2025-05-14 00:40:13,615 [INFO ] [logger.py:dumpArgs:73]	 Starting with arguments
{"output": "./results/mvce/", "threads": 2, "generator_samples": [100], "decision_samples": 1000, "cuda_index": -1, "dataset_path": "./datasets/synthetic/test.csv", "zero_variance": true, "beta_variance_scales": [0.99], "zero_imputation": false, "single_sample_imputation": true, "mean_imputation": true, "mean_shifts": [], "flip_variance": false, "correlations": [0.6, 0.8], "covariance_scales": [0.25, 4.0], "action_spaces": [{"name": "zero-one"}], "batch_size": 100, "buckets": 10, "trials": 10, "seed": 1337, "verbose": 1}
2025-05-14 00:40:13,651 [INFO ] [util.py:selectDevice:93]	 Using cpu for tensor calculations, cuda available: True
2025-05-14 00:40:13,651 [INFO ] [mvce_synthetic.py:<module>:86]	 Running on cpu
2025-05-14 00:40:13,653 [INFO ] [mvce_synthetic.py:<module>:94]	 Created classifier with weights tensor([1., 1.]) and bias -1.0
2025-05-14 00:40:13,655 [INFO ] [mvce_synthetic.py:<module>:104]	 Running ground truth generator with mean tensor([0., 0.]) and covariance tensor([[0.3000, 0.3834],
        [0.3834, 1.0000]])
2025-05-14 00:40:13,656 [INFO ] [mvce_synthetic.py:<module>:125]	 Adding 0.6 generator with tensor([0., 0.]) and covariance tensor([[0.3000, 0.3286],
        [0.3286, 1.0000]])
2025-05-14 00:40:13,657 [INFO ] [mvce_synthetic.py:<module>:125]	 Adding 0.8 generator with tensor([0., 0.]) and covariance tensor([[0.3000, 0.4382],
        [0.4382, 1.0000]])
2025-05-14 00:40:13,657 [INFO ] [mvce_synthetic.py:<module>:133]	 Adding 0.25 scaled generator with tensor([0., 0.]) and covariance tensor([[0.0750, 0.0959],
        [0.0959, 0.2500]])
2025-05-14 00:40:13,658 [INFO ] [mvce_synthetic.py:<module>:133]	 Adding 4.0 scaled generator with tensor([0., 0.]) and covariance tensor([[1.2000, 1.5336],
        [1.5336, 4.0000]])
2025-05-14 00:40:13,658 [INFO ] [mvce_synthetic.py:<module>:158]	 Including baseline imputators with single sample imputation.
2025-05-14 00:40:13,658 [INFO ] [mvce_synthetic.py:<module>:162]	 Including baseline imputators with mean imputation.
2025-05-14 00:40:13,658 [INFO ] [mvce_synthetic.py:<module>:167]	 Running all baseline imputators zero variance.
2025-05-14 00:40:13,658 [INFO ] [mvce_synthetic.py:<module>:169]	 Running all baseline imputators with [0.99] scaled beta max variance.
2025-05-14 00:40:13,666 [INFO ] [csv.py:import_from_csv:156]	 Loaded synthetic dataframe with shape: (10000, 4) and columns Index(['x1', 'x2', 'label', 'phi'], dtype='object')
2025-05-14 00:40:13,667 [INFO ] [csv.py:import_from_csv:194]	 After preprocessing, shape: (10000, 2) and columns Index(['x1', 'x2'], dtype='object')
2025-05-14 00:40:13,667 [INFO ] [csv.py:import_from_csv:201]	 Final metadata, DatasetMeta{name: 'synthetic', target: 'label', labels: ['x1', 'x2'], groups: None}
2025-05-14 00:40:13,668 [INFO ] [mvce_synthetic.py:<module>:188]	 Using imported dataset from ./datasets/synthetic/test.csv with 10000 samples
2025-05-14 00:40:13,668 [INFO ] [mvce_synthetic.py:<module>:200]	 Considering action space zero-one
2025-05-14 00:40:13,668 [INFO ] [threading_utils.py:distributeTasks:95]	 Running 18 tasks on 2 threads
2025-05-14 00:40:13,668 [INFO ] [calibration.py:__call__:194]	 Started running Beta Mean * 0.99 - Conditional Gaussian - Ground Truth missing Missing X2 in zero-one
2025-05-14 00:40:13,669 [INFO ] [calibration.py:__call__:194]	 Started running Beta Mean * 0.99 - Conditional Gaussian - Ground Truth missing Missing X1 in zero-one
2025-05-14 00:40:23,633 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.1686844527721405 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.962079487275332 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1778, 1877, 1981, 2266, 2098], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5499, 0.6500, 0.7504, 0.8503, 0.9428])
        * Final bucket consistencies: tensor([0.6822, 0.9180, 0.9975, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7662, 2338], dtype=torch.int32)
    
2025-05-14 00:40:23,676 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12537288665771484 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 10.006403120234609 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1817, 1994, 2107, 2322, 1760], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5493, 0.6491, 0.7497, 0.8496, 0.9407])
        * Final bucket consistencies: tensor([0.6208, 0.7994, 0.9364, 0.9901, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7852, 2148], dtype=torch.int32)
    
2025-05-14 00:40:32,490 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.16738787293434143 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 8.856294461991638 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1771, 1865, 1978, 2317, 2069], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5497, 0.6490, 0.7496, 0.8502, 0.9433])
        * Final bucket consistencies: tensor([0.6702, 0.9239, 0.9939, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7658, 2342], dtype=torch.int32)
    
2025-05-14 00:40:32,530 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12488584965467453 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 8.852718880865723 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1804, 2020, 2096, 2313, 1767], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5494, 0.6493, 0.7497, 0.8498, 0.9402])
        * Final bucket consistencies: tensor([0.6192, 0.8010, 0.9342, 0.9901, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7858, 2142], dtype=torch.int32)
    
2025-05-14 00:40:41,792 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.16865451633930206 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.301066359039396 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1765, 1875, 2005, 2264, 2091], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5497, 0.6498, 0.7504, 0.8508, 0.9432])
        * Final bucket consistencies: tensor([0.6856, 0.9163, 0.9950, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7686, 2314], dtype=torch.int32)
    
2025-05-14 00:40:41,847 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12521667778491974 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.316016777884215 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1798, 2015, 2125, 2287, 1775], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5497, 0.6486, 0.7497, 0.8499, 0.9400])
        * Final bucket consistencies: tensor([0.6229, 0.8010, 0.9308, 0.9908, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7859, 2141], dtype=torch.int32)
    
2025-05-14 00:40:51,350 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.16905522346496582 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.556803757324815 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1772, 1872, 1978, 2286, 2092], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5488, 0.6495, 0.7503, 0.8504, 0.9431])
        * Final bucket consistencies: tensor([0.6828, 0.9209, 0.9949, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7664, 2336], dtype=torch.int32)
    
2025-05-14 00:40:51,454 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12634959816932678 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.60543771693483 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1803, 2029, 2094, 2291, 1783], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5495, 0.6500, 0.7503, 0.8499, 0.9403])
        * Final bucket consistencies: tensor([0.6284, 0.7989, 0.9365, 0.9908, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7860, 2140], dtype=torch.int32)
    
2025-05-14 00:41:00,855 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.1248052716255188 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.400123451836407 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1818, 2020, 2069, 2339, 1754], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5492, 0.6498, 0.7503, 0.8503, 0.9407])
        * Final bucket consistencies: tensor([0.6216, 0.8025, 0.9328, 0.9897, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7852, 2148], dtype=torch.int32)
    
2025-05-14 00:41:00,890 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.1680009961128235 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.537701377645135 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1782, 1880, 1962, 2276, 2100], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5496, 0.6498, 0.7503, 0.8502, 0.9428])
        * Final bucket consistencies: tensor([0.6723, 0.9277, 0.9939, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7668, 2332], dtype=torch.int32)
    
2025-05-14 00:41:09,973 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.1258823275566101 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.116502752993256 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1798, 2036, 2075, 2320, 1771], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5495, 0.6491, 0.7497, 0.8494, 0.9403])
        * Final bucket consistencies: tensor([0.6251, 0.8001, 0.9335, 0.9909, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7864, 2136], dtype=torch.int32)
    
2025-05-14 00:41:10,005 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.16900339722633362 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.114183356054127 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1789, 1859, 1991, 2273, 2088], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5495, 0.6493, 0.7504, 0.8505, 0.9428])
        * Final bucket consistencies: tensor([0.6864, 0.9182, 0.9945, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7664, 2336], dtype=torch.int32)
    
