
        * Average consistency: 0.9334999918937683
    
2025-05-22 09:51:13,167 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.007308061700314283 for Monte Carlo - Caching Generator - 30 samples in 1035.6434972267598 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  50,   49,   67,   75, 1759], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5477, 0.6539, 0.7501, 0.8526, 0.9930])
        * Final bucket consistencies: tensor([0.5800, 0.6735, 0.6269, 0.8533, 0.9909])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1769,  231], dtype=torch.int32)
        * Average consistency: 0.9555000066757202
    
2025-05-22 10:03:48,076 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.06650000810623169 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1119.1657396471128 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([0.9335])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1769,  231], dtype=torch.int32)
        * Average consistency: 0.9334999918937683
    
2025-05-22 10:03:48,221 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.05083145573735237 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1119.3224143786356 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 177,   40,   17,   41, 1725], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5676, 0.6262, 0.7565, 0.8551, 0.9961])
        * Final bucket consistencies: tensor([0.7740, 0.7250, 0.4706, 0.6098, 0.9693])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1813,  187], dtype=torch.int32)
        * Average consistency: 0.9355000257492065
    
2025-05-22 10:03:48,243 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0465419664978981 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1119.3460919307545 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  16,   22,   31,   46, 1885], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5468, 0.6545, 0.7553, 0.8550, 0.9951])
        * Final bucket consistencies: tensor([0.5625, 0.5455, 0.6129, 0.3913, 0.9607])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1769,  231], dtype=torch.int32)
        * Average consistency: 0.934499979019165
    
2025-05-22 10:11:53,496 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.009230957366526127 for Monte Carlo - Caching Generator - 30 samples in 1240.2508409144357 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  45,   52,   68,   77, 1758], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5444, 0.6488, 0.7482, 0.8549, 0.9932])
        * Final bucket consistencies: tensor([0.6889, 0.6538, 0.6324, 0.8442, 0.9915])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1772,  228], dtype=torch.int32)
        * Average consistency: 0.9580000042915344
    
2025-05-22 10:21:43,569 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.06650000810623169 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1075.4622075594962 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([0.9335])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1769,  231], dtype=torch.int32)
        * Average consistency: 0.9334999918937683
    
2025-05-22 10:21:43,620 [INFO ] [calibration.py:__call__:249]	 Finished running Basic Imputation - 1 Sample Mean Caching Generator Imputation missing top in zero-one in 4358.239291058853 seconds
2025-05-22 10:21:43,620 [INFO ] [calibration.py:__call__:223]	 Started running Monte Carlo - Caching Generator - 3 samples missing top in zero-one
2025-05-22 10:21:43,645 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.04885352402925491 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1075.4181483499706 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 174,   43,   15,   42, 1726], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5669, 0.6255, 0.7489, 0.8553, 0.9960])
        * Final bucket consistencies: tensor([0.7586, 0.7209, 0.6000, 0.5952, 0.9687])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1813,  187], dtype=torch.int32)
        * Average consistency: 0.934499979019165
    
2025-05-22 10:21:43,645 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation missing top in zero-one in 4358.26499391906 seconds
2025-05-22 10:21:43,646 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 1 stopping
2025-05-22 10:21:43,663 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.04636455699801445 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1075.4175445409492 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  16,   24,   28,   46, 1886], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5486, 0.6565, 0.7480, 0.8493, 0.9951])
        * Final bucket consistencies: tensor([0.5625, 0.5833, 0.5357, 0.4783, 0.9592])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1771,  229], dtype=torch.int32)
        * Average consistency: 0.934499979019165
    
2025-05-22 10:21:43,663 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing top in zero-one in 4358.282428620383 seconds
2025-05-22 10:21:43,663 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 2 stopping
2025-05-22 10:27:38,367 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.010099019855260849 for Monte Carlo - Caching Generator - 30 samples in 944.8146734656766 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  52,   48,   65,   82, 1753], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5488, 0.6594, 0.7516, 0.8557, 0.9934])
        * Final bucket consistencies: tensor([0.6346, 0.7083, 0.6000, 0.8415, 0.9920])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1772,  228], dtype=torch.int32)
        * Average consistency: 0.9570000171661377
    
2025-05-22 10:27:38,367 [INFO ] [calibration.py:__call__:249]	 Finished running Monte Carlo - Caching Generator - 30 samples missing top in zero-one in 4405.365782100707 seconds
2025-05-22 10:27:38,367 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 4 stopping
2025-05-22 10:33:28,532 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.039803992956876755 for Monte Carlo - Caching Generator - 3 samples in 704.9104359131306 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  40,   80,   52,   41, 1787], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5467, 0.6576, 0.7480, 0.8409, 0.9986])
        * Final bucket consistencies: tensor([0.6000, 0.3875, 0.5385, 0.7561, 0.9754])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1749,  251], dtype=torch.int32)
        * Average consistency: 0.9284999966621399
    
2025-05-22 10:45:20,292 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03935198485851288 for Monte Carlo - Caching Generator - 3 samples in 711.7440957194194 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  40,   77,   54,   41, 1788], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5494, 0.6567, 0.7492, 0.8381, 0.9985])
        * Final bucket consistencies: tensor([0.5000, 0.3766, 0.5741, 0.7317, 0.9754])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Average consistency: 0.9269999861717224
    
2025-05-22 11:00:45,711 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.039779480546712875 for Monte Carlo - Caching Generator - 3 samples in 925.3786866134033 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  41,   74,   57,   40, 1788], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5538, 0.6541, 0.7472, 0.8388, 0.9985])
        * Final bucket consistencies: tensor([0.4390, 0.3919, 0.5614, 0.7500, 0.9754])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1751,  249], dtype=torch.int32)
        * Average consistency: 0.9265000224113464
    
2025-05-22 11:12:03,412 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.040414486080408096 for Monte Carlo - Caching Generator - 3 samples in 677.674809422344 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  40,   76,   55,   42, 1787], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5460, 0.6558, 0.7491, 0.8370, 0.9986])
        * Final bucket consistencies: tensor([0.6250, 0.3553, 0.5636, 0.7619, 0.9754])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1748,  252], dtype=torch.int32)
        * Average consistency: 0.9290000200271606
    
2025-05-22 11:12:03,420 [INFO ] [calibration.py:__call__:249]	 Finished running Monte Carlo - Caching Generator - 3 samples missing top in zero-one in 3019.7999643348157 seconds
2025-05-22 11:12:03,421 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 3 stopping
2025-05-22 11:12:03,421 [INFO ] [mvce_dataset.py:<module>:222]	 Finished running 8/8 experiments.
2025-05-22 11:12:03,422 [INFO ] [mvce_dataset.py:<module>:227]	 Saving results to ./results/mvce-25/Blond_Hair/top/celeba-20250522-090859.csv
2025-05-22 11:12:03,487 [INFO ] [mvce_dataset.py:<module>:237]	 Finished saving results
