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INFO - Distribution Fit: Inverse Gamma +2025-04-19 18:08:52,142 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 18:08:56,308 - INFO - Validation and Training Set Split Statistics +2025-04-19 18:08:56,308 - INFO - Timestamp: 2025-04-19 18:08:56 +2025-04-19 18:08:56,309 - INFO - Validation set size: 1695 (10.26%) +2025-04-19 18:08:56,309 - INFO - Training set size: 14819 (89.74%) +2025-04-19 18:08:56,309 - INFO - Minimum Distance Statistics (km): +2025-04-19 18:08:56,309 - INFO - Mean: 4.7021 +2025-04-19 18:08:56,309 - INFO - Median: 4.3064 +2025-04-19 18:08:56,309 - INFO - Min: 0.0406 +2025-04-19 18:08:56,309 - INFO - Max: 20.3992 +2025-04-19 18:08:56,309 - INFO - Std: 2.9395 +2025-04-19 18:08:56,309 - INFO - OC Distribution Statistics: +2025-04-19 18:08:56,309 - INFO - Validation Mean: 16.6693 +2025-04-19 18:08:56,309 - INFO - Validation Std: 7.8770 +2025-04-19 18:08:56,309 - INFO - Training Mean: 23.1968 +2025-04-19 18:08:56,309 - INFO - Training Std: 20.4839 +2025-04-19 18:08:56,309 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 18:08:56,309 - INFO - KS Statistic: 0.1890 +2025-04-19 18:08:56,309 - INFO - P-value: 0.0000 +2025-04-19 18:08:56,309 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 18:08:56,309 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 18:08:57,183 - INFO - Validation and Training Set Split Statistics +2025-04-19 18:08:57,183 - INFO - Timestamp: 2025-04-19 18:08:57 +2025-04-19 18:08:57,183 - INFO - Validation set size: 1409 (8.53%) +2025-04-19 18:08:57,183 - INFO - Training set size: 15105 (91.47%) +2025-04-19 18:08:57,184 - INFO - Minimum Distance Statistics (km): +2025-04-19 18:08:57,184 - INFO - Mean: 4.8111 +2025-04-19 18:08:57,184 - INFO - Median: 4.4604 +2025-04-19 18:08:57,184 - INFO - Min: 0.0712 +2025-04-19 18:08:57,184 - INFO - Max: 18.8528 +2025-04-19 18:08:57,184 - INFO - Std: 2.6398 +2025-04-19 18:08:57,184 - INFO - OC Distribution Statistics: +2025-04-19 18:08:57,184 - INFO - Validation Mean: 17.1966 +2025-04-19 18:08:57,184 - INFO - Validation Std: 8.5294 +2025-04-19 18:08:57,184 - INFO - Training Mean: 23.0240 +2025-04-19 18:08:57,184 - INFO - Training Std: 20.3276 +2025-04-19 18:08:57,184 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 18:08:57,184 - INFO - KS Statistic: 0.1582 +2025-04-19 18:08:57,184 - INFO - P-value: 0.0000 +2025-04-19 18:08:57,184 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 18:08:57,184 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 18:08:57,928 - INFO - Validation and Training Set Split Statistics +2025-04-19 18:08:57,929 - INFO - Timestamp: 2025-04-19 18:08:57 +2025-04-19 18:08:57,929 - INFO - Validation set size: 1561 (9.45%) +2025-04-19 18:08:57,929 - INFO - Training set size: 14953 (90.55%) +2025-04-19 18:08:57,929 - INFO - Minimum Distance Statistics (km): +2025-04-19 18:08:57,929 - INFO - Mean: 4.6939 +2025-04-19 18:08:57,929 - INFO - Median: 3.8909 +2025-04-19 18:08:57,929 - INFO - Min: 0.3424 +2025-04-19 18:08:57,929 - INFO - Max: 18.8528 +2025-04-19 18:08:57,929 - INFO - Std: 2.9706 +2025-04-19 18:08:57,929 - INFO - OC Distribution Statistics: +2025-04-19 18:08:57,929 - INFO - Validation Mean: 17.6388 +2025-04-19 18:08:57,929 - INFO - Validation Std: 9.0573 +2025-04-19 18:08:57,929 - INFO - Training Mean: 23.0371 +2025-04-19 18:08:57,929 - INFO - Training Std: 20.3931 +2025-04-19 18:08:57,929 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 18:08:57,929 - INFO - KS Statistic: 0.1346 +2025-04-19 18:08:57,929 - INFO - P-value: 0.0000 +2025-04-19 18:08:57,929 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 18:08:57,929 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 19:17:22,029 - INFO - Validation and Training Set Split Statistics +2025-04-19 19:17:22,030 - INFO - Timestamp: 2025-04-19 19:17:22 +2025-04-19 19:17:22,030 - INFO - Validation set size: 1590 (9.63%) +2025-04-19 19:17:22,030 - INFO - Training set size: 14924 (90.37%) +2025-04-19 19:17:22,030 - INFO - Minimum Distance Statistics (km): +2025-04-19 19:17:22,030 - INFO - Mean: 4.9021 +2025-04-19 19:17:22,030 - INFO - Median: 4.2512 +2025-04-19 19:17:22,030 - INFO - Min: 0.0406 +2025-04-19 19:17:22,030 - INFO - Max: 20.3992 +2025-04-19 19:17:22,030 - INFO - Std: 2.8136 +2025-04-19 19:17:22,030 - INFO - OC Distribution Statistics: +2025-04-19 19:17:22,030 - INFO - Validation Mean: 17.0102 +2025-04-19 19:17:22,030 - INFO - Validation Std: 7.8971 +2025-04-19 19:17:22,030 - INFO - Training Mean: 23.1145 +2025-04-19 19:17:22,030 - INFO - Training Std: 20.4400 +2025-04-19 19:17:22,030 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 19:17:22,030 - INFO - KS Statistic: 0.1731 +2025-04-19 19:17:22,030 - INFO - P-value: 0.0000 +2025-04-19 19:17:22,030 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 19:17:22,030 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 19:17:22,452 - INFO - Validation and Training Set Split Statistics +2025-04-19 19:17:22,453 - INFO - Timestamp: 2025-04-19 19:17:22 +2025-04-19 19:17:22,453 - INFO - Validation set size: 1369 (8.29%) +2025-04-19 19:17:22,453 - INFO - Training set size: 15145 (91.71%) +2025-04-19 19:17:22,453 - INFO - Minimum Distance Statistics (km): +2025-04-19 19:17:22,453 - INFO - Mean: 4.8553 +2025-04-19 19:17:22,453 - INFO - Median: 4.2248 +2025-04-19 19:17:22,453 - INFO - Min: 0.0358 +2025-04-19 19:17:22,453 - INFO - Max: 18.4135 +2025-04-19 19:17:22,453 - INFO - Std: 2.7134 +2025-04-19 19:17:22,453 - INFO - OC Distribution Statistics: +2025-04-19 19:17:22,453 - INFO - Validation Mean: 17.3997 +2025-04-19 19:17:22,453 - INFO - Validation Std: 8.1351 +2025-04-19 19:17:22,453 - INFO - Training Mean: 22.9902 +2025-04-19 19:17:22,453 - INFO - Training Std: 20.3275 +2025-04-19 19:17:22,453 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 19:17:22,453 - INFO - KS Statistic: 0.1623 +2025-04-19 19:17:22,453 - INFO - P-value: 0.0000 +2025-04-19 19:17:22,453 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 19:17:22,453 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 19:17:25,716 - INFO - Validation and Training Set Split Statistics +2025-04-19 19:17:25,717 - INFO - Timestamp: 2025-04-19 19:17:25 +2025-04-19 19:17:25,717 - INFO - Validation set size: 1598 (9.68%) +2025-04-19 19:17:25,717 - INFO - Training set size: 14916 (90.32%) +2025-04-19 19:17:25,717 - INFO - Minimum Distance Statistics (km): +2025-04-19 19:17:25,717 - INFO - Mean: 4.4554 +2025-04-19 19:17:25,717 - INFO - Median: 3.7798 +2025-04-19 19:17:25,717 - INFO - Min: 0.3976 +2025-04-19 19:17:25,717 - INFO - Max: 20.3992 +2025-04-19 19:17:25,717 - INFO - Std: 2.7133 +2025-04-19 19:17:25,717 - INFO - OC Distribution Statistics: +2025-04-19 19:17:25,717 - INFO - Validation Mean: 17.3910 +2025-04-19 19:17:25,717 - INFO - Validation Std: 8.9890 +2025-04-19 19:17:25,717 - INFO - Training Mean: 23.0770 +2025-04-19 19:17:25,717 - INFO - Training Std: 20.4076 +2025-04-19 19:17:25,717 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 19:17:25,717 - INFO - KS Statistic: 0.1690 +2025-04-19 19:17:25,717 - INFO - P-value: 0.0000 +2025-04-19 19:17:25,717 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 19:17:25,717 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 19:17:25,950 - INFO - Validation and Training Set Split Statistics +2025-04-19 19:17:25,951 - INFO - Timestamp: 2025-04-19 19:17:25 +2025-04-19 19:17:25,951 - INFO - Validation set size: 1323 (8.01%) +2025-04-19 19:17:25,951 - INFO - Training set size: 15191 (91.99%) +2025-04-19 19:17:25,951 - INFO - Minimum Distance Statistics (km): +2025-04-19 19:17:25,951 - INFO - Mean: 4.7890 +2025-04-19 19:17:25,951 - INFO - Median: 4.3369 +2025-04-19 19:17:25,951 - INFO - Min: 0.1121 +2025-04-19 19:17:25,951 - INFO - Max: 18.8528 +2025-04-19 19:17:25,951 - INFO - Std: 2.8569 +2025-04-19 19:17:25,951 - INFO - OC Distribution Statistics: +2025-04-19 19:17:25,951 - INFO - Validation Mean: 17.4841 +2025-04-19 19:17:25,951 - INFO - Validation Std: 8.6016 +2025-04-19 19:17:25,951 - INFO - Training Mean: 22.9660 +2025-04-19 19:17:25,951 - INFO - Training Std: 20.2892 +2025-04-19 19:17:25,951 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 19:17:25,951 - INFO - KS Statistic: 0.1214 +2025-04-19 19:17:25,951 - INFO - P-value: 0.0000 +2025-04-19 19:17:25,951 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 19:17:25,951 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 20:23:43,724 - INFO - Validation and Training Set Split Statistics +2025-04-19 20:23:43,725 - INFO - Timestamp: 2025-04-19 20:23:43 +2025-04-19 20:23:43,725 - INFO - Validation set size: 1368 (8.28%) +2025-04-19 20:23:43,725 - INFO - Training set size: 15146 (91.72%) +2025-04-19 20:23:43,725 - INFO - Minimum Distance Statistics (km): +2025-04-19 20:23:43,725 - INFO - Mean: 4.7497 +2025-04-19 20:23:43,725 - INFO - Median: 4.2807 +2025-04-19 20:23:43,725 - INFO - Min: 0.3705 +2025-04-19 20:23:43,725 - INFO - Max: 17.1898 +2025-04-19 20:23:43,725 - INFO - Std: 2.6068 +2025-04-19 20:23:43,725 - INFO - OC Distribution Statistics: +2025-04-19 20:23:43,725 - INFO - Validation Mean: 17.1562 +2025-04-19 20:23:43,725 - INFO - Validation Std: 7.9032 +2025-04-19 20:23:43,725 - INFO - Training Mean: 23.0119 +2025-04-19 20:23:43,725 - INFO - Training Std: 20.3290 +2025-04-19 20:23:43,725 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 20:23:43,725 - INFO - KS Statistic: 0.1769 +2025-04-19 20:23:43,725 - INFO - P-value: 0.0000 +2025-04-19 20:23:43,725 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 20:23:43,725 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 20:23:44,579 - INFO - Validation and Training Set Split Statistics +2025-04-19 20:23:44,580 - INFO - Timestamp: 2025-04-19 20:23:44 +2025-04-19 20:23:44,580 - INFO - Validation set size: 1343 (8.13%) +2025-04-19 20:23:44,580 - INFO - Training set size: 15171 (91.87%) +2025-04-19 20:23:44,580 - INFO - Minimum Distance Statistics (km): +2025-04-19 20:23:44,580 - INFO - Mean: 4.8440 +2025-04-19 20:23:44,580 - INFO - Median: 4.1596 +2025-04-19 20:23:44,580 - INFO - Min: 0.0565 +2025-04-19 20:23:44,580 - INFO - Max: 18.8528 +2025-04-19 20:23:44,580 - INFO - Std: 3.1360 +2025-04-19 20:23:44,580 - INFO - OC Distribution Statistics: +2025-04-19 20:23:44,580 - INFO - Validation Mean: 18.0474 +2025-04-19 20:23:44,580 - INFO - Validation Std: 9.0509 +2025-04-19 20:23:44,580 - INFO - Training Mean: 22.9233 +2025-04-19 20:23:44,580 - INFO - Training Std: 20.2946 +2025-04-19 20:23:44,580 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 20:23:44,580 - INFO - KS Statistic: 0.1104 +2025-04-19 20:23:44,580 - INFO - P-value: 0.0000 +2025-04-19 20:23:44,580 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 20:23:44,580 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 20:23:47,765 - INFO - Validation and Training Set Split Statistics +2025-04-19 20:23:47,766 - INFO - Timestamp: 2025-04-19 20:23:47 +2025-04-19 20:23:47,766 - INFO - Validation set size: 1762 (10.67%) +2025-04-19 20:23:47,766 - INFO - Training set size: 14752 (89.33%) +2025-04-19 20:23:47,766 - INFO - Minimum Distance Statistics (km): +2025-04-19 20:23:47,766 - INFO - Mean: 4.7451 +2025-04-19 20:23:47,766 - INFO - Median: 3.9866 +2025-04-19 20:23:47,766 - INFO - Min: 0.0712 +2025-04-19 20:23:47,766 - INFO - Max: 20.3992 +2025-04-19 20:23:47,766 - INFO - Std: 3.0522 +2025-04-19 20:23:47,766 - INFO - OC Distribution Statistics: +2025-04-19 20:23:47,766 - INFO - Validation Mean: 17.3461 +2025-04-19 20:23:47,766 - INFO - Validation Std: 8.5104 +2025-04-19 20:23:47,766 - INFO - Training Mean: 23.1456 +2025-04-19 20:23:47,766 - INFO - Training Std: 20.5128 +2025-04-19 20:23:47,766 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 20:23:47,766 - INFO - KS Statistic: 0.1309 +2025-04-19 20:23:47,766 - INFO - P-value: 0.0000 +2025-04-19 20:23:47,766 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 20:23:47,766 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 20:23:51,860 - INFO - Validation and Training Set Split Statistics +2025-04-19 20:23:51,861 - INFO - Timestamp: 2025-04-19 20:23:51 +2025-04-19 20:23:51,861 - INFO - Validation set size: 1859 (11.26%) +2025-04-19 20:23:51,861 - INFO - Training set size: 14655 (88.74%) +2025-04-19 20:23:51,861 - INFO - Minimum Distance Statistics (km): +2025-04-19 20:23:51,861 - INFO - Mean: 4.7583 +2025-04-19 20:23:51,861 - INFO - Median: 4.2086 +2025-04-19 20:23:51,861 - INFO - Min: 0.0655 +2025-04-19 20:23:51,861 - INFO - Max: 18.8528 +2025-04-19 20:23:51,861 - INFO - Std: 2.8263 +2025-04-19 20:23:51,861 - INFO - OC Distribution Statistics: +2025-04-19 20:23:51,861 - INFO - Validation Mean: 16.6123 +2025-04-19 20:23:51,861 - INFO - Validation Std: 7.9416 +2025-04-19 20:23:51,861 - INFO - Training Mean: 23.2770 +2025-04-19 20:23:51,861 - INFO - Training Std: 20.5641 +2025-04-19 20:23:51,861 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 20:23:51,861 - INFO - KS Statistic: 0.2127 +2025-04-19 20:23:51,861 - INFO - P-value: 0.0000 +2025-04-19 20:23:51,861 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 20:23:51,861 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 21:30:18,334 - INFO - Validation and Training Set Split Statistics +2025-04-19 21:30:18,335 - INFO - Timestamp: 2025-04-19 21:30:18 +2025-04-19 21:30:18,335 - INFO - Validation set size: 1462 (8.85%) +2025-04-19 21:30:18,335 - INFO - Training set size: 15052 (91.15%) +2025-04-19 21:30:18,335 - INFO - Minimum Distance Statistics (km): +2025-04-19 21:30:18,335 - INFO - Mean: 4.6211 +2025-04-19 21:30:18,335 - INFO - Median: 4.0224 +2025-04-19 21:30:18,335 - INFO - Min: 0.1121 +2025-04-19 21:30:18,335 - INFO - Max: 18.8528 +2025-04-19 21:30:18,335 - INFO - Std: 2.7748 +2025-04-19 21:30:18,335 - INFO - OC Distribution Statistics: +2025-04-19 21:30:18,335 - INFO - Validation Mean: 17.2269 +2025-04-19 21:30:18,335 - INFO - Validation Std: 8.7020 +2025-04-19 21:30:18,335 - INFO - Training Mean: 23.0416 +2025-04-19 21:30:18,335 - INFO - Training Std: 20.3479 +2025-04-19 21:30:18,335 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 21:30:18,335 - INFO - KS Statistic: 0.1712 +2025-04-19 21:30:18,335 - INFO - P-value: 0.0000 +2025-04-19 21:30:18,335 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 21:30:18,335 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 21:30:21,369 - INFO - Validation and Training Set Split Statistics +2025-04-19 21:30:21,369 - INFO - Timestamp: 2025-04-19 21:30:21 +2025-04-19 21:30:21,369 - INFO - Validation set size: 1509 (9.14%) +2025-04-19 21:30:21,369 - INFO - Training set size: 15005 (90.86%) +2025-04-19 21:30:21,369 - INFO - Minimum Distance Statistics (km): +2025-04-19 21:30:21,369 - INFO - Mean: 5.0807 +2025-04-19 21:30:21,369 - INFO - Median: 4.0846 +2025-04-19 21:30:21,369 - INFO - Min: 0.4281 +2025-04-19 21:30:21,369 - INFO - Max: 20.3992 +2025-04-19 21:30:21,369 - INFO - Std: 3.2646 +2025-04-19 21:30:21,369 - INFO - OC Distribution Statistics: +2025-04-19 21:30:21,369 - INFO - Validation Mean: 17.4920 +2025-04-19 21:30:21,369 - INFO - Validation Std: 8.1639 +2025-04-19 21:30:21,369 - INFO - Training Mean: 23.0331 +2025-04-19 21:30:21,369 - INFO - Training Std: 20.4011 +2025-04-19 21:30:21,369 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 21:30:21,369 - INFO - KS Statistic: 0.1446 +2025-04-19 21:30:21,369 - INFO - P-value: 0.0000 +2025-04-19 21:30:21,369 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 21:30:21,369 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 21:30:21,621 - INFO - Validation and Training Set Split Statistics +2025-04-19 21:30:21,621 - INFO - Timestamp: 2025-04-19 21:30:21 +2025-04-19 21:30:21,621 - INFO - Validation set size: 1324 (8.02%) +2025-04-19 21:30:21,621 - INFO - Training set size: 15190 (91.98%) +2025-04-19 21:30:21,621 - INFO - Minimum Distance Statistics (km): +2025-04-19 21:30:21,621 - INFO - Mean: 4.7386 +2025-04-19 21:30:21,621 - INFO - Median: 4.2672 +2025-04-19 21:30:21,621 - INFO - Min: 0.3763 +2025-04-19 21:30:21,621 - INFO - Max: 13.9571 +2025-04-19 21:30:21,621 - INFO - Std: 2.4103 +2025-04-19 21:30:21,621 - INFO - OC Distribution Statistics: +2025-04-19 21:30:21,621 - INFO - Validation Mean: 17.6698 +2025-04-19 21:30:21,621 - INFO - Validation Std: 8.6342 +2025-04-19 21:30:21,621 - INFO - Training Mean: 22.9501 +2025-04-19 21:30:21,622 - INFO - Training Std: 20.2928 +2025-04-19 21:30:21,622 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 21:30:21,622 - INFO - KS Statistic: 0.1409 +2025-04-19 21:30:21,622 - INFO - P-value: 0.0000 +2025-04-19 21:30:21,622 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 21:30:21,622 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 21:30:21,988 - INFO - Validation and Training Set Split Statistics +2025-04-19 21:30:21,988 - INFO - Timestamp: 2025-04-19 21:30:21 +2025-04-19 21:30:21,988 - INFO - Validation set size: 1958 (11.86%) +2025-04-19 21:30:21,988 - INFO - Training set size: 14556 (88.14%) +2025-04-19 21:30:21,988 - INFO - Minimum Distance Statistics (km): +2025-04-19 21:30:21,988 - INFO - Mean: 4.8221 +2025-04-19 21:30:21,988 - INFO - Median: 4.2253 +2025-04-19 21:30:21,988 - INFO - Min: 0.0712 +2025-04-19 21:30:21,988 - INFO - Max: 18.8528 +2025-04-19 21:30:21,988 - INFO - Std: 2.8298 +2025-04-19 21:30:21,988 - INFO - OC Distribution Statistics: +2025-04-19 21:30:21,988 - INFO - Validation Mean: 16.4043 +2025-04-19 21:30:21,988 - INFO - Validation Std: 7.4433 +2025-04-19 21:30:21,988 - INFO - Training Mean: 23.3503 +2025-04-19 21:30:21,988 - INFO - Training Std: 20.6319 +2025-04-19 21:30:21,988 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 21:30:21,988 - INFO - KS Statistic: 0.2173 +2025-04-19 21:30:21,988 - INFO - P-value: 0.0000 +2025-04-19 21:30:21,988 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 21:30:21,988 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 22:38:07,326 - INFO - Validation and Training Set Split Statistics +2025-04-19 22:38:07,327 - INFO - Timestamp: 2025-04-19 22:38:07 +2025-04-19 22:38:07,327 - INFO - Validation set size: 1362 (8.25%) +2025-04-19 22:38:07,327 - INFO - Training set size: 15152 (91.75%) +2025-04-19 22:38:07,327 - INFO - Minimum Distance Statistics (km): +2025-04-19 22:38:07,327 - INFO - Mean: 4.5987 +2025-04-19 22:38:07,327 - INFO - Median: 4.1378 +2025-04-19 22:38:07,327 - INFO - Min: 0.0712 +2025-04-19 22:38:07,327 - INFO - Max: 18.8528 +2025-04-19 22:38:07,327 - INFO - Std: 2.7670 +2025-04-19 22:38:07,327 - INFO - OC Distribution Statistics: +2025-04-19 22:38:07,327 - INFO - Validation Mean: 17.1828 +2025-04-19 22:38:07,327 - INFO - Validation Std: 8.7365 +2025-04-19 22:38:07,327 - INFO - Training Mean: 23.0072 +2025-04-19 22:38:07,327 - INFO - Training Std: 20.2960 +2025-04-19 22:38:07,327 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 22:38:07,327 - INFO - KS Statistic: 0.1438 +2025-04-19 22:38:07,327 - INFO - P-value: 0.0000 +2025-04-19 22:38:07,327 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 22:38:07,327 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 22:38:10,877 - INFO - Validation and Training Set Split Statistics +2025-04-19 22:38:10,877 - INFO - Timestamp: 2025-04-19 22:38:10 +2025-04-19 22:38:10,877 - INFO - Validation set size: 1448 (8.77%) +2025-04-19 22:38:10,877 - INFO - Training set size: 15066 (91.23%) +2025-04-19 22:38:10,877 - INFO - Minimum Distance Statistics (km): +2025-04-19 22:38:10,877 - INFO - Mean: 4.5965 +2025-04-19 22:38:10,877 - INFO - Median: 3.9768 +2025-04-19 22:38:10,877 - INFO - Min: 0.3705 +2025-04-19 22:38:10,877 - INFO - Max: 14.3959 +2025-04-19 22:38:10,877 - INFO - Std: 2.7298 +2025-04-19 22:38:10,877 - INFO - OC Distribution Statistics: +2025-04-19 22:38:10,878 - INFO - Validation Mean: 17.8779 +2025-04-19 22:38:10,878 - INFO - Validation Std: 9.0486 +2025-04-19 22:38:10,878 - INFO - Training Mean: 22.9736 +2025-04-19 22:38:10,878 - INFO - Training Std: 20.3432 +2025-04-19 22:38:10,878 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 22:38:10,878 - INFO - KS Statistic: 0.1395 +2025-04-19 22:38:10,878 - INFO - P-value: 0.0000 +2025-04-19 22:38:10,878 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 22:38:10,878 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 22:38:11,190 - INFO - Validation and Training Set Split Statistics +2025-04-19 22:38:11,190 - INFO - Timestamp: 2025-04-19 22:38:11 +2025-04-19 22:38:11,190 - INFO - Validation set size: 1736 (10.51%) +2025-04-19 22:38:11,190 - INFO - Training set size: 14778 (89.49%) +2025-04-19 22:38:11,190 - INFO - Minimum Distance Statistics (km): +2025-04-19 22:38:11,190 - INFO - Mean: 4.9587 +2025-04-19 22:38:11,190 - INFO - Median: 4.3057 +2025-04-19 22:38:11,190 - INFO - Min: 0.3763 +2025-04-19 22:38:11,190 - INFO - Max: 20.8290 +2025-04-19 22:38:11,190 - INFO - Std: 2.9046 +2025-04-19 22:38:11,190 - INFO - OC Distribution Statistics: +2025-04-19 22:38:11,190 - INFO - Validation Mean: 17.1643 +2025-04-19 22:38:11,190 - INFO - Validation Std: 8.5592 +2025-04-19 22:38:11,190 - INFO - Training Mean: 23.1567 +2025-04-19 22:38:11,190 - INFO - Training Std: 20.4908 +2025-04-19 22:38:11,190 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 22:38:11,190 - INFO - KS Statistic: 0.1660 +2025-04-19 22:38:11,190 - INFO - P-value: 0.0000 +2025-04-19 22:38:11,190 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 22:38:11,190 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 22:38:12,016 - INFO - Validation and Training Set Split Statistics +2025-04-19 22:38:12,016 - INFO - Timestamp: 2025-04-19 22:38:12 +2025-04-19 22:38:12,016 - INFO - Validation set size: 1413 (8.56%) +2025-04-19 22:38:12,016 - INFO - Training set size: 15101 (91.44%) +2025-04-19 22:38:12,016 - INFO - Minimum Distance Statistics (km): +2025-04-19 22:38:12,016 - INFO - Mean: 4.8024 +2025-04-19 22:38:12,016 - INFO - Median: 4.1592 +2025-04-19 22:38:12,016 - INFO - Min: 0.3424 +2025-04-19 22:38:12,016 - INFO - Max: 18.4150 +2025-04-19 22:38:12,016 - INFO - Std: 2.8132 +2025-04-19 22:38:12,016 - INFO - OC Distribution Statistics: +2025-04-19 22:38:12,016 - INFO - Validation Mean: 17.4360 +2025-04-19 22:38:12,016 - INFO - Validation Std: 8.0347 +2025-04-19 22:38:12,016 - INFO - Training Mean: 23.0031 +2025-04-19 22:38:12,016 - INFO - Training Std: 20.3548 +2025-04-19 22:38:12,016 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 22:38:12,016 - INFO - KS Statistic: 0.1234 +2025-04-19 22:38:12,016 - INFO - P-value: 0.0000 +2025-04-19 22:38:12,016 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 22:38:12,016 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 00:04:29,114 - INFO - Validation and Training Set Split Statistics +2025-04-20 00:04:29,115 - INFO - Timestamp: 2025-04-20 00:04:29 +2025-04-20 00:04:29,115 - INFO - Validation set size: 1343 (8.13%) +2025-04-20 00:04:29,115 - INFO - Training set size: 15171 (91.87%) +2025-04-20 00:04:29,115 - INFO - Minimum Distance Statistics (km): +2025-04-20 00:04:29,115 - INFO - Mean: 4.5027 +2025-04-20 00:04:29,115 - INFO - Median: 4.0184 +2025-04-20 00:04:29,115 - INFO - Min: 0.0358 +2025-04-20 00:04:29,115 - INFO - Max: 18.8528 +2025-04-20 00:04:29,115 - INFO - Std: 2.6158 +2025-04-20 00:04:29,115 - INFO - OC Distribution Statistics: +2025-04-20 00:04:29,115 - INFO - Validation Mean: 17.8298 +2025-04-20 00:04:29,115 - INFO - Validation Std: 9.3717 +2025-04-20 00:04:29,115 - INFO - Training Mean: 22.9426 +2025-04-20 00:04:29,115 - INFO - Training Std: 20.2770 +2025-04-20 00:04:29,115 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 00:04:29,115 - INFO - KS Statistic: 0.1355 +2025-04-20 00:04:29,115 - INFO - P-value: 0.0000 +2025-04-20 00:04:29,115 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 00:04:29,115 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 00:04:30,143 - INFO - Validation and Training Set Split Statistics +2025-04-20 00:04:30,144 - INFO - Timestamp: 2025-04-20 00:04:30 +2025-04-20 00:04:30,144 - INFO - Validation set size: 1383 (8.37%) +2025-04-20 00:04:30,144 - INFO - Training set size: 15131 (91.63%) +2025-04-20 00:04:30,144 - INFO - Minimum Distance Statistics (km): +2025-04-20 00:04:30,144 - INFO - Mean: 4.6946 +2025-04-20 00:04:30,144 - INFO - Median: 4.1795 +2025-04-20 00:04:30,144 - INFO - Min: 0.3763 +2025-04-20 00:04:30,144 - INFO - Max: 18.4150 +2025-04-20 00:04:30,144 - INFO - Std: 2.6046 +2025-04-20 00:04:30,144 - INFO - OC Distribution Statistics: +2025-04-20 00:04:30,144 - INFO - Validation Mean: 17.4145 +2025-04-20 00:04:30,144 - INFO - Validation Std: 8.2781 +2025-04-20 00:04:30,144 - INFO - Training Mean: 22.9941 +2025-04-20 00:04:30,144 - INFO - Training Std: 20.3298 +2025-04-20 00:04:30,144 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 00:04:30,144 - INFO - KS Statistic: 0.1506 +2025-04-20 00:04:30,144 - INFO - P-value: 0.0000 +2025-04-20 00:04:30,144 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 00:04:30,144 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 00:04:32,502 - INFO - Validation and Training Set Split Statistics +2025-04-20 00:04:32,502 - INFO - Timestamp: 2025-04-20 00:04:32 +2025-04-20 00:04:32,502 - INFO - Validation set size: 1726 (10.45%) +2025-04-20 00:04:32,502 - INFO - Training set size: 14788 (89.55%) +2025-04-20 00:04:32,502 - INFO - Minimum Distance Statistics (km): +2025-04-20 00:04:32,502 - INFO - Mean: 5.1200 +2025-04-20 00:04:32,502 - INFO - Median: 4.3765 +2025-04-20 00:04:32,502 - INFO - Min: 0.0358 +2025-04-20 00:04:32,502 - INFO - Max: 20.8657 +2025-04-20 00:04:32,502 - INFO - Std: 2.9539 +2025-04-20 00:04:32,502 - INFO - OC Distribution Statistics: +2025-04-20 00:04:32,502 - INFO - Validation Mean: 16.9745 +2025-04-20 00:04:32,502 - INFO - Validation Std: 8.5677 +2025-04-20 00:04:32,502 - INFO - Training Mean: 23.1748 +2025-04-20 00:04:32,502 - INFO - Training Std: 20.4787 +2025-04-20 00:04:32,502 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 00:04:32,502 - INFO - KS Statistic: 0.1781 +2025-04-20 00:04:32,502 - INFO - P-value: 0.0000 +2025-04-20 00:04:32,502 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 00:04:32,502 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 00:04:32,843 - INFO - Validation and Training Set Split Statistics +2025-04-20 00:04:32,843 - INFO - Timestamp: 2025-04-20 00:04:32 +2025-04-20 00:04:32,843 - INFO - Validation set size: 1558 (9.43%) +2025-04-20 00:04:32,843 - INFO - Training set size: 14956 (90.57%) +2025-04-20 00:04:32,843 - INFO - Minimum Distance Statistics (km): +2025-04-20 00:04:32,843 - INFO - Mean: 4.9949 +2025-04-20 00:04:32,843 - INFO - Median: 4.2677 +2025-04-20 00:04:32,843 - INFO - Min: 0.4281 +2025-04-20 00:04:32,843 - INFO - Max: 20.3992 +2025-04-20 00:04:32,843 - INFO - Std: 2.9825 +2025-04-20 00:04:32,843 - INFO - OC Distribution Statistics: +2025-04-20 00:04:32,843 - INFO - Validation Mean: 17.6962 +2025-04-20 00:04:32,843 - INFO - Validation Std: 8.3530 +2025-04-20 00:04:32,843 - INFO - Training Mean: 23.0300 +2025-04-20 00:04:32,843 - INFO - Training Std: 20.4244 +2025-04-20 00:04:32,843 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 00:04:32,843 - INFO - KS Statistic: 0.1332 +2025-04-20 00:04:32,843 - INFO - P-value: 0.0000 +2025-04-20 00:04:32,843 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 00:04:32,843 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 01:13:05,229 - INFO - Validation and Training Set Split Statistics +2025-04-20 01:13:05,229 - INFO - Timestamp: 2025-04-20 01:13:05 +2025-04-20 01:13:05,229 - INFO - Validation set size: 1605 (9.72%) +2025-04-20 01:13:05,229 - INFO - Training set size: 14909 (90.28%) +2025-04-20 01:13:05,229 - INFO - Minimum Distance Statistics (km): +2025-04-20 01:13:05,230 - INFO - Mean: 5.1838 +2025-04-20 01:13:05,230 - INFO - Median: 4.4875 +2025-04-20 01:13:05,230 - INFO - Min: 0.0882 +2025-04-20 01:13:05,230 - INFO - Max: 18.8528 +2025-04-20 01:13:05,230 - INFO - Std: 2.8171 +2025-04-20 01:13:05,230 - INFO - OC Distribution Statistics: +2025-04-20 01:13:05,230 - INFO - Validation Mean: 17.5648 +2025-04-20 01:13:05,230 - INFO - Validation Std: 8.5821 +2025-04-20 01:13:05,230 - INFO - Training Mean: 23.0610 +2025-04-20 01:13:05,230 - INFO - Training Std: 20.4351 +2025-04-20 01:13:05,230 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 01:13:05,230 - INFO - KS Statistic: 0.1453 +2025-04-20 01:13:05,230 - INFO - P-value: 0.0000 +2025-04-20 01:13:05,230 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 01:13:05,230 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 01:13:05,529 - INFO - Validation and Training Set Split Statistics +2025-04-20 01:13:05,529 - INFO - Timestamp: 2025-04-20 01:13:05 +2025-04-20 01:13:05,529 - INFO - Validation set size: 1778 (10.77%) +2025-04-20 01:13:05,529 - INFO - Training set size: 14736 (89.23%) +2025-04-20 01:13:05,529 - INFO - Minimum Distance Statistics (km): +2025-04-20 01:13:05,530 - INFO - Mean: 5.0178 +2025-04-20 01:13:05,530 - INFO - Median: 4.4671 +2025-04-20 01:13:05,530 - INFO - Min: 0.0882 +2025-04-20 01:13:05,530 - INFO - Max: 18.9132 +2025-04-20 01:13:05,530 - INFO - Std: 3.0195 +2025-04-20 01:13:05,530 - INFO - OC Distribution Statistics: +2025-04-20 01:13:05,530 - INFO - Validation Mean: 17.0342 +2025-04-20 01:13:05,530 - INFO - Validation Std: 8.8828 +2025-04-20 01:13:05,530 - INFO - Training Mean: 23.1895 +2025-04-20 01:13:05,530 - INFO - Training Std: 20.4911 +2025-04-20 01:13:05,530 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 01:13:05,530 - INFO - KS Statistic: 0.1731 +2025-04-20 01:13:05,530 - INFO - P-value: 0.0000 +2025-04-20 01:13:05,530 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 01:13:05,530 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 01:13:06,171 - INFO - Validation and Training Set Split Statistics +2025-04-20 01:13:06,171 - INFO - Timestamp: 2025-04-20 01:13:06 +2025-04-20 01:13:06,171 - INFO - Validation set size: 1756 (10.63%) +2025-04-20 01:13:06,171 - INFO - Training set size: 14758 (89.37%) +2025-04-20 01:13:06,171 - INFO - Minimum Distance Statistics (km): +2025-04-20 01:13:06,171 - INFO - Mean: 4.8405 +2025-04-20 01:13:06,171 - INFO - Median: 3.9975 +2025-04-20 01:13:06,171 - INFO - Min: 0.0712 +2025-04-20 01:13:06,171 - INFO - Max: 18.9773 +2025-04-20 01:13:06,171 - INFO - Std: 3.0812 +2025-04-20 01:13:06,171 - INFO - OC Distribution Statistics: +2025-04-20 01:13:06,171 - INFO - Validation Mean: 17.2808 +2025-04-20 01:13:06,171 - INFO - Validation Std: 8.1762 +2025-04-20 01:13:06,171 - INFO - Training Mean: 23.1510 +2025-04-20 01:13:06,172 - INFO - Training Std: 20.5237 +2025-04-20 01:13:06,172 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 01:13:06,172 - INFO - KS Statistic: 0.1615 +2025-04-20 01:13:06,172 - INFO - P-value: 0.0000 +2025-04-20 01:13:06,172 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 01:13:06,172 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 01:13:09,079 - INFO - Validation and Training Set Split Statistics +2025-04-20 01:13:09,080 - INFO - Timestamp: 2025-04-20 01:13:09 +2025-04-20 01:13:09,080 - INFO - Validation set size: 1740 (10.54%) +2025-04-20 01:13:09,080 - INFO - Training set size: 14774 (89.46%) +2025-04-20 01:13:09,080 - INFO - Minimum Distance Statistics (km): +2025-04-20 01:13:09,080 - INFO - Mean: 4.5984 +2025-04-20 01:13:09,080 - INFO - Median: 4.0432 +2025-04-20 01:13:09,080 - INFO - Min: 0.1121 +2025-04-20 01:13:09,080 - INFO - Max: 18.8528 +2025-04-20 01:13:09,080 - INFO - Std: 2.4854 +2025-04-20 01:13:09,080 - INFO - OC Distribution Statistics: +2025-04-20 01:13:09,080 - INFO - Validation Mean: 16.8684 +2025-04-20 01:13:09,080 - INFO - Validation Std: 8.2183 +2025-04-20 01:13:09,080 - INFO - Training Mean: 23.1932 +2025-04-20 01:13:09,080 - INFO - Training Std: 20.4988 +2025-04-20 01:13:09,080 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 01:13:09,080 - INFO - KS Statistic: 0.1778 +2025-04-20 01:13:09,080 - INFO - P-value: 0.0000 +2025-04-20 01:13:09,080 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 01:13:09,080 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 02:17:09,534 - INFO - Validation and Training Set Split Statistics +2025-04-20 02:17:09,534 - INFO - Timestamp: 2025-04-20 02:17:09 +2025-04-20 02:17:09,534 - INFO - Validation set size: 1470 (8.90%) +2025-04-20 02:17:09,534 - INFO - Training set size: 15044 (91.10%) +2025-04-20 02:17:09,534 - INFO - Minimum Distance Statistics (km): +2025-04-20 02:17:09,534 - INFO - Mean: 5.0744 +2025-04-20 02:17:09,534 - INFO - Median: 4.3793 +2025-04-20 02:17:09,534 - INFO - Min: 0.4134 +2025-04-20 02:17:09,534 - INFO - Max: 20.3992 +2025-04-20 02:17:09,534 - INFO - Std: 3.1246 +2025-04-20 02:17:09,534 - INFO - OC Distribution Statistics: +2025-04-20 02:17:09,534 - INFO - Validation Mean: 17.2751 +2025-04-20 02:17:09,534 - INFO - Validation Std: 8.3453 +2025-04-20 02:17:09,534 - INFO - Training Mean: 23.0399 +2025-04-20 02:17:09,534 - INFO - Training Std: 20.3678 +2025-04-20 02:17:09,534 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 02:17:09,534 - INFO - KS Statistic: 0.1514 +2025-04-20 02:17:09,534 - INFO - P-value: 0.0000 +2025-04-20 02:17:09,534 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 02:17:09,534 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 02:17:10,607 - INFO - Validation and Training Set Split Statistics +2025-04-20 02:17:10,607 - INFO - Timestamp: 2025-04-20 02:17:10 +2025-04-20 02:17:10,607 - INFO - Validation set size: 1355 (8.21%) +2025-04-20 02:17:10,607 - INFO - Training set size: 15159 (91.79%) +2025-04-20 02:17:10,607 - INFO - Minimum Distance Statistics (km): +2025-04-20 02:17:10,607 - INFO - Mean: 5.0970 +2025-04-20 02:17:10,607 - INFO - Median: 4.2214 +2025-04-20 02:17:10,607 - INFO - Min: 0.3763 +2025-04-20 02:17:10,607 - INFO - Max: 16.4385 +2025-04-20 02:17:10,607 - INFO - Std: 2.8098 +2025-04-20 02:17:10,607 - INFO - OC Distribution Statistics: +2025-04-20 02:17:10,607 - INFO - Validation Mean: 17.8006 +2025-04-20 02:17:10,607 - INFO - Validation Std: 8.9621 +2025-04-20 02:17:10,607 - INFO - Training Mean: 22.9492 +2025-04-20 02:17:10,607 - INFO - Training Std: 20.2987 +2025-04-20 02:17:10,607 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 02:17:10,607 - INFO - KS Statistic: 0.1440 +2025-04-20 02:17:10,607 - INFO - P-value: 0.0000 +2025-04-20 02:17:10,607 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 02:17:10,607 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 02:17:13,316 - INFO - Validation and Training Set Split Statistics +2025-04-20 02:17:13,316 - INFO - Timestamp: 2025-04-20 02:17:13 +2025-04-20 02:17:13,316 - INFO - Validation set size: 1784 (10.80%) +2025-04-20 02:17:13,316 - INFO - Training set size: 14730 (89.20%) +2025-04-20 02:17:13,316 - INFO - Minimum Distance Statistics (km): +2025-04-20 02:17:13,316 - INFO - Mean: 5.0185 +2025-04-20 02:17:13,316 - INFO - Median: 4.2088 +2025-04-20 02:17:13,316 - INFO - Min: 0.3705 +2025-04-20 02:17:13,316 - INFO - Max: 20.3992 +2025-04-20 02:17:13,316 - INFO - Std: 3.0062 +2025-04-20 02:17:13,316 - INFO - OC Distribution Statistics: +2025-04-20 02:17:13,316 - INFO - Validation Mean: 16.7472 +2025-04-20 02:17:13,316 - INFO - Validation Std: 8.0501 +2025-04-20 02:17:13,316 - INFO - Training Mean: 23.2268 +2025-04-20 02:17:13,316 - INFO - Training Std: 20.5251 +2025-04-20 02:17:13,316 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 02:17:13,316 - INFO - KS Statistic: 0.1769 +2025-04-20 02:17:13,316 - INFO - P-value: 0.0000 +2025-04-20 02:17:13,316 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 02:17:13,316 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 02:17:14,241 - INFO - Validation and Training Set Split Statistics +2025-04-20 02:17:14,241 - INFO - Timestamp: 2025-04-20 02:17:14 +2025-04-20 02:17:14,241 - INFO - Validation set size: 1411 (8.54%) +2025-04-20 02:17:14,241 - INFO - Training set size: 15103 (91.46%) +2025-04-20 02:17:14,241 - INFO - Minimum Distance Statistics (km): +2025-04-20 02:17:14,241 - INFO - Mean: 4.9926 +2025-04-20 02:17:14,241 - INFO - Median: 4.2359 +2025-04-20 02:17:14,241 - INFO - Min: 0.3976 +2025-04-20 02:17:14,241 - INFO - Max: 18.8528 +2025-04-20 02:17:14,241 - INFO - Std: 3.0631 +2025-04-20 02:17:14,241 - INFO - OC Distribution Statistics: +2025-04-20 02:17:14,241 - INFO - Validation Mean: 17.3157 +2025-04-20 02:17:14,241 - INFO - Validation Std: 7.9914 +2025-04-20 02:17:14,241 - INFO - Training Mean: 23.0136 +2025-04-20 02:17:14,241 - INFO - Training Std: 20.3522 +2025-04-20 02:17:14,241 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 02:17:14,241 - INFO - KS Statistic: 0.1509 +2025-04-20 02:17:14,241 - INFO - P-value: 0.0000 +2025-04-20 02:17:14,241 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 02:17:14,241 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 03:24:29,988 - INFO - Validation and Training Set Split Statistics +2025-04-20 03:24:29,988 - INFO - Timestamp: 2025-04-20 03:24:29 +2025-04-20 03:24:29,988 - INFO - Validation set size: 1501 (9.09%) +2025-04-20 03:24:29,988 - INFO - Training set size: 15013 (90.91%) +2025-04-20 03:24:29,988 - INFO - Minimum Distance Statistics (km): +2025-04-20 03:24:29,988 - INFO - Mean: 5.3885 +2025-04-20 03:24:29,988 - INFO - Median: 4.5705 +2025-04-20 03:24:29,988 - INFO - Min: 0.3763 +2025-04-20 03:24:29,988 - INFO - Max: 22.7826 +2025-04-20 03:24:29,989 - INFO - Std: 3.0685 +2025-04-20 03:24:29,989 - INFO - OC Distribution Statistics: +2025-04-20 03:24:29,989 - INFO - Validation Mean: 17.4877 +2025-04-20 03:24:29,989 - INFO - Validation Std: 8.6160 +2025-04-20 03:24:29,989 - INFO - Training Mean: 23.0306 +2025-04-20 03:24:29,989 - INFO - Training Std: 20.3783 +2025-04-20 03:24:29,989 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 03:24:29,989 - INFO - KS Statistic: 0.1602 +2025-04-20 03:24:29,989 - INFO - P-value: 0.0000 +2025-04-20 03:24:29,989 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 03:24:29,989 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 03:24:30,239 - INFO - Validation and Training Set Split Statistics +2025-04-20 03:24:30,239 - INFO - Timestamp: 2025-04-20 03:24:30 +2025-04-20 03:24:30,239 - INFO - Validation set size: 1379 (8.35%) +2025-04-20 03:24:30,239 - INFO - Training set size: 15135 (91.65%) +2025-04-20 03:24:30,239 - INFO - Minimum Distance Statistics (km): +2025-04-20 03:24:30,239 - INFO - Mean: 5.0479 +2025-04-20 03:24:30,239 - INFO - Median: 4.2598 +2025-04-20 03:24:30,239 - INFO - Min: 0.4511 +2025-04-20 03:24:30,239 - INFO - Max: 18.8528 +2025-04-20 03:24:30,239 - INFO - Std: 3.0651 +2025-04-20 03:24:30,239 - INFO - OC Distribution Statistics: +2025-04-20 03:24:30,239 - INFO - Validation Mean: 17.4132 +2025-04-20 03:24:30,239 - INFO - Validation Std: 8.6382 +2025-04-20 03:24:30,239 - INFO - Training Mean: 22.9927 +2025-04-20 03:24:30,240 - INFO - Training Std: 20.3140 +2025-04-20 03:24:30,240 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 03:24:30,240 - INFO - KS Statistic: 0.1424 +2025-04-20 03:24:30,240 - INFO - P-value: 0.0000 +2025-04-20 03:24:30,240 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 03:24:30,240 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 03:24:33,607 - INFO - Validation and Training Set Split Statistics +2025-04-20 03:24:33,607 - INFO - Timestamp: 2025-04-20 03:24:33 +2025-04-20 03:24:33,607 - INFO - Validation set size: 1424 (8.62%) +2025-04-20 03:24:33,607 - INFO - Training set size: 15090 (91.38%) +2025-04-20 03:24:33,608 - INFO - Minimum Distance Statistics (km): +2025-04-20 03:24:33,608 - INFO - Mean: 4.8254 +2025-04-20 03:24:33,608 - INFO - Median: 4.3625 +2025-04-20 03:24:33,608 - INFO - Min: 0.3976 +2025-04-20 03:24:33,608 - INFO - Max: 16.4391 +2025-04-20 03:24:33,608 - INFO - Std: 2.7378 +2025-04-20 03:24:33,608 - INFO - OC Distribution Statistics: +2025-04-20 03:24:33,608 - INFO - Validation Mean: 17.8840 +2025-04-20 03:24:33,608 - INFO - Validation Std: 8.4841 +2025-04-20 03:24:33,608 - INFO - Training Mean: 22.9649 +2025-04-20 03:24:33,608 - INFO - Training Std: 20.3543 +2025-04-20 03:24:33,608 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 03:24:33,608 - INFO - KS Statistic: 0.1110 +2025-04-20 03:24:33,608 - INFO - P-value: 0.0000 +2025-04-20 03:24:33,608 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 03:24:33,608 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 03:24:36,987 - INFO - Validation and Training Set Split Statistics +2025-04-20 03:24:36,987 - INFO - Timestamp: 2025-04-20 03:24:36 +2025-04-20 03:24:36,987 - INFO - Validation set size: 1486 (9.00%) +2025-04-20 03:24:36,987 - INFO - Training set size: 15028 (91.00%) +2025-04-20 03:24:36,987 - INFO - Minimum Distance Statistics (km): +2025-04-20 03:24:36,987 - INFO - Mean: 4.8178 +2025-04-20 03:24:36,987 - INFO - Median: 4.0588 +2025-04-20 03:24:36,987 - INFO - Min: 0.3763 +2025-04-20 03:24:36,987 - INFO - Max: 17.8824 +2025-04-20 03:24:36,987 - INFO - Std: 2.8301 +2025-04-20 03:24:36,987 - INFO - OC Distribution Statistics: +2025-04-20 03:24:36,987 - INFO - Validation Mean: 17.4779 +2025-04-20 03:24:36,987 - INFO - Validation Std: 8.5085 +2025-04-20 03:24:36,987 - INFO - Training Mean: 23.0260 +2025-04-20 03:24:36,987 - INFO - Training Std: 20.3749 +2025-04-20 03:24:36,987 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 03:24:36,987 - INFO - KS Statistic: 0.1484 +2025-04-20 03:24:36,987 - INFO - P-value: 0.0000 +2025-04-20 03:24:36,987 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 03:24:36,987 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 04:32:26,726 - INFO - Validation and Training Set Split Statistics +2025-04-20 