2025-05-14 00:41:19,381 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12559176981449127 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.406088777817786 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1805, 2020, 2098, 2275, 1802], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5497, 0.6489, 0.7499, 0.8489, 0.9393])
        * Final bucket consistencies: tensor([0.6233, 0.8015, 0.9333, 0.9899, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7856, 2144], dtype=torch.int32)
    
2025-05-14 00:41:19,458 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.16900749504566193 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.45173595706001 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1773, 1880, 1960, 2288, 2099], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5501, 0.6494, 0.7503, 0.8500, 0.9430])
        * Final bucket consistencies: tensor([0.6830, 0.9213, 0.9954, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7681, 2319], dtype=torch.int32)
    
2025-05-14 00:41:28,821 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12582390010356903 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.438348006922752 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1825, 2011, 2121, 2292, 1751], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5500, 0.6497, 0.7505, 0.8507, 0.9411])
        * Final bucket consistencies: tensor([0.6268, 0.7996, 0.9359, 0.9904, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7864, 2136], dtype=torch.int32)
    
2025-05-14 00:41:29,008 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.1684657782316208 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.549729992635548 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1773, 1873, 1977, 2257, 2120], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5495, 0.6491, 0.7501, 0.8497, 0.9422])
        * Final bucket consistencies: tensor([0.6813, 0.9178, 0.9960, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7671, 2329], dtype=torch.int32)
    
2025-05-14 00:41:37,885 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12609826028347015 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.062907330226153 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1815, 1994, 2125, 2304, 1762], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5495, 0.6489, 0.7497, 0.8499, 0.9406])
        * Final bucket consistencies: tensor([0.6237, 0.8009, 0.9351, 0.9909, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7855, 2145], dtype=torch.int32)
    
2025-05-14 00:41:38,226 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.16797631978988647 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.215841091237962 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1793, 1863, 1983, 2282, 2079], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5501, 0.6501, 0.7505, 0.8507, 0.9429])
        * Final bucket consistencies: tensor([0.6793, 0.9200, 0.9955, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7665, 2335], dtype=torch.int32)
    
2025-05-14 00:41:47,093 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12623615562915802 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.206434695981443 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1827, 1986, 2138, 2292, 1757], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5498, 0.6491, 0.7499, 0.8505, 0.9405])
        * Final bucket consistencies: tensor([0.6273, 0.8021, 0.9350, 0.9887, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7879, 2121], dtype=torch.int32)
    
2025-05-14 00:41:47,093 [INFO ] [calibration.py:__call__:217]	 Finished running Beta Mean * 0.99 - Conditional Gaussian - Ground Truth missing Missing X2 in zero-one in 93.42421273002401 seconds
2025-05-14 00:41:47,093 [INFO ] [calibration.py:__call__:194]	 Started running Basic Imputation - Conditional Gaussian - Ground Truth missing Missing X2 in zero-one
2025-05-14 00:41:47,308 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.16930460929870605 for Beta Mean * 0.99 - Conditional Gaussian - Ground Truth in 9.080597823951393 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1760, 1887, 1970, 2307, 2076], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5494, 0.6495, 0.7502, 0.8505, 0.9434])
        * Final bucket consistencies: tensor([0.6818, 0.9237, 0.9939, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7668, 2332], dtype=torch.int32)
    
2025-05-14 00:41:47,308 [INFO ] [calibration.py:__call__:217]	 Finished running Beta Mean * 0.99 - Conditional Gaussian - Ground Truth missing Missing X1 in zero-one in 93.63867457583547 seconds
2025-05-14 00:41:47,308 [INFO ] [calibration.py:__call__:194]	 Started running Basic Imputation - Conditional Gaussian - Ground Truth missing Missing X1 in zero-one
2025-05-14 00:41:53,703 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12349998950958252 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.609060982242227 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8765])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7857, 2143], dtype=torch.int32)
    
2025-05-14 00:41:53,935 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.07179999351501465 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.626606277190149 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9282])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7670, 2330], dtype=torch.int32)
    
2025-05-14 00:42:00,211 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12349998950958252 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.507446898147464 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8765])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7857, 2143], dtype=torch.int32)
    
2025-05-14 00:42:00,450 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.07179999351501465 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.513323809951544 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9282])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7670, 2330], dtype=torch.int32)
    
2025-05-14 00:42:06,634 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12349998950958252 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.421341294888407 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8765])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7857, 2143], dtype=torch.int32)
    
2025-05-14 00:42:06,967 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.07179999351501465 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.516271769069135 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9282])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7670, 2330], dtype=torch.int32)
    
2025-05-14 00:42:13,321 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12349998950958252 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.686471734195948 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8765])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7857, 2143], dtype=torch.int32)
    
2025-05-14 00:42:13,549 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.07179999351501465 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.581012901850045 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9282])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7670, 2330], dtype=torch.int32)
    
2025-05-14 00:42:19,930 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12349998950958252 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.607871629763395 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8765])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7857, 2143], dtype=torch.int32)
    
2025-05-14 00:42:20,144 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.07179999351501465 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.594027112238109 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9282])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7670, 2330], dtype=torch.int32)
    
2025-05-14 00:42:26,425 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12349998950958252 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.493743279948831 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8765])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7857, 2143], dtype=torch.int32)
    
2025-05-14 00:42:26,645 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.07179999351501465 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.499725935980678 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9282])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7670, 2330], dtype=torch.int32)
    
2025-05-14 00:42:32,861 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12349998950958252 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.434696848038584 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8765])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7857, 2143], dtype=torch.int32)
    
2025-05-14 00:42:33,099 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.07179999351501465 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.453416603151709 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9282])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7670, 2330], dtype=torch.int32)
    
2025-05-14 00:42:39,469 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12349998950958252 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.6071017468348145 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8765])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7857, 2143], dtype=torch.int32)
    
2025-05-14 00:42:39,539 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.07179999351501465 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.43833216978237 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9282])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7670, 2330], dtype=torch.int32)
    
2025-05-14 00:42:46,152 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12349998950958252 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.681965985801071 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8765])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7857, 2143], dtype=torch.int32)
    
2025-05-14 00:42:46,233 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.07179999351501465 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.692981124855578 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9282])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7670, 2330], dtype=torch.int32)
    
2025-05-14 00:42:52,745 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.07179999351501465 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.511203893925995 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9282])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7670, 2330], dtype=torch.int32)
    
2025-05-14 00:42:52,745 [INFO ] [calibration.py:__call__:217]	 Finished running Basic Imputation - Conditional Gaussian - Ground Truth missing Missing X1 in zero-one in 65.43742559710518 seconds
2025-05-14 00:42:52,745 [INFO ] [calibration.py:__call__:194]	 Started running Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation missing Missing X2 in zero-one
2025-05-14 00:42:52,780 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.12349998950958252 for Basic Imputation - Conditional Gaussian - Ground Truth in 6.6265897592529655 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8765])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7857, 2143], dtype=torch.int32)
    
2025-05-14 00:42:52,780 [INFO ] [calibration.py:__call__:217]	 Finished running Basic Imputation - Conditional Gaussian - Ground Truth missing Missing X2 in zero-one in 65.68693674029782 seconds
2025-05-14 00:42:52,780 [INFO ] [calibration.py:__call__:194]	 Started running Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation missing Missing X1 in zero-one
2025-05-14 00:43:07,312 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.14114755392074585 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.531204101163894 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1725, 1822, 1974, 2281, 2198], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5498, 0.6499, 0.7502, 0.8508, 0.9442])
        * Final bucket consistencies: tensor([0.6336, 0.8622, 0.9645, 0.9974, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7573, 2427], dtype=torch.int32)
    
2025-05-14 00:43:07,364 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.0676342248916626 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.617652928922325 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1768, 1800, 2052, 2250, 2130], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5490, 0.6499, 0.7508, 0.8511, 0.9449])
        * Final bucket consistencies: tensor([0.5922, 0.7200, 0.8387, 0.9413, 0.9873])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7537, 2463], dtype=torch.int32)
    
2025-05-14 00:43:21,589 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.1372530311346054 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.274910463020205 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1773, 1787, 1995, 2266, 2179], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5497, 0.6488, 0.7505, 0.8513, 0.9437])
        * Final bucket consistencies: tensor([0.6204, 0.8500, 0.9689, 0.9965, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7561, 2439], dtype=torch.int32)
    
