2025-05-22 09:09:33,541 [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/bottom_test", "output": "./results/mvce-25/Blond_Hair/bottom/", "classifier": "./models/celeba/Blond_Hair/celeba-20250508-133714.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 09:09:33,567 [INFO ] [util.py:selectDevice:88]	 Using cuda:0 for tensor calculations
2025-05-22 09:09:33,706 [INFO ] [mvce_dataset.py:<module>:90]	 Running on cuda:0
2025-05-22 09:09:33,985 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 09:09:39,345 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Blond_Hair']
2025-05-22 09:09:39,404 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 09:09:39,404 [INFO ] [mvce_dataset.py:<module>:102]	 Using dataset celeba with 2000 test samples
2025-05-22 09:09:39,404 [INFO ] [mvce_dataset.py:<module>:111]	 Loading mask bottom
2025-05-22 09:09:39,404 [INFO ] [mvce_dataset.py:<module>:128]	 Setting classifier activation function to sigmoid for single class
2025-05-22 09:09:39,404 [INFO ] [mvce_dataset.py:<module>:143]	 Creating generator using cache at ../../datasets/CelebAMask/cache/256/bottom_test
2025-05-22 09:09:39,456 [INFO ] [mvce_dataset.py:<module>:156]	 Including baseline imputators with zero imputation.
2025-05-22 09:09:39,456 [INFO ] [mvce_dataset.py:<module>:161]	 Including batch mean imputators with sizes [1].
2025-05-22 09:09:39,457 [INFO ] [mvce_dataset.py:<module>:166]	 Running all baseline imputators zero variance.
2025-05-22 09:09:39,457 [INFO ] [mvce_dataset.py:<module>:168]	 Running all baseline imputators with [0.5, 0.99] scaled beta max variance.
2025-05-22 09:09:39,457 [INFO ] [mvce_dataset.py:<module>:184]	 Adding 8 methods.
2025-05-22 09:09:39,457 [INFO ] [mvce_dataset.py:<module>:192]	 Using masked dataset with mask {'name': 'bottom'}
2025-05-22 09:09:39,457 [INFO ] [mvce_dataset.py:<module>:208]	 Considering action space zero-one
2025-05-22 09:09:39,457 [INFO ] [threading_utils.py:distributeTasks:95]	 Running 8 tasks on 4 threads
2025-05-22 09:09:39,458 [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 09:09:39,458 [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 09:09:39,458 [INFO ] [calibration.py:__call__:223]	 Started running Basic Imputation - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one
2025-05-22 09:09:39,459 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.99 - Zero Imputation missing bottom in zero-one
2025-05-22 09:10:06,705 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.029535481706261635 for Beta Mean * 0.99 - Zero Imputation in 27.24214226473123 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 234,   62,   29,   39, 1636], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5645, 0.6320, 0.7442, 0.8531, 0.9924])
        * Final bucket consistencies: tensor([0.7778, 0.7419, 0.6897, 0.8462, 0.9921])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1760,  240], dtype=torch.int32)
        * Average consistency: 0.9520000219345093
    
2025-05-22 09:10:38,226 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03127747401595116 for Beta Mean * 0.99 - Zero Imputation in 31.473212860524654 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 239,   58,   28,   38, 1637], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5667, 0.6316, 0.7456, 0.8510, 0.9924])
        * Final bucket consistencies: tensor([0.7699, 0.8103, 0.7143, 0.7895, 0.9927])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1760,  240], dtype=torch.int32)
        * Average consistency: 0.953000009059906
    
2025-05-22 09:11:10,531 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.030626904219388962 for Beta Mean * 0.99 - Zero Imputation in 32.29073451831937 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 244,   54,   30,   36, 1636], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5657, 0.6333, 0.7532, 0.8553, 0.9925])
        * Final bucket consistencies: tensor([0.7746, 0.7778, 0.7000, 0.8611, 0.9921])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1762,  238], dtype=torch.int32)
        * Average consistency: 0.953000009059906
    
2025-05-22 09:11:43,359 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03034997545182705 for Beta Mean * 0.99 - Zero Imputation in 32.82074286788702 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 238,   60,   25,   37, 1640], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5653, 0.6345, 0.7426, 0.8476, 0.9923])
        * Final bucket consistencies: tensor([0.7689, 0.7833, 0.6800, 0.8108, 0.9921])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1762,  238], dtype=torch.int32)
        * Average consistency: 0.9520000219345093
    