04:32:26,727 - INFO - Timestamp: 2025-04-20 04:32:26 +2025-04-20 04:32:26,727 - INFO - Validation set size: 1342 (8.13%) +2025-04-20 04:32:26,727 - INFO - Training set size: 15172 (91.87%) +2025-04-20 04:32:26,727 - INFO - Minimum Distance Statistics (km): +2025-04-20 04:32:26,727 - INFO - Mean: 4.8701 +2025-04-20 04:32:26,727 - INFO - Median: 4.4374 +2025-04-20 04:32:26,727 - INFO - Min: 0.6217 +2025-04-20 04:32:26,727 - INFO - Max: 16.0917 +2025-04-20 04:32:26,727 - INFO - Std: 2.6180 +2025-04-20 04:32:26,727 - INFO - OC Distribution Statistics: +2025-04-20 04:32:26,727 - INFO - Validation Mean: 18.1110 +2025-04-20 04:32:26,727 - INFO - Validation Std: 9.4689 +2025-04-20 04:32:26,727 - INFO - Training Mean: 22.9174 +2025-04-20 04:32:26,727 - INFO - Training Std: 20.2786 +2025-04-20 04:32:26,727 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 04:32:26,727 - INFO - KS Statistic: 0.1023 +2025-04-20 04:32:26,727 - INFO - P-value: 0.0000 +2025-04-20 04:32:26,727 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 04:32:26,727 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 04:32:30,896 - INFO - Validation and Training Set Split Statistics +2025-04-20 04:32:30,896 - INFO - Timestamp: 2025-04-20 04:32:30 +2025-04-20 04:32:30,896 - INFO - Validation set size: 1628 (9.86%) +2025-04-20 04:32:30,896 - INFO - Training set size: 14886 (90.14%) +2025-04-20 04:32:30,896 - INFO - Minimum Distance Statistics (km): +2025-04-20 04:32:30,896 - INFO - Mean: 4.7736 +2025-04-20 04:32:30,896 - INFO - Median: 4.2009 +2025-04-20 04:32:30,896 - INFO - Min: 0.3763 +2025-04-20 04:32:30,896 - INFO - Max: 17.8414 +2025-04-20 04:32:30,896 - INFO - Std: 2.7409 +2025-04-20 04:32:30,896 - INFO - OC Distribution Statistics: +2025-04-20 04:32:30,896 - INFO - Validation Mean: 16.9865 +2025-04-20 04:32:30,897 - INFO - Validation Std: 8.1871 +2025-04-20 04:32:30,897 - INFO - Training Mean: 23.1327 +2025-04-20 04:32:30,897 - INFO - Training Std: 20.4466 +2025-04-20 04:32:30,897 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 04:32:30,897 - INFO - KS Statistic: 0.1690 +2025-04-20 04:32:30,897 - INFO - P-value: 0.0000 +2025-04-20 04:32:30,897 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 04:32:30,897 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 04:32:31,360 - INFO - Validation and Training Set Split Statistics +2025-04-20 04:32:31,360 - INFO - Timestamp: 2025-04-20 04:32:31 +2025-04-20 04:32:31,361 - INFO - Validation set size: 1595 (9.66%) +2025-04-20 04:32:31,361 - INFO - Training set size: 14919 (90.34%) +2025-04-20 04:32:31,361 - INFO - Minimum Distance Statistics (km): +2025-04-20 04:32:31,361 - INFO - Mean: 5.0203 +2025-04-20 04:32:31,361 - INFO - Median: 4.4602 +2025-04-20 04:32:31,361 - INFO - Min: 0.3763 +2025-04-20 04:32:31,361 - INFO - Max: 18.8528 +2025-04-20 04:32:31,361 - INFO - Std: 2.6887 +2025-04-20 04:32:31,361 - INFO - OC Distribution Statistics: +2025-04-20 04:32:31,361 - INFO - Validation Mean: 17.2924 +2025-04-20 04:32:31,361 - INFO - Validation Std: 8.9142 +2025-04-20 04:32:31,361 - INFO - Training Mean: 23.0864 +2025-04-20 04:32:31,361 - INFO - Training Std: 20.4066 +2025-04-20 04:32:31,361 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 04:32:31,361 - INFO - KS Statistic: 0.1551 +2025-04-20 04:32:31,361 - INFO - P-value: 0.0000 +2025-04-20 04:32:31,361 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 04:32:31,361 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 04:32:35,041 - INFO - Validation and Training Set Split Statistics +2025-04-20 04:32:35,042 - INFO - Timestamp: 2025-04-20 04:32:35 +2025-04-20 04:32:35,042 - INFO - Validation set size: 1585 (9.60%) +2025-04-20 04:32:35,042 - INFO - Training set size: 14929 (90.40%) +2025-04-20 04:32:35,042 - INFO - Minimum Distance Statistics (km): +2025-04-20 04:32:35,042 - INFO - Mean: 5.0028 +2025-04-20 04:32:35,042 - INFO - Median: 4.3484 +2025-04-20 04:32:35,042 - INFO - Min: 0.3763 +2025-04-20 04:32:35,042 - INFO - Max: 18.8528 +2025-04-20 04:32:35,042 - INFO - Std: 3.1305 +2025-04-20 04:32:35,042 - INFO - OC Distribution Statistics: +2025-04-20 04:32:35,042 - INFO - Validation Mean: 17.5444 +2025-04-20 04:32:35,042 - INFO - Validation Std: 9.1511 +2025-04-20 04:32:35,042 - INFO - Training Mean: 23.0558 +2025-04-20 04:32:35,042 - INFO - Training Std: 20.3979 +2025-04-20 04:32:35,042 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 04:32:35,042 - INFO - KS Statistic: 0.1543 +2025-04-20 04:32:35,042 - INFO - P-value: 0.0000 +2025-04-20 04:32:35,042 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 04:32:35,042 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:01:25,410 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:01:25,410 - INFO - Timestamp: 2025-04-20 13:01:25 +2025-04-20 13:01:25,410 - INFO - Validation set size: 1610 (9.75%) +2025-04-20 13:01:25,410 - INFO - Training set size: 14904 (90.25%) +2025-04-20 13:01:25,410 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:01:25,410 - INFO - Mean: 4.8576 +2025-04-20 13:01:25,410 - INFO - Median: 3.9884 +2025-04-20 13:01:25,410 - INFO - Min: 0.4134 +2025-04-20 13:01:25,410 - INFO - Max: 20.3992 +2025-04-20 13:01:25,410 - INFO - Std: 2.7507 +2025-04-20 13:01:25,410 - INFO - OC Distribution Statistics: +2025-04-20 13:01:25,410 - INFO - Validation Mean: 16.6843 +2025-04-20 13:01:25,410 - INFO - Validation Std: 8.9255 +2025-04-20 13:01:25,410 - INFO - Training Mean: 23.1579 +2025-04-20 13:01:25,410 - INFO - Training Std: 20.3938 +2025-04-20 13:01:25,410 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:01:25,410 - INFO - KS Statistic: 0.1983 +2025-04-20 13:01:25,410 - INFO - P-value: 0.0000 +2025-04-20 13:01:25,410 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:01:25,410 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:01:25,885 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:01:25,885 - INFO - Timestamp: 2025-04-20 13:01:25 +2025-04-20 13:01:25,885 - INFO - Validation set size: 1385 (8.39%) +2025-04-20 13:01:25,885 - INFO - Training set size: 15129 (91.61%) +2025-04-20 13:01:25,885 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:01:25,885 - INFO - Mean: 5.0372 +2025-04-20 13:01:25,885 - INFO - Median: 4.2807 +2025-04-20 13:01:25,885 - INFO - Min: 0.3763 +2025-04-20 13:01:25,885 - INFO - Max: 20.3992 +2025-04-20 13:01:25,885 - INFO - Std: 3.0328 +2025-04-20 13:01:25,885 - INFO - OC Distribution Statistics: +2025-04-20 13:01:25,885 - INFO - Validation Mean: 17.5074 +2025-04-20 13:01:25,885 - INFO - Validation Std: 8.7109 +2025-04-20 13:01:25,885 - INFO - Training Mean: 22.9863 +2025-04-20 13:01:25,885 - INFO - Training Std: 20.3165 +2025-04-20 13:01:25,885 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:01:25,885 - INFO - KS Statistic: 0.1299 +2025-04-20 13:01:25,885 - INFO - P-value: 0.0000 +2025-04-20 13:01:25,885 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:01:25,885 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:01:29,199 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:01:29,199 - INFO - Timestamp: 2025-04-20 13:01:29 +2025-04-20 13:01:29,199 - INFO - Validation set size: 1565 (9.48%) +2025-04-20 13:01:29,200 - INFO - Training set size: 14949 (90.52%) +2025-04-20 13:01:29,200 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:01:29,200 - INFO - Mean: 5.0242 +2025-04-20 13:01:29,200 - INFO - Median: 4.4530 +2025-04-20 13:01:29,200 - INFO - Min: 0.0406 +2025-04-20 13:01:29,200 - INFO - Max: 18.8528 +2025-04-20 13:01:29,200 - INFO - Std: 2.7558 +2025-04-20 13:01:29,200 - INFO - OC Distribution Statistics: +2025-04-20 13:01:29,200 - INFO - Validation Mean: 17.9527 +2025-04-20 13:01:29,200 - INFO - Validation Std: 9.0352 +2025-04-20 13:01:29,200 - INFO - Training Mean: 23.0056 +2025-04-20 13:01:29,200 - INFO - Training Std: 20.4045 +2025-04-20 13:01:29,200 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:01:29,200 - INFO - KS Statistic: 0.1358 +2025-04-20 13:01:29,200 - INFO - P-value: 0.0000 +2025-04-20 13:01:29,200 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:01:29,200 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:01:29,374 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:01:29,374 - INFO - Timestamp: 2025-04-20 13:01:29 +2025-04-20 13:01:29,374 - INFO - Validation set size: 1584 (9.59%) +2025-04-20 13:01:29,374 - INFO - Training set size: 14930 (90.41%) +2025-04-20 13:01:29,374 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:01:29,374 - INFO - Mean: 5.0299 +2025-04-20 13:01:29,374 - INFO - Median: 4.4671 +2025-04-20 13:01:29,374 - INFO - Min: 0.3763 +2025-04-20 13:01:29,374 - INFO - Max: 18.8528 +2025-04-20 13:01:29,374 - INFO - Std: 3.0175 +2025-04-20 13:01:29,374 - INFO - OC Distribution Statistics: +2025-04-20 13:01:29,374 - INFO - Validation Mean: 17.1062 +2025-04-20 13:01:29,374 - INFO - Validation Std: 8.1121 +2025-04-20 13:01:29,374 - INFO - Training Mean: 23.1019 +2025-04-20 13:01:29,374 - INFO - Training Std: 20.4310 +2025-04-20 13:01:29,374 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:01:29,374 - INFO - KS Statistic: 0.1533 +2025-04-20 13:01:29,374 - INFO - P-value: 0.0000 +2025-04-20 13:01:29,374 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:01:29,374 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:10:09,067 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:10:09,068 - INFO - Timestamp: 2025-04-20 14:10:09 +2025-04-20 14:10:09,068 - INFO - Validation set size: 1363 (8.25%) +2025-04-20 14:10:09,068 - INFO - Training set size: 15151 (91.75%) +2025-04-20 14:10:09,068 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:10:09,068 - INFO - Mean: 4.7252 +2025-04-20 14:10:09,068 - INFO - Median: 4.2512 +2025-04-20 14:10:09,068 - INFO - Min: 0.0358 +2025-04-20 14:10:09,068 - INFO - Max: 14.1436 +2025-04-20 14:10:09,068 - INFO - Std: 2.5519 +2025-04-20 14:10:09,068 - INFO - OC Distribution Statistics: +2025-04-20 14:10:09,068 - INFO - Validation Mean: 17.1003 +2025-04-20 14:10:09,068 - INFO - Validation Std: 8.3069 +2025-04-20 14:10:09,068 - INFO - Training Mean: 23.0150 +2025-04-20 14:10:09,068 - INFO - Training Std: 20.3105 +2025-04-20 14:10:09,068 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:10:09,068 - INFO - KS Statistic: 0.1651 +2025-04-20 14:10:09,068 - INFO - P-value: 0.0000 +2025-04-20 14:10:09,068 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:10:09,068 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:10:09,249 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:10:09,249 - INFO - Timestamp: 2025-04-20 14:10:09 +2025-04-20 14:10:09,249 - INFO - Validation set size: 1378 (8.34%) +2025-04-20 14:10:09,249 - INFO - Training set size: 15136 (91.66%) +2025-04-20 14:10:09,249 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:10:09,249 - INFO - Mean: 4.7579 +2025-04-20 14:10:09,249 - INFO - Median: 4.2081 +2025-04-20 14:10:09,249 - INFO - Min: 0.3763 +2025-04-20 14:10:09,249 - INFO - Max: 17.8824 +2025-04-20 14:10:09,249 - INFO - Std: 2.7897 +2025-04-20 14:10:09,249 - INFO - OC Distribution Statistics: +2025-04-20 14:10:09,249 - INFO - Validation Mean: 17.9489 +2025-04-20 14:10:09,249 - INFO - Validation Std: 9.2869 +2025-04-20 14:10:09,249 - INFO - Training Mean: 22.9436 +2025-04-20 14:10:09,249 - INFO - Training Std: 20.3002 +2025-04-20 14:10:09,249 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:10:09,249 - INFO - KS Statistic: 0.1078 +2025-04-20 14:10:09,249 - INFO - P-value: 0.0000 +2025-04-20 14:10:09,249 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:10:09,249 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:10:12,904 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:10:12,904 - INFO - Timestamp: 2025-04-20 14:10:12 +2025-04-20 14:10:12,904 - INFO - Validation set size: 1408 (8.53%) +2025-04-20 14:10:12,904 - INFO - Training set size: 15106 (91.47%) +2025-04-20 14:10:12,904 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:10:12,904 - INFO - Mean: 4.8231 +2025-04-20 14:10:12,904 - INFO - Median: 4.3979 +2025-04-20 14:10:12,904 - INFO - Min: 0.3763 +2025-04-20 14:10:12,904 - INFO - Max: 18.4135 +2025-04-20 14:10:12,904 - INFO - Std: 2.6735 +2025-04-20 14:10:12,904 - INFO - OC Distribution Statistics: +2025-04-20 14:10:12,904 - INFO - Validation Mean: 17.7883 +2025-04-20 14:10:12,904 - INFO - Validation Std: 8.6157 +2025-04-20 14:10:12,904 - INFO - Training Mean: 22.9684 +2025-04-20 14:10:12,904 - INFO - Training Std: 20.3387 +2025-04-20 14:10:12,904 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:10:12,904 - INFO - KS Statistic: 0.1163 +2025-04-20 14:10:12,904 - INFO - P-value: 0.0000 +2025-04-20 14:10:12,904 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:10:12,904 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:10:13,538 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:10:13,538 - INFO - Timestamp: 2025-04-20 14:10:13 +2025-04-20 14:10:13,538 - INFO - Validation set size: 1435 (8.69%) +2025-04-20 14:10:13,538 - INFO - Training set size: 15079 (91.31%) +2025-04-20 14:10:13,538 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:10:13,538 - INFO - Mean: 4.6360 +2025-04-20 14:10:13,538 - INFO - Median: 4.1251 +2025-04-20 14:10:13,538 - INFO - Min: 0.4281 +2025-04-20 14:10:13,539 - INFO - Max: 15.1878 +2025-04-20 14:10:13,539 - INFO - Std: 2.5717 +2025-04-20 14:10:13,539 - INFO - OC Distribution Statistics: +2025-04-20 14:10:13,539 - INFO - Validation Mean: 17.6771 +2025-04-20 14:10:13,539 - INFO - Validation Std: 9.3328 +2025-04-20 14:10:13,539 - INFO - Training Mean: 22.9883 +2025-04-20 14:10:13,539 - INFO - Training Std: 20.3196 +2025-04-20 14:10:13,539 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:10:13,539 - INFO - KS Statistic: 0.1336 +2025-04-20 14:10:13,539 - INFO - P-value: 0.0000 +2025-04-20 14:10:13,539 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:10:13,539 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:15:43,132 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:15:43,132 - INFO - Timestamp: 2025-04-20 15:15:43 +2025-04-20 15:15:43,132 - INFO - Validation set size: 1338 (8.10%) +2025-04-20 15:15:43,132 - INFO - Training set size: 15176 (91.90%) +2025-04-20 15:15:43,132 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:15:43,132 - INFO - Mean: 5.3312 +2025-04-20 15:15:43,132 - INFO - Median: 4.4606 +2025-04-20 15:15:43,132 - INFO - Min: 0.0565 +2025-04-20 15:15:43,132 - INFO - Max: 18.8528 +2025-04-20 15:15:43,132 - INFO - Std: 3.3579 +2025-04-20 15:15:43,132 - INFO - OC Distribution Statistics: +2025-04-20 15:15:43,132 - INFO - Validation Mean: 17.7556 +2025-04-20 15:15:43,132 - INFO - Validation Std: 8.8919 +2025-04-20 15:15:43,132 - INFO - Training Mean: 22.9474 +2025-04-20 15:15:43,132 - INFO - Training Std: 20.2919 +2025-04-20 15:15:43,132 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:15:43,132 - INFO - KS Statistic: 0.1149 +2025-04-20 15:15:43,132 - INFO - P-value: 0.0000 +2025-04-20 15:15:43,132 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:15:43,132 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:15:43,331 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:15:43,331 - INFO - Timestamp: 2025-04-20 15:15:43 +2025-04-20 15:15:43,331 - INFO - Validation set size: 1352 (8.19%) +2025-04-20 15:15:43,331 - INFO - Training set size: 15162 (91.81%) +2025-04-20 15:15:43,331 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:15:43,331 - INFO - Mean: 5.1057 +2025-04-20 15:15:43,331 - INFO - Median: 4.4695 +2025-04-20 15:15:43,331 - INFO - Min: 0.4134 +2025-04-20 15:15:43,331 - INFO - Max: 18.8528 +2025-04-20 15:15:43,331 - INFO - Std: 2.8395 +2025-04-20 15:15:43,331 - INFO - OC Distribution Statistics: +2025-04-20 15:15:43,331 - INFO - Validation Mean: 18.0521 +2025-04-20 15:15:43,331 - INFO - Validation Std: 8.6316 +2025-04-20 15:15:43,331 - INFO - Training Mean: 22.9258 +2025-04-20 15:15:43,331 - INFO - Training Std: 20.3155 +2025-04-20 15:15:43,331 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:15:43,331 - INFO - KS Statistic: 0.1219 +2025-04-20 15:15:43,331 - INFO - P-value: 0.0000 +2025-04-20 15:15:43,331 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:15:43,331 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:15:46,866 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:15:46,867 - INFO - Timestamp: 2025-04-20 15:15:46 +2025-04-20 15:15:46,867 - INFO - Validation set size: 1608 (9.74%) +2025-04-20 15:15:46,867 - INFO - Training set size: 14906 (90.26%) +2025-04-20 15:15:46,867 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:15:46,867 - INFO - Mean: 4.9130 +2025-04-20 15:15:46,867 - INFO - Median: 4.1281 +2025-04-20 15:15:46,867 - INFO - Min: 0.0406 +2025-04-20 15:15:46,867 - INFO - Max: 21.4000 +2025-04-20 15:15:46,867 - INFO - Std: 3.1347 +2025-04-20 15:15:46,867 - INFO - OC Distribution Statistics: +2025-04-20 15:15:46,867 - INFO - Validation Mean: 17.4282 +2025-04-20 15:15:46,867 - INFO - Validation Std: 7.9482 +2025-04-20 15:15:46,867 - INFO - Training Mean: 23.0768 +2025-04-20 15:15:46,867 - INFO - Training Std: 20.4602 +2025-04-20 15:15:46,867 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:15:46,867 - INFO - KS Statistic: 0.1596 +2025-04-20 15:15:46,867 - INFO - P-value: 0.0000 +2025-04-20 15:15:46,867 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:15:46,867 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:15:47,579 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:15:47,579 - INFO - Timestamp: 2025-04-20 15:15:47 +2025-04-20 15:15:47,579 - INFO - Validation set size: 1380 (8.36%) +2025-04-20 15:15:47,579 - INFO - Training set size: 15134 (91.64%) +2025-04-20 15:15:47,579 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:15:47,579 - INFO - Mean: 5.0328 +2025-04-20 15:15:47,579 - INFO - Median: 4.4799 +2025-04-20 15:15:47,579 - INFO - Min: 0.3705 +2025-04-20 15:15:47,579 - INFO - Max: 26.6740 +2025-04-20 15:15:47,579 - INFO - Std: 2.9857 +2025-04-20 15:15:47,579 - INFO - OC Distribution Statistics: +2025-04-20 15:15:47,579 - INFO - Validation Mean: 17.6714 +2025-04-20 15:15:47,579 - INFO - Validation Std: 9.1103 +2025-04-20 15:15:47,579 - INFO - Training Mean: 22.9695 +2025-04-20 15:15:47,579 - INFO - Training Std: 20.3020 +2025-04-20 15:15:47,579 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:15:47,579 - INFO - KS Statistic: 0.1432 +2025-04-20 15:15:47,579 - INFO - P-value: 0.0000 +2025-04-20 15:15:47,579 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:15:47,579 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 16:23:09,180 - INFO - Validation and Training Set Split Statistics +2025-04-20 16:23:09,181 - INFO - Timestamp: 2025-04-20 16:23:09 +2025-04-20 16:23:09,181 - INFO - Validation set size: 1384 (8.38%) +2025-04-20 16:23:09,181 - INFO - Training set size: 15130 (91.62%) +2025-04-20 16:23:09,181 - INFO - Minimum Distance Statistics (km): +2025-04-20 16:23:09,181 - INFO - Mean: 4.6217 +2025-04-20 16:23:09,181 - INFO - Median: 4.0282 +2025-04-20 16:23:09,181 - INFO - Min: 0.0712 +2025-04-20 16:23:09,181 - INFO - Max: 16.1156 +2025-04-20 16:23:09,181 - INFO - Std: 2.7424 +2025-04-20 16:23:09,181 - INFO - OC Distribution Statistics: +2025-04-20 16:23:09,181 - INFO - Validation Mean: 17.7888 +2025-04-20 16:23:09,181 - INFO - Validation Std: 9.1516 +2025-04-20 16:23:09,181 - INFO - Training Mean: 22.9602 +2025-04-20 16:23:09,181 - INFO - Training Std: 20.3051 +2025-04-20 16:23:09,181 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 16:23:09,181 - INFO - KS Statistic: 0.1478 +2025-04-20 16:23:09,181 - INFO - P-value: 0.0000 +2025-04-20 16:23:09,181 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 16:23:09,181 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 16:23:12,603 - INFO - Validation and Training Set Split Statistics +2025-04-20 16:23:12,603 - INFO - Timestamp: 2025-04-20 16:23:12 +2025-04-20 16:23:12,603 - INFO - Validation set size: 1525 (9.23%) +2025-04-20 16:23:12,603 - INFO - Training set size: 14989 (90.77%) +2025-04-20 16:23:12,603 - INFO - Minimum Distance Statistics (km): +2025-04-20 16:23:12,603 - INFO - Mean: 4.9232 +2025-04-20 16:23:12,603 - INFO - Median: 4.3127 +2025-04-20 16:23:12,603 - INFO - Min: 0.0712 +2025-04-20 16:23:12,603 - INFO - Max: 20.3992 +2025-04-20 16:23:12,603 - INFO - Std: 2.9960 +2025-04-20 16:23:12,603 - INFO - OC Distribution Statistics: +2025-04-20 16:23:12,603 - INFO - Validation Mean: 17.8306 +2025-04-20 16:23:12,603 - INFO - Validation Std: 8.7281 +2025-04-20 16:23:12,603 - INFO - Training Mean: 23.0046 +2025-04-20 16:23:12,603 - INFO - Training Std: 20.3948 +2025-04-20 16:23:12,603 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 16:23:12,603 - INFO - KS Statistic: 0.1308 +2025-04-20 16:23:12,603 - INFO - P-value: 0.0000 +2025-04-20 16:23:12,603 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 16:23:12,603 