2025-05-14 00:43:21,680 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05767775699496269 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.31425129622221 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1751, 1827, 1953, 2319, 2150], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5500, 0.6494, 0.7507, 0.8514, 0.9448])
        * Final bucket consistencies: tensor([0.5705, 0.7198, 0.8208, 0.9271, 0.9912])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7542, 2458], dtype=torch.int32)
    
2025-05-14 00:43:35,955 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.14311158657073975 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.364829618949443 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1766, 1767, 2040, 2232, 2195], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5491, 0.6495, 0.7508, 0.8510, 0.9442])
        * Final bucket consistencies: tensor([0.6484, 0.8540, 0.9691, 0.9973, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7556, 2444], dtype=torch.int32)
    
2025-05-14 00:43:36,023 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.055519670248031616 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.341098449658602 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1721, 1892, 1962, 2199, 2226], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5490, 0.6511, 0.7502, 0.8502, 0.9450])
        * Final bucket consistencies: tensor([0.5665, 0.7093, 0.8191, 0.9350, 0.9870])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7493, 2507], dtype=torch.int32)
    
2025-05-14 00:43:50,762 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.13818661868572235 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.805984574835747 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1750, 1816, 1962, 2267, 2205], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5504, 0.6492, 0.7510, 0.8504, 0.9445])
        * Final bucket consistencies: tensor([0.6223, 0.8541, 0.9699, 0.9969, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7584, 2416], dtype=torch.int32)
    
2025-05-14 00:43:51,003 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.06281332671642303 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.97863504011184 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1823, 1803, 1988, 2204, 2182], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5506, 0.6507, 0.7499, 0.8511, 0.9452])
        * Final bucket consistencies: tensor([0.5721, 0.7127, 0.8375, 0.9433, 0.9908])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7538, 2462], dtype=torch.int32)
    
2025-05-14 00:44:05,647 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.1368035078048706 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.883208134677261 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1723, 1824, 1978, 2243, 2232], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5504, 0.6498, 0.7506, 0.8515, 0.9440])
        * Final bucket consistencies: tensor([0.6286, 0.8394, 0.9702, 0.9978, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7561, 2439], dtype=torch.int32)
    
2025-05-14 00:44:06,012 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.058526623994112015 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 15.008367889095098 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1739, 1828, 2006, 2269, 2158], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5502, 0.6494, 0.7511, 0.8507, 0.9444])
        * Final bucket consistencies: tensor([0.5699, 0.7133, 0.8325, 0.9229, 0.9940])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7578, 2422], dtype=torch.int32)
    
2025-05-14 00:44:19,973 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.1410001814365387 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.324809441808611 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1800, 1819, 2000, 2202, 2179], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5497, 0.6506, 0.7502, 0.8510, 0.9448])
        * Final bucket consistencies: tensor([0.6378, 0.8631, 0.9615, 0.9973, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7568, 2432], dtype=torch.int32)
    
2025-05-14 00:44:20,364 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05956963449716568 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.350344897713512 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1775, 1798, 2008, 2219, 2200], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5508, 0.6497, 0.7501, 0.8498, 0.9448])
        * Final bucket consistencies: tensor([0.5606, 0.7141, 0.8396, 0.9342, 0.9882])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7594, 2406], dtype=torch.int32)
    
2025-05-14 00:44:34,488 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.1361735463142395 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.513553573284298 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1781, 1748, 2018, 2238, 2215], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5505, 0.6497, 0.7498, 0.8505, 0.9443])
        * Final bucket consistencies: tensor([0.6176, 0.8553, 0.9633, 0.9973, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7530, 2470], dtype=torch.int32)
    
2025-05-14 00:44:34,822 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.06100928410887718 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.456618568394333 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1770, 1772, 1957, 2275, 2226], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5492, 0.6501, 0.7504, 0.8496, 0.9445])
        * Final bucket consistencies: tensor([0.5757, 0.7144, 0.8319, 0.9323, 0.9901])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7598, 2402], dtype=torch.int32)
    
2025-05-14 00:44:48,867 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.14104507863521576 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.377986710052937 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1741, 1812, 1972, 2241, 2234], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5491, 0.6500, 0.7506, 0.8507, 0.9436])
        * Final bucket consistencies: tensor([0.6370, 0.8626, 0.9625, 0.9973, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7619, 2381], dtype=torch.int32)
    
2025-05-14 00:44:49,272 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.06741984933614731 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.449770980980247 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1780, 1936, 1927, 2203, 2154], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5499, 0.6501, 0.7509, 0.8510, 0.9446])
        * Final bucket consistencies: tensor([0.5798, 0.7211, 0.8490, 0.9374, 0.9930])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7616, 2384], dtype=torch.int32)
    
2025-05-14 00:45:03,230 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.13809403777122498 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.362083183135837 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1723, 1817, 1971, 2245, 2244], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5494, 0.6505, 0.7485, 0.8498, 0.9436])
        * Final bucket consistencies: tensor([0.6280, 0.8531, 0.9630, 0.9960, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7574, 2426], dtype=torch.int32)
    
2025-05-14 00:45:03,579 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.060296498239040375 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.305201556067914 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1755, 1800, 2058, 2201, 2186], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5485, 0.6506, 0.7500, 0.8506, 0.9440])
        * Final bucket consistencies: tensor([0.5613, 0.7289, 0.8222, 0.9378, 0.9895])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7558, 2442], dtype=torch.int32)
    
2025-05-14 00:45:17,472 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.13980381190776825 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.240195508114994 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1836, 1803, 1913, 2225, 2223], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5487, 0.6511, 0.7508, 0.8512, 0.9440])
        * Final bucket consistencies: tensor([0.6383, 0.8536, 0.9692, 0.9978, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7568, 2432], dtype=torch.int32)
    
2025-05-14 00:45:17,472 [INFO ] [calibration.py:__call__:217]	 Finished running Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation missing Missing X1 in zero-one in 144.6921497732401 seconds
2025-05-14 00:45:17,472 [INFO ] [calibration.py:__call__:194]	 Started running Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation missing Missing X2 in zero-one
2025-05-14 00:45:17,875 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.058897923678159714 for Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation in 14.294267060235143 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1825, 1789, 1985, 2213, 2188], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5492, 0.6509, 0.7507, 0.8507, 0.9454])
        * Final bucket consistencies: tensor([0.5671, 0.6959, 0.8403, 0.9413, 0.9899])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7549, 2451], dtype=torch.int32)
    
2025-05-14 00:45:17,875 [INFO ] [calibration.py:__call__:217]	 Finished running Beta Mean * 0.99 - Single Sample Conditional Gaussian - Ground Truth Imputation missing Missing X2 in zero-one in 145.1293927859515 seconds
2025-05-14 00:45:17,875 [INFO ] [calibration.py:__call__:194]	 Started running Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation missing Missing X1 in zero-one
2025-05-14 00:45:28,900 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.17599999904632568 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.426316658966243 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8240])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7506, 2494], dtype=torch.int32)
    
2025-05-14 00:45:29,369 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.10019999742507935 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.493605567142367 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8998])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7540, 2460], dtype=torch.int32)
    
2025-05-14 00:45:40,591 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.17640000581741333 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.690099828876555 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8236])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7546, 2454], dtype=torch.int32)
    
2025-05-14 00:45:40,930 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09520000219345093 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.559661781881005 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9048])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7558, 2442], dtype=torch.int32)
    
2025-05-14 00:45:52,104 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.17650002241134644 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.51128055434674 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8235])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7481, 2519], dtype=torch.int32)
    
2025-05-14 00:45:52,464 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09670001268386841 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.532686344813555 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9033])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7543, 2457], dtype=torch.int32)
    
2025-05-14 00:46:03,767 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.17989999055862427 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.661988041829318 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8201])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7629, 2371], dtype=torch.int32)
    
2025-05-14 00:46:04,255 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09570002555847168 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.789982987102121 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9043])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7579, 2421], dtype=torch.int32)
    
2025-05-14 00:46:15,517 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.17409998178482056 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.748888912145048 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8259])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7565, 2435], dtype=torch.int32)
    
2025-05-14 00:46:15,906 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09670001268386841 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.650142397731543 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9033])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7549, 2451], dtype=torch.int32)
    
2025-05-14 00:46:27,173 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.17400002479553223 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.654814168810844 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8260])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7566, 2434], dtype=torch.int32)
    
2025-05-14 00:46:27,569 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.0974000096321106 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.661952116061002 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9026])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7564, 2436], dtype=torch.int32)
    
