2025-05-22 09:09:38,634 [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/bottom_test", "output": "./results/mvce-25/Eyeglasses/bottom/", "classifier": "./models/celeba/Eyeglasses/celeba-20250508-134104.pklz", "classifier_feature": null, "mask": {"name": "bottom"}, "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 09:09:38,690 [INFO ] [util.py:selectDevice:88]	 Using cuda:0 for tensor calculations
2025-05-22 09:09:38,806 [INFO ] [mvce_dataset.py:<module>:90]	 Running on cuda:0
2025-05-22 09:09:39,089 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 09:09:45,243 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Eyeglasses']
2025-05-22 09:09:45,325 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 09:09:45,325 [INFO ] [mvce_dataset.py:<module>:102]	 Using dataset celeba with 2000 test samples
2025-05-22 09:09:45,325 [INFO ] [mvce_dataset.py:<module>:111]	 Loading mask bottom
2025-05-22 09:09:45,335 [INFO ] [mvce_dataset.py:<module>:128]	 Setting classifier activation function to sigmoid for single class
2025-05-22 09:09:45,335 [INFO ] [mvce_dataset.py:<module>:143]	 Creating generator using cache at ../../datasets/CelebAMask/cache/256/bottom_test
2025-05-22 09:09:45,336 [INFO ] [mvce_dataset.py:<module>:156]	 Including baseline imputators with zero imputation.
2025-05-22 09:09:45,336 [INFO ] [mvce_dataset.py:<module>:161]	 Including batch mean imputators with sizes [1].
2025-05-22 09:09:45,336 [INFO ] [mvce_dataset.py:<module>:166]	 Running all baseline imputators zero variance.
2025-05-22 09:09:45,336 [INFO ] [mvce_dataset.py:<module>:168]	 Running all baseline imputators with [0.5, 0.99] scaled beta max variance.
2025-05-22 09:09:45,336 [INFO ] [mvce_dataset.py:<module>:184]	 Adding 8 methods.
2025-05-22 09:09:45,336 [INFO ] [mvce_dataset.py:<module>:192]	 Using masked dataset with mask {'name': 'bottom'}
2025-05-22 09:09:45,337 [INFO ] [mvce_dataset.py:<module>:208]	 Considering action space zero-one
2025-05-22 09:09:45,339 [INFO ] [threading_utils.py:distributeTasks:95]	 Running 8 tasks on 4 threads
2025-05-22 09:09:45,339 [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 09:09:45,340 [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 09:09:45,340 [INFO ] [calibration.py:__call__:223]	 Started running Basic Imputation - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one
2025-05-22 09:09:45,340 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.99 - Zero Imputation missing bottom in zero-one
2025-05-22 09:10:16,911 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.06899707019329071 for Beta Mean * 0.99 - Zero Imputation in 31.56434476748109 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 199,   95,   98,  243, 1365], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5577, 0.6477, 0.7514, 0.8551, 0.9678])
        * Final bucket consistencies: tensor([0.4925, 0.9053, 0.9898, 1.0000, 0.9985])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1800,  200], dtype=torch.int32)
        * Average consistency: 0.9434999823570251
    
2025-05-22 09:10:50,839 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.07303546369075775 for Beta Mean * 0.99 - Zero Imputation in 33.917241708375514 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 202,   89,   99,  260, 1350], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5584, 0.6473, 0.7530, 0.8570, 0.9687])
        * Final bucket consistencies: tensor([0.4554, 0.9213, 0.9899, 1.0000, 0.9985])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1793,  207], dtype=torch.int32)
        * Average consistency: 0.9399999976158142
    
2025-05-22 09:11:25,534 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.07198701798915863 for Beta Mean * 0.99 - Zero Imputation in 34.68448696658015 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 197,  102,   90,  248, 1363], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5564, 0.6476, 0.7545, 0.8550, 0.9682])
        * Final bucket consistencies: tensor([0.4721, 0.9314, 0.9889, 1.0000, 0.9985])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1799,  201], dtype=torch.int32)
        * Average consistency: 0.9430000185966492
    
2025-05-22 09:12:01,722 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.06902801245450974 for Beta Mean * 0.99 - Zero Imputation in 36.179061631672084 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 189,  108,   97,  235, 1371], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5555, 0.6418, 0.7551, 0.8545, 0.9676])
        * Final bucket consistencies: tensor([0.4921, 0.8889, 0.9897, 1.0000, 0.9985])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1802,  198], dtype=torch.int32)
        * Average consistency: 0.9445000290870667
    
