2025-05-23 01:30:41,024 [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, "calibration_map": "./results/calibration/celeba/Eyeglasses/celeba.csv", "mask": {"name": "top"}, "drop": null, "threads": 4, "generator_samples": [], "decision_samples": 1000, "cuda_index": 2, "zero_imputation": true, "zero_variance": false, "beta_variance_scales": [0.5], "action_spaces": [{"name": "zero-one"}], "class_count": 1, "batch_size": 100, "batch_mean_imputation": [1], "buckets": 10, "trials": 4, "seed": 1337, "verbose": 1}
2025-05-23 01:30:41,190 [INFO ] [util.py:selectDevice:88]	 Using cuda:2 for tensor calculations
2025-05-23 01:30:42,427 [INFO ] [mvce_dataset.py:<module>:93]	 Running on cuda:2
2025-05-23 01:30:43,703 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-23 01:30:50,616 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Eyeglasses']
2025-05-23 01:30:50,751 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-23 01:30:50,751 [INFO ] [mvce_dataset.py:<module>:106]	 Using dataset celeba with 2000 test samples and 1 classes
2025-05-23 01:30:50,775 [INFO ] [mvce_dataset.py:<module>:119]	 Found calibration map at ./results/calibration/celeba/Eyeglasses/celeba.csv: {'Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation': 0.1, 'Beta Mean * 0.5 - Zero Imputation': 0.25, 'Dirichlet Network': 1.0}
2025-05-23 01:30:50,776 [INFO ] [mvce_dataset.py:<module>:124]	 Loading mask top
2025-05-23 01:30:50,776 [INFO ] [mvce_dataset.py:<module>:141]	 Setting classifier activation function to sigmoid for single class
2025-05-23 01:30:50,776 [INFO ] [mvce_dataset.py:<module>:156]	 Creating generator using cache at ../../datasets/CelebAMask/cache/256/top_test
2025-05-23 01:30:50,780 [INFO ] [mvce_dataset.py:<module>:169]	 Including baseline imputators with zero imputation.
2025-05-23 01:30:50,780 [INFO ] [mvce_dataset.py:<module>:174]	 Including batch mean imputators with sizes [1].
2025-05-23 01:30:50,780 [INFO ] [mvce_dataset.py:<module>:181]	 Running all baseline imputators with [0.5] scaled beta max variance.
2025-05-23 01:30:50,780 [INFO ] [mvce_dataset.py:<module>:197]	 Adding 2 methods.
2025-05-23 01:30:50,780 [INFO ] [mvce_dataset.py:<module>:205]	 Using masked dataset with mask {'name': 'top'}
2025-05-23 01:30:50,781 [INFO ] [mvce_dataset.py:<module>:220]	 Considering action space zero-one
2025-05-23 01:30:50,781 [INFO ] [threading_utils.py:distributeTasks:91]	 Running 2 tasks each on their own thread
2025-05-23 01:30:50,781 [INFO ] [calibration.py:__call__:310]	 Started running Beta Mean * 0.5 - Zero Imputation missing top in zero-one
2025-05-23 01:30:50,782 [INFO ] [calibration.py:__call__:310]	 Started running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing top in zero-one
2025-05-23 01:31:09,245 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.11947000026702881 for Beta Mean * 0.5 - Zero Imputation in 18.46150105446577 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([212, 231, 306, 382, 869], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5501, 0.6508, 0.7517, 0.8534, 0.9597])
        * Final bucket consistencies: tensor([0.5472, 0.5974, 0.7157, 0.7539, 0.7560])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1331,  669], dtype=torch.int32)
        * Average consistency: 0.7089999914169312
    
2025-05-23 01:31:23,136 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.11929403245449066 for Beta Mean * 0.5 - Zero Imputation in 13.889626410789788 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([219, 228, 307, 377, 869], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5490, 0.6512, 0.7531, 0.8539, 0.9594])
        * Final bucket consistencies: tensor([0.5342, 0.6316, 0.6971, 0.7507, 0.7583])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1328,  672], dtype=torch.int32)
        * Average consistency: 0.7085000276565552
    
2025-05-23 01:31:36,526 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.12416999787092209 for Beta Mean * 0.5 - Zero Imputation in 13.388199361041188 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([219, 235, 293, 381, 872], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5513, 0.6519, 0.7528, 0.8525, 0.9593])
        * Final bucket consistencies: tensor([0.5799, 0.5872, 0.7167, 0.7533, 0.7546])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1333,  667], dtype=torch.int32)
        * Average consistency: 0.7099999785423279
    
2025-05-23 01:31:49,336 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.12079199403524399 for Beta Mean * 0.5 - Zero Imputation in 12.807818405330181 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([223, 220, 312, 378, 867], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5516, 0.6524, 0.7515, 0.8533, 0.9597])
        * Final bucket consistencies: tensor([0.5650, 0.6182, 0.6987, 0.7540, 0.7555])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1333,  667], dtype=torch.int32)
        * Average consistency: 0.7099999785423279
    
2025-05-23 01:31:49,336 [INFO ] [calibration.py:__call__:336]	 Finished running Beta Mean * 0.5 - Zero Imputation missing top in zero-one in 58.55454719439149 seconds
2025-05-23 01:35:20,771 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.003275682684034109 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 269.9881363110617 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   3,    4,    2, 1991], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6490, 0.7660, 0.8925, 0.9988])
        * Final bucket consistencies: tensor([0.6667, 0.5000, 0.5000, 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
    
2025-05-23 01:39:34,337 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.0033515903633087873 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 253.5642549097538 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   3,    3,    3, 1991], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6497, 0.7820, 0.8587, 0.9989])
        * Final bucket consistencies: tensor([0.6667, 0.6667, 0.3333, 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
    
2025-05-23 01:43:47,841 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.0032720693852752447 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 253.50248648319393 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   3,    3,    4, 1990], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6433, 0.7830, 0.8608, 0.9988])
        * Final bucket consistencies: tensor([0.6667, 0.3333, 0.7500, 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
    
2025-05-23 01:48:00,214 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.003261601086705923 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 252.3713288148865 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    5,    2, 1991], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6375, 0.7630, 0.8775, 0.9988])
        * Final bucket consistencies: tensor([0.5000, 0.6000, 0.5000, 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
    
2025-05-23 01:48:00,214 [INFO ] [calibration.py:__call__:336]	 Finished running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing top in zero-one in 1029.432130601257 seconds
2025-05-23 01:48:00,214 [INFO ] [mvce_dataset.py:<module>:234]	 Finished running 2/2 experiments.
2025-05-23 01:48:00,214 [INFO ] [mvce_dataset.py:<module>:239]	 Saving results to ./results/mvce-25/Eyeglasses/top/celeba-20250523-013041.csv
2025-05-23 01:48:00,216 [INFO ] [mvce_dataset.py:<module>:249]	 Finished saving results