2025-05-14 00:46:38,998 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.17259997129440308 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.823721944820136 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8274])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7558, 2442], dtype=torch.int32)
    
2025-05-14 00:46:39,410 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.10250002145767212 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.839999210089445 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8975])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7569, 2431], dtype=torch.int32)
    
2025-05-14 00:46:50,604 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.17650002241134644 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.60470416303724 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8235])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7533, 2467], dtype=torch.int32)
    
2025-05-14 00:46:51,099 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09509998559951782 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.687881565187126 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9049])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7625, 2375], dtype=torch.int32)
    
2025-05-14 00:47:02,319 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.17159998416900635 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.713488091249019 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8284])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7544, 2456], dtype=torch.int32)
    
2025-05-14 00:47:02,651 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09579998254776001 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.550599406939 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.9042])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7566, 2434], dtype=torch.int32)
    
2025-05-14 00:47:13,949 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.17900002002716064 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.62912215711549 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8210])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7512, 2488], dtype=torch.int32)
    
2025-05-14 00:47:13,949 [INFO ] [calibration.py:__call__:217]	 Finished running Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation missing Missing X2 in zero-one in 116.47679406311363 seconds
2025-05-14 00:47:13,949 [INFO ] [calibration.py:__call__:194]	 Started running Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples missing Missing X2 in zero-one
2025-05-14 00:47:14,085 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.1032000258564949 for Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation in 11.432229194790125 seconds with 10 buckets:
        * Non-zero buckets: 9
        * Final bucket sizes: tensor([10000], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([1.])
        * Final bucket consistencies: tensor([0.8968])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7480, 2520], dtype=torch.int32)
    
2025-05-14 00:47:14,085 [INFO ] [calibration.py:__call__:217]	 Finished running Basic Imputation - Single Sample Conditional Gaussian - Ground Truth Imputation missing Missing X1 in zero-one in 116.21022524405271 seconds
2025-05-14 00:47:14,085 [INFO ] [calibration.py:__call__:194]	 Started running Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples missing Missing X1 in zero-one
2025-05-14 00:47:28,577 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05768650397658348 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.490531131625175 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 914,  873, 1107, 1328, 5778], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5513, 0.6507, 0.7500, 0.8539, 0.9806])
        * Final bucket consistencies: tensor([0.5875, 0.7835, 0.8916, 0.9752, 0.9997])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7639, 2361], dtype=torch.int32)
    
2025-05-14 00:47:28,654 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09725142270326614 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.70358207076788 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1688, 1743, 1879, 1950, 2740], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5503, 0.6490, 0.7489, 0.8517, 0.9570])
        * Final bucket consistencies: tensor([0.6214, 0.7688, 0.9063, 0.9718, 0.9985])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7835, 2165], dtype=torch.int32)
    
2025-05-14 00:47:43,346 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.057887427508831024 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.766963039990515 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 894,  907, 1096, 1278, 5825], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5487, 0.6502, 0.7505, 0.8523, 0.9799])
        * Final bucket consistencies: tensor([0.5940, 0.7718, 0.8996, 0.9695, 0.9997])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7663, 2337], dtype=torch.int32)
    
2025-05-14 00:47:43,698 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09817753732204437 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 15.04273840598762 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1677, 1764, 1826, 2030, 2703], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5513, 0.6499, 0.7499, 0.8506, 0.9580])
        * Final bucket consistencies: tensor([0.6172, 0.7891, 0.8976, 0.9704, 0.9996])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7820, 2180], dtype=torch.int32)
    
2025-05-14 00:47:57,807 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05710674449801445 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.45927546499297 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 885,  902, 1069, 1344, 5800], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5503, 0.6497, 0.7497, 0.8524, 0.9805])
        * Final bucket consistencies: tensor([0.6056, 0.7661, 0.8877, 0.9717, 0.9993])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7674, 2326], dtype=torch.int32)
    
2025-05-14 00:47:58,126 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.0983986184000969 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.426957949995995 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1681, 1781, 1834, 1957, 2747], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5507, 0.6504, 0.7498, 0.8497, 0.9571])
        * Final bucket consistencies: tensor([0.6086, 0.7951, 0.8926, 0.9811, 0.9971])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7835, 2165], dtype=torch.int32)
    
2025-05-14 00:48:12,284 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05461341515183449 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.476069491822273 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 893,  906, 1063, 1333, 5805], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5484, 0.6523, 0.7507, 0.8525, 0.9801])
        * Final bucket consistencies: tensor([0.5733, 0.7638, 0.8918, 0.9730, 0.9995])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7643, 2357], dtype=torch.int32)
    
2025-05-14 00:48:12,668 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09835617244243622 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.5403191302903 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1669, 1778, 1838, 2034, 2681], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5502, 0.6501, 0.7501, 0.8510, 0.9581])
        * Final bucket consistencies: tensor([0.6171, 0.7823, 0.8950, 0.9798, 0.9985])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7818, 2182], dtype=torch.int32)
    
2025-05-14 00:48:27,031 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05676502734422684 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.745365665759891 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 874,  953, 1038, 1334, 5801], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5491, 0.6520, 0.7511, 0.8535, 0.9805])
        * Final bucket consistencies: tensor([0.5892, 0.7733, 0.8892, 0.9738, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7669, 2331], dtype=torch.int32)
    
2025-05-14 00:48:27,372 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09706322103738785 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.702773436903954 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1640, 1781, 1874, 1979, 2726], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5497, 0.6498, 0.7502, 0.8510, 0.9573])
        * Final bucket consistencies: tensor([0.6037, 0.7850, 0.8975, 0.9808, 0.9971])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7869, 2131], dtype=torch.int32)
    
2025-05-14 00:48:41,962 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05676955729722977 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.930111438035965 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 894,  929, 1074, 1303, 5800], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5498, 0.6510, 0.7511, 0.8529, 0.9805])
        * Final bucket consistencies: tensor([0.5828, 0.7761, 0.8939, 0.9754, 0.9993])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7661, 2339], dtype=torch.int32)
    
2025-05-14 00:48:42,352 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09932060539722443 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.979228883981705 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1666, 1810, 1821, 1983, 2720], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5505, 0.6501, 0.7496, 0.8508, 0.9570])
        * Final bucket consistencies: tensor([0.6146, 0.7845, 0.9044, 0.9748, 0.9993])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7858, 2142], dtype=torch.int32)
    
2025-05-14 00:48:56,274 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05568576231598854 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.310658850241452 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 904,  918, 1073, 1317, 5788], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5483, 0.6531, 0.7509, 0.8536, 0.9805])
        * Final bucket consistencies: tensor([0.5586, 0.7985, 0.8872, 0.9742, 0.9993])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7647, 2353], dtype=torch.int32)
    
2025-05-14 00:48:56,709 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.0966162234544754 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.355407711584121 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1729, 1731, 1819, 1957, 2764], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5506, 0.6505, 0.7502, 0.8509, 0.9573])
        * Final bucket consistencies: tensor([0.6113, 0.7811, 0.8999, 0.9785, 0.9982])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7834, 2166], dtype=torch.int32)
    
2025-05-14 00:49:11,426 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05605900287628174 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 15.150675537064672 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 907,  900, 1062, 1303, 5828], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5490, 0.6523, 0.7506, 0.8512, 0.9800])
        * Final bucket consistencies: tensor([0.5854, 0.7711, 0.8879, 0.9747, 0.9995])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7637, 2363], dtype=torch.int32)
    
2025-05-14 00:49:11,815 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09834205359220505 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 15.104310479946434 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1660, 1783, 1862, 1957, 2738], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5492, 0.6496, 0.7490, 0.8499, 0.9572])
        * Final bucket consistencies: tensor([0.6127, 0.7801, 0.8985, 0.9806, 0.9978])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7848, 2152], dtype=torch.int32)
    
2025-05-14 00:49:26,221 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05798569321632385 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.792869581840932 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 892,  904, 1065, 1325, 5814], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5500, 0.6494, 0.7505, 0.8517, 0.9800])
        * Final bucket consistencies: tensor([0.6009, 0.7732, 0.8883, 0.9751, 0.9993])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7661, 2339], dtype=torch.int32)
    
2025-05-14 00:49:26,538 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09919939935207367 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.721999912057072 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1673, 1757, 1864, 1981, 2725], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5499, 0.6497, 0.7497, 0.8505, 0.9579])
        * Final bucket consistencies: tensor([0.6133, 0.7826, 0.9024, 0.9813, 0.9978])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7842, 2158], dtype=torch.int32)
    