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 16:23:13,674 - INFO - Validation and Training Set Split Statistics +2025-04-20 16:23:13,674 - INFO - Timestamp: 2025-04-20 16:23:13 +2025-04-20 16:23:13,674 - INFO - Validation set size: 1573 (9.53%) +2025-04-20 16:23:13,674 - INFO - Training set size: 14941 (90.47%) +2025-04-20 16:23:13,674 - INFO - Minimum Distance Statistics (km): +2025-04-20 16:23:13,674 - INFO - Mean: 4.7220 +2025-04-20 16:23:13,674 - INFO - Median: 4.2801 +2025-04-20 16:23:13,674 - INFO - Min: 0.3976 +2025-04-20 16:23:13,674 - INFO - Max: 18.8528 +2025-04-20 16:23:13,674 - INFO - Std: 2.7303 +2025-04-20 16:23:13,674 - INFO - OC Distribution Statistics: +2025-04-20 16:23:13,674 - INFO - Validation Mean: 17.6998 +2025-04-20 16:23:13,674 - INFO - Validation Std: 9.2662 +2025-04-20 16:23:13,674 - INFO - Training Mean: 23.0350 +2025-04-20 16:23:13,674 - INFO - Training Std: 20.3909 +2025-04-20 16:23:13,674 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 16:23:13,674 - INFO - KS Statistic: 0.1321 +2025-04-20 16:23:13,674 - INFO - P-value: 0.0000 +2025-04-20 16:23:13,674 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 16:23:13,674 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 16:23:17,219 - INFO - Validation and Training Set Split Statistics +2025-04-20 16:23:17,219 - INFO - Timestamp: 2025-04-20 16:23:17 +2025-04-20 16:23:17,219 - INFO - Validation set size: 1601 (9.69%) +2025-04-20 16:23:17,219 - INFO - Training set size: 14913 (90.31%) +2025-04-20 16:23:17,219 - INFO - Minimum Distance Statistics (km): +2025-04-20 16:23:17,219 - INFO - Mean: 4.8988 +2025-04-20 16:23:17,219 - INFO - Median: 4.4530 +2025-04-20 16:23:17,219 - INFO - Min: 0.3763 +2025-04-20 16:23:17,219 - INFO - Max: 26.6740 +2025-04-20 16:23:17,219 - INFO - Std: 3.0049 +2025-04-20 16:23:17,219 - INFO - OC Distribution Statistics: +2025-04-20 16:23:17,219 - INFO - Validation Mean: 16.8163 +2025-04-20 16:23:17,219 - INFO - Validation Std: 8.1386 +2025-04-20 16:23:17,219 - INFO - Training Mean: 23.1398 +2025-04-20 16:23:17,219 - INFO - Training Std: 20.4292 +2025-04-20 16:23:17,219 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 16:23:17,220 - INFO - KS Statistic: 0.1896 +2025-04-20 16:23:17,220 - INFO - P-value: 0.0000 +2025-04-20 16:23:17,220 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 16:23:17,220 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 17:32:58,815 - INFO - Validation and Training Set Split Statistics +2025-04-20 17:32:58,815 - INFO - Timestamp: 2025-04-20 17:32:58 +2025-04-20 17:32:58,815 - INFO - Validation set size: 1625 (9.84%) +2025-04-20 17:32:58,815 - INFO - Training set size: 14889 (90.16%) +2025-04-20 17:32:58,815 - INFO - Minimum Distance Statistics (km): +2025-04-20 17:32:58,815 - INFO - Mean: 4.4314 +2025-04-20 17:32:58,815 - INFO - Median: 3.8005 +2025-04-20 17:32:58,815 - INFO - Min: 0.3763 +2025-04-20 17:32:58,815 - INFO - Max: 20.3992 +2025-04-20 17:32:58,815 - INFO - Std: 2.7074 +2025-04-20 17:32:58,815 - INFO - OC Distribution Statistics: +2025-04-20 17:32:58,815 - INFO - Validation Mean: 17.5217 +2025-04-20 17:32:58,815 - INFO - Validation Std: 8.4147 +2025-04-20 17:32:58,815 - INFO - Training Mean: 23.0730 +2025-04-20 17:32:58,815 - INFO - Training Std: 20.4517 +2025-04-20 17:32:58,816 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 17:32:58,816 - INFO - KS Statistic: 0.1254 +2025-04-20 17:32:58,816 - INFO - P-value: 0.0000 +2025-04-20 17:32:58,816 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 17:32:58,816 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 17:32:59,161 - INFO - Validation and Training Set Split Statistics +2025-04-20 17:32:59,162 - INFO - Timestamp: 2025-04-20 17:32:59 +2025-04-20 17:32:59,162 - INFO - Validation set size: 1752 (10.61%) +2025-04-20 17:32:59,162 - INFO - Training set size: 14762 (89.39%) +2025-04-20 17:32:59,162 - INFO - Minimum Distance Statistics (km): +2025-04-20 17:32:59,162 - INFO - Mean: 5.0854 +2025-04-20 17:32:59,162 - INFO - Median: 4.2320 +2025-04-20 17:32:59,162 - INFO - Min: 0.3763 +2025-04-20 17:32:59,162 - INFO - Max: 20.3992 +2025-04-20 17:32:59,162 - INFO - Std: 2.9891 +2025-04-20 17:32:59,162 - INFO - OC Distribution Statistics: +2025-04-20 17:32:59,162 - INFO - Validation Mean: 17.2754 +2025-04-20 17:32:59,162 - INFO - Validation Std: 8.3674 +2025-04-20 17:32:59,162 - INFO - Training Mean: 23.1500 +2025-04-20 17:32:59,162 - INFO - Training Std: 20.5122 +2025-04-20 17:32:59,162 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 17:32:59,162 - INFO - KS Statistic: 0.1557 +2025-04-20 17:32:59,162 - INFO - P-value: 0.0000 +2025-04-20 17:32:59,162 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 17:32:59,162 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 17:32:59,258 - INFO - Validation and Training Set Split Statistics +2025-04-20 17:32:59,258 - INFO - Timestamp: 2025-04-20 17:32:59 +2025-04-20 17:32:59,258 - INFO - Validation set size: 1832 (11.09%) +2025-04-20 17:32:59,258 - INFO - Training set size: 14682 (88.91%) +2025-04-20 17:32:59,258 - INFO - Minimum Distance Statistics (km): +2025-04-20 17:32:59,258 - INFO - Mean: 4.7595 +2025-04-20 17:32:59,258 - INFO - Median: 4.3997 +2025-04-20 17:32:59,258 - INFO - Min: 0.3763 +2025-04-20 17:32:59,258 - INFO - Max: 18.4150 +2025-04-20 17:32:59,258 - INFO - Std: 2.7800 +2025-04-20 17:32:59,258 - INFO - OC Distribution Statistics: +2025-04-20 17:32:59,258 - INFO - Validation Mean: 17.2343 +2025-04-20 17:32:59,258 - INFO - Validation Std: 8.1086 +2025-04-20 17:32:59,258 - INFO - Training Mean: 23.1872 +2025-04-20 17:32:59,258 - INFO - Training Std: 20.5656 +2025-04-20 17:32:59,258 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 17:32:59,258 - INFO - KS Statistic: 0.1800 +2025-04-20 17:32:59,258 - INFO - P-value: 0.0000 +2025-04-20 17:32:59,258 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 17:32:59,258 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 17:32:59,908 - INFO - Validation and Training Set Split Statistics +2025-04-20 17:32:59,908 - INFO - Timestamp: 2025-04-20 17:32:59 +2025-04-20 17:32:59,908 - INFO - Validation set size: 1343 (8.13%) +2025-04-20 17:32:59,908 - INFO - Training set size: 15171 (91.87%) +2025-04-20 17:32:59,908 - INFO - Minimum Distance Statistics (km): +2025-04-20 17:32:59,908 - INFO - Mean: 4.8474 +2025-04-20 17:32:59,908 - INFO - Median: 4.0191 +2025-04-20 17:32:59,908 - INFO - Min: 0.0712 +2025-04-20 17:32:59,908 - INFO - Max: 23.3270 +2025-04-20 17:32:59,908 - INFO - Std: 3.1443 +2025-04-20 17:32:59,908 - INFO - OC Distribution Statistics: +2025-04-20 17:32:59,908 - INFO - Validation Mean: 17.5737 +2025-04-20 17:32:59,908 - INFO - Validation Std: 8.7974 +2025-04-20 17:32:59,908 - INFO - Training Mean: 22.9653 +2025-04-20 17:32:59,908 - INFO - Training Std: 20.2939 +2025-04-20 17:32:59,908 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 17:32:59,908 - INFO - KS Statistic: 0.1352 +2025-04-20 17:32:59,908 - INFO - P-value: 0.0000 +2025-04-20 17:32:59,909 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 17:32:59,909 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 23:20:00,327 - INFO - Validation and Training Set Split Statistics +2025-04-20 23:20:00,327 - INFO - Timestamp: 2025-04-20 23:20:00 +2025-04-20 23:20:00,328 - INFO - Validation set size: 1348 (8.16%) +2025-04-20 23:20:00,328 - INFO - Training set size: 15166 (91.84%) +2025-04-20 23:20:00,328 - INFO - Minimum Distance Statistics (km): +2025-04-20 23:20:00,328 - INFO - Mean: 4.8966 +2025-04-20 23:20:00,328 - INFO - Median: 4.0352 +2025-04-20 23:20:00,328 - INFO - Min: 0.3424 +2025-04-20 23:20:00,328 - INFO - Max: 17.1898 +2025-04-20 23:20:00,328 - INFO - Std: 2.9343 +2025-04-20 23:20:00,328 - INFO - OC Distribution Statistics: +2025-04-20 23:20:00,328 - INFO - Validation Mean: 17.2109 +2025-04-20 23:20:00,328 - INFO - Validation Std: 8.3515 +2025-04-20 23:20:00,328 - INFO - Training Mean: 22.9993 +2025-04-20 23:20:00,328 - INFO - Training Std: 20.3042 +2025-04-20 23:20:00,328 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 23:20:00,328 - INFO - KS Statistic: 0.1608 +2025-04-20 23:20:00,328 - INFO - P-value: 0.0000 +2025-04-20 23:20:00,328 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 23:20:00,328 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 23:20:01,516 - INFO - Validation and Training Set Split Statistics +2025-04-20 23:20:01,516 - INFO - Timestamp: 2025-04-20 23:20:01 +2025-04-20 23:20:01,517 - INFO - Validation set size: 1364 (8.26%) +2025-04-20 23:20:01,517 - INFO - Training set size: 15150 (91.74%) +2025-04-20 23:20:01,517 - INFO - Minimum Distance Statistics (km): +2025-04-20 23:20:01,517 - INFO - Mean: 4.8411 +2025-04-20 23:20:01,517 - INFO - Median: 3.9312 +2025-04-20 23:20:01,517 - INFO - Min: 0.0219 +2025-04-20 23:20:01,517 - INFO - Max: 17.1898 +2025-04-20 23:20:01,517 - INFO - Std: 3.1809 +2025-04-20 23:20:01,517 - INFO - OC Distribution Statistics: +2025-04-20 23:20:01,517 - INFO - Validation Mean: 18.1538 +2025-04-20 23:20:01,517 - INFO - Validation Std: 9.3310 +2025-04-20 23:20:01,517 - INFO - Training Mean: 22.9205 +2025-04-20 23:20:01,517 - INFO - Training Std: 20.2959 +2025-04-20 23:20:01,517 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 23:20:01,517 - INFO - KS Statistic: 0.1073 +2025-04-20 23:20:01,517 - INFO - P-value: 0.0000 +2025-04-20 23:20:01,517 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 23:20:01,517 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 23:20:03,576 - INFO - Validation and Training Set Split Statistics +2025-04-20 23:20:03,576 - INFO - Timestamp: 2025-04-20 23:20:03 +2025-04-20 23:20:03,576 - INFO - Validation set size: 1554 (9.41%) +2025-04-20 23:20:03,576 - INFO - Training set size: 14960 (90.59%) +2025-04-20 23:20:03,576 - INFO - Minimum Distance Statistics (km): +2025-04-20 23:20:03,576 - INFO - Mean: 4.7096 +2025-04-20 23:20:03,576 - INFO - Median: 3.8154 +2025-04-20 23:20:03,576 - INFO - Min: 0.3705 +2025-04-20 23:20:03,576 - INFO - Max: 18.4150 +2025-04-20 23:20:03,576 - INFO - Std: 3.1502 +2025-04-20 23:20:03,576 - INFO - OC Distribution Statistics: +2025-04-20 23:20:03,576 - INFO - Validation Mean: 17.2812 +2025-04-20 23:20:03,577 - INFO - Validation Std: 8.5726 +2025-04-20 23:20:03,577 - INFO - Training Mean: 23.0717 +2025-04-20 23:20:03,577 - INFO - Training Std: 20.4011 +2025-04-20 23:20:03,577 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 23:20:03,577 - INFO - KS Statistic: 0.1554 +2025-04-20 23:20:03,577 - INFO - P-value: 0.0000 +2025-04-20 23:20:03,577 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 23:20:03,577 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 23:20:03,796 - INFO - Validation and Training Set Split Statistics +2025-04-20 23:20:03,796 - INFO - Timestamp: 2025-04-20 23:20:03 +2025-04-20 23:20:03,796 - INFO - Validation set size: 1562 (9.46%) +2025-04-20 23:20:03,796 - INFO - Training set size: 14952 (90.54%) +2025-04-20 23:20:03,796 - INFO - Minimum Distance Statistics (km): +2025-04-20 23:20:03,796 - INFO - Mean: 4.8106 +2025-04-20 23:20:03,796 - INFO - Median: 4.2533 +2025-04-20 23:20:03,796 - INFO - Min: 0.0819 +2025-04-20 23:20:03,796 - INFO - Max: 18.9773 +2025-04-20 23:20:03,796 - INFO - Std: 2.8678 +2025-04-20 23:20:03,796 - INFO - OC Distribution Statistics: +2025-04-20 23:20:03,796 - INFO - Validation Mean: 17.3744 +2025-04-20 23:20:03,796 - INFO - Validation Std: 8.6397 +2025-04-20 23:20:03,796 - INFO - Training Mean: 23.0650 +2025-04-20 23:20:03,796 - INFO - Training Std: 20.4050 +2025-04-20 23:20:03,796 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 23:20:03,796 - INFO - KS Statistic: 0.1589 +2025-04-20 23:20:03,796 - INFO - P-value: 0.0000 +2025-04-20 23:20:03,796 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 23:20:03,796 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 00:23:43,250 - INFO - Validation and Training Set Split Statistics +2025-04-21 00:23:43,251 - INFO - Timestamp: 2025-04-21 00:23:43 +2025-04-21 00:23:43,251 - INFO - Validation set size: 1484 (8.99%) +2025-04-21 00:23:43,251 - INFO - Training set size: 15030 (91.01%) +2025-04-21 00:23:43,251 - INFO - Minimum Distance Statistics (km): +2025-04-21 00:23:43,251 - INFO - Mean: 5.0474 +2025-04-21 00:23:43,251 - INFO - Median: 4.4895 +2025-04-21 00:23:43,251 - INFO - Min: 0.0219 +2025-04-21 00:23:43,251 - INFO - Max: 20.3992 +2025-04-21 00:23:43,251 - INFO - Std: 2.9602 +2025-04-21 00:23:43,251 - INFO - OC Distribution Statistics: +2025-04-21 00:23:43,251 - INFO - Validation Mean: 17.4610 +2025-04-21 00:23:43,251 - INFO - Validation Std: 8.8749 +2025-04-21 00:23:43,251 - INFO - Training Mean: 23.0270 +2025-04-21 00:23:43,251 - INFO - Training Std: 20.3579 +2025-04-21 00:23:43,251 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 00:23:43,251 - INFO - KS Statistic: 0.1271 +2025-04-21 00:23:43,251 - INFO - P-value: 0.0000 +2025-04-21 00:23:43,251 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 00:23:43,251 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 00:23:43,409 - INFO - Validation and Training Set Split Statistics +2025-04-21 00:23:43,409 - INFO - Timestamp: 2025-04-21 00:23:43 +2025-04-21 00:23:43,409 - INFO - Validation set size: 1539 (9.32%) +2025-04-21 00:23:43,409 - INFO - Training set size: 14975 (90.68%) +2025-04-21 00:23:43,409 - INFO - Minimum Distance Statistics (km): +2025-04-21 00:23:43,409 - INFO - Mean: 4.9437 +2025-04-21 00:23:43,409 - INFO - Median: 4.3127 +2025-04-21 00:23:43,409 - INFO - Min: 0.0406 +2025-04-21 00:23:43,409 - INFO - Max: 20.3992 +2025-04-21 00:23:43,409 - INFO - Std: 3.0905 +2025-04-21 00:23:43,409 - INFO - OC Distribution Statistics: +2025-04-21 00:23:43,409 - INFO - Validation Mean: 17.6956 +2025-04-21 00:23:43,409 - INFO - Validation Std: 8.7063 +2025-04-21 00:23:43,409 - INFO - Training Mean: 23.0233 +2025-04-21 00:23:43,409 - INFO - Training Std: 20.3993 +2025-04-21 00:23:43,409 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 00:23:43,409 - INFO - KS Statistic: 0.1119 +2025-04-21 00:23:43,409 - INFO - P-value: 0.0000 +2025-04-21 00:23:43,409 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 00:23:43,409 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 00:23:43,661 - INFO - Validation and Training Set Split Statistics +2025-04-21 00:23:43,662 - INFO - Timestamp: 2025-04-21 00:23:43 +2025-04-21 00:23:43,662 - INFO - Validation set size: 1581 (9.57%) +2025-04-21 00:23:43,662 - INFO - Training set size: 14933 (90.43%) +2025-04-21 00:23:43,662 - INFO - Minimum Distance Statistics (km): +2025-04-21 00:23:43,662 - INFO - Mean: 4.8819 +2025-04-21 00:23:43,662 - INFO - Median: 4.2214 +2025-04-21 00:23:43,662 - INFO - Min: 0.0655 +2025-04-21 00:23:43,662 - INFO - Max: 20.8290 +2025-04-21 00:23:43,662 - INFO - Std: 2.8816 +2025-04-21 00:23:43,662 - INFO - OC Distribution Statistics: +2025-04-21 00:23:43,662 - INFO - Validation Mean: 17.9111 +2025-04-21 00:23:43,662 - INFO - Validation Std: 9.1277 +2025-04-21 00:23:43,662 - INFO - Training Mean: 23.0155 +2025-04-21 00:23:43,662 - INFO - Training Std: 20.4072 +2025-04-21 00:23:43,662 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 00:23:43,662 - INFO - KS Statistic: 0.1367 +2025-04-21 00:23:43,662 - INFO - P-value: 0.0000 +2025-04-21 00:23:43,662 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 00:23:43,662 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 00:23:44,082 - INFO - Validation and Training Set Split Statistics +2025-04-21 00:23:44,082 - INFO - Timestamp: 2025-04-21 00:23:44 +2025-04-21 00:23:44,082 - INFO - Validation set size: 1443 (8.74%) +2025-04-21 00:23:44,082 - INFO - Training set size: 15071 (91.26%) +2025-04-21 00:23:44,082 - INFO - Minimum Distance Statistics (km): +2025-04-21 00:23:44,082 - INFO - Mean: 5.4381 +2025-04-21 00:23:44,082 - INFO - Median: 4.4602 +2025-04-21 00:23:44,082 - INFO - Min: 0.4134 +2025-04-21 00:23:44,082 - INFO - Max: 26.6740 +2025-04-21 00:23:44,082 - INFO - Std: 3.2942 +2025-04-21 00:23:44,082 - INFO - OC Distribution Statistics: +2025-04-21 00:23:44,083 - INFO - Validation Mean: 17.3597 +2025-04-21 00:23:44,083 - INFO - Validation Std: 8.5073 +2025-04-21 00:23:44,083 - INFO - Training Mean: 23.0215 +2025-04-21 00:23:44,083 - INFO - Training Std: 20.3499 +2025-04-21 00:23:44,083 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 00:23:44,083 - INFO - KS Statistic: 0.1670 +2025-04-21 00:23:44,083 - INFO - P-value: 0.0000 +2025-04-21 00:23:44,083 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 00:23:44,083 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 00:58:01,285 - INFO - Validation and Training Set Split Statistics +2025-04-21 00:58:01,285 - INFO - Timestamp: 2025-04-21 00:58:01 +2025-04-21 00:58:01,285 - INFO - Validation set size: 1370 (8.30%) +2025-04-21 00:58:01,285 - INFO - Training set size: 15144 (91.70%) +2025-04-21 00:58:01,285 - INFO - Minimum Distance Statistics (km): +2025-04-21 00:58:01,285 - INFO - Mean: 4.8126 +2025-04-21 00:58:01,285 - INFO - Median: 4.3484 +2025-04-21 00:58:01,285 - INFO - Min: 0.4134 +2025-04-21 00:58:01,285 - INFO - Max: 18.8528 +2025-04-21 00:58:01,285 - INFO - Std: 2.8140 +2025-04-21 00:58:01,285 - INFO - OC Distribution Statistics: +2025-04-21 00:58:01,285 - INFO - Validation Mean: 18.0476 +2025-04-21 00:58:01,285 - INFO - Validation Std: 9.4825 +2025-04-21 00:58:01,285 - INFO - Training Mean: 22.9320 +2025-04-21 00:58:01,285 - INFO - Training Std: 20.2902 +2025-04-21 00:58:01,285 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 00:58:01,285 - INFO - KS Statistic: 0.1142 +2025-04-21 00:58:01,285 - INFO - P-value: 0.0000 +2025-04-21 00:58:01,285 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 00:58:01,285 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 00:58:05,315 - INFO - Validation and Training Set Split Statistics +2025-04-21 00:58:05,315 - INFO - Timestamp: 2025-04-21 00:58:05 +2025-04-21 00:58:05,315 - INFO - Validation set size: 1622 (9.82%) +2025-04-21 00:58:05,315 - INFO - Training set size: 14892 (90.18%) +2025-04-21 00:58:05,315 - INFO - Minimum Distance Statistics (km): +2025-04-21 00:58:05,315 - INFO - Mean: 5.1304 +2025-04-21 00:58:05,315 - INFO - Median: 4.4690 +2025-04-21 00:58:05,315 - INFO - Min: 0.0358 +2025-04-21 00:58:05,315 - INFO - Max: 18.8528 +2025-04-21 00:58:05,315 - INFO - Std: 2.9674 +2025-04-21 00:58:05,315 - INFO - OC Distribution Statistics: +2025-04-21 00:58:05,315 - INFO - Validation Mean: 17.7292 +2025-04-21 00:58:05,315 - INFO - Validation Std: 8.8058 +2025-04-21 00:58:05,315 - INFO - Training Mean: 23.0493 +2025-04-21 00:58:05,315 - INFO - Training Std: 20.4382 +2025-04-21 00:58:05,315 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 00:58:05,315 - INFO - KS Statistic: 0.1312 +2025-04-21 00:58:05,315 - INFO - P-value: 0.0000 +2025-04-21 00:58:05,315 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 00:58:05,316 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 00:58:05,617 - INFO - Validation and Training Set Split Statistics +2025-04-21 00:58:05,617 - INFO - Timestamp: 2025-04-21 00:58:05 +2025-04-21 00:58:05,617 - INFO - Validation set size: 1708 (10.34%) +2025-04-21 00:58:05,617 - INFO - Training set size: 14806 (89.66%) +2025-04-21 00:58:05,617 - INFO - Minimum Distance Statistics (km): +2025-04-21 00:58:05,617 - INFO - Mean: 4.7864 +2025-04-21 00:58:05,617 - INFO - Median: 4.2382 +2025-04-21 00:58:05,617 - INFO - Min: 0.0655 +2025-04-21 00:58:05,617 - INFO - Max: 16.4391 +2025-04-21 00:58:05,617 - INFO - Std: 2.8026 +2025-04-21 00:58:05,617 - INFO - OC Distribution Statistics: +2025-04-21 00:58:05,617 - INFO - Validation Mean: 17.0716 +2025-04-21 00:58:05,618 - INFO - Validation Std: 8.5681 +2025-04-21 00:58:05,618 - INFO - Training Mean: 23.1561 +2025-04-21 00:58:05,618 - INFO - Training Std: 20.4729 +2025-04-21 00:58:05,618 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 00:58:05,618 - INFO - KS Statistic: 0.1703 +2025-04-21 00:58:05,618 - INFO - P-value: 0.0000 +2025-04-21 00:58:05,618 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 00:58:05,618 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 00:58:19,159 - INFO - Validation and Training Set Split Statistics +2025-04-21 00:58:19,160 - INFO - Timestamp: 2025-04-21 00:58:19 +2025-04-21 00:58:19,160 - INFO - Validation set size: 1385 (8.39%) +2025-04-21 00:58:19,160 - INFO - Training set size: 15129 (91.61%) +2025-04-21 00:58:19,160 - INFO - Minimum Distance Statistics (km): +2025-04-21 00:58:19,160 - INFO - Mean: 4.7208 +2025-04-21 00:58:19,160 - INFO - Median: 4.2801 +2025-04-21 00:58:19,160 - INFO - Min: 0.0406 +2025-04-21 00:58:19,160 - INFO - Max: 18.8528 +2025-04-21 00:58:19,160 - INFO - Std: 2.5195 +2025-04-21 00:58:19,160 - INFO - OC Distribution Statistics: +2025-04-21 00:58:19,160 - INFO - Validation Mean: 