2025-05-23 01:30:43,282 [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, "calibration_map": "./results/calibration/celeba/Eyeglasses/celeba.csv", "mask": {"name": "bottom"}, "drop": null, "threads": 4, "generator_samples": [], "decision_samples": 1000, "cuda_index": 3, "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 01:30:43,370 [INFO ] [util.py:selectDevice:88]	 Using cuda:3 for tensor calculations
2025-05-23 01:30:43,609 [INFO ] [mvce_dataset.py:<module>:93]	 Running on cuda:3
2025-05-23 01:30:43,944 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-23 01:30:50,790 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Eyeglasses']
2025-05-23 01:30:50,863 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-23 01:30:50,863 [INFO ] [mvce_dataset.py:<module>:106]	 Using dataset celeba with 2000 test samples and 1 classes
2025-05-23 01:30:50,865 [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 01:30:50,865 [INFO ] [mvce_dataset.py:<module>:124]	 Loading mask bottom
2025-05-23 01:30:50,868 [INFO ] [mvce_dataset.py:<module>:141]	 Setting classifier activation function to sigmoid for single class
2025-05-23 01:30:50,868 [INFO ] [mvce_dataset.py:<module>:156]	 Creating generator using cache at ../../datasets/CelebAMask/cache/256/bottom_test
2025-05-23 01:30:50,870 [INFO ] [mvce_dataset.py:<module>:169]	 Including baseline imputators with zero imputation.
2025-05-23 01:30:50,870 [INFO ] [mvce_dataset.py:<module>:174]	 Including batch mean imputators with sizes [1].
2025-05-23 01:30:50,870 [INFO ] [mvce_dataset.py:<module>:181]	 Running all baseline imputators with [0.5] scaled beta max variance.
2025-05-23 01:30:50,870 [INFO ] [mvce_dataset.py:<module>:197]	 Adding 2 methods.
2025-05-23 01:30:50,870 [INFO ] [mvce_dataset.py:<module>:205]	 Using masked dataset with mask {'name': 'bottom'}
2025-05-23 01:30:50,870 [INFO ] [mvce_dataset.py:<module>:220]	 Considering action space zero-one
2025-05-23 01:30:50,870 [INFO ] [threading_utils.py:distributeTasks:91]	 Running 2 tasks each on their own thread
2025-05-23 01:30:50,871 [INFO ] [calibration.py:__call__:310]	 Started running Beta Mean * 0.5 - Zero Imputation missing bottom in zero-one
2025-05-23 01:30:50,871 [INFO ] [calibration.py:__call__:310]	 Started running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one
2025-05-23 01:31:09,762 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.035340528935194016 for Beta Mean * 0.5 - Zero Imputation in 18.88877988886088 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 102,  126,  177,  360, 1235], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5544, 0.6513, 0.7520, 0.8583, 0.9653])
        * Final bucket consistencies: tensor([0.4804, 0.6349, 0.7966, 0.8667, 0.9247])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1637,  363], dtype=torch.int32)
        * Average consistency: 0.8619999885559082
    
2025-05-23 01:31:24,555 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.03935346379876137 for Beta Mean * 0.5 - Zero Imputation in 14.791221584193408 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 101,  129,  177,  341, 1252], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5540, 0.6521, 0.7526, 0.8566, 0.9648])
        * Final bucket consistencies: tensor([0.4455, 0.6512, 0.7797, 0.8827, 0.9217])
        * 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
    
2025-05-23 01:31:38,283 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.03780648484826088 for Beta Mean * 0.5 - Zero Imputation in 13.726591188460588 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 104,  128,  170,  350, 1248], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5532, 0.6533, 0.7514, 0.8559, 0.9646])
        * Final bucket consistencies: tensor([0.4615, 0.6250, 0.8000, 0.8657, 0.9239])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Prediction Action Counts: tensor([1633,  367], dtype=torch.int32)
        * Average consistency: 0.8600000143051147
    
2025-05-23 01:31:51,716 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.03780147433280945 for Beta Mean * 0.5 - Zero Imputation in 13.430766416713595 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  98,  133,  171,  356, 1242], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5528, 0.6521, 0.7524, 0.8577, 0.9651])
        * Final bucket consistencies: tensor([0.4694, 0.6391, 0.7778, 0.8792, 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
    
2025-05-23 01:31:51,716 [INFO ] [calibration.py:__call__:336]	 Finished running Beta Mean * 0.5 - Zero Imputation missing bottom in zero-one in 60.845005492679775 seconds
2025-05-23 01:35:25,588 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.004797053523361683 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 274.7151497239247 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    1,    1,    6, 1990], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5655, 0.6730, 0.7940, 0.8577, 0.9988])
        * Final bucket consistencies: tensor([1.0000, 0.0000, 1.0000, 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
    
2025-05-23 01:39:36,006 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.0048180813901126385 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 250.41661503631622 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    1,    1,    6, 1990], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5525, 0.6430, 0.7610, 0.8635, 0.9988])
        * Final bucket consistencies: tensor([1.0000, 0.0000, 1.0000, 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
    
2025-05-23 01:43:46,343 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.004800050985068083 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 250.335396559909 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    1,    1,    6, 1990], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5600, 0.6620, 0.7920, 0.8668, 0.9988])
        * Final bucket consistencies: tensor([1.0000, 0.0000, 1.0000, 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
    
2025-05-23 01:47:54,789 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.004820043686777353 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 248.44512100331485 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    1,    1,    6, 1990], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5695, 0.6880, 0.7830, 0.8603, 0.9989])
        * Final bucket consistencies: tensor([1.0000, 0.0000, 1.0000, 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
    
2025-05-23 01:47:54,789 [INFO ] [calibration.py:__call__:336]	 Finished running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one in 1023.9182422244921 seconds
2025-05-23 01:47:54,790 [INFO ] [mvce_dataset.py:<module>:234]	 Finished running 2/2 experiments.
2025-05-23 01:47:54,790 [INFO ] [mvce_dataset.py:<module>:239]	 Saving results to ./results/mvce-25/Eyeglasses/bottom/celeba-20250523-013043.csv
2025-05-23 01:47:54,791 [INFO ] [mvce_dataset.py:<module>:249]	 Finished saving results