2025-05-14 00:49:40,890 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05550098419189453 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.668395420070738 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 893,  913, 1038, 1337, 5819], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5499, 0.6505, 0.7500, 0.8522, 0.9803])
        * Final bucket consistencies: tensor([0.5700, 0.7755, 0.8950, 0.9723, 0.9995])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7642, 2358], dtype=torch.int32)
    
2025-05-14 00:49:40,891 [INFO ] [calibration.py:__call__:217]	 Finished running Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples missing Missing X1 in zero-one in 146.80502746300772 seconds
2025-05-14 00:49:40,891 [INFO ] [calibration.py:__call__:194]	 Started running Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples missing Missing X2 in zero-one
2025-05-14 00:49:41,387 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.09812568873167038 for Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples in 14.847537487279624 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([1640, 1794, 1898, 1935, 2733], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5500, 0.6488, 0.7515, 0.8500, 0.9574])
        * Final bucket consistencies: tensor([0.6104, 0.7821, 0.9004, 0.9788, 0.9982])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7853, 2147], dtype=torch.int32)
    
2025-05-14 00:49:41,387 [INFO ] [calibration.py:__call__:217]	 Finished running Monte Carlo - Conditional Gaussian - 4.0 * Covariance - 100 samples missing Missing X2 in zero-one in 147.43764430098236 seconds
2025-05-14 00:49:41,387 [INFO ] [calibration.py:__call__:194]	 Started running Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples missing Missing X1 in zero-one
2025-05-14 00:49:55,370 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.06043688952922821 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.478171430062503 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 421,  429,  500,  691, 7959], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5487, 0.6496, 0.7507, 0.8545, 0.9907])
        * Final bucket consistencies: tensor([0.5154, 0.6014, 0.6600, 0.7135, 0.9371])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7856, 2144], dtype=torch.int32)
    
2025-05-14 00:49:55,926 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.037680573761463165 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.537938863039017 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 241,  228,  270,  351, 8910], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5489, 0.6514, 0.7495, 0.8535, 0.9955])
        * Final bucket consistencies: tensor([0.5021, 0.5570, 0.6074, 0.7123, 0.9668])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7656, 2344], dtype=torch.int32)
    
2025-05-14 00:50:09,799 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.06009877473115921 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.427992133889347 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 435,  418,  489,  701, 7957], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5495, 0.6495, 0.7513, 0.8551, 0.9907])
        * Final bucket consistencies: tensor([0.5632, 0.5694, 0.6769, 0.7233, 0.9363])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7853, 2147], dtype=torch.int32)
    
2025-05-14 00:50:10,300 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.03576377406716347 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.37173491762951 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 251,  225,  278,  343, 8903], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5485, 0.6473, 0.7538, 0.8556, 0.9955])
        * Final bucket consistencies: tensor([0.5458, 0.5689, 0.6475, 0.6764, 0.9677])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7668, 2332], dtype=torch.int32)
    
2025-05-14 00:50:24,087 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05931633338332176 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.28641914203763 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 431,  437,  479,  691, 7962], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5508, 0.6494, 0.7519, 0.8540, 0.9905])
        * Final bucket consistencies: tensor([0.5499, 0.5904, 0.6514, 0.7221, 0.9368])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7861, 2139], dtype=torch.int32)
    
2025-05-14 00:50:24,604 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.03638722375035286 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.302435171790421 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 250,  223,  273,  344, 8910], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5492, 0.6506, 0.7507, 0.8547, 0.9955])
        * Final bucket consistencies: tensor([0.5160, 0.5964, 0.6337, 0.6860, 0.9670])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7675, 2325], dtype=torch.int32)
    
2025-05-14 00:50:38,505 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05954780429601669 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.416694597806782 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 425,  421,  482,  717, 7955], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5492, 0.6499, 0.7495, 0.8533, 0.9906])
        * Final bucket consistencies: tensor([0.5388, 0.5914, 0.6660, 0.7099, 0.9374])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7849, 2151], dtype=torch.int32)
    
2025-05-14 00:50:38,962 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.03734583407640457 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.356577389407903 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 252,  215,  275,  367, 8891], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5502, 0.6496, 0.7501, 0.8568, 0.9957])
        * Final bucket consistencies: tensor([0.4881, 0.5767, 0.6436, 0.7003, 0.9669])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7676, 2324], dtype=torch.int32)
    
2025-05-14 00:50:53,100 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05982863903045654 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.59344489686191 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 421,  413,  510,  708, 7948], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5505, 0.6488, 0.7512, 0.8530, 0.9907])
        * Final bucket consistencies: tensor([0.5582, 0.5690, 0.6725, 0.7105, 0.9377])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7855, 2145], dtype=torch.int32)
    
2025-05-14 00:50:53,409 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.037108901888132095 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.446198216173798 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 235,  236,  271,  338, 8920], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5482, 0.6495, 0.7509, 0.8543, 0.9954])
        * Final bucket consistencies: tensor([0.5574, 0.5636, 0.6384, 0.6657, 0.9669])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7657, 2343], dtype=torch.int32)
    
2025-05-14 00:51:07,765 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05995488911867142 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.664340917021036 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 420,  447,  480,  715, 7938], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5493, 0.6507, 0.7519, 0.8540, 0.9907])
        * Final bucket consistencies: tensor([0.5405, 0.5839, 0.6354, 0.7343, 0.9373])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7856, 2144], dtype=torch.int32)
    
2025-05-14 00:51:07,982 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.03738434612751007 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.57144651003182 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 243,  237,  253,  355, 8912], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5477, 0.6481, 0.7477, 0.8541, 0.9955])
        * Final bucket consistencies: tensor([0.4897, 0.5738, 0.6443, 0.6986, 0.9662])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7665, 2335], dtype=torch.int32)
    
2025-05-14 00:51:22,076 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.0601758249104023 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.30912773590535 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 421,  427,  492,  712, 7948], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5493, 0.6525, 0.7505, 0.8537, 0.9907])
        * Final bucket consistencies: tensor([0.5202, 0.5972, 0.6707, 0.7135, 0.9370])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7843, 2157], dtype=torch.int32)
    
2025-05-14 00:51:22,312 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.03716574236750603 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.328609904274344 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 255,  216,  251,  375, 8903], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5514, 0.6486, 0.7510, 0.8539, 0.9955])
        * Final bucket consistencies: tensor([0.5333, 0.5602, 0.6255, 0.6667, 0.9679])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7675, 2325], dtype=torch.int32)
    
2025-05-14 00:51:36,966 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.061334483325481415 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.889475033152848 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 420,  432,  486,  683, 7979], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5495, 0.6489, 0.7494, 0.8544, 0.9904])
        * Final bucket consistencies: tensor([0.5667, 0.5764, 0.6379, 0.7247, 0.9362])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7860, 2140], dtype=torch.int32)
    
2025-05-14 00:51:37,152 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.036301445215940475 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.838971273973584 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 239,  234,  273,  349, 8905], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5496, 0.6475, 0.7520, 0.8561, 0.9955])
        * Final bucket consistencies: tensor([0.5188, 0.5940, 0.6520, 0.6476, 0.9682])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7667, 2333], dtype=torch.int32)
    
2025-05-14 00:51:51,365 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.06044907867908478 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.396896474994719 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 420,  441,  477,  709, 7953], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5488, 0.6506, 0.7522, 0.8532, 0.9906])
        * Final bucket consistencies: tensor([0.5333, 0.5782, 0.6583, 0.7306, 0.9360])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7871, 2129], dtype=torch.int32)
    
2025-05-14 00:51:51,587 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.03710334002971649 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.432603877037764 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 245,  225,  280,  355, 8895], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5499, 0.6489, 0.7531, 0.8557, 0.9956])
        * Final bucket consistencies: tensor([0.4980, 0.5822, 0.6000, 0.7042, 0.9678])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7676, 2324], dtype=torch.int32)
    
2025-05-14 00:52:05,815 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.05946769937872887 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.449339877814054 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 428,  427,  491,  722, 7932], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5482, 0.6482, 0.7536, 0.8548, 0.9907])
        * Final bucket consistencies: tensor([0.5561, 0.5948, 0.6558, 0.7202, 0.9373])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7847, 2153], dtype=torch.int32)
    