2025-05-22 09:12:01,722 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.99 - Zero Imputation missing bottom in zero-one in 136.38194931671023 seconds
2025-05-22 09:12:01,723 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.5 - Zero Imputation missing bottom in zero-one
2025-05-22 09:12:37,144 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.041649531573057175 for Beta Mean * 0.5 - Zero Imputation in 35.412901662290096 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  83,  117,  154,  289, 1357], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5521, 0.6503, 0.7537, 0.8586, 0.9678])
        * Final bucket consistencies: tensor([0.4458, 0.5897, 0.7727, 0.8512, 0.9219])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1635,  365], dtype=torch.int32)
        * Average consistency: 0.8610000014305115
    
2025-05-22 09:13:08,650 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.04030149057507515 for Beta Mean * 0.5 - Zero Imputation in 31.499795340932906 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  89,  108,  157,  279, 1367], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5531, 0.6521, 0.7530, 0.8561, 0.9675])
        * Final bucket consistencies: tensor([0.4944, 0.5926, 0.7389, 0.8674, 0.9210])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1638,  362], dtype=torch.int32)
        * Average consistency: 0.862500011920929
    
2025-05-22 09:13:36,626 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03873596712946892 for Beta Mean * 0.5 - Zero Imputation in 27.970855969935656 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  91,  101,  160,  288, 1360], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5530, 0.6518, 0.7501, 0.8571, 0.9680])
        * Final bucket consistencies: tensor([0.5055, 0.5743, 0.7437, 0.8542, 0.9213])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1635,  365], dtype=torch.int32)
        * Average consistency: 0.8610000014305115
    
2025-05-22 09:14:08,795 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.044832952320575714 for Beta Mean * 0.5 - Zero Imputation in 32.15846341010183 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  88,  107,  153,  298, 1354], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5546, 0.6522, 0.7511, 0.8579, 0.9680])
        * Final bucket consistencies: tensor([0.4886, 0.5514, 0.7843, 0.8624, 0.9188])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1636,  364], dtype=torch.int32)
        * Average consistency: 0.8615000247955322
    
2025-05-22 09:14:08,795 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.5 - Zero Imputation missing bottom in zero-one in 127.07228480558842 seconds
2025-05-22 09:14:08,795 [INFO ] [calibration.py:__call__:223]	 Started running Basic Imputation - Zero Imputation missing bottom in zero-one
2025-05-22 09:14:35,322 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.13899999856948853 for Basic Imputation - Zero Imputation in 26.523860127665102 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([0.8610])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1635,  365], dtype=torch.int32)
        * Average consistency: 0.8610000014305115
    
2025-05-22 09:14:54,852 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.13899999856948853 for Basic Imputation - Zero Imputation in 19.525637893937528 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([0.8610])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1635,  365], dtype=torch.int32)
        * Average consistency: 0.8610000014305115
    
2025-05-22 09:15:13,262 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.13899999856948853 for Basic Imputation - Zero Imputation in 18.40769013389945 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([0.8610])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1635,  365], dtype=torch.int32)
        * Average consistency: 0.8610000014305115
    
2025-05-22 09:15:32,321 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.13899999856948853 for Basic Imputation - Zero Imputation in 19.056954039260745 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([0.8610])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1635,  365], dtype=torch.int32)
        * Average consistency: 0.8610000014305115
    
2025-05-22 09:15:32,321 [INFO ] [calibration.py:__call__:249]	 Finished running Basic Imputation - Zero Imputation missing bottom in zero-one in 83.52593635488302 seconds
2025-05-22 09:15:32,321 [INFO ] [calibration.py:__call__:223]	 Started running Monte Carlo - Caching Generator - 30 samples missing bottom in zero-one
2025-05-22 09:24:54,015 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0059999823570251465 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 908.6714415876195 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([0.9940])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1911,   89], dtype=torch.int32)
        * Average consistency: 0.9940000176429749
    
2025-05-22 09:24:54,077 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.005053069908171892 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 908.7343535861 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    1,    6, 1991], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5660, 0.7010, 0.8755, 0.9991])
        * Final bucket consistencies: tensor([1.0000, 0.0000, 0.6667, 0.9955])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1911,   89], dtype=torch.int32)
        * Average consistency: 0.9940000176429749
    
