2025-05-22 09:09:36,135 [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": ["Eyeglasses"]}, "cache_directory": "../../datasets/CelebAMask/cache/256/top_test", "output": "./results/mvce-25/Eyeglasses/top/", "classifier": "./models/celeba/Eyeglasses/celeba-20250508-134104.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 09:09:36,162 [INFO ] [util.py:selectDevice:88]	 Using cuda:0 for tensor calculations
2025-05-22 09:09:36,303 [INFO ] [mvce_dataset.py:<module>:90]	 Running on cuda:0
2025-05-22 09:09:36,754 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 09:09:42,636 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Eyeglasses']
2025-05-22 09:09:42,699 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 09:09:42,699 [INFO ] [mvce_dataset.py:<module>:102]	 Using dataset celeba with 2000 test samples
2025-05-22 09:09:42,699 [INFO ] [mvce_dataset.py:<module>:111]	 Loading mask top
2025-05-22 09:09:42,699 [INFO ] [mvce_dataset.py:<module>:128]	 Setting classifier activation function to sigmoid for single class
2025-05-22 09:09:42,699 [INFO ] [mvce_dataset.py:<module>:143]	 Creating generator using cache at ../../datasets/CelebAMask/cache/256/top_test
2025-05-22 09:09:42,701 [INFO ] [mvce_dataset.py:<module>:156]	 Including baseline imputators with zero imputation.
2025-05-22 09:09:42,701 [INFO ] [mvce_dataset.py:<module>:161]	 Including batch mean imputators with sizes [1].
2025-05-22 09:09:42,701 [INFO ] [mvce_dataset.py:<module>:166]	 Running all baseline imputators zero variance.
2025-05-22 09:09:42,701 [INFO ] [mvce_dataset.py:<module>:168]	 Running all baseline imputators with [0.5, 0.99] scaled beta max variance.
2025-05-22 09:09:42,701 [INFO ] [mvce_dataset.py:<module>:184]	 Adding 8 methods.
2025-05-22 09:09:42,701 [INFO ] [mvce_dataset.py:<module>:192]	 Using masked dataset with mask {'name': 'top'}
2025-05-22 09:09:42,701 [INFO ] [mvce_dataset.py:<module>:208]	 Considering action space zero-one
2025-05-22 09:09:42,701 [INFO ] [threading_utils.py:distributeTasks:95]	 Running 8 tasks on 4 threads
2025-05-22 09:09:42,702 [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 09:09:42,708 [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 09:09:42,708 [INFO ] [calibration.py:__call__:223]	 Started running Basic Imputation - 1 Sample Mean Caching Generator Imputation missing top in zero-one
2025-05-22 09:09:42,709 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.99 - Zero Imputation missing top in zero-one
2025-05-22 09:10:11,901 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.11875253170728683 for Beta Mean * 0.99 - Zero Imputation in 29.17701253760606 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([418, 182, 212, 348, 840], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5532, 0.6451, 0.7504, 0.8534, 0.9598])
        * Final bucket consistencies: tensor([0.4282, 0.9341, 0.9953, 0.9971, 0.9964])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1663,  337], dtype=torch.int32)
        * Average consistency: 0.871999979019165
    
2025-05-22 09:10:40,150 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.11508052796125412 for Beta Mean * 0.99 - Zero Imputation in 28.24168241303414 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([419, 187, 208, 357, 829], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5531, 0.6483, 0.7538, 0.8544, 0.9608])
        * Final bucket consistencies: tensor([0.4415, 0.9305, 0.9952, 0.9972, 0.9964])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1667,  333], dtype=torch.int32)
        * Average consistency: 0.8740000128746033
    
2025-05-22 09:11:10,558 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.1224764809012413 for Beta Mean * 0.99 - Zero Imputation in 30.397077679634094 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([428, 174, 216, 353, 829], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5541, 0.6466, 0.7524, 0.8554, 0.9605])
        * Final bucket consistencies: tensor([0.4229, 0.9655, 1.0000, 0.9972, 0.9964])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1662,  338], dtype=torch.int32)
        * Average consistency: 0.8715000152587891
    
2025-05-22 09:11:41,522 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.1142810508608818 for Beta Mean * 0.99 - Zero Imputation in 30.95902187936008 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([424, 176, 206, 365, 829], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5529, 0.6453, 0.7496, 0.8541, 0.9603])
        * Final bucket consistencies: tensor([0.4458, 0.9318, 0.9951, 0.9973, 0.9964])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1667,  333], dtype=torch.int32)
        * Average consistency: 0.8740000128746033
    