2025-05-14 00:52:05,816 [INFO ] [calibration.py:__call__:217]	 Finished running Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples missing Missing X2 in zero-one in 144.92482938291505 seconds
2025-05-14 00:52:05,816 [INFO ] [calibration.py:__call__:194]	 Started running Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples missing Missing X2 in zero-one
2025-05-14 00:52:05,927 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.03692261129617691 for Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples in 14.338455853052437 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 250,  226,  259,  361, 8904], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5481, 0.6517, 0.7507, 0.8537, 0.9955])
        * Final bucket consistencies: tensor([0.5680, 0.5221, 0.6602, 0.6704, 0.9680])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7678, 2322], dtype=torch.int32)
    
2025-05-14 00:52:05,927 [INFO ] [calibration.py:__call__:217]	 Finished running Monte Carlo - Conditional Gaussian - 0.25 * Covariance - 100 samples missing Missing X1 in zero-one in 144.54031776078045 seconds
2025-05-14 00:52:05,927 [INFO ] [calibration.py:__call__:194]	 Started running Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples missing Missing X1 in zero-one
2025-05-14 00:52:20,384 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.0260794498026371 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.566789435222745 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 676,  659,  872, 1126, 6667], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5490, 0.6499, 0.7507, 0.8526, 0.9846])
        * Final bucket consistencies: tensor([0.5769, 0.6434, 0.7202, 0.7833, 0.9646])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7699, 2301], dtype=torch.int32)
    
2025-05-14 00:52:20,730 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.017095647752285004 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.801468711346388 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 382,  382,  420,  602, 8214], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5483, 0.6498, 0.7497, 0.8524, 0.9923])
        * Final bucket consistencies: tensor([0.5052, 0.6571, 0.6762, 0.8023, 0.9813])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7569, 2431], dtype=torch.int32)
    
2025-05-14 00:52:34,710 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.0235290490090847 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.324300789739937 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 651,  722,  811, 1169, 6647], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5492, 0.6493, 0.7509, 0.8529, 0.9848])
        * Final bucket consistencies: tensor([0.5530, 0.6288, 0.7300, 0.7904, 0.9655])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7698, 2302], dtype=torch.int32)
    
2025-05-14 00:52:35,078 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.015978632494807243 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.346763425972313 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 397,  369,  437,  590, 8207], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5490, 0.6490, 0.7509, 0.8549, 0.9922])
        * Final bucket consistencies: tensor([0.5592, 0.6179, 0.6957, 0.7932, 0.9820])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7549, 2451], dtype=torch.int32)
    
2025-05-14 00:52:49,140 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.025396743789315224 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.428039906080812 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 672,  704,  801, 1173, 6650], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5505, 0.6500, 0.7512, 0.8528, 0.9851])
        * Final bucket consistencies: tensor([0.5804, 0.6420, 0.7179, 0.8082, 0.9627])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7703, 2297], dtype=torch.int32)
    
2025-05-14 00:52:49,523 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.016791779547929764 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.444096809718758 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 399,  366,  441,  577, 8217], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5501, 0.6501, 0.7520, 0.8542, 0.9921])
        * Final bucket consistencies: tensor([0.5815, 0.6230, 0.7075, 0.7799, 0.9820])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7571, 2429], dtype=torch.int32)
    
2025-05-14 00:53:04,056 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.022810781374573708 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.91452274704352 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 670,  698,  841, 1119, 6672], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5507, 0.6489, 0.7498, 0.8519, 0.9848])
        * Final bucket consistencies: tensor([0.5418, 0.6418, 0.7229, 0.8159, 0.9616])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7686, 2314], dtype=torch.int32)
    
2025-05-14 00:53:04,434 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.016334783285856247 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.908708645030856 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 383,  398,  407,  624, 8188], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5489, 0.6503, 0.7532, 0.8550, 0.9925])
        * Final bucket consistencies: tensor([0.5248, 0.6206, 0.7150, 0.7853, 0.9823])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7570, 2430], dtype=torch.int32)
    
2025-05-14 00:53:18,901 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.024734416976571083 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.843225184828043 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 680,  700,  847, 1115, 6658], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5493, 0.6504, 0.7507, 0.8532, 0.9851])
        * Final bucket consistencies: tensor([0.5191, 0.6543, 0.7367, 0.7857, 0.9646])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7657, 2343], dtype=torch.int32)
    
2025-05-14 00:53:19,211 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.01711888611316681 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.775746027939022 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 386,  398,  399,  614, 8203], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5496, 0.6520, 0.7506, 0.8536, 0.9924])
        * Final bucket consistencies: tensor([0.5829, 0.6005, 0.7293, 0.7801, 0.9821])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7570, 2430], dtype=torch.int32)
    
2025-05-14 00:53:33,498 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.0237064640969038 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.596362325828522 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 657,  731,  815, 1134, 6663], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5493, 0.6497, 0.7512, 0.8512, 0.9848])
        * Final bucket consistencies: tensor([0.5266, 0.6484, 0.7325, 0.7937, 0.9637])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7690, 2310], dtype=torch.int32)
    
2025-05-14 00:53:33,879 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.016610462218523026 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.666924878954887 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 379,  385,  419,  613, 8204], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5480, 0.6490, 0.7488, 0.8532, 0.9922])
        * Final bucket consistencies: tensor([0.5673, 0.6000, 0.6826, 0.8010, 0.9824])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7561, 2439], dtype=torch.int32)
    
2025-05-14 00:53:48,266 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.0238360445946455 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.765902690123767 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 663,  696,  836, 1141, 6664], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5490, 0.6492, 0.7505, 0.8520, 0.9848])
        * Final bucket consistencies: tensor([0.5294, 0.6422, 0.7321, 0.7958, 0.9637])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7676, 2324], dtype=torch.int32)
    
2025-05-14 00:53:48,706 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.017586788162589073 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 14.82576439389959 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 385,  369,  440,  609, 8197], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5484, 0.6512, 0.7523, 0.8557, 0.9924])
        * Final bucket consistencies: tensor([0.4961, 0.6152, 0.7023, 0.7964, 0.9821])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7576, 2424], dtype=torch.int32)
    
2025-05-14 00:54:03,771 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.024191226810216904 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 15.503765261732042 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 666,  708,  848, 1094, 6684], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5492, 0.6510, 0.7527, 0.8515, 0.9845])
        * Final bucket consistencies: tensor([0.5345, 0.6257, 0.7394, 0.8026, 0.9621])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7677, 2323], dtype=torch.int32)
    
2025-05-14 00:54:03,870 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.01764814369380474 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 15.163366117980331 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 377,  387,  441,  587, 8208], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5486, 0.6509, 0.7519, 0.8555, 0.9922])
        * Final bucket consistencies: tensor([0.5013, 0.6227, 0.6757, 0.8126, 0.9814])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7568, 2432], dtype=torch.int32)
    
2025-05-14 00:54:22,386 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.01589055545628071 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 18.514476479962468 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 370,  383,  438,  632, 8177], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5486, 0.6488, 0.7489, 0.8538, 0.9924])
        * Final bucket consistencies: tensor([0.5405, 0.6057, 0.7078, 0.7896, 0.9825])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7559, 2441], dtype=torch.int32)
    
2025-05-14 00:54:22,622 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.024297524243593216 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 18.850491533987224 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 663,  705,  853, 1131, 6648], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5505, 0.6500, 0.7515, 0.8533, 0.9851])
        * Final bucket consistencies: tensor([0.5490, 0.6071, 0.7292, 0.8099, 0.9634])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7671, 2329], dtype=torch.int32)
    
2025-05-14 00:54:40,907 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.022386467084288597 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 18.283217038027942 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 665,  716,  854, 1122, 6643], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5490, 0.6515, 0.7534, 0.8529, 0.9851])
        * Final bucket consistencies: tensor([0.5504, 0.6508, 0.7295, 0.8021, 0.9633])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7672, 2328], dtype=torch.int32)
    
2025-05-14 00:54:40,907 [INFO ] [calibration.py:__call__:217]	 Finished running Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples missing Missing X2 in zero-one in 155.09168904135004 seconds
2025-05-14 00:54:40,907 [INFO ] [calibration.py:__call__:194]	 Started running Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples missing Missing X2 in zero-one
2025-05-14 00:54:41,596 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.01607724092900753 for Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples in 19.208454684820026 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 389,  359,  448,  601, 8203], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5495, 0.6508, 0.7499, 0.8538, 0.9923])
        * Final bucket consistencies: tensor([0.5553, 0.5877, 0.6987, 0.8070, 0.9820])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7568, 2432], dtype=torch.int32)
    