2025-05-22 09:24:54,081 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.006669546477496624 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 908.7385712787509 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 8, 9])
        * Final bucket sizes: tensor([  24,    5,    1, 1970], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5604, 0.6264, 0.8960, 0.9994])
        * Final bucket consistencies: tensor([0.8333, 0.6000, 0.0000, 0.9964])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1917,   83], dtype=torch.int32)
        * Average consistency: 0.9929999709129333
    
2025-05-22 09:31:55,835 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0037635164335370064 for Monte Carlo - Caching Generator - 30 samples in 983.4684635922313 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    6,    4,    9, 1979], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5595, 0.6428, 0.7697, 0.8653, 0.9994])
        * Final bucket consistencies: tensor([1.0000, 0.6667, 1.0000, 0.7778, 0.9970])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1917,   83], dtype=torch.int32)
        * Average consistency: 0.9950000047683716
    
2025-05-22 09:42:55,424 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0059999823570251465 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1081.3807417536154 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([0.9940])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1911,   89], dtype=torch.int32)
        * Average consistency: 0.9940000176429749
    
2025-05-22 09:42:55,443 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.006682453211396933 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1081.3597093280405 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 9])
        * Final bucket sizes: tensor([  24,    5, 1971], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5616, 0.6340, 0.9993])
        * Final bucket consistencies: tensor([0.8333, 0.6000, 0.9959])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1917,   83], dtype=torch.int32)
        * Average consistency: 0.9929999709129333
    
2025-05-22 09:42:55,449 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.005032027140259743 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1081.3702394282445 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    1,    6, 1991], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5740, 0.7150, 0.8693, 0.9991])
        * Final bucket consistencies: tensor([1.0000, 0.0000, 0.6667, 0.9955])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1911,   89], dtype=torch.int32)
        * Average consistency: 0.9940000176429749
    
2025-05-22 09:48:25,813 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.003890994470566511 for Monte Carlo - Caching Generator - 30 samples in 989.9749225815758 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    6,    4,    8, 1980], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5825, 0.6353, 0.7695, 0.8600, 0.9994])
        * Final bucket consistencies: tensor([1.0000, 0.6667, 1.0000, 0.8750, 0.9965])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1917,   83], dtype=torch.int32)
        * Average consistency: 0.9950000047683716
    
2025-05-22 09:59:59,592 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0059999823570251465 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1024.0335533479229 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([0.9940])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1911,   89], dtype=torch.int32)
        * Average consistency: 0.9940000176429749
    
2025-05-22 09:59:59,649 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.00754261901602149 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1024.0914326524362 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 9])
        * Final bucket sizes: tensor([  26,    3, 1971], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5615, 0.6650, 0.9993])
        * Final bucket consistencies: tensor([0.8462, 0.3333, 0.9959])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1917,   83], dtype=torch.int32)
        * Average consistency: 0.9929999709129333
    
2025-05-22 09:59:59,673 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.005026550497859716 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1024.114313130267 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 8, 9])
        * Final bucket sizes: tensor([   2,    1,    6, 1991], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5675, 0.6880, 0.8723, 0.9991])
        * Final bucket consistencies: tensor([1.0000, 0.0000, 0.6667, 0.9955])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1911,   89], dtype=torch.int32)
        * Average consistency: 0.9940000176429749
    
2025-05-22 10:05:12,173 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0037130757700651884 for Monte Carlo - Caching Generator - 30 samples in 1006.3276930591092 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    6,    4,    6, 1982], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5670, 0.6523, 0.7747, 0.8425, 0.9993])
        * Final bucket consistencies: tensor([1.0000, 0.6667, 1.0000, 0.8333, 0.9965])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1917,   83], dtype=torch.int32)
        * Average consistency: 0.9950000047683716
    
2025-05-22 10:17:09,200 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0059999823570251465 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1029.5529141798615 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([0.9940])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1911,   89], dtype=torch.int32)
        * Average consistency: 0.9940000176429749
    
