2025-05-22 09:09:44,064 [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/top_test", "output": "./results/mvce-25/Smiling/top/", "classifier": "./models/celeba/Smiling/celeba-20250508-133956.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 09:09:44,111 [INFO ] [util.py:selectDevice:88]	 Using cuda:0 for tensor calculations
2025-05-22 09:09:44,256 [INFO ] [mvce_dataset.py:<module>:90]	 Running on cuda:0
2025-05-22 09:09:44,719 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 09:09:51,589 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Smiling']
2025-05-22 09:09:51,666 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 09:09:51,667 [INFO ] [mvce_dataset.py:<module>:102]	 Using dataset celeba with 2000 test samples
2025-05-22 09:09:51,667 [INFO ] [mvce_dataset.py:<module>:111]	 Loading mask top
2025-05-22 09:09:51,667 [INFO ] [mvce_dataset.py:<module>:128]	 Setting classifier activation function to sigmoid for single class
2025-05-22 09:09:51,667 [INFO ] [mvce_dataset.py:<module>:143]	 Creating generator using cache at ../../datasets/CelebAMask/cache/256/top_test
2025-05-22 09:09:51,668 [INFO ] [mvce_dataset.py:<module>:156]	 Including baseline imputators with zero imputation.
2025-05-22 09:09:51,668 [INFO ] [mvce_dataset.py:<module>:161]	 Including batch mean imputators with sizes [1].
2025-05-22 09:09:51,668 [INFO ] [mvce_dataset.py:<module>:166]	 Running all baseline imputators zero variance.
2025-05-22 09:09:51,668 [INFO ] [mvce_dataset.py:<module>:168]	 Running all baseline imputators with [0.5, 0.99] scaled beta max variance.
2025-05-22 09:09:51,668 [INFO ] [mvce_dataset.py:<module>:184]	 Adding 8 methods.
2025-05-22 09:09:51,668 [INFO ] [mvce_dataset.py:<module>:192]	 Using masked dataset with mask {'name': 'top'}
2025-05-22 09:09:51,669 [INFO ] [mvce_dataset.py:<module>:208]	 Considering action space zero-one
2025-05-22 09:09:51,669 [INFO ] [threading_utils.py:distributeTasks:95]	 Running 8 tasks on 4 threads
2025-05-22 09:09:51,670 [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 09:09:51,670 [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 09:09:51,670 [INFO ] [calibration.py:__call__:223]	 Started running Basic Imputation - 1 Sample Mean Caching Generator Imputation missing top in zero-one
2025-05-22 09:09:51,671 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.99 - Zero Imputation missing top in zero-one
2025-05-22 09:10:23,361 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.06728962808847427 for Beta Mean * 0.99 - Zero Imputation in 31.68615777976811 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 308,   58,   29,   42, 1563], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5698, 0.6230, 0.7453, 0.8521, 0.9922])
        * Final bucket consistencies: tensor([0.8831, 0.8448, 0.5862, 0.6667, 0.9840])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1065,  935], dtype=torch.int32)
        * Average consistency: 0.9520000219345093
    
2025-05-22 09:10:54,995 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.06833754479885101 for Beta Mean * 0.99 - Zero Imputation in 31.62721946183592 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 311,   56,   26,   50, 1557], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5717, 0.6250, 0.7497, 0.8566, 0.9924])
        * Final bucket consistencies: tensor([0.9035, 0.7857, 0.5769, 0.7200, 0.9839])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1065,  935], dtype=torch.int32)
        * Average consistency: 0.9539999961853027
    
2025-05-22 09:11:25,623 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.07184898108243942 for Beta Mean * 0.99 - Zero Imputation in 30.62472099252045 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 301,   64,   29,   47, 1559], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5706, 0.6204, 0.7458, 0.8566, 0.9925])
        * Final bucket consistencies: tensor([0.9037, 0.8750, 0.5172, 0.7021, 0.9840])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1063,  937], dtype=torch.int32)
        * Average consistency: 0.9549999833106995
    
2025-05-22 09:11:56,369 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0674094706773758 for Beta Mean * 0.99 - Zero Imputation in 30.74066251050681 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 305,   60,   30,   47, 1558], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5696, 0.6219, 0.7462, 0.8560, 0.9924])
        * Final bucket consistencies: tensor([0.8852, 0.8500, 0.6000, 0.6809, 0.9846])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1068,  932], dtype=torch.int32)
        * Average consistency: 0.9524999856948853
    