2025-05-22 09:11:43,361 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.99 - Zero Imputation missing bottom in zero-one in 123.90223742835224 seconds
2025-05-22 09:11:43,362 [INFO ] [calibration.py:__call__:223]	 Started running Beta Mean * 0.5 - Zero Imputation missing bottom in zero-one
2025-05-22 09:12:19,224 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.02800205536186695 for Beta Mean * 0.5 - Zero Imputation in 35.855543351732194 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  29,   35,   39,   60, 1837], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5415, 0.6445, 0.7443, 0.8613, 0.9913])
        * Final bucket consistencies: tensor([0.4483, 0.3714, 0.6667, 0.7500, 0.9728])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1690,  310], dtype=torch.int32)
        * Average consistency: 0.9419999718666077
    
2025-05-22 09:12:50,139 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.02801496721804142 for Beta Mean * 0.5 - Zero Imputation in 30.91117190849036 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  29,   37,   36,   58, 1840], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5427, 0.6471, 0.7477, 0.8567, 0.9912])
        * Final bucket consistencies: tensor([0.4138, 0.3784, 0.6667, 0.7586, 0.9728])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1690,  310], dtype=torch.int32)
        * Average consistency: 0.9419999718666077
    
2025-05-22 09:13:20,452 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.028549998998641968 for Beta Mean * 0.5 - Zero Imputation in 30.307919543236494 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  31,   34,   39,   55, 1841], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5471, 0.6463, 0.7508, 0.8579, 0.9911])
        * Final bucket consistencies: tensor([0.3548, 0.4412, 0.6923, 0.7273, 0.9723])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1689,  311], dtype=torch.int32)
        * Average consistency: 0.9415000081062317
    
2025-05-22 09:13:49,601 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.028707008808851242 for Beta Mean * 0.5 - Zero Imputation in 29.13883361313492 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  28,   39,   35,   55, 1843], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5506, 0.6478, 0.7512, 0.8541, 0.9910])
        * Final bucket consistencies: tensor([0.3929, 0.4103, 0.6857, 0.6909, 0.9734])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1687,  313], dtype=torch.int32)
        * Average consistency: 0.9415000081062317
    
2025-05-22 09:13:49,602 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.5 - Zero Imputation missing bottom in zero-one in 126.24012821353972 seconds
2025-05-22 09:13:49,602 [INFO ] [calibration.py:__call__:223]	 Started running Basic Imputation - Zero Imputation missing bottom in zero-one
2025-05-22 09:14:17,669 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.05849999189376831 for Basic Imputation - Zero Imputation in 28.061445945873857 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([0.9415])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1687,  313], dtype=torch.int32)
        * Average consistency: 0.9415000081062317
    
2025-05-22 09:14:41,885 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.05849999189376831 for Basic Imputation - Zero Imputation in 24.21287194173783 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([0.9415])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1687,  313], dtype=torch.int32)
        * Average consistency: 0.9415000081062317
    
2025-05-22 09:15:03,295 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.05849999189376831 for Basic Imputation - Zero Imputation in 21.405267763882875 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([0.9415])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1687,  313], dtype=torch.int32)
        * Average consistency: 0.9415000081062317
    
2025-05-22 09:15:25,595 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.05849999189376831 for Basic Imputation - Zero Imputation in 22.290270019322634 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([0.9415])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1687,  313], dtype=torch.int32)
        * Average consistency: 0.9415000081062317
    
2025-05-22 09:15:25,596 [INFO ] [calibration.py:__call__:249]	 Finished running Basic Imputation - Zero Imputation missing bottom in zero-one in 95.99301377683878 seconds
2025-05-22 09:15:25,596 [INFO ] [calibration.py:__call__:223]	 Started running Monte Carlo - Caching Generator - 30 samples missing bottom in zero-one
2025-05-22 09:24:54,071 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03799998760223389 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 914.6082886746153 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([0.9620])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1752,  248], dtype=torch.int32)
        * Average consistency: 0.9620000123977661
    
2025-05-22 09:24:54,107 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03484547883272171 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 914.6418787641451 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 188,   48,   24,   29, 1711], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5638, 0.6269, 0.7512, 0.8548, 0.9965])
        * Final bucket consistencies: tensor([0.7979, 0.6875, 0.5000, 0.6897, 0.9895])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1806,  194], dtype=torch.int32)
        * Average consistency: 0.9539999961853027
    