17.6834 +2025-04-21 00:58:19,160 - INFO - Validation Std: 8.4582 +2025-04-21 00:58:19,160 - INFO - Training Mean: 22.9702 +2025-04-21 00:58:19,160 - INFO - Training Std: 20.3306 +2025-04-21 00:58:19,160 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 00:58:19,160 - INFO - KS Statistic: 0.1303 +2025-04-21 00:58:19,160 - INFO - P-value: 0.0000 +2025-04-21 00:58:19,160 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 00:58:19,160 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 02:07:18,266 - INFO - Validation and Training Set Split Statistics +2025-04-21 02:07:18,266 - INFO - Timestamp: 2025-04-21 02:07:18 +2025-04-21 02:07:18,266 - INFO - Validation set size: 1387 (8.40%) +2025-04-21 02:07:18,267 - INFO - Training set size: 15127 (91.60%) +2025-04-21 02:07:18,267 - INFO - Minimum Distance Statistics (km): +2025-04-21 02:07:18,267 - INFO - Mean: 5.1085 +2025-04-21 02:07:18,267 - INFO - Median: 4.4375 +2025-04-21 02:07:18,267 - INFO - Min: 0.3763 +2025-04-21 02:07:18,267 - INFO - Max: 16.4391 +2025-04-21 02:07:18,267 - INFO - Std: 2.6804 +2025-04-21 02:07:18,267 - INFO - OC Distribution Statistics: +2025-04-21 02:07:18,267 - INFO - Validation Mean: 16.6458 +2025-04-21 02:07:18,267 - INFO - Validation Std: 7.2938 +2025-04-21 02:07:18,267 - INFO - Training Mean: 23.0660 +2025-04-21 02:07:18,267 - INFO - Training Std: 20.3456 +2025-04-21 02:07:18,267 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 02:07:18,267 - INFO - KS Statistic: 0.1821 +2025-04-21 02:07:18,267 - INFO - P-value: 0.0000 +2025-04-21 02:07:18,267 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 02:07:18,267 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 02:07:20,904 - INFO - Validation and Training Set Split Statistics +2025-04-21 02:07:20,905 - INFO - Timestamp: 2025-04-21 02:07:20 +2025-04-21 02:07:20,905 - INFO - Validation set size: 1823 (11.04%) +2025-04-21 02:07:20,905 - INFO - Training set size: 14691 (88.96%) +2025-04-21 02:07:20,905 - INFO - Minimum Distance Statistics (km): +2025-04-21 02:07:20,905 - INFO - Mean: 4.6788 +2025-04-21 02:07:20,905 - INFO - Median: 3.9027 +2025-04-21 02:07:20,905 - INFO - Min: 0.0712 +2025-04-21 02:07:20,905 - INFO - Max: 18.8528 +2025-04-21 02:07:20,905 - INFO - Std: 3.0015 +2025-04-21 02:07:20,905 - INFO - OC Distribution Statistics: +2025-04-21 02:07:20,905 - INFO - Validation Mean: 17.0075 +2025-04-21 02:07:20,905 - INFO - Validation Std: 9.0628 +2025-04-21 02:07:20,905 - INFO - Training Mean: 23.2117 +2025-04-21 02:07:20,905 - INFO - Training Std: 20.5030 +2025-04-21 02:07:20,905 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 02:07:20,905 - INFO - KS Statistic: 0.1731 +2025-04-21 02:07:20,905 - INFO - P-value: 0.0000 +2025-04-21 02:07:20,905 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 02:07:20,905 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 02:07:21,175 - INFO - Validation and Training Set Split Statistics +2025-04-21 02:07:21,175 - INFO - Timestamp: 2025-04-21 02:07:21 +2025-04-21 02:07:21,175 - INFO - Validation set size: 1632 (9.88%) +2025-04-21 02:07:21,175 - INFO - Training set size: 14882 (90.12%) +2025-04-21 02:07:21,175 - INFO - Minimum Distance Statistics (km): +2025-04-21 02:07:21,175 - INFO - Mean: 4.8322 +2025-04-21 02:07:21,175 - INFO - Median: 4.3625 +2025-04-21 02:07:21,175 - INFO - Min: 0.3705 +2025-04-21 02:07:21,175 - INFO - Max: 18.8528 +2025-04-21 02:07:21,175 - INFO - Std: 2.7831 +2025-04-21 02:07:21,175 - INFO - OC Distribution Statistics: +2025-04-21 02:07:21,175 - INFO - Validation Mean: 17.1144 +2025-04-21 02:07:21,175 - INFO - Validation Std: 8.7079 +2025-04-21 02:07:21,175 - INFO - Training Mean: 23.1203 +2025-04-21 02:07:21,175 - INFO - Training Std: 20.4292 +2025-04-21 02:07:21,175 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 02:07:21,175 - INFO - KS Statistic: 0.1623 +2025-04-21 02:07:21,175 - INFO - P-value: 0.0000 +2025-04-21 02:07:21,175 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 02:07:21,175 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 02:07:39,777 - INFO - Validation and Training Set Split Statistics +2025-04-21 02:07:39,777 - INFO - Timestamp: 2025-04-21 02:07:39 +2025-04-21 02:07:39,777 - INFO - Validation set size: 1501 (9.09%) +2025-04-21 02:07:39,777 - INFO - Training set size: 15013 (90.91%) +2025-04-21 02:07:39,777 - INFO - Minimum Distance Statistics (km): +2025-04-21 02:07:39,777 - INFO - Mean: 4.7464 +2025-04-21 02:07:39,777 - INFO - Median: 4.0653 +2025-04-21 02:07:39,777 - INFO - Min: 0.0519 +2025-04-21 02:07:39,777 - INFO - Max: 18.8528 +2025-04-21 02:07:39,777 - INFO - Std: 2.8561 +2025-04-21 02:07:39,777 - INFO - OC Distribution Statistics: +2025-04-21 02:07:39,777 - INFO - Validation Mean: 17.3850 +2025-04-21 02:07:39,777 - INFO - Validation Std: 8.7608 +2025-04-21 02:07:39,777 - INFO - Training Mean: 23.0409 +2025-04-21 02:07:39,777 - INFO - Training Std: 20.3693 +2025-04-21 02:07:39,777 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 02:07:39,777 - INFO - KS Statistic: 0.1504 +2025-04-21 02:07:39,777 - INFO - P-value: 0.0000 +2025-04-21 02:07:39,777 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 02:07:39,777 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 15:25:08,626 - INFO - Validation and Training Set Split Statistics +2025-04-21 15:25:08,626 - INFO - Timestamp: 2025-04-21 15:25:08 +2025-04-21 15:25:08,626 - INFO - Validation set size: 1349 (8.17%) +2025-04-21 15:25:08,626 - INFO - Training set size: 15165 (91.83%) +2025-04-21 15:25:08,626 - INFO - Minimum Distance Statistics (km): +2025-04-21 15:25:08,626 - INFO - Mean: 5.5980 +2025-04-21 15:25:08,626 - INFO - Median: 5.2044 +2025-04-21 15:25:08,626 - INFO - Min: 0.3763 +2025-04-21 15:25:08,626 - INFO - Max: 18.8528 +2025-04-21 15:25:08,626 - INFO - Std: 3.0572 +2025-04-21 15:25:08,626 - INFO - OC Distribution Statistics: +2025-04-21 15:25:08,627 - INFO - Validation Mean: 17.1680 +2025-04-21 15:25:08,627 - INFO - Validation Std: 8.1987 +2025-04-21 15:25:08,627 - INFO - Training Mean: 23.0035 +2025-04-21 15:25:08,627 - INFO - Training Std: 20.3092 +2025-04-21 15:25:08,627 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 15:25:08,627 - INFO - KS Statistic: 0.1368 +2025-04-21 15:25:08,627 - INFO - P-value: 0.0000 +2025-04-21 15:25:08,627 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 15:25:08,627 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 15:25:13,163 - INFO - Validation and Training Set Split Statistics +2025-04-21 15:25:13,163 - INFO - Timestamp: 2025-04-21 15:25:13 +2025-04-21 15:25:13,163 - INFO - Validation set size: 1528 (9.25%) +2025-04-21 15:25:13,163 - INFO - Training set size: 14986 (90.75%) +2025-04-21 15:25:13,163 - INFO - Minimum Distance Statistics (km): +2025-04-21 15:25:13,163 - INFO - Mean: 4.8043 +2025-04-21 15:25:13,163 - INFO - Median: 4.1302 +2025-04-21 15:25:13,163 - INFO - Min: 0.3705 +2025-04-21 15:25:13,163 - INFO - Max: 20.3992 +2025-04-21 15:25:13,164 - INFO - Std: 2.8213 +2025-04-21 15:25:13,164 - INFO - OC Distribution Statistics: +2025-04-21 15:25:13,164 - INFO - Validation Mean: 17.5714 +2025-04-21 15:25:13,164 - INFO - Validation Std: 9.1471 +2025-04-21 15:25:13,164 - INFO - Training Mean: 23.0320 +2025-04-21 15:25:13,164 - INFO - Training Std: 20.3707 +2025-04-21 15:25:13,164 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 15:25:13,164 - INFO - KS Statistic: 0.1655 +2025-04-21 15:25:13,164 - INFO - P-value: 0.0000 +2025-04-21 15:25:13,164 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 15:25:13,164 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 15:25:13,478 - INFO - Validation and Training Set Split Statistics +2025-04-21 15:25:13,478 - INFO - Timestamp: 2025-04-21 15:25:13 +2025-04-21 15:25:13,478 - INFO - Validation set size: 1693 (10.25%) +2025-04-21 15:25:13,478 - INFO - Training set size: 14821 (89.75%) +2025-04-21 15:25:13,478 - INFO - Minimum Distance Statistics (km): +2025-04-21 15:25:13,478 - INFO - Mean: 5.0695 +2025-04-21 15:25:13,478 - INFO - Median: 4.4245 +2025-04-21 15:25:13,478 - INFO - Min: 0.3705 +2025-04-21 15:25:13,478 - INFO - Max: 16.4391 +2025-04-21 15:25:13,478 - INFO - Std: 2.8680 +2025-04-21 15:25:13,478 - INFO - OC Distribution Statistics: +2025-04-21 15:25:13,478 - INFO - Validation Mean: 17.6899 +2025-04-21 15:25:13,478 - INFO - Validation Std: 8.9997 +2025-04-21 15:25:13,478 - INFO - Training Mean: 23.0793 +2025-04-21 15:25:13,478 - INFO - Training Std: 20.4639 +2025-04-21 15:25:13,478 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 15:25:13,478 - INFO - KS Statistic: 0.1379 +2025-04-21 15:25:13,479 - INFO - P-value: 0.0000 +2025-04-21 15:25:13,479 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 15:25:13,479 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 15:25:14,583 - INFO - Validation and Training Set Split Statistics +2025-04-21 15:25:14,583 - INFO - Timestamp: 2025-04-21 15:25:14 +2025-04-21 15:25:14,583 - INFO - Validation set size: 1499 (9.08%) +2025-04-21 15:25:14,583 - INFO - Training set size: 15015 (90.92%) +2025-04-21 15:25:14,583 - INFO - Minimum Distance Statistics (km): +2025-04-21 15:25:14,583 - INFO - Mean: 4.8553 +2025-04-21 15:25:14,583 - INFO - Median: 4.3127 +2025-04-21 15:25:14,583 - INFO - Min: 0.0712 +2025-04-21 15:25:14,583 - INFO - Max: 17.8824 +2025-04-21 15:25:14,583 - INFO - Std: 2.9486 +2025-04-21 15:25:14,583 - INFO - OC Distribution Statistics: +2025-04-21 15:25:14,583 - INFO - Validation Mean: 16.9803 +2025-04-21 15:25:14,583 - INFO - Validation Std: 8.7560 +2025-04-21 15:25:14,583 - INFO - Training Mean: 23.0805 +2025-04-21 15:25:14,583 - INFO - Training Std: 20.3568 +2025-04-21 15:25:14,583 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 15:25:14,583 - INFO - KS Statistic: 0.1592 +2025-04-21 15:25:14,583 - INFO - P-value: 0.0000 +2025-04-21 15:25:14,583 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 15:25:14,583 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 10:30:21,443 - INFO - Validation and Training Set Split Statistics +2025-04-22 10:30:21,443 - INFO - Timestamp: 2025-04-22 10:30:21 +2025-04-22 10:30:21,443 - INFO - Validation set size: 1365 (8.27%) +2025-04-22 10:30:21,443 - INFO - Training set size: 15149 (91.73%) +2025-04-22 10:30:21,443 - INFO - Minimum Distance Statistics (km): +2025-04-22 10:30:21,443 - INFO - Mean: 5.1066 +2025-04-22 10:30:21,443 - INFO - Median: 4.4306 +2025-04-22 10:30:21,443 - INFO - Min: 0.4134 +2025-04-22 10:30:21,443 - INFO - Max: 18.8528 +2025-04-22 10:30:21,443 - INFO - Std: 2.9939 +2025-04-22 10:30:21,443 - INFO - OC Distribution Statistics: +2025-04-22 10:30:21,443 - INFO - Validation Mean: 17.6593 +2025-04-22 10:30:21,443 - INFO - Validation Std: 8.2611 +2025-04-22 10:30:21,443 - INFO - Training Mean: 22.9654 +2025-04-22 10:30:21,444 - INFO - Training Std: 20.3271 +2025-04-22 10:30:21,444 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 10:30:21,444 - INFO - KS Statistic: 0.1206 +2025-04-22 10:30:21,444 - INFO - P-value: 0.0000 +2025-04-22 10:30:21,444 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 10:30:21,444 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 10:30:24,995 - INFO - Validation and Training Set Split Statistics +2025-04-22 10:30:24,995 - INFO - Timestamp: 2025-04-22 10:30:24 +2025-04-22 10:30:24,995 - INFO - Validation set size: 1504 (9.11%) +2025-04-22 10:30:24,995 - INFO - Training set size: 15010 (90.89%) +2025-04-22 10:30:24,995 - INFO - Minimum Distance Statistics (km): +2025-04-22 10:30:24,995 - INFO - Mean: 4.9028 +2025-04-22 10:30:24,995 - INFO - Median: 4.2551 +2025-04-22 10:30:24,995 - INFO - Min: 0.3763 +2025-04-22 10:30:24,995 - INFO - Max: 20.3992 +2025-04-22 10:30:24,995 - INFO - Std: 3.0186 +2025-04-22 10:30:24,995 - INFO - OC Distribution Statistics: +2025-04-22 10:30:24,995 - INFO - Validation Mean: 17.5883 +2025-04-22 10:30:24,995 - INFO - Validation Std: 8.6667 +2025-04-22 10:30:24,995 - INFO - Training Mean: 23.0216 +2025-04-22 10:30:24,995 - INFO - Training Std: 20.3803 +2025-04-22 10:30:24,995 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 10:30:24,995 - INFO - KS Statistic: 0.1325 +2025-04-22 10:30:24,995 - INFO - P-value: 0.0000 +2025-04-22 10:30:24,995 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 10:30:24,995 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 10:30:25,074 - INFO - Validation and Training Set Split Statistics +2025-04-22 10:30:25,074 - INFO - Timestamp: 2025-04-22 10:30:25 +2025-04-22 10:30:25,074 - INFO - Validation set size: 1584 (9.59%) +2025-04-22 10:30:25,074 - INFO - Training set size: 14930 (90.41%) +2025-04-22 10:30:25,074 - INFO - Minimum Distance Statistics (km): +2025-04-22 10:30:25,074 - INFO - Mean: 5.0301 +2025-04-22 10:30:25,074 - INFO - Median: 4.3765 +2025-04-22 10:30:25,074 - INFO - Min: 0.3763 +2025-04-22 10:30:25,074 - INFO - Max: 20.8657 +2025-04-22 10:30:25,074 - INFO - Std: 3.1060 +2025-04-22 10:30:25,074 - INFO - OC Distribution Statistics: +2025-04-22 10:30:25,074 - INFO - Validation Mean: 17.2535 +2025-04-22 10:30:25,074 - INFO - Validation Std: 7.9594 +2025-04-22 10:30:25,074 - INFO - Training Mean: 23.0863 +2025-04-22 10:30:25,074 - INFO - Training Std: 20.4418 +2025-04-22 10:30:25,074 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 10:30:25,074 - INFO - KS Statistic: 0.1540 +2025-04-22 10:30:25,074 - INFO - P-value: 0.0000 +2025-04-22 10:30:25,074 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 10:30:25,074 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 10:30:25,382 - INFO - Validation and Training Set Split Statistics +2025-04-22 10:30:25,382 - INFO - Timestamp: 2025-04-22 10:30:25 +2025-04-22 10:30:25,382 - INFO - Validation set size: 1967 (11.91%) +2025-04-22 10:30:25,382 - INFO - Training set size: 14547 (88.09%) +2025-04-22 10:30:25,382 - INFO - Minimum Distance Statistics (km): +2025-04-22 10:30:25,382 - INFO - Mean: 4.9214 +2025-04-22 10:30:25,382 - INFO - Median: 4.4202 +2025-04-22 10:30:25,382 - INFO - Min: 0.0712 +2025-04-22 10:30:25,382 - INFO - Max: 26.6740 +2025-04-22 10:30:25,382 - INFO - Std: 3.0978 +2025-04-22 10:30:25,382 - INFO - OC Distribution Statistics: +2025-04-22 10:30:25,382 - INFO - Validation Mean: 16.5157 +2025-04-22 10:30:25,382 - INFO - Validation Std: 7.5641 +2025-04-22 10:30:25,382 - INFO - Training Mean: 23.3396 +2025-04-22 10:30:25,382 - INFO - Training Std: 20.6358 +2025-04-22 10:30:25,382 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 10:30:25,382 - INFO - KS Statistic: 0.2062 +2025-04-22 10:30:25,382 - INFO - P-value: 0.0000 +2025-04-22 10:30:25,382 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 10:30:25,382 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 10:30:25,779 - INFO - Validation and Training Set Split Statistics +2025-04-22 10:30:25,780 - INFO - Timestamp: 2025-04-22 10:30:25 +2025-04-22 10:30:25,780 - INFO - Validation set size: 1476 (8.94%) +2025-04-22 10:30:25,780 - INFO - Training set size: 15038 (91.06%) +2025-04-22 10:30:25,780 - INFO - Minimum Distance Statistics (km): +2025-04-22 10:30:25,780 - INFO - Mean: 4.9871 +2025-04-22 10:30:25,780 - INFO - Median: 4.3765 +2025-04-22 10:30:25,780 - INFO - Min: 0.0358 +2025-04-22 10:30:25,780 - INFO - Max: 22.8404 +2025-04-22 10:30:25,780 - INFO - Std: 3.0652 +2025-04-22 10:30:25,780 - INFO - OC Distribution Statistics: +2025-04-22 10:30:25,780 - INFO - Validation Mean: 17.7163 +2025-04-22 10:30:25,780 - INFO - Validation Std: 8.7820 +2025-04-22 10:30:25,780 - INFO - Training Mean: 22.9989 +2025-04-22 10:30:25,780 - INFO - Training Std: 20.3646 +2025-04-22 10:30:25,780 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 10:30:25,780 - INFO - KS Statistic: 0.1311 +2025-04-22 10:30:25,780 - INFO - P-value: 0.0000 +2025-04-22 10:30:25,780 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 10:30:25,780 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 10:30:25,842 - INFO - Validation and Training Set Split Statistics +2025-04-22 10:30:25,842 - INFO - Timestamp: 2025-04-22 10:30:25 +2025-04-22 10:30:25,842 - INFO - Validation set size: 1824 (11.05%) +2025-04-22 10:30:25,842 - INFO - Training set size: 14690 (88.95%) +2025-04-22 10:30:25,842 - INFO - Minimum Distance Statistics (km): +2025-04-22 10:30:25,842 - INFO - Mean: 4.7175 +2025-04-22 10:30:25,842 - INFO - Median: 4.3420 +2025-04-22 10:30:25,842 - INFO - Min: 0.0665 +2025-04-22 10:30:25,842 - INFO - Max: 20.3992 +2025-04-22 10:30:25,842 - INFO - Std: 2.8331 +2025-04-22 10:30:25,842 - INFO - OC Distribution Statistics: +2025-04-22 10:30:25,842 - INFO - Validation Mean: 17.3310 +2025-04-22 10:30:25,842 - INFO - Validation Std: 8.4872 +2025-04-22 10:30:25,842 - INFO - Training Mean: 23.1719 +2025-04-22 10:30:25,842 - INFO - Training Std: 20.5458 +2025-04-22 10:30:25,842 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 10:30:25,842 - INFO - KS Statistic: 0.1747 +2025-04-22 10:30:25,842 - INFO - P-value: 0.0000 +2025-04-22 10:30:25,842 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 10:30:25,842 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 10:30:26,450 - INFO - Validation and Training Set Split Statistics +2025-04-22 10:30:26,451 - INFO - Timestamp: 2025-04-22 10:30:26 +2025-04-22 10:30:26,451 - INFO - Validation set size: 1750 (10.60%) +2025-04-22 10:30:26,451 - INFO - Training set size: 14764 (89.40%) +2025-04-22 10:30:26,451 - INFO - Minimum Distance Statistics (km): +2025-04-22 10:30:26,451 - INFO - Mean: 4.9083 +2025-04-22 10:30:26,451 - INFO - Median: 4.4190 +2025-04-22 10:30:26,451 - INFO - Min: 0.0712 +2025-04-22 10:30:26,451 - INFO - Max: 18.8528 +2025-04-22 10:30:26,451 - INFO - Std: 2.7821 +2025-04-22 10:30:26,451 - INFO - OC Distribution Statistics: +2025-04-22 10:30:26,451 - INFO - Validation Mean: 17.3330 +2025-04-22 10:30:26,451 - INFO - Validation Std: 7.9068 +2025-04-22 10:30:26,451 - INFO - Training Mean: 23.1424 +2025-04-22 10:30:26,452 - INFO - Training Std: 20.5348 +2025-04-22 10:30:26,452 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 10:30:26,452 - INFO - KS Statistic: 0.1545 +2025-04-22 10:30:26,452 - INFO - P-value: 0.0000 +2025-04-22 10:30:26,452 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 10:30:26,452 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 10:30:27,193 - INFO - Validation and Training Set Split Statistics +2025-04-22 10:30:27,193 - INFO - Timestamp: 2025-04-22 10:30:27 +2025-04-22 10:30:27,193 - INFO - Validation set size: 1419 (8.59%) +2025-04-22 10:30:27,193 - INFO - Training set size: 15095 (91.41%) +2025-04-22 10:30:27,193 - INFO - Minimum Distance Statistics (km): +2025-04-22 10:30:27,193 - INFO - Mean: 4.8545 +2025-04-22 10:30:27,193 - INFO - Median: 4.1623 +2025-04-22 10:30:27,193 - INFO - Min: 0.3705 +2025-04-22 10:30:27,193 - INFO - Max: 20.3992 +2025-04-22 10:30:27,193 - INFO - Std: 2.9972 +2025-04-22 10:30:27,193 - INFO - OC Distribution Statistics: +2025-04-22 10:30:27,193 - INFO - Validation Mean: 17.8485 +2025-04-22 10:30:27,193 - INFO - Validation Std: 8.3146 +2025-04-22 10:30:27,193 - INFO - Training Mean: 22.9666 +2025-04-22 10:30:27,193 - INFO - Training Std: 20.3575 +2025-04-22 10:30:27,193 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 10:30:27,193 - INFO - KS Statistic: 0.1251 +2025-04-22 10:30:27,193 - INFO - P-value: 0.0000 +2025-04-22 10:30:27,193 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 10:30:27,193 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 11:36:06,344 - INFO - Validation and Training Set Split Statistics +2025-04-22 11:36:06,345 - INFO - Timestamp: 2025-04-22 11:36:06 +2025-04-22 11:36:06,345 - INFO - Validation set size: 1385 (8.39%) +2025-04-22 11:36:06,345 - INFO - Training set size: 15129 (91.61%) +2025-04-22 11:36:06,345 - INFO - Minimum Distance Statistics (km): +2025-04-22 11:36:06,345 - INFO - Mean: 4.5649 +2025-04-22 11:36:06,345 - INFO - Median: 3.7773 +2025-04-22 11:36:06,345 - INFO - Min: 0.4134 +2025-04-22 11:36:06,345 - INFO - Max: 18.8528 +2025-04-22 11:36:06,345 - INFO - Std: 2.7226 +2025-04-22 11:36:06,345 - INFO - OC Distribution Statistics: +2025-04-22 11:36:06,345 - INFO - Validation Mean: 17.4142 +2025-04-22 11:36:06,345 - INFO - Validation Std: 8.2419 +2025-04-22 11:36:06,345 - INFO - Training Mean: 22.9948 +2025-04-22 11:36:06,345 - INFO - Training Std: 20.3321 +2025-04-22 11:36:06,345 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 11:36:06,345 - INFO - KS Statistic: 0.1490 +2025-04-22 11:36:06,345 - INFO - P-value: 0.0000 +2025-04-22 11:36:06,345 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 11:36:06,345 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 11:36:06,777 - INFO - Validation and Training Set Split Statistics +2025-04-22 11:36:06,777 - INFO - Timestamp: 2025-04-22 11:36:06 +2025-04-22 11:36:06,777 - INFO - Validation set size: 1595 (9.66%) +2025-04-22 11:36:06,777 - INFO - Training set size: 14919 (90.34%) +2025-04-22 11:36:06,778 - INFO - Minimum Distance Statistics (km): +2025-04-22 11:36:06,778 - INFO - Mean: 5.0353 +2025-04-22 11:36:06,778 - INFO - Median: 4.5005 +2025-04-22 11:36:06,778 - INFO - Min: 0.3763 +2025-04-22 11:36:06,778 - INFO - Max: 17.8824 +2025-04-22 11:36:06,778 - INFO - Std: 2.7564 +2025-04-22 11:36:06,778 - INFO - OC Distribution Statistics: +2025-04-22 11:36:06,778 - INFO - Validation Mean: 17.1368 +2025-04-22 11:36:06,778 - INFO - Validation Std: 8.3206 +2025-04-22 11:36:06,778 - INFO - Training Mean: 23.1030 +2025-04-22 11:36:06,778 - INFO - Training Std: 20.4286 +2025-04-22 11:36:06,778 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 11:36:06,778 - INFO - KS Statistic: 0.1752 +2025-04-22 11:36:06,778 - INFO - P-value: 0.0000 +2025-04-22 11:36:06,778 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 11:36:06,778 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 11:36:10,121 - INFO - Validation and Training Set Split Statistics +2025-04-22 11:36:10,121 - INFO - Timestamp: 2025-04-22 11:36:10 +2025-04-22 11:36:10,121 - INFO - Validation set size: 1355 (8.21%) +2025-04-22 11:36:10,121 - INFO - Training set size: 15159 (91.79%) +2025-04-22 11:36:10,121 - INFO - Minimum Distance Statistics (km): +2025-04-22 11:36:10,121 - INFO - Mean: 4.4683 +2025-04-22 11:36:10,121 - INFO - Median: 3.8089 +2025-04-22 11:36:10,121 - INFO - Min: 0.3424 +2025-04-22 11:36:10,121 - INFO - Max: 18.8528 +2025-04-22 11:36:10,121 - INFO - Std: 2.8051 +2025-04-22 11:36:10,121 - INFO - OC Distribution Statistics: +2025-04-22 11:36:10,121 - INFO - Validation Mean: 17.8273 +2025-04-22 11:36:10,121 - INFO - Validation Std: 8.3130 +2025-04-22 11:36:10,121 - INFO - Training Mean: 22.9469 +2025-04-22 11:36:10,121 - INFO - Training Std: 20.3239 +2025-04-22 11:36:10,121 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 11:36:10,121 - INFO - KS Statistic: 0.1113 +2025-04-22 11:36:10,121 - INFO - P-value: 0.0000 +2025-04-22 11:36:10,121 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 11:36:10,121 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 11:36:10,458 - INFO - Validation and Training Set Split Statistics +2025-04-22 11:36:10,458 - INFO - Timestamp: 2025-04-22 11:36:10 +2025-04-22 11:36:10,458 - INFO - Validation set size: 1658 (10.04%) +2025-04-22 11:36:10,458 - INFO - Training set size: 14856 (89.96%) +2025-04-22 11:36:10,458 - INFO - Minimum Distance Statistics (km): +2025-04-22 11:36:10,458 - INFO - Mean: 4.7103 +2025-04-22 11:36:10,459 - INFO - Median: 4.2359 +2025-04-22 11:36:10,459 - INFO - Min: 0.0712 +2025-04-22 11:36:10,459 - INFO - Max: 18.8528 +2025-04-22 11:36:10,459 - INFO - Std: 2.7871 +2025-04-22 11:36:10,459 - INFO - OC Distribution Statistics: +2025-04-22 11:36:10,459 - INFO - Validation Mean: 17.2706 +2025-04-22 11:36:10,459 - INFO - Validation Std: 8.7513 +2025-04-22 11:36:10,459 - INFO - Training Mean: 23.1134 +2025-04-22 11:36:10,459 - INFO - Training Std: 20.4453 +2025-04-22 11:36:10,459 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 11:36:10,459 - INFO - KS Statistic: 0.1694 +2025-04-22 11:36:10,459 - INFO - P-value: 0.0000 +2025-04-22 11:36:10,459 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 11:36:10,459 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 11:36:10,743 - INFO - Validation and Training Set Split Statistics +2025-04-22 11:36:10,743 - INFO - Timestamp: 2025-04-22 11:36:10 +2025-04-22 11:36:10,743 - INFO - Validation set size: 1696 (10.27%) +2025-04-22 11:36:10,743 - INFO - Training set size: 14818 (89.73%) +2025-04-22 11:36:10,744 - INFO - Minimum Distance Statistics (km): +2025-04-22 11:36:10,744 - INFO - Mean: 5.1129 +2025-04-22 11:36:10,744 - INFO - Median: 4.4595 +2025-04-22 11:36:10,744 - INFO - Min: 0.0219 +2025-04-22 11:36:10,744 - INFO - Max: 26.6740 +2025-04-22 11:36:10,744 - INFO - Std: 3.2474 +2025-04-22 11:36:10,744 - INFO - OC Distribution Statistics: +2025-04-22 11:36:10,744 - INFO - Validation Mean: 17.2472 +2025-04-22 11:36:10,744 - INFO - Validation Std: 9.1305 +2025-04-22 11:36:10,744 - INFO - Training Mean: 23.1311 +2025-04-22 11:36:10,744 - INFO - Training Std: 20.4448 +2025-04-22 11:36:10,744 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 11:36:10,744 - INFO - KS Statistic: 0.1527 +2025-04-22 11:36:10,744 - INFO - P-value: 0.0000 +2025-04-22 11:36:10,744 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 11:36:10,744 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 11:36:10,832 - INFO - Validation and Training Set Split Statistics +2025-04-22 11:36:10,832 - INFO - Timestamp: 2025-04-22 11:36:10 +2025-04-22 11:36:10,833 - INFO - Validation set size: 1554 (9.41%) +2025-04-22 11:36:10,833 - INFO - Training set size: 14960 (90.59%) +2025-04-22 11:36:10,833 - INFO - Minimum Distance Statistics (km): +2025-04-22 11:36:10,833 - INFO - Mean: 4.6388 +2025-04-22 11:36:10,833 - INFO - Median: 3.8554 +2025-04-22 11:36:10,833 - INFO - Min: 0.0293 +2025-04-22 11:36:10,833 - INFO - Max: 20.8290 +2025-04-22 11:36:10,833 - INFO - Std: 2.8372 +2025-04-22 11:36:10,833 - INFO - OC Distribution Statistics: +2025-04-22 11:36:10,833 - INFO - Validation Mean: 17.8345 +2025-04-22 11:36:10,833 - INFO - Validation Std: 9.4651 +2025-04-22 11:36:10,833 - INFO - Training Mean: 23.0142 +2025-04-22 11:36:10,833 - INFO - Training Std: 20.3756 +2025-04-22 11:36:10,833 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 11:36:10,833 - INFO - KS Statistic: 0.1452 +2025-04-22 11:36:10,833 - INFO - P-value: 0.0000 +2025-04-22 11:36:10,833 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 11:36:10,833 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 11:36:10,904 - INFO - Validation and Training Set Split Statistics +2025-04-22 11:36:10,904 - INFO - Timestamp: 2025-04-22 11:36:10 +2025-04-22 11:36:10,904 - INFO - Validation set size: 1823 (11.04%) +2025-04-22 11:36:10,904 - INFO - Training set size: 14691 (88.96%) +2025-04-22 11:36:10,904 - INFO - Minimum Distance Statistics (km): +2025-04-22 11:36:10,904 - INFO - Mean: 5.3570 +2025-04-22 11:36:10,904 - INFO - Median: 4.6902 +2025-04-22 11:36:10,904 - INFO - Min: 0.3976 +2025-04-22 11:36:10,904 - INFO - Max: 20.3992 +2025-04-22 11:36:10,904 - INFO - Std: 2.8383 +2025-04-22 11:36:10,904 - INFO - OC Distribution Statistics: +2025-04-22 11:36:10,904 - INFO - Validation Mean: 17.1449 +2025-04-22 11:36:10,904 - INFO - Validation Std: 8.4758 +2025-04-22 11:36:10,904 - INFO - Training Mean: 23.1946 +2025-04-22 11:36:10,904 - INFO - Training Std: 20.5392 +2025-04-22 11:36:10,904 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 11:36:10,904 - INFO - KS Statistic: 0.1755 +2025-04-22 11:36:10,904 - INFO - P-value: 0.0000 +2025-04-22 11:36:10,904 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 11:36:10,904 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 11:36:11,134 - INFO - Validation and Training Set Split Statistics +2025-04-22 11:36:11,134 - INFO - Timestamp: 2025-04-22 11:36:11 +2025-04-22 11:36:11,135 - INFO - Validation set size: 1418 (8.59%) +2025-04-22 11:36:11,135 - INFO - Training set size: 15096 (91.41%) +2025-04-22 11:36:11,135 - INFO - Minimum Distance Statistics (km): +2025-04-22 11:36:11,135 - INFO - Mean: 4.7429 +2025-04-22 11:36:11,135 - INFO - Median: 4.1366 +2025-04-22 11:36:11,135 - INFO - Min: 0.3898 +2025-04-22 11:36:11,135 - INFO - Max: 26.6740 +2025-04-22 11:36:11,135 - INFO - Std: 2.8637 +2025-04-22 11:36:11,135 - INFO - OC Distribution Statistics: +2025-04-22 11:36:11,135 - INFO - Validation Mean: 17.9801 +2025-04-22 11:36:11,135 - INFO - Validation Std: 9.1581 +2025-04-22 11:36:11,135 - INFO - Training Mean: 22.9539 +2025-04-22 11:36:11,135 - INFO - Training Std: 20.3260 +2025-04-22 11:36:11,135 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 11:36:11,135 - INFO - KS Statistic: 0.1204 +2025-04-22 11:36:11,135 - INFO - P-value: 0.0000 +2025-04-22 11:36:11,135 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 11:36:11,135 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 12:44:15,830 - INFO - Validation and Training Set Split Statistics +2025-04-22 12:44:15,830 - INFO - Timestamp: 2025-04-22 12:44:15 +2025-04-22 12:44:15,831 - INFO - Validation set size: 1575 (9.54%) +2025-04-22 12:44:15,831 - INFO - Training set size: 14939 (90.46%) +2025-04-22 12:44:15,831 - INFO - Minimum Distance Statistics (km): +2025-04-22 12:44:15,831 - INFO - Mean: 4.8959 +2025-04-22 12:44:15,831 - INFO - Median: 4.2241 +2025-04-22 12:44:15,831 - INFO - Min: 0.0293 +2025-04-22 12:44:15,831 - INFO - Max: 20.3992 +2025-04-22 12:44:15,831 - INFO - Std: 2.8041 +2025-04-22 12:44:15,831 - INFO - OC Distribution Statistics: +2025-04-22 12:44:15,831 - INFO - Validation Mean: 17.3093 +2025-04-22 12:44:15,831 - INFO - Validation Std: 8.8984 +2025-04-22 12:44:15,831 - INFO - Training Mean: 23.0769 +2025-04-22 12:44:15,831 - INFO - Training Std: 20.3979 +2025-04-22 12:44:15,831 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 12:44:15,831 - INFO - KS Statistic: 0.1808 +2025-04-22 12:44:15,831 - INFO - P-value: 0.0000 +2025-04-22 12:44:15,831 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 12:44:15,831 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 12:44:16,198 - INFO - Validation and Training Set Split Statistics +2025-04-22 12:44:16,198 - INFO - Timestamp: 2025-04-22 12:44:16 +2025-04-22 12:44:16,198 - INFO - Validation set size: 1407 (8.52%) +2025-04-22 12:44:16,199 - INFO - Training set size: 15107 (91.48%) +2025-04-22 12:44:16,199 - INFO - Minimum Distance Statistics (km): +2025-04-22 12:44:16,199 - INFO - Mean: 5.1120 +2025-04-22 12:44:16,199 - INFO - Median: 4.3596 +2025-04-22 12:44:16,199 - INFO - Min: 0.0406 +2025-04-22 12:44:16,199 - INFO - Max: 24.0895 +2025-04-22 12:44:16,199 - INFO - Std: 3.3775 +2025-04-22 12:44:16,199 - INFO - OC Distribution Statistics: +2025-04-22 12:44:16,199 - INFO - Validation Mean: 17.8751 +2025-04-22 12:44:16,199 - INFO - Validation Std: 8.5307 +2025-04-22 12:44:16,199 - INFO - Training Mean: 22.9600 +2025-04-22 12:44:16,199 - INFO - Training Std: 20.3435 +2025-04-22 12:44:16,199 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 12:44:16,199 - INFO - KS Statistic: 0.1302 +2025-04-22 12:44:16,199 - INFO - P-value: 0.0000 +2025-04-22 12:44:16,199 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 12:44:16,199 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 12:44:19,776 - INFO - Validation and Training Set Split Statistics +2025-04-22 12:44:19,776 - INFO - Timestamp: 2025-04-22 12:44:19 +2025-04-22 12:44:19,776 - INFO - Validation set size: 1674 (10.14%) +2025-04-22 12:44:19,776 - INFO - Training set size: 14840 (89.86%) +2025-04-22 12:44:19,776 - INFO - Minimum Distance Statistics (km): +2025-04-22 12:44:19,776 - INFO - Mean: 4.7246 +2025-04-22 12:44:19,776 - INFO - Median: 3.9882 +2025-04-22 12:44:19,776 - INFO - Min: 0.0406 +2025-04-22 12:44:19,776 - INFO - Max: 21.4000 +2025-04-22 12:44:19,776 - INFO - Std: 2.9719 +2025-04-22 12:44:19,776 - INFO - OC Distribution Statistics: +2025-04-22 12:44:19,776 - INFO - Validation Mean: 17.3141 +2025-04-22 12:44:19,776 - INFO - Validation Std: 8.6533 +2025-04-22 12:44:19,776 - INFO - Training Mean: 23.1148 +2025-04-22 12:44:19,776 - INFO - Training Std: 20.4595 +2025-04-22 12:44:19,776 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 12:44:19,776 - INFO - KS Statistic: 0.1467 +2025-04-22 12:44:19,776 - INFO - P-value: 0.0000 +2025-04-22 12:44:19,776 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 12:44:19,776 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 12:44:20,000 - INFO - Validation and Training Set Split Statistics +2025-04-22 12:44:20,000 - INFO - Timestamp: 2025-04-22 12:44:19 +2025-04-22 12:44:20,000 - INFO - Validation set size: 1510 (9.14%) +2025-04-22 12:44:20,000 - INFO - Training set size: 15004 (90.86%) +2025-04-22 12:44:20,000 - INFO - Minimum Distance Statistics (km): +2025-04-22 12:44:20,000 - INFO - Mean: 4.9440 +2025-04-22 12:44:20,000 - INFO - Median: 4.3420 +2025-04-22 12:44:20,000 - INFO - Min: 0.0819 +2025-04-22 12:44:20,000 - INFO - Max: 18.8528 +2025-04-22 12:44:20,000 - INFO - Std: 2.9846 +2025-04-22 12:44:20,000 - INFO - OC Distribution Statistics: +2025-04-22 12:44:20,000 - INFO - Validation Mean: 17.2289 +2025-04-22 12:44:20,000 - INFO - Validation Std: 8.2896 +2025-04-22 12:44:20,000 - INFO - Training Mean: 23.0600 +2025-04-22 12:44:20,000 - INFO - Training Std: 20.3892 +2025-04-22 12:44:20,000 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 12:44:20,000 - INFO - KS Statistic: 0.1554 +2025-04-22 12:44:20,000 - INFO - P-value: 0.0000 +2025-04-22 12:44:20,000 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 12:44:20,000 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 12:44:20,095 - INFO - Validation and Training Set Split Statistics +2025-04-22 12:44:20,095 - INFO - Timestamp: 2025-04-22 12:44:20 +2025-04-22 12:44:20,095 - INFO - Validation set size: 1384 (8.38%) +2025-04-22 12:44:20,095 - INFO - Training set size: 15130 (91.62%) +2025-04-22 12:44:20,095 - INFO - Minimum Distance Statistics (km): +2025-04-22 12:44:20,095 - INFO - Mean: 5.1736 +2025-04-22 12:44:20,095 - INFO - Median: 4.4190 +2025-04-22 12:44:20,095 - INFO - Min: 0.3763 +2025-04-22 12:44:20,095 - INFO - Max: 20.3992 +2025-04-22 12:44:20,095 - INFO - Std: 3.1244 +2025-04-22 12:44:20,095 - INFO - OC Distribution Statistics: +2025-04-22 12:44:20,095 - INFO - Validation Mean: 17.7641 +2025-04-22 12:44:20,095 - INFO - Validation Std: 8.1347 +2025-04-22 12:44:20,095 - INFO - Training Mean: 22.9624 +2025-04-22 12:44:20,095 - INFO - Training Std: 20.3440 +2025-04-22 12:44:20,095 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 12:44:20,095 - INFO - KS Statistic: 0.1261 +2025-04-22 12:44:20,096 - INFO - P-value: 0.0000 +2025-04-22 12:44:20,096 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 12:44:20,096 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 12:44:20,180 - INFO - Validation and Training Set Split Statistics +2025-04-22 12:44:20,181 - INFO - Timestamp: 2025-04-22 12:44:20 +2025-04-22 12:44:20,181 - INFO - Validation set size: 1654 (10.02%) +2025-04-22 12:44:20,181 - INFO - Training set size: 14860 (89.98%) +2025-04-22 12:44:20,181 - INFO - Minimum Distance Statistics (km): +2025-04-22 12:44:20,181 - INFO - Mean: 4.9751 +2025-04-22 12:44:20,181 - INFO - Median: 4.4306 +2025-04-22 12:44:20,181 - INFO - Min: 0.3763 +2025-04-22 12:44:20,181 - INFO - Max: 18.8528 +2025-04-22 12:44:20,181 - INFO - Std: 2.8576 +2025-04-22 12:44:20,181 - INFO - OC Distribution Statistics: +2025-04-22 12:44:20,181 - INFO - Validation Mean: 16.9644 +2025-04-22 12:44:20,181 - INFO - Validation Std: 8.8216 +2025-04-22 12:44:20,181 - INFO - Training Mean: 23.1459 +2025-04-22 12:44:20,181 - INFO - Training Std: 20.4299 +2025-04-22 12:44:20,181 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 12:44:20,181 - INFO - KS Statistic: 0.2019 +2025-04-22 12:44:20,181 - INFO - P-value: 0.0000 +2025-04-22 12:44:20,181 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 12:44:20,181 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 12:44:20,717 - INFO - Validation and Training Set Split Statistics +2025-04-22 12:44:20,717 - INFO - Timestamp: 2025-04-22 12:44:20 +2025-04-22 12:44:20,717 - INFO - Validation set size: 1664 (10.08%) +2025-04-22 12:44:20,717 - INFO - Training set size: 14850 (89.92%) +2025-04-22 12:44:20,717 - INFO - Minimum Distance Statistics (km): +2025-04-22 12:44:20,717 - INFO - Mean: 4.9072 +2025-04-22 12:44:20,717 - INFO - Median: 4.3179 +2025-04-22 12:44:20,717 - INFO - Min: 0.0712 +2025-04-22 12:44:20,717 - INFO - Max: 18.9132 +2025-04-22 12:44:20,717 - INFO - Std: 3.0756 +2025-04-22 12:44:20,717 - INFO - OC Distribution Statistics: +2025-04-22 12:44:20,717 - INFO - Validation Mean: 17.0479 +2025-04-22 12:44:20,717 - INFO - Validation Std: 8.4486 +2025-04-22 12:44:20,717 - INFO - Training Mean: 23.1407 +2025-04-22 12:44:20,717 - INFO - Training Std: 20.4553 +2025-04-22 12:44:20,718 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 12:44:20,718 - INFO - KS Statistic: 0.1733 +2025-04-22 12:44:20,718 - INFO - P-value: 0.0000 +2025-04-22 12:44:20,718 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 12:44:20,718 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 12:44:24,485 - INFO - Validation and Training Set Split Statistics +2025-04-22 12:44:24,485 - INFO - Timestamp: 2025-04-22 12:44:24 +2025-04-22 12:44:24,485 - INFO - Validation set size: 1659 (10.05%) +2025-04-22 12:44:24,485 - INFO - Training set size: 14855 (89.95%) +2025-04-22 12:44:24,485 - INFO - Minimum Distance Statistics (km): +2025-04-22 12:44:24,485 - INFO - Mean: 4.7426 +2025-04-22 12:44:24,485 - INFO - Median: 4.4338 +2025-04-22 12:44:24,485 - INFO - Min: 0.0665 +2025-04-22 12:44:24,485 - INFO - Max: 18.8528 +2025-04-22 12:44:24,485 - INFO - Std: 2.5808 +2025-04-22 12:44:24,485 - INFO - OC Distribution Statistics: +2025-04-22 12:44:24,485 - INFO - Validation Mean: 17.1495 +2025-04-22 12:44:24,485 - INFO - Validation Std: 9.1284 +2025-04-22 12:44:24,485 - INFO - Training Mean: 23.1273 +2025-04-22 12:44:24,485 - INFO - Training Std: 20.4235 +2025-04-22 12:44:24,485 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 12:44:24,485 - INFO - KS Statistic: 0.1700 +2025-04-22 12:44:24,485 - INFO - P-value: 0.0000 +2025-04-22 12:44:24,485 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 12:44:24,485 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 13:49:03,597 - INFO - Validation and Training Set Split Statistics +2025-04-22 13:49:03,597 - INFO - Timestamp: 2025-04-22 13:49:03 +2025-04-22 13:49:03,597 - INFO - Validation set size: 1400 (8.48%) +2025-04-22 13:49:03,597 - INFO - Training set size: 15114 (91.52%) +2025-04-22 13:49:03,597 - INFO - Minimum Distance Statistics (km): +2025-04-22 13:49:03,597 - INFO - Mean: 4.6532 +2025-04-22 13:49:03,597 - INFO - Median: 4.2005 +2025-04-22 13:49:03,597 - INFO - Min: 0.3763 +2025-04-22 13:49:03,597 - INFO - Max: 14.5627 +2025-04-22 13:49:03,597 - INFO - Std: 2.8082 +2025-04-22 13:49:03,597 - INFO - OC Distribution Statistics: +2025-04-22 13:49:03,597 - INFO - Validation Mean: 17.3601 +2025-04-22 13:49:03,597 - INFO - Validation Std: 8.5497 +2025-04-22 13:49:03,597 - INFO - Training Mean: 23.0054 +2025-04-22 13:49:03,597 - INFO - Training Std: 20.3266 +2025-04-22 13:49:03,597 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 13:49:03,597 - INFO - KS Statistic: 0.1378 +2025-04-22 13:49:03,597 - INFO - P-value: 0.0000 +2025-04-22 13:49:03,597 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 13:49:03,597 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 13:49:07,598 - INFO - Validation and Training Set Split Statistics +2025-04-22 13:49:07,598 - INFO - Timestamp: 2025-04-22 13:49:07 +2025-04-22 13:49:07,599 - INFO - Validation set size: 1678 (10.16%) +2025-04-22 13:49:07,599 - INFO - Training set size: 14836 (89.84%) +2025-04-22 13:49:07,599 - INFO - Minimum Distance Statistics (km): +2025-04-22 13:49:07,599 - INFO - Mean: 4.8573 +2025-04-22 13:49:07,599 - INFO - Median: 4.2850 +2025-04-22 13:49:07,599 - INFO - Min: 0.0819 +2025-04-22 13:49:07,599 - INFO - Max: 26.6740 +2025-04-22 13:49:07,599 - INFO - Std: 3.2714 +2025-04-22 13:49:07,599 - INFO - OC Distribution Statistics: +2025-04-22 13:49:07,599 - INFO - Validation Mean: 17.2259 +2025-04-22 13:49:07,599 - INFO - Validation Std: 9.0592 +2025-04-22 13:49:07,599 - INFO - Training Mean: 23.1263 +2025-04-22 13:49:07,599 - INFO - Training Std: 20.4388 +2025-04-22 13:49:07,599 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 13:49:07,599 - INFO - KS Statistic: 0.1719 +2025-04-22 13:49:07,599 - INFO - P-value: 0.0000 +2025-04-22 13:49:07,599 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 13:49:07,599 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 13:49:08,333 - INFO - Validation and Training Set Split Statistics +2025-04-22 13:49:08,333 - INFO - Timestamp: 2025-04-22 13:49:08 +2025-04-22 13:49:08,333 - INFO - Validation set size: 1506 (9.12%) +2025-04-22 13:49:08,333 - INFO - Training set size: 15008 (90.88%) +2025-04-22 13:49:08,333 - INFO - Minimum Distance Statistics (km): +2025-04-22 13:49:08,333 - INFO - Mean: 4.5233 +2025-04-22 13:49:08,333 - INFO - Median: 3.8909 +2025-04-22 13:49:08,333 - INFO - Min: 0.3424 +2025-04-22 13:49:08,333 - INFO - Max: 18.8528 +2025-04-22 13:49:08,333 - INFO - Std: 2.8464 +2025-04-22 13:49:08,333 - INFO - OC Distribution Statistics: +2025-04-22 13:49:08,333 - INFO - Validation Mean: 17.0086 +2025-04-22 13:49:08,333 - INFO - Validation Std: 8.2170 +2025-04-22 13:49:08,333 - INFO - Training Mean: 23.0805 +2025-04-22 13:49:08,333 - INFO - Training Std: 20.3836 +2025-04-22 13:49:08,333 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 