2025-05-22 09:11:56,370 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.99 - Zero Imputation missing top in zero-one in 124.69934973306954 seconds
2025-05-22 09:11:56,371 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.5 - Zero Imputation missing top in zero-one
2025-05-22 09:12:29,150 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.025101467967033386 for Beta Mean * 0.5 - Zero Imputation in 32.756375833414495 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  26,   26,   41,   64, 1843], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5509, 0.6504, 0.7520, 0.8574, 0.9920])
        * Final bucket consistencies: tensor([0.4231, 0.5769, 0.5366, 0.6719, 0.9788])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1010,  990], dtype=torch.int32)
        * Average consistency: 0.9474999904632568
    
2025-05-22 09:13:01,890 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.025573881343007088 for Beta Mean * 0.5 - Zero Imputation in 32.73401884641498 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  25,   26,   43,   67, 1839], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5462, 0.6448, 0.7568, 0.8590, 0.9922])
        * Final bucket consistencies: tensor([0.3600, 0.6154, 0.5814, 0.6418, 0.9793])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1009,  991], dtype=torch.int32)
        * Average consistency: 0.9470000267028809
    
2025-05-22 09:13:31,761 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.025326088070869446 for Beta Mean * 0.5 - Zero Imputation in 29.862104072235525 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  25,   25,   38,   70, 1842], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5493, 0.6434, 0.7518, 0.8560, 0.9920])
        * Final bucket consistencies: tensor([0.4000, 0.6000, 0.6053, 0.6000, 0.9799])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1010,  990], dtype=torch.int32)
        * Average consistency: 0.9474999904632568
    
2025-05-22 09:14:03,412 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.02452845312654972 for Beta Mean * 0.5 - Zero Imputation in 31.645154796540737 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  27,   28,   35,   68, 1842], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5494, 0.6572, 0.7587, 0.8536, 0.9920])
        * Final bucket consistencies: tensor([0.4444, 0.6071, 0.5429, 0.6471, 0.9794])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1011,  989], dtype=torch.int32)
        * Average consistency: 0.9480000138282776
    
2025-05-22 09:14:03,412 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.5 - Zero Imputation missing top in zero-one in 127.04156320542097 seconds
2025-05-22 09:14:03,413 [INFO ] [calibration.py:__call__:223]	 Started running Basic Imputation - Zero Imputation missing top in zero-one
2025-05-22 09:14:31,473 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.05199998617172241 for Basic Imputation - Zero Imputation in 28.05655932519585 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([0.9480])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1009,  991], dtype=torch.int32)
        * Average consistency: 0.9480000138282776
    
2025-05-22 09:14:54,540 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.05199998617172241 for Basic Imputation - Zero Imputation in 23.064248712733388 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([0.9480])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1009,  991], dtype=torch.int32)
        * Average consistency: 0.9480000138282776
    
2025-05-22 09:15:16,822 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.05199998617172241 for Basic Imputation - Zero Imputation in 22.27837198972702 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([0.9480])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1009,  991], dtype=torch.int32)
        * Average consistency: 0.9480000138282776
    
2025-05-22 09:15:36,280 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.05199998617172241 for Basic Imputation - Zero Imputation in 19.455508476123214 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([0.9480])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1009,  991], dtype=torch.int32)
        * Average consistency: 0.9480000138282776
    
2025-05-22 09:15:36,280 [INFO ] [calibration.py:__call__:249]	 Finished running Basic Imputation - Zero Imputation missing top in zero-one in 92.86740641202778 seconds
2025-05-22 09:15:36,280 [INFO ] [calibration.py:__call__:223]	 Started running Monte Carlo - Caching Generator - 30 samples missing top in zero-one
2025-05-22 09:25:09,109 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.038999974727630615 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 917.4311200026423 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([0.9610])
        * 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: 0.9610000252723694
    
2025-05-22 09:25:09,182 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.04215805605053902 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 917.5057297972962 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 241,   69,   33,   65, 1592], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5694, 0.6301, 0.7432, 0.8577, 0.9920])
        * Final bucket consistencies: tensor([0.8755, 0.7391, 0.8182, 0.8615, 0.9918])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1057,  943], dtype=torch.int32)
        * Average consistency: 0.9620000123977661
    