2025-05-22 09:11:41,523 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.99 - Zero Imputation missing top in zero-one in 118.81413175817579 seconds
2025-05-22 09:11:41,523 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.5 - Zero Imputation missing top in zero-one
2025-05-22 09:12:18,273 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.1370849907398224 for Beta Mean * 0.5 - Zero Imputation in 36.74593317415565 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([191, 200, 275, 367, 967], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5499, 0.6521, 0.7535, 0.8534, 0.9631])
        * Final bucket consistencies: tensor([0.5393, 0.5850, 0.6691, 0.7575, 0.7559])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1326,  674], dtype=torch.int32)
        * Average consistency: 0.7064999938011169
    
2025-05-22 09:12:52,711 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.13388046622276306 for Beta Mean * 0.5 - Zero Imputation in 34.425871153362095 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([196, 187, 284, 368, 965], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5507, 0.6482, 0.7529, 0.8544, 0.9631])
        * Final bucket consistencies: tensor([0.5357, 0.6257, 0.6796, 0.7310, 0.7617])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1332,  668], dtype=torch.int32)
        * Average consistency: 0.7095000147819519
    
2025-05-22 09:13:25,390 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.13465557992458344 for Beta Mean * 0.5 - Zero Imputation in 32.674893563613296 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([199, 190, 268, 367, 976], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5524, 0.6518, 0.7543, 0.8526, 0.9624])
        * Final bucket consistencies: tensor([0.5528, 0.6158, 0.6903, 0.7384, 0.7541])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1332,  668], dtype=torch.int32)
        * Average consistency: 0.7095000147819519
    
2025-05-22 09:13:59,325 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.13864098489284515 for Beta Mean * 0.5 - Zero Imputation in 33.92904908210039 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([187, 197, 279, 365, 972], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5477, 0.6499, 0.7518, 0.8550, 0.9628])
        * Final bucket consistencies: tensor([0.5775, 0.5685, 0.7061, 0.7425, 0.7551])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1335,  665], dtype=torch.int32)
        * Average consistency: 0.7110000252723694
    
2025-05-22 09:13:59,326 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.5 - Zero Imputation missing top in zero-one in 137.8021313128993 seconds
2025-05-22 09:13:59,326 [INFO ] [calibration.py:__call__:223]	 Started running Basic Imputation - Zero Imputation missing top in zero-one
2025-05-22 09:14:28,132 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.2904999852180481 for Basic Imputation - Zero Imputation in 28.802963512018323 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([0.7095])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1332,  668], dtype=torch.int32)
        * Average consistency: 0.7095000147819519
    
2025-05-22 09:14:52,753 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.2904999852180481 for Basic Imputation - Zero Imputation in 24.614953896962106 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([0.7095])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1332,  668], dtype=torch.int32)
        * Average consistency: 0.7095000147819519
    
2025-05-22 09:15:14,461 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.2904999852180481 for Basic Imputation - Zero Imputation in 21.7047945773229 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([0.7095])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1332,  668], dtype=torch.int32)
        * Average consistency: 0.7095000147819519
    
2025-05-22 09:15:35,563 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.2904999852180481 for Basic Imputation - Zero Imputation in 21.098819211125374 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([0.7095])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1332,  668], dtype=torch.int32)
        * Average consistency: 0.7095000147819519
    
2025-05-22 09:15:35,563 [INFO ] [calibration.py:__call__:249]	 Finished running Basic Imputation - Zero Imputation missing top in zero-one in 96.23669137526304 seconds
2025-05-22 09:15:35,563 [INFO ] [calibration.py:__call__:223]	 Started running Monte Carlo - Caching Generator - 30 samples missing top in zero-one
2025-05-22 09:25:09,098 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.00550001859664917 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 926.3854817934334 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([0.9945])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1912,   88], dtype=torch.int32)
        * Average consistency: 0.9944999814033508
    
2025-05-22 09:25:09,237 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.008108620531857014 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 926.5313185183331 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  33,    2,    2,    3, 1960], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5698, 0.6035, 0.7875, 0.8530, 0.9993])
        * Final bucket consistencies: tensor([0.9091, 0.5000, 0.5000, 0.6667, 0.9974])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1914,   86], dtype=torch.int32)
        * Average consistency: 0.9944999814033508
    