13:49:08,333 - INFO - KS Statistic: 0.1427 +2025-04-22 13:49:08,333 - INFO - P-value: 0.0000 +2025-04-22 13:49:08,333 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 13:49:08,333 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 13:49:11,909 - INFO - Validation and Training Set Split Statistics +2025-04-22 13:49:11,909 - INFO - Timestamp: 2025-04-22 13:49:11 +2025-04-22 13:49:11,910 - INFO - Validation set size: 1361 (8.24%) +2025-04-22 13:49:11,910 - INFO - Training set size: 15153 (91.76%) +2025-04-22 13:49:11,910 - INFO - Minimum Distance Statistics (km): +2025-04-22 13:49:11,910 - INFO - Mean: 4.7479 +2025-04-22 13:49:11,910 - INFO - Median: 4.0870 +2025-04-22 13:49:11,910 - INFO - Min: 0.3763 +2025-04-22 13:49:11,910 - INFO - Max: 22.8404 +2025-04-22 13:49:11,910 - INFO - Std: 2.8973 +2025-04-22 13:49:11,910 - INFO - OC Distribution Statistics: +2025-04-22 13:49:11,910 - INFO - Validation Mean: 17.4456 +2025-04-22 13:49:11,910 - INFO - Validation Std: 8.8802 +2025-04-22 13:49:11,910 - INFO - Training Mean: 22.9832 +2025-04-22 13:49:11,910 - INFO - Training Std: 20.2965 +2025-04-22 13:49:11,910 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 13:49:11,910 - INFO - KS Statistic: 0.1548 +2025-04-22 13:49:11,910 - INFO - P-value: 0.0000 +2025-04-22 13:49:11,910 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 13:49:11,910 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 13:56:04,071 - INFO - Validation and Training Set Split Statistics +2025-04-22 13:56:04,071 - INFO - Timestamp: 2025-04-22 13:56:04 +2025-04-22 13:56:04,071 - INFO - Validation set size: 1378 (8.34%) +2025-04-22 13:56:04,071 - INFO - Training set size: 15136 (91.66%) +2025-04-22 13:56:04,071 - INFO - Minimum Distance Statistics (km): +2025-04-22 13:56:04,071 - INFO - Mean: 4.9555 +2025-04-22 13:56:04,071 - INFO - Median: 4.3979 +2025-04-22 13:56:04,071 - INFO - Min: 0.3763 +2025-04-22 13:56:04,071 - INFO - Max: 17.8414 +2025-04-22 13:56:04,071 - INFO - Std: 2.9063 +2025-04-22 13:56:04,071 - INFO - OC Distribution Statistics: +2025-04-22 13:56:04,071 - INFO - Validation Mean: 17.5256 +2025-04-22 13:56:04,071 - INFO - Validation Std: 8.4882 +2025-04-22 13:56:04,071 - INFO - Training Mean: 22.9821 +2025-04-22 13:56:04,071 - INFO - Training Std: 20.3221 +2025-04-22 13:56:04,071 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 13:56:04,071 - INFO - KS Statistic: 0.1411 +2025-04-22 13:56:04,071 - INFO - P-value: 0.0000 +2025-04-22 13:56:04,071 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 13:56:04,071 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 13:56:04,124 - INFO - Validation and Training Set Split Statistics +2025-04-22 13:56:04,124 - INFO - Timestamp: 2025-04-22 13:56:04 +2025-04-22 13:56:04,124 - INFO - Validation set size: 1396 (8.45%) +2025-04-22 13:56:04,124 - INFO - Training set size: 15118 (91.55%) +2025-04-22 13:56:04,124 - INFO - Minimum Distance Statistics (km): +2025-04-22 13:56:04,124 - INFO - Mean: 4.7825 +2025-04-22 13:56:04,124 - INFO - Median: 4.1795 +2025-04-22 13:56:04,124 - INFO - Min: 0.3976 +2025-04-22 13:56:04,124 - INFO - Max: 26.6740 +2025-04-22 13:56:04,124 - INFO - Std: 2.9008 +2025-04-22 13:56:04,124 - INFO - OC Distribution Statistics: +2025-04-22 13:56:04,124 - INFO - Validation Mean: 17.8100 +2025-04-22 13:56:04,124 - INFO - Validation Std: 9.0527 +2025-04-22 13:56:04,124 - INFO - Training Mean: 22.9623 +2025-04-22 13:56:04,124 - INFO - Training Std: 20.3155 +2025-04-22 13:56:04,124 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 13:56:04,124 - INFO - KS Statistic: 0.1215 +2025-04-22 13:56:04,124 - INFO - P-value: 0.0000 +2025-04-22 13:56:04,124 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 13:56:04,124 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 13:56:04,559 - INFO - Validation and Training Set Split Statistics +2025-04-22 13:56:04,559 - INFO - Timestamp: 2025-04-22 13:56:04 +2025-04-22 13:56:04,559 - INFO - Validation set size: 1611 (9.76%) +2025-04-22 13:56:04,559 - INFO - Training set size: 14903 (90.24%) +2025-04-22 13:56:04,559 - INFO - Minimum Distance Statistics (km): +2025-04-22 13:56:04,559 - INFO - Mean: 4.8453 +2025-04-22 13:56:04,559 - INFO - Median: 4.3831 +2025-04-22 13:56:04,559 - INFO - Min: 0.0712 +2025-04-22 13:56:04,559 - INFO - Max: 18.8528 +2025-04-22 13:56:04,559 - INFO - Std: 2.8258 +2025-04-22 13:56:04,559 - INFO - OC Distribution Statistics: +2025-04-22 13:56:04,559 - INFO - Validation Mean: 16.7390 +2025-04-22 13:56:04,560 - INFO - Validation Std: 8.0133 +2025-04-22 13:56:04,560 - INFO - Training Mean: 23.1524 +2025-04-22 13:56:04,560 - INFO - Training Std: 20.4371 +2025-04-22 13:56:04,560 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 13:56:04,560 - INFO - KS Statistic: 0.1787 +2025-04-22 13:56:04,560 - INFO - P-value: 0.0000 +2025-04-22 13:56:04,560 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 13:56:04,560 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 13:56:04,741 - INFO - Validation and Training Set Split Statistics +2025-04-22 13:56:04,741 - INFO - Timestamp: 2025-04-22 13:56:04 +2025-04-22 13:56:04,741 - INFO - Validation set size: 1541 (9.33%) +2025-04-22 13:56:04,741 - INFO - Training set size: 14973 (90.67%) +2025-04-22 13:56:04,741 - INFO - Minimum Distance Statistics (km): +2025-04-22 13:56:04,741 - INFO - Mean: 5.1449 +2025-04-22 13:56:04,741 - INFO - Median: 4.5955 +2025-04-22 13:56:04,741 - INFO - Min: 0.3763 +2025-04-22 13:56:04,741 - INFO - Max: 18.8528 +2025-04-22 13:56:04,741 - INFO - Std: 2.8299 +2025-04-22 13:56:04,741 - INFO - OC Distribution Statistics: +2025-04-22 13:56:04,741 - INFO - Validation Mean: 16.7738 +2025-04-22 13:56:04,741 - INFO - Validation Std: 8.1607 +2025-04-22 13:56:04,741 - INFO - Training Mean: 23.1189 +2025-04-22 13:56:04,741 - INFO - Training Std: 20.3964 +2025-04-22 13:56:04,741 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 13:56:04,741 - INFO - KS Statistic: 0.2040 +2025-04-22 13:56:04,741 - INFO - P-value: 0.0000 +2025-04-22 13:56:04,741 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 13:56:04,741 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 13:56:04,982 - INFO - Validation and Training Set Split Statistics +2025-04-22 13:56:04,982 - INFO - Timestamp: 2025-04-22 13:56:04 +2025-04-22 13:56:04,982 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 13:56:04,982 - INFO - Training set size: 15123 (91.58%) +2025-04-22 13:56:04,982 - INFO - Minimum Distance Statistics (km): +2025-04-22 13:56:04,982 - INFO - Mean: 4.8627 +2025-04-22 13:56:04,982 - INFO - Median: 4.1633 +2025-04-22 13:56:04,982 - INFO - Min: 0.3763 +2025-04-22 13:56:04,982 - INFO - Max: 18.8528 +2025-04-22 13:56:04,982 - INFO - Std: 2.8853 +2025-04-22 13:56:04,982 - INFO - OC Distribution Statistics: +2025-04-22 13:56:04,983 - INFO - Validation Mean: 17.6090 +2025-04-22 13:56:04,983 - INFO - Validation Std: 8.3406 +2025-04-22 13:56:04,983 - INFO - Training Mean: 22.9791 +2025-04-22 13:56:04,983 - INFO - Training Std: 20.3363 +2025-04-22 13:56:04,983 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 13:56:04,983 - INFO - KS Statistic: 0.1228 +2025-04-22 13:56:04,983 - INFO - P-value: 0.0000 +2025-04-22 13:56:04,983 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 13:56:04,983 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 13:56:08,318 - INFO - Validation and Training Set Split Statistics +2025-04-22 13:56:08,318 - INFO - Timestamp: 2025-04-22 13:56:08 +2025-04-22 13:56:08,318 - INFO - Validation set size: 1704 (10.32%) +2025-04-22 13:56:08,318 - INFO - Training set size: 14810 (89.68%) +2025-04-22 13:56:08,318 - INFO - Minimum Distance Statistics (km): +2025-04-22 13:56:08,318 - INFO - Mean: 5.3616 +2025-04-22 13:56:08,318 - INFO - Median: 4.7083 +2025-04-22 13:56:08,318 - INFO - Min: 0.3705 +2025-04-22 13:56:08,318 - INFO - Max: 22.7461 +2025-04-22 13:56:08,318 - INFO - Std: 2.9767 +2025-04-22 13:56:08,318 - INFO - OC Distribution Statistics: +2025-04-22 13:56:08,318 - INFO - Validation Mean: 17.4924 +2025-04-22 13:56:08,318 - INFO - Validation Std: 8.4372 +2025-04-22 13:56:08,318 - INFO - Training Mean: 23.1060 +2025-04-22 13:56:08,318 - INFO - Training Std: 20.4909 +2025-04-22 13:56:08,318 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 13:56:08,318 - INFO - KS Statistic: 0.1390 +2025-04-22 13:56:08,318 - INFO - P-value: 0.0000 +2025-04-22 13:56:08,318 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 13:56:08,318 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 13:56:08,728 - INFO - Validation and Training Set Split Statistics +2025-04-22 13:56:08,728 - INFO - Timestamp: 2025-04-22 13:56:08 +2025-04-22 13:56:08,728 - INFO - Validation set size: 1876 (11.36%) +2025-04-22 13:56:08,728 - INFO - Training set size: 14638 (88.64%) +2025-04-22 13:56:08,728 - INFO - Minimum Distance Statistics (km): +2025-04-22 13:56:08,728 - INFO - Mean: 5.0239 +2025-04-22 13:56:08,728 - INFO - Median: 4.4530 +2025-04-22 13:56:08,728 - INFO - Min: 0.3557 +2025-04-22 13:56:08,728 - INFO - Max: 16.0699 +2025-04-22 13:56:08,728 - INFO - Std: 2.6615 +2025-04-22 13:56:08,728 - INFO - OC Distribution Statistics: +2025-04-22 13:56:08,728 - INFO - Validation Mean: 16.6110 +2025-04-22 13:56:08,728 - INFO - Validation Std: 7.6467 +2025-04-22 13:56:08,728 - INFO - Training Mean: 23.2849 +2025-04-22 13:56:08,728 - INFO - Training Std: 20.5872 +2025-04-22 13:56:08,728 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 13:56:08,728 - INFO - KS Statistic: 0.1954 +2025-04-22 13:56:08,728 - INFO - P-value: 0.0000 +2025-04-22 13:56:08,728 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 13:56:08,729 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 13:56:12,789 - INFO - Validation and Training Set Split Statistics +2025-04-22 13:56:12,789 - INFO - Timestamp: 2025-04-22 13:56:12 +2025-04-22 13:56:12,789 - INFO - Validation set size: 1612 (9.76%) +2025-04-22 13:56:12,789 - INFO - Training set size: 14902 (90.24%) +2025-04-22 13:56:12,789 - INFO - Minimum Distance Statistics (km): +2025-04-22 13:56:12,789 - INFO - Mean: 4.9605 +2025-04-22 13:56:12,789 - INFO - Median: 4.2798 +2025-04-22 13:56:12,789 - INFO - Min: 0.0358 +2025-04-22 13:56:12,789 - INFO - Max: 18.8528 +2025-04-22 13:56:12,789 - INFO - Std: 3.0077 +2025-04-22 13:56:12,789 - INFO - OC Distribution Statistics: +2025-04-22 13:56:12,789 - INFO - Validation Mean: 17.0449 +2025-04-22 13:56:12,789 - INFO - Validation Std: 7.8858 +2025-04-22 13:56:12,789 - INFO - Training Mean: 23.1198 +2025-04-22 13:56:12,789 - INFO - Training Std: 20.4531 +2025-04-22 13:56:12,789 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 13:56:12,789 - INFO - KS Statistic: 0.1631 +2025-04-22 13:56:12,789 - INFO - P-value: 0.0000 +2025-04-22 13:56:12,789 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 13:56:12,789 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 14:05:55,704 - INFO - Validation and Training Set Split Statistics +2025-04-22 14:05:55,705 - INFO - Timestamp: 2025-04-22 14:05:55 +2025-04-22 14:05:55,705 - INFO - Validation set size: 1516 (9.18%) +2025-04-22 14:05:55,705 - INFO - Training set size: 14998 (90.82%) +2025-04-22 14:05:55,705 - INFO - Minimum Distance Statistics (km): +2025-04-22 14:05:55,705 - INFO - Mean: 4.6898 +2025-04-22 14:05:55,705 - INFO - Median: 3.9877 +2025-04-22 14:05:55,705 - INFO - Min: 0.3763 +2025-04-22 14:05:55,705 - INFO - Max: 21.4000 +2025-04-22 14:05:55,705 - INFO - Std: 2.7768 +2025-04-22 14:05:55,705 - INFO - OC Distribution Statistics: +2025-04-22 14:05:55,705 - INFO - Validation Mean: 18.2154 +2025-04-22 14:05:55,705 - INFO - Validation Std: 9.0944 +2025-04-22 14:05:55,705 - INFO - Training Mean: 22.9626 +2025-04-22 14:05:55,705 - INFO - Training Std: 20.3834 +2025-04-22 14:05:55,705 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 14:05:55,705 - INFO - KS Statistic: 0.1186 +2025-04-22 14:05:55,705 - INFO - P-value: 0.0000 +2025-04-22 14:05:55,705 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 14:05:55,705 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 14:11:17,509 - INFO - Validation and Training Set Split Statistics +2025-04-22 14:11:17,510 - INFO - Timestamp: 2025-04-22 14:11:17 +2025-04-22 14:11:17,510 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 14:11:17,510 - INFO - Training set size: 15123 (91.58%) +2025-04-22 14:11:17,510 - INFO - Minimum Distance Statistics (km): +2025-04-22 14:11:17,510 - INFO - Mean: 4.7899 +2025-04-22 14:11:17,510 - INFO - Median: 4.2255 +2025-04-22 14:11:17,510 - INFO - Min: 0.3763 +2025-04-22 14:11:17,510 - INFO - Max: 18.8528 +2025-04-22 14:11:17,510 - INFO - Std: 2.8893 +2025-04-22 14:11:17,510 - INFO - OC Distribution Statistics: +2025-04-22 14:11:17,510 - INFO - Validation Mean: 17.3772 +2025-04-22 14:11:17,510 - INFO - Validation Std: 8.9271 +2025-04-22 14:11:17,510 - INFO - Training Mean: 23.0004 +2025-04-22 14:11:17,510 - INFO - Training Std: 20.3076 +2025-04-22 14:11:17,510 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 14:11:17,510 - INFO - KS Statistic: 0.1538 +2025-04-22 14:11:17,510 - INFO - P-value: 0.0000 +2025-04-22 14:11:17,510 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 14:11:17,510 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 14:11:17,940 - INFO - Validation and Training Set Split Statistics +2025-04-22 14:11:17,940 - INFO - Timestamp: 2025-04-22 14:11:17 +2025-04-22 14:11:17,940 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 14:11:17,940 - INFO - Training set size: 15123 (91.58%) +2025-04-22 14:11:17,940 - INFO - Minimum Distance Statistics (km): +2025-04-22 14:11:17,940 - INFO - Mean: 4.7899 +2025-04-22 14:11:17,940 - INFO - Median: 4.2255 +2025-04-22 14:11:17,940 - INFO - Min: 0.3763 +2025-04-22 14:11:17,940 - INFO - Max: 18.8528 +2025-04-22 14:11:17,940 - INFO - Std: 2.8893 +2025-04-22 14:11:17,940 - INFO - OC Distribution Statistics: +2025-04-22 14:11:17,940 - INFO - Validation Mean: 17.3772 +2025-04-22 14:11:17,940 - INFO - Validation Std: 8.9271 +2025-04-22 14:11:17,940 - INFO - Training Mean: 23.0004 +2025-04-22 14:11:17,940 - INFO - Training Std: 20.3076 +2025-04-22 14:11:17,940 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 14:11:17,940 - INFO - KS Statistic: 0.1538 +2025-04-22 14:11:17,940 - INFO - P-value: 0.0000 +2025-04-22 14:11:17,940 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 14:11:17,940 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 14:11:29,106 - INFO - Validation and Training Set Split Statistics +2025-04-22 14:11:29,106 - INFO - Timestamp: 2025-04-22 14:11:29 +2025-04-22 14:11:29,106 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 14:11:29,106 - INFO - Training set size: 15123 (91.58%) +2025-04-22 14:11:29,106 - INFO - Minimum Distance Statistics (km): +2025-04-22 14:11:29,106 - INFO - Mean: 4.7899 +2025-04-22 14:11:29,106 - INFO - Median: 4.2255 +2025-04-22 14:11:29,106 - INFO - Min: 0.3763 +2025-04-22 14:11:29,106 - INFO - Max: 18.8528 +2025-04-22 14:11:29,106 - INFO - Std: 2.8893 +2025-04-22 14:11:29,106 - INFO - OC Distribution Statistics: +2025-04-22 14:11:29,106 - INFO - Validation Mean: 17.3772 +2025-04-22 14:11:29,106 - INFO - Validation Std: 8.9271 +2025-04-22 14:11:29,106 - INFO - Training Mean: 23.0004 +2025-04-22 14:11:29,106 - INFO - Training Std: 20.3076 +2025-04-22 14:11:29,106 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 14:11:29,106 - INFO - KS Statistic: 0.1538 +2025-04-22 14:11:29,106 - INFO - P-value: 0.0000 +2025-04-22 14:11:29,106 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 14:11:29,106 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 14:12:01,580 - INFO - Validation and Training Set Split Statistics +2025-04-22 14:12:01,581 - INFO - Timestamp: 2025-04-22 14:12:01 +2025-04-22 14:12:01,581 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 14:12:01,581 - INFO - Training set size: 15123 (91.58%) +2025-04-22 14:12:01,581 - INFO - Minimum Distance Statistics (km): +2025-04-22 14:12:01,581 - INFO - Mean: 4.7899 +2025-04-22 14:12:01,581 - INFO - Median: 4.2255 +2025-04-22 14:12:01,581 - INFO - Min: 0.3763 +2025-04-22 14:12:01,581 - INFO - Max: 18.8528 +2025-04-22 14:12:01,581 - INFO - Std: 2.8893 +2025-04-22 14:12:01,581 - INFO - OC Distribution Statistics: +2025-04-22 14:12:01,581 - INFO - Validation Mean: 17.3772 +2025-04-22 14:12:01,581 - INFO - Validation Std: 8.9271 +2025-04-22 14:12:01,581 - INFO - Training Mean: 23.0004 +2025-04-22 14:12:01,581 - INFO - Training Std: 20.3076 +2025-04-22 14:12:01,581 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 14:12:01,581 - INFO - KS Statistic: 0.1538 +2025-04-22 14:12:01,581 - INFO - P-value: 0.0000 +2025-04-22 14:12:01,581 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 14:12:01,581 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 15:17:46,131 - INFO - Validation and Training Set Split Statistics +2025-04-22 15:17:46,131 - INFO - Timestamp: 2025-04-22 15:17:46 +2025-04-22 15:17:46,131 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 15:17:46,131 - INFO - Training set size: 15123 (91.58%) +2025-04-22 15:17:46,131 - INFO - Minimum Distance Statistics (km): +2025-04-22 15:17:46,131 - INFO - Mean: 4.7899 +2025-04-22 15:17:46,131 - INFO - Median: 4.2255 +2025-04-22 15:17:46,131 - INFO - Min: 0.3763 +2025-04-22 15:17:46,131 - INFO - Max: 18.8528 +2025-04-22 15:17:46,131 - INFO - Std: 2.8893 +2025-04-22 15:17:46,131 - INFO - OC Distribution Statistics: +2025-04-22 15:17:46,131 - INFO - Validation Mean: 17.3772 +2025-04-22 15:17:46,131 - INFO - Validation Std: 8.9271 +2025-04-22 15:17:46,131 - INFO - Training Mean: 23.0004 +2025-04-22 15:17:46,131 - INFO - Training Std: 20.3076 +2025-04-22 15:17:46,131 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 15:17:46,132 - INFO - KS Statistic: 0.1538 +2025-04-22 15:17:46,132 - INFO - P-value: 0.0000 +2025-04-22 15:17:46,132 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 15:17:46,132 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 15:17:53,534 - INFO - Validation and Training Set Split Statistics +2025-04-22 15:17:53,534 - INFO - Timestamp: 2025-04-22 15:17:53 +2025-04-22 15:17:53,534 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 15:17:53,534 - INFO - Training set size: 15123 (91.58%) +2025-04-22 15:17:53,534 - INFO - Minimum Distance Statistics (km): +2025-04-22 15:17:53,534 - INFO - Mean: 4.7899 +2025-04-22 15:17:53,534 - INFO - Median: 4.2255 +2025-04-22 15:17:53,534 - INFO - Min: 0.3763 +2025-04-22 15:17:53,534 - INFO - Max: 18.8528 +2025-04-22 15:17:53,534 - INFO - Std: 2.8893 +2025-04-22 15:17:53,534 - INFO - OC Distribution Statistics: +2025-04-22 15:17:53,534 - INFO - Validation Mean: 17.3772 +2025-04-22 15:17:53,534 - INFO - Validation Std: 8.9271 +2025-04-22 15:17:53,534 - INFO - Training Mean: 23.0004 +2025-04-22 15:17:53,534 - INFO - Training Std: 20.3076 +2025-04-22 15:17:53,534 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 15:17:53,534 - INFO - KS Statistic: 0.1538 +2025-04-22 15:17:53,534 - INFO - P-value: 0.0000 +2025-04-22 15:17:53,534 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 15:17:53,535 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 15:17:59,191 - INFO - Validation and Training Set Split Statistics +2025-04-22 15:17:59,191 - INFO - Timestamp: 2025-04-22 15:17:59 +2025-04-22 15:17:59,191 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 15:17:59,191 - INFO - Training set size: 15123 (91.58%) +2025-04-22 15:17:59,191 - INFO - Minimum Distance Statistics (km): +2025-04-22 15:17:59,191 - INFO - Mean: 4.7899 +2025-04-22 15:17:59,191 - INFO - Median: 4.2255 +2025-04-22 15:17:59,191 - INFO - Min: 0.3763 +2025-04-22 15:17:59,191 - INFO - Max: 18.8528 +2025-04-22 15:17:59,191 - INFO - Std: 2.8893 +2025-04-22 15:17:59,191 - INFO - OC Distribution Statistics: +2025-04-22 15:17:59,191 - INFO - Validation Mean: 17.3772 +2025-04-22 15:17:59,191 - INFO - Validation Std: 8.9271 +2025-04-22 15:17:59,191 - INFO - Training Mean: 23.0004 +2025-04-22 15:17:59,191 - INFO - Training Std: 20.3076 +2025-04-22 15:17:59,191 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 15:17:59,191 - INFO - KS Statistic: 0.1538 +2025-04-22 15:17:59,191 - INFO - P-value: 0.0000 +2025-04-22 15:17:59,191 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 15:17:59,191 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 15:18:14,969 - INFO - Validation and Training Set Split Statistics +2025-04-22 15:18:14,969 - INFO - Timestamp: 2025-04-22 15:18:14 +2025-04-22 15:18:14,969 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 15:18:14,969 - INFO - Training set size: 15123 (91.58%) +2025-04-22 15:18:14,969 - INFO - Minimum Distance Statistics (km): +2025-04-22 15:18:14,969 - INFO - Mean: 4.7899 +2025-04-22 15:18:14,969 - INFO - Median: 4.2255 +2025-04-22 15:18:14,969 - INFO - Min: 0.3763 +2025-04-22 