2025-05-22 09:24:54,137 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.018984392285346985 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 914.6753846826032 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  22,   24,   31,   47, 1876], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5376, 0.6509, 0.7571, 0.8580, 0.9954])
        * Final bucket consistencies: tensor([0.6364, 0.5000, 0.6452, 0.7021, 0.9840])
        * 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: 0.9624999761581421
    
2025-05-22 09:31:55,848 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.003745997790247202 for Monte Carlo - Caching Generator - 30 samples in 990.2510822257027 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  28,   37,   33,   57, 1845], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5463, 0.6662, 0.7486, 0.8612, 0.9969])
        * Final bucket consistencies: tensor([0.5714, 0.6216, 0.7273, 0.8246, 0.9957])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1761,  239], dtype=torch.int32)
        * Average consistency: 0.9735000133514404
    
2025-05-22 09:42:55,308 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03799998760223389 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1081.1737849228084 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([0.9620])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1752,  248], dtype=torch.int32)
        * Average consistency: 0.9620000123977661
    
2025-05-22 09:42:55,413 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.019038459286093712 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1081.2732722321525 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  23,   23,   32,   46, 1876], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5346, 0.6465, 0.7533, 0.8581, 0.9954])
        * Final bucket consistencies: tensor([0.4348, 0.4783, 0.6875, 0.6739, 0.9840])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1754,  246], dtype=torch.int32)
        * Average consistency: 0.9599999785423279
    
2025-05-22 09:42:55,423 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03612406551837921 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1081.2892897604033 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 190,   45,   22,   31, 1712], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5652, 0.6274, 0.7464, 0.8535, 0.9965])
        * Final bucket consistencies: tensor([0.7947, 0.7111, 0.4091, 0.6774, 0.9895])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1805,  195], dtype=torch.int32)
        * Average consistency: 0.953499972820282
    
2025-05-22 09:48:25,819 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.004492020234465599 for Monte Carlo - Caching Generator - 30 samples in 989.8045482672751 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  28,   30,   41,   54, 1847], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5434, 0.6562, 0.7447, 0.8610, 0.9968])
        * Final bucket consistencies: tensor([0.5000, 0.5667, 0.7561, 0.7963, 0.9962])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1759,  241], dtype=torch.int32)
        * Average consistency: 0.9725000262260437
    
2025-05-22 09:59:59,567 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03799998760223389 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1024.1455548461527 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([0.9620])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1752,  248], dtype=torch.int32)
        * Average consistency: 0.9620000123977661
    
2025-05-22 09:59:59,650 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.018091998994350433 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1024.2295532282442 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  21,   27,   29,   49, 1874], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5353, 0.6492, 0.7518, 0.8601, 0.9955])
        * Final bucket consistencies: tensor([0.5714, 0.5185, 0.7586, 0.6531, 0.9840])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1752,  248], dtype=torch.int32)
        * Average consistency: 0.9620000123977661
    
2025-05-22 09:59:59,657 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03727858141064644 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1024.2307495586574 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 193,   41,   24,   32, 1710], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5648, 0.6279, 0.7488, 0.8539, 0.9965])
        * Final bucket consistencies: tensor([0.8083, 0.7073, 0.4583, 0.6250, 0.9906])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1804,  196], dtype=torch.int32)
        * Average consistency: 0.9549999833106995
    
2025-05-22 10:05:12,182 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0036628651432693005 for Monte Carlo - Caching Generator - 30 samples in 1006.3392688883469 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  28,   35,   32,   57, 1848], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5504, 0.6565, 0.7468, 0.8565, 0.9968])
        * Final bucket consistencies: tensor([0.5000, 0.6286, 0.7500, 0.8070, 0.9957])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1758,  242], dtype=torch.int32)
        * Average consistency: 0.9729999899864197
    
2025-05-22 10:17:09,209 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03799998760223389 for Basic Imputation - 1 Sample Mean Caching Generator Imputation in 1029.6375743309036 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([0.9620])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1752,  248], dtype=torch.int32)
        * Average consistency: 0.9620000123977661
    