2025-05-14 00:54:41,597 [INFO ] [calibration.py:__call__:217]	 Finished running Monte Carlo - Conditional Gaussian - 0.8 Correlation - 100 samples missing Missing X1 in zero-one in 155.66918378323317 seconds
2025-05-14 00:54:41,597 [INFO ] [calibration.py:__call__:194]	 Started running Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples missing Missing X1 in zero-one
2025-05-14 00:54:59,090 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.01654474437236786 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 18.181997454259545 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 920, 1027, 1180, 1483, 5390], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5488, 0.6495, 0.7503, 0.8506, 0.9762])
        * Final bucket consistencies: tensor([0.5793, 0.6904, 0.7678, 0.8739, 0.9837])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([8048, 1952], dtype=torch.int32)
    
2025-05-14 00:54:59,793 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.0056620710529387 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 18.195056003984064 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 491,  526,  652,  837, 7494], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5493, 0.6491, 0.7523, 0.8551, 0.9894])
        * Final bucket consistencies: tensor([0.5397, 0.6578, 0.7914, 0.8542, 0.9923])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7760, 2240], dtype=torch.int32)
    
2025-05-14 00:55:16,578 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.007421291898936033 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 16.783946600742638 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 502,  518,  632,  847, 7501], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5504, 0.6503, 0.7525, 0.8541, 0.9894])
        * Final bucket consistencies: tensor([0.5916, 0.6409, 0.7832, 0.8701, 0.9915])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7764, 2236], dtype=torch.int32)
    
2025-05-14 00:55:16,867 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.015580984763801098 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 17.775808990933 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 969, 1022, 1191, 1480, 5338], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5510, 0.6498, 0.7523, 0.8536, 0.9767])
        * Final bucket consistencies: tensor([0.5635, 0.6888, 0.7741, 0.8791, 0.9843])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([8041, 1959], dtype=torch.int32)
    
2025-05-14 00:55:32,871 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.00694489199668169 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 16.29177524941042 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 509,  515,  622,  820, 7534], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5510, 0.6513, 0.7530, 0.8519, 0.9891])
        * Final bucket consistencies: tensor([0.5265, 0.6466, 0.8167, 0.8500, 0.9908])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7753, 2247], dtype=torch.int32)
    
2025-05-14 00:55:33,589 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.016768986359238625 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 16.72033144161105 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 954, 1018, 1183, 1481, 5364], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5512, 0.6497, 0.7506, 0.8529, 0.9767])
        * Final bucket consistencies: tensor([0.5692, 0.6857, 0.7836, 0.8731, 0.9851])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([8037, 1963], dtype=torch.int32)
    
2025-05-14 00:55:49,011 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.005740568973124027 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 16.13887183740735 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 501,  513,  632,  835, 7519], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5485, 0.6505, 0.7508, 0.8521, 0.9892])
        * Final bucket consistencies: tensor([0.5429, 0.6608, 0.7896, 0.8623, 0.9914])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7762, 2238], dtype=torch.int32)
    
2025-05-14 00:55:49,727 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.016861386597156525 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 16.136718897614628 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 920, 1057, 1160, 1455, 5408], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5487, 0.6491, 0.7506, 0.8516, 0.9759])
        * Final bucket consistencies: tensor([0.5761, 0.6935, 0.7750, 0.8742, 0.9824])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([8053, 1947], dtype=torch.int32)
    
2025-05-14 00:56:05,516 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.005313635803759098 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 16.503253490198404 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 516,  494,  635,  833, 7522], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5486, 0.6497, 0.7509, 0.8534, 0.9891])
        * Final bucket consistencies: tensor([0.5407, 0.6721, 0.7811, 0.8535, 0.9916])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7773, 2227], dtype=torch.int32)
    
2025-05-14 00:56:06,220 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.016286320984363556 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 16.492226153612137 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 955,  996, 1196, 1445, 5408], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5502, 0.6506, 0.7508, 0.8525, 0.9763])
        * Final bucket consistencies: tensor([0.5728, 0.6898, 0.7793, 0.8692, 0.9845])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([8064, 1936], dtype=torch.int32)
    
2025-05-14 00:56:22,073 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.006200519390404224 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 16.55536161083728 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 505,  518,  619,  847, 7511], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5488, 0.6508, 0.7511, 0.8527, 0.9892])
        * Final bucket consistencies: tensor([0.5644, 0.6622, 0.7884, 0.8477, 0.9920])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7759, 2241], dtype=torch.int32)
    
2025-05-14 00:56:22,962 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.016234129667282104 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 16.740431360900402 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 962, 1022, 1156, 1491, 5369], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5501, 0.6510, 0.7507, 0.8530, 0.9764])
        * Final bucket consistencies: tensor([0.5728, 0.6869, 0.7742, 0.8793, 0.9834])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([8045, 1955], dtype=torch.int32)
    
2025-05-14 00:56:39,405 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.006233980879187584 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 17.33085736585781 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 502,  533,  607,  829, 7529], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5503, 0.6512, 0.7515, 0.8518, 0.9890])
        * Final bucket consistencies: tensor([0.5239, 0.6792, 0.7743, 0.8528, 0.9916])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7752, 2248], dtype=torch.int32)
    
2025-05-14 00:56:39,880 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.01451557595282793 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 16.9160568267107 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 998,  966, 1181, 1496, 5359], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5510, 0.6498, 0.7511, 0.8530, 0.9767])
        * Final bucket consistencies: tensor([0.5601, 0.6925, 0.7714, 0.8730, 0.9843])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([8050, 1950], dtype=torch.int32)
    
2025-05-14 00:56:57,534 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.007478234823793173 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 18.127370125148445 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 500,  521,  627,  849, 7503], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5480, 0.6518, 0.7532, 0.8539, 0.9893])
        * Final bucket consistencies: tensor([0.5960, 0.6814, 0.7608, 0.8681, 0.9917])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7756, 2244], dtype=torch.int32)
    
2025-05-14 00:56:58,882 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.018602434545755386 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 19.00031903432682 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 926, 1011, 1207, 1481, 5375], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5492, 0.6500, 0.7516, 0.8513, 0.9764])
        * Final bucket consistencies: tensor([0.5259, 0.6904, 0.7970, 0.8724, 0.9834])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([8039, 1961], dtype=torch.int32)
    
2025-05-14 00:57:16,188 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.006869287230074406 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 18.653200164437294 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 512,  522,  619,  818, 7529], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5494, 0.6511, 0.7525, 0.8525, 0.9890])
        * Final bucket consistencies: tensor([0.5176, 0.6743, 0.7722, 0.8619, 0.9918])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7755, 2245], dtype=torch.int32)
    
2025-05-14 00:57:17,275 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.016541652381420135 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 18.39163688989356 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 945, 1038, 1166, 1476, 5375], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5494, 0.6499, 0.7502, 0.8528, 0.9763])
        * Final bucket consistencies: tensor([0.5587, 0.6936, 0.7882, 0.8726, 0.9833])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([8048, 1952], dtype=torch.int32)
    
2025-05-14 00:57:35,063 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.008438840508460999 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 18.872878711204976 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 495,  526,  641,  830, 7508], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5509, 0.6502, 0.7513, 0.8538, 0.9893])
        * Final bucket consistencies: tensor([0.5030, 0.6692, 0.7894, 0.8506, 0.9924])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7766, 2234], dtype=torch.int32)
    
2025-05-14 00:57:35,063 [INFO ] [calibration.py:__call__:217]	 Finished running Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples missing Missing X1 in zero-one in 173.46636298811063 seconds
2025-05-14 00:57:35,063 [INFO ] [calibration.py:__call__:194]	 Started running Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples missing Missing X2 in zero-one
2025-05-14 00:57:35,171 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.018148446455597878 for Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples in 17.894973013084382 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 978, 1027, 1142, 1504, 5349], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5506, 0.6523, 0.7514, 0.8527, 0.9766])
        * Final bucket consistencies: tensor([0.5859, 0.6767, 0.8004, 0.8723, 0.9834])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([8064, 1936], dtype=torch.int32)
    