2025-05-22 09:25:09,196 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.01164050493389368 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 917.5199578953907 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  39,   30,   48,   81, 1802], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5512, 0.6562, 0.7504, 0.8606, 0.9916])
        * Final bucket consistencies: tensor([0.6154, 0.5667, 0.6875, 0.9012, 0.9850])
        * 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: 0.9610000252723694
    
2025-05-22 09:33:57,348 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.036760009825229645 for Monte Carlo - Caching Generator - 30 samples in 1101.0654997061938 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  32,   28,   43,   57, 1840], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5488, 0.6485, 0.7529, 0.8612, 0.9970])
        * Final bucket consistencies: tensor([0.8438, 0.5357, 0.7442, 0.6316, 0.9712])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1050,  950], dtype=torch.int32)
        * Average consistency: 0.9484999775886536
    
2025-05-22 09:45:08,765 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.038999974727630615 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1199.5972744710743 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([0.9610])
        * 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: 0.9610000252723694
    
2025-05-22 09:45:08,826 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.04119403660297394 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1199.6316422531381 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 250,   60,   34,   59, 1597], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5695, 0.6306, 0.7465, 0.8576, 0.9916])
        * Final bucket consistencies: tensor([0.8560, 0.7833, 0.7647, 0.8475, 0.9919])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1058,  942], dtype=torch.int32)
        * Average consistency: 0.9605000019073486
    
2025-05-22 09:45:08,841 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.013288985937833786 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1199.6433071186766 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  38,   31,   46,   78, 1807], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5509, 0.6528, 0.7480, 0.8548, 0.9914])
        * Final bucket consistencies: tensor([0.6053, 0.6129, 0.6304, 0.9231, 0.9845])
        * 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: 0.9610000252723694
    
2025-05-22 09:51:13,151 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03648597374558449 for Monte Carlo - Caching Generator - 30 samples in 1035.7814878914505 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  35,   26,   36,   61, 1842], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5545, 0.6527, 0.7496, 0.8574, 0.9968])
        * Final bucket consistencies: tensor([0.8286, 0.5769, 0.6944, 0.6721, 0.9707])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1051,  949], dtype=torch.int32)
        * Average consistency: 0.9490000009536743
    
2025-05-22 10:03:48,113 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.038999974727630615 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1119.2887905174866 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([0.9610])
        * 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: 0.9610000252723694
    
2025-05-22 10:03:48,172 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0123825054615736 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1119.327392716892 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  37,   28,   46,   84, 1805], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5524, 0.6510, 0.7420, 0.8547, 0.9915])
        * Final bucket consistencies: tensor([0.5946, 0.6071, 0.6087, 0.9048, 0.9850])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 992, 1008], dtype=torch.int32)
        * Average consistency: 0.9605000019073486
    
2025-05-22 10:03:48,201 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03982098773121834 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1119.3665418941528 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 238,   69,   34,   64, 1595], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5694, 0.6263, 0.7436, 0.8534, 0.9917])
        * Final bucket consistencies: tensor([0.8613, 0.7536, 0.7647, 0.8594, 0.9918])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1059,  941], dtype=torch.int32)
        * Average consistency: 0.9599999785423279
    
2025-05-22 10:11:53,497 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.033188097178936005 for Monte Carlo - Caching Generator - 30 samples in 1240.2815125929192 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  33,   30,   34,   64, 1839], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5495, 0.6580, 0.7517, 0.8556, 0.9970])
        * Final bucket consistencies: tensor([0.6667, 0.6000, 0.7647, 0.6719, 0.9706])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1047,  953], dtype=torch.int32)
        * Average consistency: 0.9470000267028809
    
2025-05-22 10:21:43,564 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.038999974727630615 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1075.3990879449993 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([0.9610])
        * 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: 0.9610000252723694
    
2025-05-22 10:21:43,601 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.04151347279548645 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1075.396312548779 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 243,   67,   33,   59, 1598], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5669, 0.6329, 0.7459, 0.8545, 0.9916])
        * Final bucket consistencies: tensor([0.8765, 0.7015, 0.8182, 0.8475, 0.9919])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1057,  943], dtype=torch.int32)
        * Average consistency: 0.9610000252723694
    