2025-05-22 09:25:09,247 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.004217092413455248 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 926.5351802483201 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    2,    4, 1992], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6590, 0.7510, 0.8400, 0.9991])
        * Final bucket consistencies: tensor([0.5000, 1.0000, 0.2500, 0.9965])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1912,   88], dtype=torch.int32)
        * Average consistency: 0.9944999814033508
    
2025-05-22 09:33:57,375 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.004995493683964014 for Monte Carlo - Caching Generator - 30 samples in 1101.8105039615184 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   4,    4,    4,    6, 1982], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5767, 0.6698, 0.7665, 0.8500, 0.9996])
        * Final bucket consistencies: tensor([0.5000, 0.7500, 0.5000, 0.6667, 0.9960])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1918,   82], dtype=torch.int32)
        * Average consistency: 0.9925000071525574
    
2025-05-22 09:45:08,727 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.00550001859664917 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1199.551824722439 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([0.9945])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1912,   88], dtype=torch.int32)
        * Average consistency: 0.9944999814033508
    
2025-05-22 09:45:08,850 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0077990093268454075 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1199.6100660767406 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  30,    5,    2,    3, 1960], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5752, 0.6030, 0.7955, 0.8503, 0.9994])
        * Final bucket consistencies: tensor([0.8667, 1.0000, 0.5000, 0.6667, 0.9974])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1914,   86], dtype=torch.int32)
        * Average consistency: 0.9944999814033508
    
2025-05-22 09:45:08,869 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.004239040892571211 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1199.618965446949 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    2,    4, 1992], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6540, 0.7405, 0.8430, 0.9991])
        * Final bucket consistencies: tensor([0.5000, 1.0000, 0.2500, 0.9965])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1912,   88], dtype=torch.int32)
        * Average consistency: 0.9944999814033508
    
2025-05-22 09:51:13,168 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0053425682708621025 for Monte Carlo - Caching Generator - 30 samples in 1035.7295754179358 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   3,    5,    5,    6, 1981], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5733, 0.6476, 0.7588, 0.8605, 0.9996])
        * Final bucket consistencies: tensor([0.3333, 0.8000, 0.6000, 0.6667, 0.9960])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1918,   82], dtype=torch.int32)
        * Average consistency: 0.9925000071525574
    
2025-05-22 10:03:48,080 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.00550001859664917 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1119.2610781788826 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([0.9945])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1912,   88], dtype=torch.int32)
        * Average consistency: 0.9944999814033508
    
2025-05-22 10:03:48,198 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.004230503924190998 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1119.3272115411237 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    2,    4, 1992], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6720, 0.7525, 0.8377, 0.9991])
        * Final bucket consistencies: tensor([0.5000, 1.0000, 0.2500, 0.9965])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1912,   88], dtype=torch.int32)
        * Average consistency: 0.9944999814033508
    
2025-05-22 10:03:48,230 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.007772097364068031 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1119.3726022858173 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  30,    5,    2,    3, 1960], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5748, 0.6076, 0.7780, 0.8533, 0.9993])
        * Final bucket consistencies: tensor([0.8667, 1.0000, 0.5000, 0.6667, 0.9974])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1914,   86], dtype=torch.int32)
        * Average consistency: 0.9944999814033508
    
2025-05-22 10:11:53,496 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.005094517488032579 for Monte Carlo - Caching Generator - 30 samples in 1240.2566381059587 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    7,    3,    6, 1982], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5565, 0.6487, 0.7880, 0.8432, 0.9996])
        * Final bucket consistencies: tensor([0.5000, 0.7143, 0.6667, 0.5000, 0.9960])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1918,   82], dtype=torch.int32)
        * Average consistency: 0.9925000071525574
    
2025-05-22 10:21:43,536 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.00550001859664917 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1075.4268158711493 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([0.9945])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1912,   88], dtype=torch.int32)
        * Average consistency: 0.9944999814033508
    
