2025-05-23 01:06:19,661 [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/top_test", "output": "./results/mvce-25/Blond_Hair/top/", "classifier": "./models/celeba/Blond_Hair/celeba-20250508-133714.pklz", "classifier_feature": null, "calibration_map": "./results/calibration/celeba/Blond_Hair/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 01:06:19,890 [INFO ] [util.py:selectDevice:88]	 Using cuda:0 for tensor calculations
2025-05-23 01:06:21,157 [INFO ] [mvce_dataset.py:<module>:93]	 Running on cuda:0
2025-05-23 01:06:21,935 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-23 01:06:29,002 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Blond_Hair']
2025-05-23 01:06:29,098 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-23 01:06:29,098 [INFO ] [mvce_dataset.py:<module>:106]	 Using dataset celeba with 2000 test samples and 1 classes
2025-05-23 01:06:29,159 [INFO ] [mvce_dataset.py:<module>:119]	 Found calibration map at ./results/calibration/celeba/Blond_Hair/celeba.csv: {'Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation': 0.5, 'Beta Mean * 0.5 - Zero Imputation': 0.25, 'Dirichlet Network': 7.5}
2025-05-23 01:06:29,159 [INFO ] [mvce_dataset.py:<module>:124]	 Loading mask top
2025-05-23 01:06:29,177 [INFO ] [mvce_dataset.py:<module>:141]	 Setting classifier activation function to sigmoid for single class
2025-05-23 01:06:29,177 [INFO ] [mvce_dataset.py:<module>:156]	 Creating generator using cache at ../../datasets/CelebAMask/cache/256/top_test
2025-05-23 01:06:29,219 [INFO ] [mvce_dataset.py:<module>:169]	 Including baseline imputators with zero imputation.
2025-05-23 01:06:29,219 [INFO ] [mvce_dataset.py:<module>:174]	 Including batch mean imputators with sizes [1].
2025-05-23 01:06:29,219 [INFO ] [mvce_dataset.py:<module>:181]	 Running all baseline imputators with [0.5] scaled beta max variance.
2025-05-23 01:06:29,219 [INFO ] [mvce_dataset.py:<module>:197]	 Adding 2 methods.
2025-05-23 01:06:29,220 [INFO ] [mvce_dataset.py:<module>:205]	 Using masked dataset with mask {'name': 'top'}
2025-05-23 01:06:29,220 [INFO ] [mvce_dataset.py:<module>:220]	 Considering action space zero-one
2025-05-23 01:06:29,220 [INFO ] [threading_utils.py:distributeTasks:91]	 Running 2 tasks each on their own thread
2025-05-23 01:06:29,221 [INFO ] [calibration.py:__call__:310]	 Started running Beta Mean * 0.5 - Zero Imputation missing top in zero-one
2025-05-23 01:06:29,222 [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 01:07:34,131 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.09652850031852722 for Beta Mean * 0.5 - Zero Imputation in 64.75604718830436 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   9,   18,   16,   24, 1933], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5314, 0.6523, 0.7683, 0.8574, 0.9975])
        * Final bucket consistencies: tensor([0.5556, 0.3889, 0.3125, 0.2917, 0.9110])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1963,   37], dtype=torch.int32)
        * Average consistency: 0.8924999833106995
    
2025-05-23 01:07:46,016 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.09708696603775024 for Beta Mean * 0.5 - Zero Imputation in 11.882922742515802 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   9,   17,   14,   29, 1931], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5324, 0.6549, 0.7595, 0.8530, 0.9977])
        * Final bucket consistencies: tensor([0.6667, 0.4118, 0.4286, 0.2069, 0.9120])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1962,   38], dtype=torch.int32)
        * Average consistency: 0.8930000066757202
    
2025-05-23 01:07:59,404 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.09796702116727829 for Beta Mean * 0.5 - Zero Imputation in 13.386087419465184 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  10,   16,   15,   28, 1931], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5388, 0.6584, 0.7627, 0.8551, 0.9976])
        * Final bucket consistencies: tensor([0.8000, 0.3750, 0.3333, 0.2500, 0.9120])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1961,   39], dtype=torch.int32)
        * Average consistency: 0.8934999704360962
    
2025-05-23 01:08:12,330 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.0971209704875946 for Beta Mean * 0.5 - Zero Imputation in 12.923772718757391 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   9,   16,   16,   26, 1933], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5256, 0.6516, 0.7558, 0.8509, 0.9976])
        * Final bucket consistencies: tensor([0.6667, 0.3125, 0.5000, 0.1923, 0.9115])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1962,   38], dtype=torch.int32)
        * Average consistency: 0.8930000066757202
    
2025-05-23 01:08:12,331 [INFO ] [calibration.py:__call__:336]	 Finished running Beta Mean * 0.5 - Zero Imputation missing top in zero-one in 103.10951276589185 seconds
2025-05-23 01:24:25,919 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.04520757123827934 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 1076.6950299944729 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  16,   29,   33,   52, 1870], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5502, 0.6502, 0.7525, 0.8555, 0.9949])
        * Final bucket consistencies: tensor([0.5000, 0.4138, 0.6061, 0.5577, 0.9615])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1769,  231], dtype=torch.int32)
        * Average consistency: 0.9334999918937683
    
2025-05-23 01:29:50,089 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.04498794674873352 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 324.14182943478227 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  16,   26,   38,   51, 1869], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5501, 0.6490, 0.7560, 0.8591, 0.9948])
        * Final bucket consistencies: tensor([0.5625, 0.4615, 0.5263, 0.5490, 0.9625])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1770,  230], dtype=torch.int32)
        * Average consistency: 0.9340000152587891
    
2025-05-23 01:34:07,933 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.04541600123047829 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 257.8425298286602 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  14,   28,   32,   57, 1869], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5417, 0.6479, 0.7510, 0.8553, 0.9949])
        * Final bucket consistencies: tensor([0.6429, 0.5000, 0.5625, 0.5263, 0.9625])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1770,  230], dtype=torch.int32)
        * Average consistency: 0.9350000023841858
    
2025-05-23 01:38:19,099 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.04551655426621437 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 251.16396593861282 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  15,   29,   33,   54, 1869], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5457, 0.6537, 0.7567, 0.8591, 0.9949])
        * Final bucket consistencies: tensor([0.4667, 0.4828, 0.6061, 0.5000, 0.9625])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1769,  231], dtype=torch.int32)
        * Average consistency: 0.9334999918937683
    
2025-05-23 01:38:19,100 [INFO ] [calibration.py:__call__:336]	 Finished running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing top in zero-one in 1909.877876249142 seconds
2025-05-23 01:38:19,100 [INFO ] [mvce_dataset.py:<module>:234]	 Finished running 2/2 experiments.
2025-05-23 01:38:19,100 [INFO ] [mvce_dataset.py:<module>:239]	 Saving results to ./results/mvce-25/Blond_Hair/top/celeba-20250523-010619.csv
2025-05-23 01:38:19,110 [INFO ] [mvce_dataset.py:<module>:249]	 Finished saving results