2025-05-14 00:57:35,171 [INFO ] [calibration.py:__call__:217]	 Finished running Monte Carlo - Conditional Gaussian - 0.6 Correlation - 100 samples missing Missing X2 in zero-one in 174.26359033863991 seconds
2025-05-14 00:57:35,171 [INFO ] [calibration.py:__call__:194]	 Started running Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples missing Missing X1 in zero-one
2025-05-14 00:57:51,964 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.008302904665470123 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.899608264211565 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 818,  874, 1027, 1326, 5955], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5489, 0.6506, 0.7533, 0.8531, 0.9805])
        * Final bucket consistencies: tensor([0.5685, 0.6808, 0.7488, 0.8462, 0.9760])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7851, 2149], dtype=torch.int32)
    
2025-05-14 00:57:52,631 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.005113956052809954 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 17.458845760673285 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 471,  435,  541,  746, 7807], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5466, 0.6508, 0.7528, 0.8536, 0.9906])
        * Final bucket consistencies: tensor([0.5605, 0.6345, 0.7412, 0.8405, 0.9878])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7670, 2330], dtype=torch.int32)
    
2025-05-14 00:58:08,440 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.007567815948277712 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.474838647060096 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 829,  861, 1034, 1316, 5960], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5480, 0.6509, 0.7516, 0.8507, 0.9805])
        * Final bucket consistencies: tensor([0.5501, 0.6748, 0.7650, 0.8374, 0.9768])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7846, 2154], dtype=torch.int32)
    
2025-05-14 00:58:08,913 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.005882974248379469 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.28056665463373 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 461,  438,  533,  758, 7810], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5491, 0.6500, 0.7512, 0.8535, 0.9906])
        * Final bucket consistencies: tensor([0.5098, 0.6370, 0.7561, 0.8272, 0.9890])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7676, 2324], dtype=torch.int32)
    
2025-05-14 00:58:24,638 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.007243577390909195 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.19686851883307 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 817,  862, 1029, 1350, 5942], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5490, 0.6500, 0.7508, 0.8509, 0.9805])
        * Final bucket consistencies: tensor([0.5569, 0.6705, 0.7570, 0.8319, 0.9778])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7855, 2145], dtype=torch.int32)
    
2025-05-14 00:58:25,074 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.006067910231649876 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.1600519339554 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 454,  464,  503,  761, 7818], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5472, 0.6489, 0.7504, 0.8528, 0.9905])
        * Final bucket consistencies: tensor([0.5198, 0.6509, 0.7634, 0.8371, 0.9868])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7662, 2338], dtype=torch.int32)
    
2025-05-14 00:58:40,965 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.006334750447422266 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 15.88953467225656 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 459,  420,  552,  750, 7819], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5499, 0.6479, 0.7512, 0.8541, 0.9906])
        * Final bucket consistencies: tensor([0.5795, 0.6214, 0.7409, 0.8307, 0.9886])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7665, 2335], dtype=torch.int32)
    
2025-05-14 00:58:41,131 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.009565845131874084 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.491825822740793 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 833,  882, 1018, 1329, 5938], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5484, 0.6504, 0.7526, 0.8522, 0.9806])
        * Final bucket consistencies: tensor([0.5774, 0.6701, 0.7662, 0.8382, 0.9769])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7863, 2137], dtype=torch.int32)
    
2025-05-14 00:58:56,477 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.004842857830226421 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 15.51092910533771 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 475,  433,  518,  757, 7817], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5493, 0.6494, 0.7531, 0.8518, 0.9905])
        * Final bucket consistencies: tensor([0.5600, 0.6189, 0.7394, 0.8520, 0.9876])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7662, 2338], dtype=torch.int32)
    
2025-05-14 00:58:56,897 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.008115757256746292 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 15.764472687616944 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 843,  834, 1062, 1323, 5938], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5492, 0.6515, 0.7510, 0.8512, 0.9806])
        * Final bucket consistencies: tensor([0.5753, 0.6703, 0.7571, 0.8466, 0.9754])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7844, 2156], dtype=torch.int32)
    
2025-05-14 00:59:12,850 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.009132563136518002 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 15.952494672965258 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 823,  869, 1070, 1253, 5985], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5499, 0.6498, 0.7516, 0.8522, 0.9803])
        * Final bucket consistencies: tensor([0.5456, 0.6801, 0.7664, 0.8324, 0.9768])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7863, 2137], dtype=torch.int32)
    
2025-05-14 00:59:13,135 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.004394148476421833 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.656859256792814 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 448,  481,  513,  743, 7815], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5475, 0.6508, 0.7510, 0.8534, 0.9907])
        * Final bucket consistencies: tensor([0.5402, 0.6528, 0.7349, 0.8425, 0.9877])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7662, 2338], dtype=torch.int32)
    
2025-05-14 00:59:29,087 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.008907615207135677 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.234969859942794 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 851,  846, 1037, 1335, 5931], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5502, 0.6499, 0.7506, 0.8522, 0.9807])
        * Final bucket consistencies: tensor([0.5617, 0.6513, 0.7801, 0.8277, 0.9782])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7859, 2141], dtype=torch.int32)
    
2025-05-14 00:59:29,721 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.005511279217898846 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.58498505083844 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 455,  441,  536,  748, 7820], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5468, 0.6488, 0.7516, 0.8528, 0.9906])
        * Final bucket consistencies: tensor([0.5231, 0.6281, 0.7593, 0.8449, 0.9873])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7681, 2319], dtype=torch.int32)
    
2025-05-14 00:59:45,305 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.006802556570619345 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.21685795718804 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 814,  876, 1040, 1353, 5917], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5493, 0.6499, 0.7512, 0.8529, 0.9808])
        * Final bucket consistencies: tensor([0.5491, 0.6724, 0.7615, 0.8418, 0.9770])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7868, 2132], dtype=torch.int32)
    
2025-05-14 00:59:45,887 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.004423754755407572 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.165130133740604 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 457,  449,  516,  768, 7810], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5483, 0.6518, 0.7500, 0.8519, 0.9907])
        * Final bucket consistencies: tensor([0.5427, 0.6347, 0.7384, 0.8490, 0.9875])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7664, 2336], dtype=torch.int32)
    
2025-05-14 01:00:01,662 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.007145469076931477 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.355500552803278 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 831,  879, 1013, 1320, 5957], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5502, 0.6501, 0.7510, 0.8526, 0.9800])
        * Final bucket consistencies: tensor([0.5475, 0.6678, 0.7660, 0.8394, 0.9765])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7859, 2141], dtype=torch.int32)
    
2025-05-14 01:00:02,253 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.005436330568045378 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.365119963884354 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 456,  459,  501,  775, 7809], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5497, 0.6531, 0.7507, 0.8521, 0.9906])
        * Final bucket consistencies: tensor([0.5197, 0.6492, 0.7545, 0.8413, 0.9869])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7664, 2336], dtype=torch.int32)
    
2025-05-14 01:00:18,126 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.0077673583291471004 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.463278429117054 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 840,  857, 1038, 1286, 5979], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5500, 0.6515, 0.7504, 0.8517, 0.9802])
        * Final bucket consistencies: tensor([0.5524, 0.6896, 0.7486, 0.8390, 0.9761])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7841, 2159], dtype=torch.int32)
    
2025-05-14 01:00:18,126 [INFO ] [calibration.py:__call__:217]	 Finished running Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples missing Missing X2 in zero-one in 163.06318735703826 seconds
2025-05-14 01:00:18,127 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 1 stopping
2025-05-14 01:00:18,393 [INFO ] [calibration.py:computeMVCE:130]	 
        Computed MVCE 0.004395349882543087 for Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples in 16.13841621670872 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 462,  432,  553,  756, 7797], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.5509, 0.6476, 0.7508, 0.8540, 0.9908])
        * Final bucket consistencies: tensor([0.5260, 0.6481, 0.7450, 0.8333, 0.9891])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([7556, 2444], dtype=torch.int32)
        * Prediction Action Counts: tensor([7663, 2337], dtype=torch.int32)
    
2025-05-14 01:00:18,393 [INFO ] [calibration.py:__call__:217]	 Finished running Monte Carlo - Conditional Gaussian - Ground Truth - 100 samples missing Missing X1 in zero-one in 163.22172837518156 seconds
2025-05-14 01:00:18,393 [INFO ] [threading_utils.py:__call__:60]	 Worker thread 2 stopping
2025-05-14 01:00:18,393 [INFO ] [mvce_synthetic.py:<module>:215]	 Finished running 18/18 experiments.
2025-05-14 01:00:18,394 [INFO ] [mvce_synthetic.py:<module>:220]	 Saving results to ./results/mvce/synthetic-20250514-004013.csv
2025-05-14 01:00:18,395 [INFO ] [mvce_synthetic.py:<module>:230]	 Finished saving results