2025-05-22 10:21:43,605 [INFO ] [calibration.py:__call__:249]	 Finished running Basic Imputation - 1 Sample Mean Caching Generator Imputation missing top in zero-one in 4311.935095047578 seconds
2025-05-22 10:21:43,606 [INFO ] [calibration.py:__call__:223]	 Started running Monte Carlo - Caching Generator - 3 samples missing top in zero-one
2025-05-22 10:21:43,606 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation missing top in zero-one in 4311.9357670247555 seconds
2025-05-22 10:21:43,606 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 1 stopping
2025-05-22 10:21:43,639 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.01016854215413332 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1075.4588763713837 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  41,   30,   43,   79, 1807], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5539, 0.6607, 0.7534, 0.8541, 0.9913])
        * Final bucket consistencies: tensor([0.5610, 0.6000, 0.6744, 0.8987, 0.9851])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 992, 1008], dtype=torch.int32)
        * Average consistency: 0.9605000019073486
    
2025-05-22 10:21:43,639 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing top in zero-one in 4311.96931324061 seconds
2025-05-22 10:21:43,639 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 2 stopping
2025-05-22 10:27:38,360 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03449951112270355 for Monte Carlo - Caching Generator - 30 samples in 944.7720067854971 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  31,   30,   42,   52, 1845], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5487, 0.6500, 0.7557, 0.8608, 0.9968])
        * Final bucket consistencies: tensor([0.7419, 0.6667, 0.6905, 0.6731, 0.9696])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1049,  951], dtype=torch.int32)
        * Average consistency: 0.9480000138282776
    
2025-05-22 10:27:38,375 [INFO ] [calibration.py:__call__:249]	 Finished running Monte Carlo - Caching Generator - 30 samples missing top in zero-one in 4322.094230209477 seconds
2025-05-22 10:27:38,375 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 4 stopping
2025-05-22 10:33:28,474 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.023452028632164 for Monte Carlo - Caching Generator - 3 samples in 704.865816638805 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  24,   47,   32,   35, 1862], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5451, 0.6532, 0.7475, 0.8448, 0.9983])
        * Final bucket consistencies: tensor([0.3750, 0.6383, 0.6250, 0.8857, 0.9785])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1025,  975], dtype=torch.int32)
        * Average consistency: 0.9559999704360962
    
2025-05-22 10:45:20,242 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.022659996524453163 for Monte Carlo - Caching Generator - 3 samples in 711.7488169502467 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  27,   39,   35,   37, 1862], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5481, 0.6559, 0.7427, 0.8437, 0.9982])
        * Final bucket consistencies: tensor([0.4074, 0.5641, 0.7143, 0.8378, 0.9785])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1024,  976], dtype=torch.int32)
        * Average consistency: 0.9555000066757202
    
2025-05-22 11:00:45,692 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.022780410945415497 for Monte Carlo - Caching Generator - 3 samples in 925.3898571273312 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  26,   37,   39,   36, 1862], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5475, 0.6504, 0.7446, 0.8475, 0.9982])
        * Final bucket consistencies: tensor([0.3462, 0.5676, 0.7179, 0.8333, 0.9791])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1022,  978], dtype=torch.int32)
        * Average consistency: 0.9555000066757202
    
2025-05-22 11:12:03,397 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.02166854962706566 for Monte Carlo - Caching Generator - 3 samples in 677.6737142847851 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  26,   38,   34,   41, 1861], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5466, 0.6487, 0.7332, 0.8419, 0.9983])
        * Final bucket consistencies: tensor([0.4615, 0.5526, 0.7353, 0.8049, 0.9790])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1024,  976], dtype=torch.int32)
        * Average consistency: 0.9564999938011169
    
2025-05-22 11:12:03,411 [INFO ] [calibration.py:__call__:249]	 Finished running Monte Carlo - Caching Generator - 3 samples missing top in zero-one in 3019.8054925529286 seconds
2025-05-22 11:12:03,411 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 3 stopping
2025-05-22 11:12:03,412 [INFO ] [mvce_dataset.py:<module>:222]	 Finished running 8/8 experiments.
2025-05-22 11:12:03,412 [INFO ] [mvce_dataset.py:<module>:227]	 Saving results to ./results/mvce-25/Smiling/top/celeba-20250522-090944.csv
2025-05-22 11:12:03,457 [INFO ] [mvce_dataset.py:<module>:237]	 Finished saving results
