2025-05-23 01:06:24,528 [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/bottom_test", "output": "./results/mvce-25/Blond_Hair/bottom/", "classifier": "./models/celeba/Blond_Hair/celeba-20250508-133714.pklz", "classifier_feature": null, "calibration_map": "./results/calibration/celeba/Blond_Hair/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 01:06:24,650 [INFO ] [util.py:selectDevice:88]	 Using cuda:1 for tensor calculations
2025-05-23 01:06:24,870 [INFO ] [mvce_dataset.py:<module>:93]	 Running on cuda:1
2025-05-23 01:06:25,214 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-23 01:06:32,150 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Blond_Hair']
2025-05-23 01:06:32,222 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-23 01:06:32,222 [INFO ] [mvce_dataset.py:<module>:106]	 Using dataset celeba with 2000 test samples and 1 classes
2025-05-23 01:06:32,224 [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 01:06:32,224 [INFO ] [mvce_dataset.py:<module>:124]	 Loading mask bottom
2025-05-23 01:06:32,224 [INFO ] [mvce_dataset.py:<module>:141]	 Setting classifier activation function to sigmoid for single class
2025-05-23 01:06:32,225 [INFO ] [mvce_dataset.py:<module>:156]	 Creating generator using cache at ../../datasets/CelebAMask/cache/256/bottom_test
2025-05-23 01:06:32,246 [INFO ] [mvce_dataset.py:<module>:169]	 Including baseline imputators with zero imputation.
2025-05-23 01:06:32,246 [INFO ] [mvce_dataset.py:<module>:174]	 Including batch mean imputators with sizes [1].
2025-05-23 01:06:32,246 [INFO ] [mvce_dataset.py:<module>:181]	 Running all baseline imputators with [0.5] scaled beta max variance.
2025-05-23 01:06:32,246 [INFO ] [mvce_dataset.py:<module>:197]	 Adding 2 methods.
2025-05-23 01:06:32,246 [INFO ] [mvce_dataset.py:<module>:205]	 Using masked dataset with mask {'name': 'bottom'}
2025-05-23 01:06:32,246 [INFO ] [mvce_dataset.py:<module>:220]	 Considering action space zero-one
2025-05-23 01:06:32,247 [INFO ] [threading_utils.py:distributeTasks:91]	 Running 2 tasks each on their own thread
2025-05-23 01:06:32,247 [INFO ] [calibration.py:__call__:310]	 Started running Beta Mean * 0.5 - Zero Imputation missing bottom in zero-one
2025-05-23 01:06:32,247 [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 01:07:34,131 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.024136006832122803 for Beta Mean * 0.5 - Zero Imputation in 61.73891993239522 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  37,   39,   40,   66, 1818], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5510, 0.6532, 0.7530, 0.8612, 0.9894])
        * Final bucket consistencies: tensor([0.4324, 0.4359, 0.7750, 0.7273, 0.9752])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1689,  311], dtype=torch.int32)
        * Average consistency: 0.9424999952316284
    
2025-05-23 01:07:46,623 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.0233144648373127 for Beta Mean * 0.5 - Zero Imputation in 12.490321014076471 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  38,   34,   41,   74, 1813], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5508, 0.6458, 0.7445, 0.8619, 0.9898])
        * Final bucket consistencies: tensor([0.4211, 0.4412, 0.7317, 0.7297, 0.9763])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1689,  311], dtype=torch.int32)
        * Average consistency: 0.9424999952316284
    
2025-05-23 01:07:59,855 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.02482805773615837 for Beta Mean * 0.5 - Zero Imputation in 13.231008211150765 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  36,   38,   41,   69, 1816], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5542, 0.6497, 0.7486, 0.8612, 0.9897])
        * Final bucket consistencies: tensor([0.3889, 0.3684, 0.7561, 0.7826, 0.9747])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1689,  311], dtype=torch.int32)
        * Average consistency: 0.9415000081062317
    
2025-05-23 01:08:12,431 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.02385653182864189 for Beta Mean * 0.5 - Zero Imputation in 12.574088740162551 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  37,   33,   45,   63, 1822], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5500, 0.6456, 0.7483, 0.8575, 0.9892])
        * Final bucket consistencies: tensor([0.4324, 0.3636, 0.7333, 0.7778, 0.9737])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1688,  312], dtype=torch.int32)
        * Average consistency: 0.9419999718666077
    
2025-05-23 01:08:12,432 [INFO ] [calibration.py:__call__:336]	 Finished running Beta Mean * 0.5 - Zero Imputation missing bottom in zero-one in 100.18425316270441 seconds
2025-05-23 01:22:18,018 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.015909496694803238 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 945.7690481720492 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  24,   27,   34,   49, 1866], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5374, 0.6533, 0.7525, 0.8569, 0.9950])
        * Final bucket consistencies: tensor([0.5417, 0.4815, 0.7647, 0.6939, 0.9850])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1752,  248], dtype=torch.int32)
        * Average consistency: 0.9620000123977661
    
2025-05-23 01:28:48,703 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.017716573551297188 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 390.6744575807825 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  25,   28,   32,   50, 1865], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5398, 0.6534, 0.7515, 0.8564, 0.9950])
        * Final bucket consistencies: tensor([0.6800, 0.4643, 0.7188, 0.7200, 0.9850])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Average consistency: 0.9629999995231628
    
2025-05-23 01:33:06,806 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.015898071229457855 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 258.10187598876655 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  25,   28,   34,   45, 1868], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5436, 0.6561, 0.7596, 0.8596, 0.9948])
        * Final bucket consistencies: tensor([0.5200, 0.5000, 0.7353, 0.6889, 0.9850])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1753,  247], dtype=torch.int32)
        * Average consistency: 0.9614999890327454
    
2025-05-23 01:37:17,438 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.01635056361556053 for Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation in 250.63045898731798 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  24,   30,   29,   53, 1864], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5383, 0.6581, 0.7529, 0.8555, 0.9950])
        * Final bucket consistencies: tensor([0.5833, 0.5000, 0.7241, 0.7358, 0.9844])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Prediction Action Counts: tensor([1752,  248], dtype=torch.int32)
        * Average consistency: 0.9620000123977661
    
2025-05-23 01:37:17,438 [INFO ] [calibration.py:__call__:336]	 Finished running Beta Mean * 0.5 - 1 Sample Mean Caching Generator Imputation missing bottom in zero-one in 1845.190753698349 seconds
2025-05-23 01:37:17,439 [INFO ] [mvce_dataset.py:<module>:234]	 Finished running 2/2 experiments.
2025-05-23 01:37:17,439 [INFO ] [mvce_dataset.py:<module>:239]	 Saving results to ./results/mvce-25/Blond_Hair/bottom/celeba-20250523-010624.csv
2025-05-23 01:37:17,681 [INFO ] [mvce_dataset.py:<module>:249]	 Finished saving results
