2025-05-23 02:32:30,480 [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/bottom_test", "output": "./results/mvce-25/Smiling/bottom/", "classifier": "./models/celeba/Smiling/celeba-20250508-133956.pklz", "classifier_feature": null, "calibration_map": "./results/calibration/celeba/Smiling/celeba.csv", "mask": {"name": "bottom"}, "drop": null, "threads": 4, "generator_samples": [], "decision_samples": 1000, "cuda_index": 1, "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 02:32:30,581 [INFO ] [util.py:selectDevice:88]	 Using cuda:1 for tensor calculations
2025-05-23 02:32:30,808 [INFO ] [mvce_dataset.py:<module>:93]	 Running on cuda:1
2025-05-23 02:32:31,156 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-23 02:32:38,933 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Smiling']
2025-05-23 02:32:39,006 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-23 02:32:39,006 [INFO ] [mvce_dataset.py:<module>:106]	 Using dataset celeba with 2000 test samples and 1 classes
2025-05-23 02:32:39,009 [INFO ] [mvce_dataset.py:<module>:119]	 Found calibration map at ./results/calibration/celeba/Smiling/celeba.csv: {'Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation': 0.1, 'Beta Mean * 0.5 - Zero Imputation': 0.25, 'Dirichlet Network': 2.5}
2025-05-23 02:32:39,009 [INFO ] [mvce_dataset.py:<module>:124]	 Loading mask bottom
2025-05-23 02:32:39,009 [INFO ] [mvce_dataset.py:<module>:141]	 Setting classifier activation function to sigmoid for single class
2025-05-23 02:32:39,009 [INFO ] [mvce_dataset.py:<module>:156]	 Creating generator using cache at ../../datasets/CelebAMask/cache/256/bottom_test
2025-05-23 02:32:39,011 [INFO ] [mvce_dataset.py:<module>:169]	 Including baseline imputators with zero imputation.
2025-05-23 02:32:39,011 [INFO ] [mvce_dataset.py:<module>:174]	 Including batch mean imputators with sizes [1].
2025-05-23 02:32:39,011 [INFO ] [mvce_dataset.py:<module>:181]	 Running all baseline imputators with [0.5] scaled beta max variance.
2025-05-23 02:32:39,011 [INFO ] [mvce_dataset.py:<module>:197]	 Adding 2 methods.
2025-05-23 02:32:39,011 [INFO ] [mvce_dataset.py:<module>:205]	 Using masked dataset with mask {'name': 'bottom'}
2025-05-23 02:32:39,011 [INFO ] [mvce_dataset.py:<module>:220]	 Considering action space zero-one
2025-05-23 02:32:39,011 [INFO ] [threading_utils.py:distributeTasks:91]	 Running 2 tasks each on their own thread
2025-05-23 02:32:39,012 [INFO ] [calibration.py:__call__:310]	 Started running Beta Mean * 0.5 - Zero Imputation missing bottom in zero-one
2025-05-23 02:32:39,012 [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 02:32:54,496 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.40288859605789185 for Beta Mean * 0.5 - Zero Imputation in 15.481096449308097 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  79,   65,  100,  158, 1598], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5502, 0.6491, 0.7500, 0.8550, 0.9875])
        * Final bucket consistencies: tensor([0.4810, 0.4615, 0.5100, 0.4241, 0.5519])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 257, 1743], dtype=torch.int32)
        * Average consistency: 0.5339999794960022
    
2025-05-23 02:33:08,503 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.40258991718292236 for Beta Mean * 0.5 - Zero Imputation in 14.004712454974651 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  80,   60,  105,  154, 1601], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5522, 0.6489, 0.7502, 0.8561, 0.9872])
        * Final bucket consistencies: tensor([0.4750, 0.5167, 0.4857, 0.4286, 0.5515])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 260, 1740], dtype=torch.int32)
        * Average consistency: 0.534500002861023
    
2025-05-23 02:33:21,873 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.4041234850883484 for Beta Mean * 0.5 - Zero Imputation in 13.368721858598292 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  77,   71,   94,  154, 1604], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5510, 0.6516, 0.7506, 0.8524, 0.9874])
        * Final bucket consistencies: tensor([0.4545, 0.4648, 0.4574, 0.4545, 0.5517])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 261, 1739], dtype=torch.int32)
        * Average consistency: 0.5329999923706055
    
2025-05-23 02:33:35,619 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.4031185209751129 for Beta Mean * 0.5 - Zero Imputation in 13.726108400151134 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  80,   61,  103,  148, 1608], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5520, 0.6491, 0.7487, 0.8531, 0.9870])
        * Final bucket consistencies: tensor([0.4625, 0.4590, 0.4563, 0.4932, 0.5491])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 259, 1741], dtype=torch.int32)
        * Average consistency: 0.5339999794960022
    
2025-05-23 02:33:35,619 [INFO ] [calibration.py:__call__:336]	 Finished running Beta Mean * 0.5 - Zero Imputation missing bottom in zero-one in 56.60728141665459 seconds
2025-05-23 02:38:33,621 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.19004644453525543 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 354.60733213834465 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  46,   48,   64,  101, 1741], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5538, 0.6571, 0.7533, 0.8591, 0.9903])
        * Final bucket consistencies: tensor([0.5000, 0.6042, 0.5781, 0.4950, 0.8024])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 967, 1033], dtype=torch.int32)
        * Average consistency: 0.7680000066757202
    
2025-05-23 02:43:48,461 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.1906219869852066 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 314.8382997298613 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  41,   52,   62,  103, 1742], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5507, 0.6528, 0.7528, 0.8559, 0.9902])
        * Final bucket consistencies: tensor([0.5122, 0.5385, 0.5484, 0.5437, 0.8014])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 964, 1036], dtype=torch.int32)
        * Average consistency: 0.7674999833106995
    
2025-05-23 02:48:41,973 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.1897975206375122 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 293.51075995340943 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  43,   49,   68,   86, 1754], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5515, 0.6537, 0.7561, 0.8535, 0.9898])
        * Final bucket consistencies: tensor([0.5116, 0.5918, 0.5735, 0.4884, 0.8010])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 966, 1034], dtype=torch.int32)
        * Average consistency: 0.7684999704360962
    
2025-05-23 02:53:30,396 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.19031599164009094 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 288.4218484153971 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  45,   48,   63,   96, 1748], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5544, 0.6602, 0.7520, 0.8555, 0.9900])
        * Final bucket consistencies: tensor([0.4889, 0.6042, 0.6190, 0.4479, 0.8026])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 965, 1035], dtype=torch.int32)
        * Average consistency: 0.7680000066757202
    
2025-05-23 02:53:30,397 [INFO ] [calibration.py:__call__:336]	 Finished running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one in 1251.3844493133947 seconds
2025-05-23 02:53:30,397 [INFO ] [mvce_dataset.py:<module>:234]	 Finished running 2/2 experiments.
2025-05-23 02:53:30,397 [INFO ] [mvce_dataset.py:<module>:239]	 Saving results to ./results/mvce-25/Smiling/bottom/celeba-20250523-023230.csv
2025-05-23 02:53:30,398 [INFO ] [mvce_dataset.py:<module>:249]	 Finished saving results