2025-05-22 10:17:09,245 [INFO ] [calibration.py:__call__:249]	 Finished running Basic Imputation - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one in 4049.786168342456 seconds
2025-05-22 10:17:09,245 [INFO ] [calibration.py:__call__:223]	 Started running Monte Carlo - Caching Generator - 3 samples missing bottom in zero-one
2025-05-22 10:17:09,305 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03500348702073097 for Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation in 1029.6403467757627 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 198,   36,   25,   29, 1712], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5633, 0.6296, 0.7449, 0.8522, 0.9964])
        * Final bucket consistencies: tensor([0.7980, 0.6389, 0.5200, 0.6552, 0.9895])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1807,  193], dtype=torch.int32)
        * Average consistency: 0.953499972820282
    
2025-05-22 10:17:09,306 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.99 - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one in 4049.847636440769 seconds
2025-05-22 10:17:09,306 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 1 stopping
2025-05-22 10:17:09,313 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.007277491502463818 for Monte Carlo - Caching Generator - 30 samples in 717.1139851026237 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  29,   39,   29,   58, 1845], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5469, 0.6662, 0.7484, 0.8621, 0.9970])
        * Final bucket consistencies: tensor([0.4483, 0.5897, 0.8621, 0.7931, 0.9962])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1759,  241], dtype=torch.int32)
        * Average consistency: 0.9725000262260437
    
2025-05-22 10:17:09,313 [INFO ] [calibration.py:__call__:249]	 Finished running Monte Carlo - Caching Generator - 30 samples missing bottom in zero-one in 3703.71712524537 seconds
2025-05-22 10:17:09,313 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 4 stopping
2025-05-22 10:17:09,316 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.018150825053453445 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1029.663584713824 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  23,   23,   30,   48, 1876], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5390, 0.6497, 0.7452, 0.8535, 0.9954])
        * Final bucket consistencies: tensor([0.6087, 0.5217, 0.7333, 0.6667, 0.9835])
        * 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: 0.9624999761581421
    
2025-05-22 10:17:09,316 [INFO ] [calibration.py:__call__:249]	 Finished running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one in 4049.857701715082 seconds
2025-05-22 10:17:09,316 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 2 stopping
2025-05-22 10:31:10,777 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.01517543662339449 for Monte Carlo - Caching Generator - 3 samples in 841.5307834222913 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  27,   32,   35,   25, 1881], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5490, 0.6477, 0.7594, 0.8482, 0.9988])
        * Final bucket consistencies: tensor([0.6296, 0.6875, 0.7714, 0.9200, 0.9856])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1753,  247], dtype=torch.int32)
        * Average consistency: 0.9714999794960022
    
2025-05-22 10:42:56,838 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.015123842284083366 for Monte Carlo - Caching Generator - 3 samples in 705.9717258289456 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  25,   36,   30,   28, 1881], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5443, 0.6462, 0.7615, 0.8481, 0.9988])
        * Final bucket consistencies: tensor([0.6000, 0.6944, 0.7667, 0.9286, 0.9856])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1753,  247], dtype=torch.int32)
        * Average consistency: 0.9714999794960022
    
2025-05-22 10:58:50,800 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.01528540626168251 for Monte Carlo - Caching Generator - 3 samples in 953.9255936853588 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  29,   31,   30,   29, 1881], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5508, 0.6557, 0.7559, 0.8469, 0.9988])
        * Final bucket consistencies: tensor([0.6207, 0.6774, 0.7333, 0.9310, 0.9856])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1752,  248], dtype=torch.int32)
        * Average consistency: 0.9710000157356262
    
2025-05-22 11:11:23,566 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.014617491513490677 for Monte Carlo - Caching Generator - 3 samples in 752.7466679243371 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  30,   28,   35,   27, 1880], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5511, 0.6531, 0.7551, 0.8510, 0.9989])
        * Final bucket consistencies: tensor([0.5667, 0.6429, 0.7714, 0.9630, 0.9856])
        * 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: 0.9704999923706055
    
2025-05-22 11:11:23,724 [INFO ] [calibration.py:__call__:249]	 Finished running Monte Carlo - Caching Generator - 3 samples missing bottom in zero-one in 3254.4787900941446 seconds
2025-05-22 11:11:23,725 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 3 stopping
2025-05-22 11:11:23,725 [INFO ] [mvce_dataset.py:<module>:222]	 Finished running 8/8 experiments.
2025-05-22 11:11:23,726 [INFO ] [mvce_dataset.py:<module>:227]	 Saving results to ./results/mvce-25/Blond_Hair/bottom/celeba-20250522-090933.csv
2025-05-22 11:11:24,080 [INFO ] [mvce_dataset.py:<module>:237]	 Finished saving results