2025-05-22 10:21:43,567 [INFO ] [calibration.py:__call__:249]	 Finished running Basic Imputation - 1 Sample Mean Caching Generator Imputation missing top in zero-one in 4320.858423340134 seconds
2025-05-22 10:21:43,567 [INFO ] [calibration.py:__call__:223]	 Started running Monte Carlo - Caching Generator - 3 samples missing top in zero-one
2025-05-22 10:21:43,658 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.004192170221358538 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1075.4559798398986 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    2,    3, 1993], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6425, 0.7600, 0.8230, 0.9991])
        * Final bucket consistencies: tensor([0.5000, 1.0000, 0.3333, 0.9960])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1912,   88], dtype=torch.int32)
        * Average consistency: 0.9944999814033508
    
2025-05-22 10:21:43,659 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing top in zero-one in 4320.9509270861745 seconds
2025-05-22 10:21:43,659 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 2 stopping
2025-05-22 10:21:43,670 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.008071083575487137 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1075.4378035450354 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  32,    3,    1,    4, 1960], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5719, 0.6020, 0.7730, 0.8397, 0.9993])
        * Final bucket consistencies: tensor([0.8750, 1.0000, 1.0000, 0.5000, 0.9974])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1914,   86], dtype=torch.int32)
        * Average consistency: 0.9944999814033508
    
2025-05-22 10:21:43,671 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation missing top in zero-one in 4320.969042137265 seconds
2025-05-22 10:21:43,671 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 1 stopping
2025-05-22 10:27:38,356 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.005388941615819931 for Monte Carlo - Caching Generator - 30 samples in 944.7629001541063 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   3,    5,    5,    5, 1982], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5583, 0.6404, 0.7752, 0.8542, 0.9996])
        * Final bucket consistencies: tensor([0.3333, 0.8000, 0.6000, 0.6000, 0.9960])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1918,   82], dtype=torch.int32)
        * Average consistency: 0.9925000071525574
    
2025-05-22 10:27:38,370 [INFO ] [calibration.py:__call__:249]	 Finished running Monte Carlo - Caching Generator - 30 samples missing top in zero-one in 4322.806675045751 seconds
2025-05-22 10:27:38,370 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 4 stopping
2025-05-22 10:33:28,374 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0048314533196389675 for Monte Carlo - Caching Generator - 3 samples in 704.8055277373642 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   6,    4,    1, 1989], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6593, 0.7340, 0.8330, 0.9997])
        * Final bucket consistencies: tensor([0.8333, 1.0000, 1.0000, 0.9960])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1910,   90], dtype=torch.int32)
        * Average consistency: 0.9955000281333923
    
2025-05-22 10:45:20,287 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.004811533261090517 for Monte Carlo - Caching Generator - 3 samples in 711.8928786832839 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   5,    5,    1, 1989], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6504, 0.7400, 0.8030, 0.9997])
        * Final bucket consistencies: tensor([0.8000, 1.0000, 1.0000, 0.9960])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1910,   90], dtype=torch.int32)
        * Average consistency: 0.9955000281333923
    
2025-05-22 11:00:45,710 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0048180827870965 for Monte Carlo - Caching Generator - 3 samples in 925.3979372521862 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   6,    4,    1, 1989], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6585, 0.7393, 0.8160, 0.9997])
        * Final bucket consistencies: tensor([0.8333, 1.0000, 1.0000, 0.9960])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1910,   90], dtype=torch.int32)
        * Average consistency: 0.9955000281333923
    
2025-05-22 11:12:03,422 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.004833456128835678 for Monte Carlo - Caching Generator - 3 samples in 677.669730708003 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   7,    3,    1, 1989], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6604, 0.7630, 0.8250, 0.9997])
        * Final bucket consistencies: tensor([0.8571, 1.0000, 1.0000, 0.9960])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1910,   90], dtype=torch.int32)
        * Average consistency: 0.9955000281333923
    
2025-05-22 11:12:03,470 [INFO ] [calibration.py:__call__:249]	 Finished running Monte Carlo - Caching Generator - 3 samples missing top in zero-one in 3019.902574773878 seconds
2025-05-22 11:12:03,470 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 3 stopping
2025-05-22 11:12:03,471 [INFO ] [mvce_dataset.py:<module>:222]	 Finished running 8/8 experiments.
2025-05-22 11:12:03,471 [INFO ] [mvce_dataset.py:<module>:227]	 Saving results to ./results/mvce-25/Eyeglasses/top/celeba-20250522-090936.csv
2025-05-22 11:12:03,521 [INFO ] [mvce_dataset.py:<module>:237]	 Finished saving results