2025-05-22 10:17:09,269 [INFO ] [calibration.py:__call__:249]	 Finished running Basic Imputation - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one in 4043.9282356463373 seconds
2025-05-22 10:17:09,269 [INFO ] [calibration.py:__call__:223]	 Started running Monte Carlo - Caching Generator - 3 samples missing bottom in zero-one
2025-05-22 10:17:09,297 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.005031936336308718 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1029.620297620073 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 8, 9])
        * Final bucket sizes: tensor([   2,    1,    5, 1992], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5620, 0.6850, 0.8684, 0.9991])
        * Final bucket consistencies: tensor([1.0000, 0.0000, 0.6000, 0.9955])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1911,   89], dtype=torch.int32)
        * Average consistency: 0.9940000176429749
    
2025-05-22 10:17:09,298 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one in 4043.957262611948 seconds
2025-05-22 10:17:09,298 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 2 stopping
2025-05-22 10:17:09,303 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0038604477886110544 for Monte Carlo - Caching Generator - 30 samples in 717.1267886972055 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    6,    4,    8, 1980], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5720, 0.6450, 0.7722, 0.8602, 0.9993])
        * Final bucket consistencies: tensor([1.0000, 0.6667, 1.0000, 0.8750, 0.9965])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1917,   83], dtype=torch.int32)
        * Average consistency: 0.9950000047683716
    
2025-05-22 10:17:09,304 [INFO ] [calibration.py:__call__:249]	 Finished running Monte Carlo - Caching Generator - 30 samples missing bottom in zero-one in 3696.982206088491 seconds
2025-05-22 10:17:09,304 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 4 stopping
2025-05-22 10:17:09,315 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0055399867706000805 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1029.654816577211 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 9])
        * Final bucket sizes: tensor([  23,    6, 1971], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5573, 0.6282, 0.9993])
        * Final bucket consistencies: tensor([0.7391, 0.6667, 0.9959])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1919,   81], dtype=torch.int32)
        * Average consistency: 0.9919999837875366
    
2025-05-22 10:17:09,316 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one in 4043.975599472411 seconds
2025-05-22 10:17:09,316 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 1 stopping
2025-05-22 10:31:10,775 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0058034746907651424 for Monte Carlo - Caching Generator - 3 samples in 841.5051086526364 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    7,    3,    3, 1985], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5675, 0.6479, 0.7637, 0.8670, 0.9999])
        * Final bucket consistencies: tensor([1.0000, 0.2857, 1.0000, 0.6667, 0.9965])
        * 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: 0.9934999942779541
    
2025-05-22 10:42:56,837 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.005606519524008036 for Monte Carlo - Caching Generator - 3 samples in 705.9722043490037 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    7,    2,    4, 1985], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5560, 0.6506, 0.7665, 0.8477, 1.0000])
        * Final bucket consistencies: tensor([1.0000, 0.2857, 1.0000, 0.7500, 0.9965])
        * 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: 0.9934999942779541
    
2025-05-22 10:58:50,801 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.00478906836360693 for Monte Carlo - Caching Generator - 3 samples in 953.9533121362329 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   9,    2,    4, 1985], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6416, 0.7520, 0.8575, 0.9999])
        * Final bucket consistencies: tensor([0.4444, 1.0000, 0.7500, 0.9965])
        * 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: 0.9934999942779541
    
2025-05-22 11:11:23,566 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.005628048907965422 for Monte Carlo - Caching Generator - 3 samples in 752.7297047199681 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    7,    2,    4, 1985], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5890, 0.6587, 0.7330, 0.8493, 0.9999])
        * Final bucket consistencies: tensor([1.0000, 0.2857, 1.0000, 0.7500, 0.9965])
        * 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: 0.9934999942779541
    
2025-05-22 11:11:23,668 [INFO ] [calibration.py:__call__:249]	 Finished running Monte Carlo - Caching Generator - 3 samples missing bottom in zero-one in 3254.3987785261124 seconds
2025-05-22 11:11:23,668 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 3 stopping
2025-05-22 11:11:23,669 [INFO ] [mvce_dataset.py:<module>:222]	 Finished running 8/8 experiments.
2025-05-22 11:11:23,669 [INFO ] [mvce_dataset.py:<module>:227]	 Saving results to ./results/mvce-25/Eyeglasses/bottom/celeba-20250522-090938.csv
2025-05-22 11:11:24,080 [INFO ] [mvce_dataset.py:<module>:237]	 Finished saving results
