2025-05-23 02:32:13,167 [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, "calibration_map": "./results/calibration/celeba/Smiling/celeba.csv", "mask": {"name": "top"}, "drop": null, "threads": 4, "generator_samples": [], "decision_samples": 1000, "cuda_index": 0, "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 02:32:13,280 [INFO ] [util.py:selectDevice:88]	 Using cuda:0 for tensor calculations
2025-05-23 02:32:13,512 [INFO ] [mvce_dataset.py:<module>:93]	 Running on cuda:0
2025-05-23 02:32:13,860 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-23 02:32:20,855 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Smiling']
2025-05-23 02:32:20,926 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-23 02:32:20,927 [INFO ] [mvce_dataset.py:<module>:106]	 Using dataset celeba with 2000 test samples and 1 classes
2025-05-23 02:32:20,929 [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 02:32:20,929 [INFO ] [mvce_dataset.py:<module>:124]	 Loading mask top
2025-05-23 02:32:20,929 [INFO ] [mvce_dataset.py:<module>:141]	 Setting classifier activation function to sigmoid for single class
2025-05-23 02:32:20,929 [INFO ] [mvce_dataset.py:<module>:156]	 Creating generator using cache at ../../datasets/CelebAMask/cache/256/top_test
2025-05-23 02:32:20,931 [INFO ] [mvce_dataset.py:<module>:169]	 Including baseline imputators with zero imputation.
2025-05-23 02:32:20,931 [INFO ] [mvce_dataset.py:<module>:174]	 Including batch mean imputators with sizes [1].
2025-05-23 02:32:20,931 [INFO ] [mvce_dataset.py:<module>:181]	 Running all baseline imputators with [0.5] scaled beta max variance.
2025-05-23 02:32:20,931 [INFO ] [mvce_dataset.py:<module>:197]	 Adding 2 methods.
2025-05-23 02:32:20,931 [INFO ] [mvce_dataset.py:<module>:205]	 Using masked dataset with mask {'name': 'top'}
2025-05-23 02:32:20,931 [INFO ] [mvce_dataset.py:<module>:220]	 Considering action space zero-one
2025-05-23 02:32:20,931 [INFO ] [threading_utils.py:distributeTasks:91]	 Running 2 tasks each on their own thread
2025-05-23 02:32:20,932 [INFO ] [calibration.py:__call__:310]	 Started running Beta Mean * 0.5 - Zero Imputation missing top in zero-one
2025-05-23 02:32:20,932 [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 02:32:34,609 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.02164054848253727 for Beta Mean * 0.5 - Zero Imputation in 13.675285693258047 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  29,   31,   48,   72, 1820], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5494, 0.6504, 0.7538, 0.8576, 0.9910])
        * Final bucket consistencies: tensor([0.4483, 0.6774, 0.5625, 0.7083, 0.9802])
        * 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
    
2025-05-23 02:32:47,363 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.02149241231381893 for Beta Mean * 0.5 - Zero Imputation in 12.751417743973434 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  29,   31,   50,   73, 1817], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5472, 0.6510, 0.7537, 0.8595, 0.9909])
        * Final bucket consistencies: tensor([0.4138, 0.6129, 0.5600, 0.7260, 0.9807])
        * 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
    
2025-05-23 02:32:59,723 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.020800495520234108 for Beta Mean * 0.5 - Zero Imputation in 12.358310980722308 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  29,   31,   45,   77, 1818], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5508, 0.6540, 0.7508, 0.8576, 0.9909])
        * Final bucket consistencies: tensor([0.4483, 0.6129, 0.5778, 0.7273, 0.9802])
        * 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
    
2025-05-23 02:33:11,905 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.021120568737387657 for Beta Mean * 0.5 - Zero Imputation in 12.180763801559806 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  30,   30,   48,   76, 1816], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5521, 0.6478, 0.7535, 0.8603, 0.9910])
        * Final bucket consistencies: tensor([0.4000, 0.6333, 0.5833, 0.7237, 0.9807])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([1008,  992], dtype=torch.int32)
        * Average consistency: 0.9474999904632568
    
2025-05-23 02:33:11,922 [INFO ] [calibration.py:__call__:336]	 Finished running Beta Mean * 0.5 - Zero Imputation missing top in zero-one in 50.990210734307766 seconds
2025-05-23 02:37:37,655 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.004480121657252312 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 316.72194193769246 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  42,   43,   47,   98, 1770], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5459, 0.6626, 0.7555, 0.8570, 0.9903])
        * Final bucket consistencies: tensor([0.5476, 0.6279, 0.7660, 0.8776, 0.9876])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 991, 1009], dtype=torch.int32)
        * Average consistency: 0.9599999785423279
    
2025-05-23 02:42:05,135 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.00865113828331232 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 267.47700276970863 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  44,   41,   52,   92, 1771], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5498, 0.6633, 0.7587, 0.8604, 0.9903])
        * Final bucket consistencies: tensor([0.5682, 0.6098, 0.8077, 0.9022, 0.9859])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 994, 1006], dtype=torch.int32)
        * Average consistency: 0.9605000019073486
    
2025-05-23 02:46:37,002 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.009661517105996609 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 271.8659909153357 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  44,   44,   45,   93, 1774], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5506, 0.6646, 0.7578, 0.8571, 0.9902])
        * Final bucket consistencies: tensor([0.5000, 0.6364, 0.7778, 0.9247, 0.9853])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 990, 1010], dtype=torch.int32)
        * Average consistency: 0.9595000147819519
    
2025-05-23 02:51:04,778 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.006910995580255985 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 267.7745614564046 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  44,   38,   53,   95, 1770], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5522, 0.6577, 0.7534, 0.8591, 0.9905])
        * Final bucket consistencies: tensor([0.5000, 0.6842, 0.7358, 0.8947, 0.9870])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1015,  985], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 990, 1010], dtype=torch.int32)
        * Average consistency: 0.9595000147819519
    
2025-05-23 02:51:04,779 [INFO ] [calibration.py:__call__:336]	 Finished running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing top in zero-one in 1123.8464172035456 seconds
2025-05-23 02:51:04,779 [INFO ] [mvce_dataset.py:<module>:234]	 Finished running 2/2 experiments.
2025-05-23 02:51:04,779 [INFO ] [mvce_dataset.py:<module>:239]	 Saving results to ./results/mvce-25/Smiling/top/celeba-20250523-023213.csv
2025-05-23 02:51:04,780 [INFO ] [mvce_dataset.py:<module>:249]	 Finished saving results