15:18:14,969 - INFO - Max: 18.8528 +2025-04-22 15:18:14,969 - INFO - Std: 2.8893 +2025-04-22 15:18:14,969 - INFO - OC Distribution Statistics: +2025-04-22 15:18:14,969 - INFO - Validation Mean: 17.3772 +2025-04-22 15:18:14,969 - INFO - Validation Std: 8.9271 +2025-04-22 15:18:14,969 - INFO - Training Mean: 23.0004 +2025-04-22 15:18:14,969 - INFO - Training Std: 20.3076 +2025-04-22 15:18:14,969 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 15:18:14,969 - INFO - KS Statistic: 0.1538 +2025-04-22 15:18:14,969 - INFO - P-value: 0.0000 +2025-04-22 15:18:14,969 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 15:18:14,969 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 16:19:28,724 - INFO - Validation and Training Set Split Statistics +2025-04-22 16:19:28,724 - INFO - Timestamp: 2025-04-22 16:19:28 +2025-04-22 16:19:28,724 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 16:19:28,724 - INFO - Training set size: 15123 (91.58%) +2025-04-22 16:19:28,724 - INFO - Minimum Distance Statistics (km): +2025-04-22 16:19:28,724 - INFO - Mean: 4.7899 +2025-04-22 16:19:28,724 - INFO - Median: 4.2255 +2025-04-22 16:19:28,724 - INFO - Min: 0.3763 +2025-04-22 16:19:28,724 - INFO - Max: 18.8528 +2025-04-22 16:19:28,724 - INFO - Std: 2.8893 +2025-04-22 16:19:28,724 - INFO - OC Distribution Statistics: +2025-04-22 16:19:28,724 - INFO - Validation Mean: 17.3772 +2025-04-22 16:19:28,724 - INFO - Validation Std: 8.9271 +2025-04-22 16:19:28,724 - INFO - Training Mean: 23.0004 +2025-04-22 16:19:28,724 - INFO - Training Std: 20.3076 +2025-04-22 16:19:28,724 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 16:19:28,724 - INFO - KS Statistic: 0.1538 +2025-04-22 16:19:28,724 - INFO - P-value: 0.0000 +2025-04-22 16:19:28,724 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 16:19:28,724 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 16:19:31,320 - INFO - Validation and Training Set Split Statistics +2025-04-22 16:19:31,320 - INFO - Timestamp: 2025-04-22 16:19:31 +2025-04-22 16:19:31,320 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 16:19:31,320 - INFO - Training set size: 15123 (91.58%) +2025-04-22 16:19:31,320 - INFO - Minimum Distance Statistics (km): +2025-04-22 16:19:31,320 - INFO - Mean: 4.7899 +2025-04-22 16:19:31,320 - INFO - Median: 4.2255 +2025-04-22 16:19:31,320 - INFO - Min: 0.3763 +2025-04-22 16:19:31,320 - INFO - Max: 18.8528 +2025-04-22 16:19:31,320 - INFO - Std: 2.8893 +2025-04-22 16:19:31,320 - INFO - OC Distribution Statistics: +2025-04-22 16:19:31,320 - INFO - Validation Mean: 17.3772 +2025-04-22 16:19:31,321 - INFO - Validation Std: 8.9271 +2025-04-22 16:19:31,321 - INFO - Training Mean: 23.0004 +2025-04-22 16:19:31,321 - INFO - Training Std: 20.3076 +2025-04-22 16:19:31,321 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 16:19:31,321 - INFO - KS Statistic: 0.1538 +2025-04-22 16:19:31,321 - INFO - P-value: 0.0000 +2025-04-22 16:19:31,321 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 16:19:31,321 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 16:19:32,167 - INFO - Validation and Training Set Split Statistics +2025-04-22 16:19:32,167 - INFO - Timestamp: 2025-04-22 16:19:32 +2025-04-22 16:19:32,167 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 16:19:32,167 - INFO - Training set size: 15123 (91.58%) +2025-04-22 16:19:32,167 - INFO - Minimum Distance Statistics (km): +2025-04-22 16:19:32,167 - INFO - Mean: 4.7899 +2025-04-22 16:19:32,167 - INFO - Median: 4.2255 +2025-04-22 16:19:32,167 - INFO - Min: 0.3763 +2025-04-22 16:19:32,167 - INFO - Max: 18.8528 +2025-04-22 16:19:32,167 - INFO - Std: 2.8893 +2025-04-22 16:19:32,167 - INFO - OC Distribution Statistics: +2025-04-22 16:19:32,167 - INFO - Validation Mean: 17.3772 +2025-04-22 16:19:32,167 - INFO - Validation Std: 8.9271 +2025-04-22 16:19:32,167 - INFO - Training Mean: 23.0004 +2025-04-22 16:19:32,167 - INFO - Training Std: 20.3076 +2025-04-22 16:19:32,168 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 16:19:32,168 - INFO - KS Statistic: 0.1538 +2025-04-22 16:19:32,168 - INFO - P-value: 0.0000 +2025-04-22 16:19:32,168 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 16:19:32,168 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 16:19:33,472 - INFO - Validation and Training Set Split Statistics +2025-04-22 16:19:33,473 - INFO - Timestamp: 2025-04-22 16:19:33 +2025-04-22 16:19:33,473 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 16:19:33,473 - INFO - Training set size: 15123 (91.58%) +2025-04-22 16:19:33,473 - INFO - Minimum Distance Statistics (km): +2025-04-22 16:19:33,473 - INFO - Mean: 4.7899 +2025-04-22 16:19:33,473 - INFO - Median: 4.2255 +2025-04-22 16:19:33,473 - INFO - Min: 0.3763 +2025-04-22 16:19:33,473 - INFO - Max: 18.8528 +2025-04-22 16:19:33,473 - INFO - Std: 2.8893 +2025-04-22 16:19:33,473 - INFO - OC Distribution Statistics: +2025-04-22 16:19:33,473 - INFO - Validation Mean: 17.3772 +2025-04-22 16:19:33,473 - INFO - Validation Std: 8.9271 +2025-04-22 16:19:33,473 - INFO - Training Mean: 23.0004 +2025-04-22 16:19:33,473 - INFO - Training Std: 20.3076 +2025-04-22 16:19:33,473 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 16:19:33,473 - INFO - KS Statistic: 0.1538 +2025-04-22 16:19:33,473 - INFO - P-value: 0.0000 +2025-04-22 16:19:33,473 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 16:19:33,473 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 16:19:33,666 - INFO - Validation and Training Set Split Statistics +2025-04-22 16:19:33,667 - INFO - Timestamp: 2025-04-22 16:19:33 +2025-04-22 16:19:33,667 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 16:19:33,667 - INFO - Training set size: 15123 (91.58%) +2025-04-22 16:19:33,667 - INFO - Minimum Distance Statistics (km): +2025-04-22 16:19:33,667 - INFO - Mean: 4.7899 +2025-04-22 16:19:33,667 - INFO - Median: 4.2255 +2025-04-22 16:19:33,667 - INFO - Min: 0.3763 +2025-04-22 16:19:33,667 - INFO - Max: 18.8528 +2025-04-22 16:19:33,667 - INFO - Std: 2.8893 +2025-04-22 16:19:33,667 - INFO - OC Distribution Statistics: +2025-04-22 16:19:33,667 - INFO - Validation Mean: 17.3772 +2025-04-22 16:19:33,667 - INFO - Validation Std: 8.9271 +2025-04-22 16:19:33,667 - INFO - Training Mean: 23.0004 +2025-04-22 16:19:33,667 - INFO - Training Std: 20.3076 +2025-04-22 16:19:33,667 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 16:19:33,667 - INFO - KS Statistic: 0.1538 +2025-04-22 16:19:33,667 - INFO - P-value: 0.0000 +2025-04-22 16:19:33,667 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 16:19:33,667 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 16:19:36,276 - INFO - Validation and Training Set Split Statistics +2025-04-22 16:19:36,277 - INFO - Timestamp: 2025-04-22 16:19:36 +2025-04-22 16:19:36,277 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 16:19:36,277 - INFO - Training set size: 15123 (91.58%) +2025-04-22 16:19:36,277 - INFO - Minimum Distance Statistics (km): +2025-04-22 16:19:36,277 - INFO - Mean: 4.7899 +2025-04-22 16:19:36,277 - INFO - Median: 4.2255 +2025-04-22 16:19:36,277 - INFO - Min: 0.3763 +2025-04-22 16:19:36,277 - INFO - Max: 18.8528 +2025-04-22 16:19:36,277 - INFO - Std: 2.8893 +2025-04-22 16:19:36,277 - INFO - OC Distribution Statistics: +2025-04-22 16:19:36,277 - INFO - Validation Mean: 17.3772 +2025-04-22 16:19:36,277 - INFO - Validation Std: 8.9271 +2025-04-22 16:19:36,277 - INFO - Training Mean: 23.0004 +2025-04-22 16:19:36,277 - INFO - Training Std: 20.3076 +2025-04-22 16:19:36,277 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 16:19:36,277 - INFO - KS Statistic: 0.1538 +2025-04-22 16:19:36,277 - INFO - P-value: 0.0000 +2025-04-22 16:19:36,277 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 16:19:36,277 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 16:19:36,451 - INFO - Validation and Training Set Split Statistics +2025-04-22 16:19:36,451 - INFO - Timestamp: 2025-04-22 16:19:36 +2025-04-22 16:19:36,451 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 16:19:36,451 - INFO - Training set size: 15123 (91.58%) +2025-04-22 16:19:36,451 - INFO - Minimum Distance Statistics (km): +2025-04-22 16:19:36,451 - INFO - Mean: 4.7899 +2025-04-22 16:19:36,451 - INFO - Median: 4.2255 +2025-04-22 16:19:36,451 - INFO - Min: 0.3763 +2025-04-22 16:19:36,451 - INFO - Max: 18.8528 +2025-04-22 16:19:36,451 - INFO - Std: 2.8893 +2025-04-22 16:19:36,451 - INFO - OC Distribution Statistics: +2025-04-22 16:19:36,451 - INFO - Validation Mean: 17.3772 +2025-04-22 16:19:36,451 - INFO - Validation Std: 8.9271 +2025-04-22 16:19:36,451 - INFO - Training Mean: 23.0004 +2025-04-22 16:19:36,451 - INFO - Training Std: 20.3076 +2025-04-22 16:19:36,451 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 16:19:36,451 - INFO - KS Statistic: 0.1538 +2025-04-22 16:19:36,451 - INFO - P-value: 0.0000 +2025-04-22 16:19:36,451 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 16:19:36,451 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 16:19:36,560 - INFO - Validation and Training Set Split Statistics +2025-04-22 16:19:36,560 - INFO - Timestamp: 2025-04-22 16:19:36 +2025-04-22 16:19:36,560 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 16:19:36,560 - INFO - Training set size: 15123 (91.58%) +2025-04-22 16:19:36,560 - INFO - Minimum Distance Statistics (km): +2025-04-22 16:19:36,560 - INFO - Mean: 4.7899 +2025-04-22 16:19:36,560 - INFO - Median: 4.2255 +2025-04-22 16:19:36,560 - INFO - Min: 0.3763 +2025-04-22 16:19:36,560 - INFO - Max: 18.8528 +2025-04-22 16:19:36,560 - INFO - Std: 2.8893 +2025-04-22 16:19:36,560 - INFO - OC Distribution Statistics: +2025-04-22 16:19:36,560 - INFO - Validation Mean: 17.3772 +2025-04-22 16:19:36,560 - INFO - Validation Std: 8.9271 +2025-04-22 16:19:36,560 - INFO - Training Mean: 23.0004 +2025-04-22 16:19:36,560 - INFO - Training Std: 20.3076 +2025-04-22 16:19:36,560 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 16:19:36,560 - INFO - KS Statistic: 0.1538 +2025-04-22 16:19:36,560 - INFO - P-value: 0.0000 +2025-04-22 16:19:36,560 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 16:19:36,560 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 16:30:10,971 - INFO - Validation and Training Set Split Statistics +2025-04-22 16:30:10,971 - INFO - Timestamp: 2025-04-22 16:30:10 +2025-04-22 16:30:10,971 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 16:30:10,971 - INFO - Training set size: 15123 (91.58%) +2025-04-22 16:30:10,971 - INFO - Minimum Distance Statistics (km): +2025-04-22 16:30:10,971 - INFO - Mean: 4.7899 +2025-04-22 16:30:10,971 - INFO - Median: 4.2255 +2025-04-22 16:30:10,971 - INFO - Min: 0.3763 +2025-04-22 16:30:10,971 - INFO - Max: 18.8528 +2025-04-22 16:30:10,971 - INFO - Std: 2.8893 +2025-04-22 16:30:10,971 - INFO - OC Distribution Statistics: +2025-04-22 16:30:10,971 - INFO - Validation Mean: 17.3772 +2025-04-22 16:30:10,971 - INFO - Validation Std: 8.9271 +2025-04-22 16:30:10,971 - INFO - Training Mean: 23.0004 +2025-04-22 16:30:10,971 - INFO - Training Std: 20.3076 +2025-04-22 16:30:10,971 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 16:30:10,971 - INFO - KS Statistic: 0.1538 +2025-04-22 16:30:10,971 - INFO - P-value: 0.0000 +2025-04-22 16:30:10,971 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 16:30:10,971 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 16:30:13,026 - INFO - Validation and Training Set Split Statistics +2025-04-22 16:30:13,026 - INFO - Timestamp: 2025-04-22 16:30:13 +2025-04-22 16:30:13,026 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 16:30:13,026 - INFO - Training set size: 15123 (91.58%) +2025-04-22 16:30:13,026 - INFO - Minimum Distance Statistics (km): +2025-04-22 16:30:13,026 - INFO - Mean: 4.7899 +2025-04-22 16:30:13,026 - INFO - Median: 4.2255 +2025-04-22 16:30:13,026 - INFO - Min: 0.3763 +2025-04-22 16:30:13,026 - INFO - Max: 18.8528 +2025-04-22 16:30:13,026 - INFO - Std: 2.8893 +2025-04-22 16:30:13,026 - INFO - OC Distribution Statistics: +2025-04-22 16:30:13,026 - INFO - Validation Mean: 17.3772 +2025-04-22 16:30:13,026 - INFO - Validation Std: 8.9271 +2025-04-22 16:30:13,026 - INFO - Training Mean: 23.0004 +2025-04-22 16:30:13,026 - INFO - Training Std: 20.3076 +2025-04-22 16:30:13,026 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 16:30:13,026 - INFO - KS Statistic: 0.1538 +2025-04-22 16:30:13,026 - INFO - P-value: 0.0000 +2025-04-22 16:30:13,026 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 16:30:13,026 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 16:30:19,988 - INFO - Validation and Training Set Split Statistics +2025-04-22 16:30:19,988 - INFO - Timestamp: 2025-04-22 16:30:19 +2025-04-22 16:30:19,988 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 16:30:19,988 - INFO - Training set size: 15123 (91.58%) +2025-04-22 16:30:19,988 - INFO - Minimum Distance Statistics (km): +2025-04-22 16:30:19,988 - INFO - Mean: 4.7899 +2025-04-22 16:30:19,988 - INFO - Median: 4.2255 +2025-04-22 16:30:19,988 - INFO - Min: 0.3763 +2025-04-22 16:30:19,989 - INFO - Max: 18.8528 +2025-04-22 16:30:19,989 - INFO - Std: 2.8893 +2025-04-22 16:30:19,989 - INFO - OC Distribution Statistics: +2025-04-22 16:30:19,989 - INFO - Validation Mean: 17.3772 +2025-04-22 16:30:19,989 - INFO - Validation Std: 8.9271 +2025-04-22 16:30:19,989 - INFO - Training Mean: 23.0004 +2025-04-22 16:30:19,989 - INFO - Training Std: 20.3076 +2025-04-22 16:30:19,989 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 16:30:19,989 - INFO - KS Statistic: 0.1538 +2025-04-22 16:30:19,989 - INFO - P-value: 0.0000 +2025-04-22 16:30:19,989 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 16:30:19,989 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 16:30:41,494 - INFO - Validation and Training Set Split Statistics +2025-04-22 16:30:41,494 - INFO - Timestamp: 2025-04-22 16:30:41 +2025-04-22 16:30:41,494 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 16:30:41,494 - INFO - Training set size: 15123 (91.58%) +2025-04-22 16:30:41,494 - INFO - Minimum Distance Statistics (km): +2025-04-22 16:30:41,494 - INFO - Mean: 4.7899 +2025-04-22 16:30:41,494 - INFO - Median: 4.2255 +2025-04-22 16:30:41,494 - INFO - Min: 0.3763 +2025-04-22 16:30:41,494 - INFO - Max: 18.8528 +2025-04-22 16:30:41,494 - INFO - Std: 2.8893 +2025-04-22 16:30:41,494 - INFO - OC Distribution Statistics: +2025-04-22 16:30:41,494 - INFO - Validation Mean: 17.3772 +2025-04-22 16:30:41,494 - INFO - Validation Std: 8.9271 +2025-04-22 16:30:41,494 - INFO - Training Mean: 23.0004 +2025-04-22 16:30:41,494 - INFO - Training Std: 20.3076 +2025-04-22 16:30:41,494 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 16:30:41,494 - INFO - KS Statistic: 0.1538 +2025-04-22 16:30:41,494 - INFO - P-value: 0.0000 +2025-04-22 16:30:41,494 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 16:30:41,494 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 17:28:58,892 - INFO - Validation and Training Set Split Statistics +2025-04-22 17:28:58,892 - INFO - Timestamp: 2025-04-22 17:28:58 +2025-04-22 17:28:58,892 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 17:28:58,892 - INFO - Training set size: 15123 (91.58%) +2025-04-22 17:28:58,892 - INFO - Minimum Distance Statistics (km): +2025-04-22 17:28:58,892 - INFO - Mean: 4.7899 +2025-04-22 17:28:58,892 - INFO - Median: 4.2255 +2025-04-22 17:28:58,892 - INFO - Min: 0.3763 +2025-04-22 17:28:58,892 - INFO - Max: 18.8528 +2025-04-22 17:28:58,892 - INFO - Std: 2.8893 +2025-04-22 17:28:58,892 - INFO - OC Distribution Statistics: +2025-04-22 17:28:58,892 - INFO - Validation Mean: 17.3772 +2025-04-22 17:28:58,892 - INFO - Validation Std: 8.9271 +2025-04-22 17:28:58,892 - INFO - Training Mean: 23.0004 +2025-04-22 17:28:58,892 - INFO - Training Std: 20.3076 +2025-04-22 17:28:58,892 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 17:28:58,892 - INFO - KS Statistic: 0.1538 +2025-04-22 17:28:58,892 - INFO - P-value: 0.0000 +2025-04-22 17:28:58,892 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 17:28:58,892 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 17:28:59,120 - INFO - Validation and Training Set Split Statistics +2025-04-22 17:28:59,120 - INFO - Timestamp: 2025-04-22 17:28:59 +2025-04-22 17:28:59,120 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 17:28:59,120 - INFO - Training set size: 15123 (91.58%) +2025-04-22 17:28:59,120 - INFO - Minimum Distance Statistics (km): +2025-04-22 17:28:59,120 - INFO - Mean: 4.7899 +2025-04-22 17:28:59,120 - INFO - Median: 4.2255 +2025-04-22 17:28:59,120 - INFO - Min: 0.3763 +2025-04-22 17:28:59,120 - INFO - Max: 18.8528 +2025-04-22 17:28:59,120 - INFO - Std: 2.8893 +2025-04-22 17:28:59,120 - INFO - OC Distribution Statistics: +2025-04-22 17:28:59,120 - INFO - Validation Mean: 17.3772 +2025-04-22 17:28:59,120 - INFO - Validation Std: 8.9271 +2025-04-22 17:28:59,120 - INFO - Training Mean: 23.0004 +2025-04-22 17:28:59,120 - INFO - Training Std: 20.3076 +2025-04-22 17:28:59,120 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 17:28:59,120 - INFO - KS Statistic: 0.1538 +2025-04-22 17:28:59,120 - INFO - P-value: 0.0000 +2025-04-22 17:28:59,120 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 17:28:59,120 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 17:29:00,042 - INFO - Validation and Training Set Split Statistics +2025-04-22 17:29:00,043 - INFO - Timestamp: 2025-04-22 17:29:00 +2025-04-22 17:29:00,043 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 17:29:00,043 - INFO - Training set size: 15123 (91.58%) +2025-04-22 17:29:00,043 - INFO - Minimum Distance Statistics (km): +2025-04-22 17:29:00,043 - INFO - Mean: 4.7899 +2025-04-22 17:29:00,043 - INFO - Median: 4.2255 +2025-04-22 17:29:00,043 - INFO - Min: 0.3763 +2025-04-22 17:29:00,043 - INFO - Max: 18.8528 +2025-04-22 17:29:00,043 - INFO - Std: 2.8893 +2025-04-22 17:29:00,043 - INFO - OC Distribution Statistics: +2025-04-22 17:29:00,043 - INFO - Validation Mean: 17.3772 +2025-04-22 17:29:00,043 - INFO - Validation Std: 8.9271 +2025-04-22 17:29:00,043 - INFO - Training Mean: 23.0004 +2025-04-22 17:29:00,043 - INFO - Training Std: 20.3076 +2025-04-22 17:29:00,043 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 17:29:00,043 - INFO - KS Statistic: 0.1538 +2025-04-22 17:29:00,043 - INFO - P-value: 0.0000 +2025-04-22 17:29:00,043 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 17:29:00,043 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 17:29:00,121 - INFO - Validation and Training Set Split Statistics +2025-04-22 17:29:00,122 - INFO - Timestamp: 2025-04-22 17:29:00 +2025-04-22 17:29:00,122 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 17:29:00,122 - INFO - Training set size: 15123 (91.58%) +2025-04-22 17:29:00,122 - INFO - Minimum Distance Statistics (km): +2025-04-22 17:29:00,122 - INFO - Mean: 4.7899 +2025-04-22 17:29:00,122 - INFO - Median: 4.2255 +2025-04-22 17:29:00,122 - INFO - Min: 0.3763 +2025-04-22 17:29:00,122 - INFO - Max: 18.8528 +2025-04-22 17:29:00,122 - INFO - Std: 2.8893 +2025-04-22 17:29:00,122 - INFO - OC Distribution Statistics: +2025-04-22 17:29:00,122 - INFO - Validation Mean: 17.3772 +2025-04-22 17:29:00,122 - INFO - Validation Std: 8.9271 +2025-04-22 17:29:00,122 - INFO - Training Mean: 23.0004 +2025-04-22 17:29:00,122 - INFO - Training Std: 20.3076 +2025-04-22 17:29:00,122 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 17:29:00,122 - INFO - KS Statistic: 0.1538 +2025-04-22 17:29:00,122 - INFO - P-value: 0.0000 +2025-04-22 17:29:00,122 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 17:29:00,122 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 17:29:00,308 - INFO - Validation and Training Set Split Statistics +2025-04-22 17:29:00,309 - INFO - Timestamp: 2025-04-22 17:29:00 +2025-04-22 17:29:00,309 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 17:29:00,309 - INFO - Training set size: 15123 (91.58%) +2025-04-22 17:29:00,309 - INFO - Minimum Distance Statistics (km): +2025-04-22 17:29:00,309 - INFO - Mean: 4.7899 +2025-04-22 17:29:00,309 - INFO - Median: 4.2255 +2025-04-22 17:29:00,309 - INFO - Min: 0.3763 +2025-04-22 17:29:00,309 - INFO - Max: 18.8528 +2025-04-22 17:29:00,309 - INFO - Std: 2.8893 +2025-04-22 17:29:00,309 - INFO - OC Distribution Statistics: +2025-04-22 17:29:00,309 - INFO - Validation Mean: 17.3772 +2025-04-22 17:29:00,309 - INFO - Validation Std: 8.9271 +2025-04-22 17:29:00,309 - INFO - Training Mean: 23.0004 +2025-04-22 17:29:00,309 - INFO - Training Std: 20.3076 +2025-04-22 17:29:00,309 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 17:29:00,309 - INFO - KS Statistic: 0.1538 +2025-04-22 17:29:00,309 - INFO - P-value: 0.0000 +2025-04-22 17:29:00,309 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 17:29:00,309 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 17:29:03,378 - INFO - Validation and Training Set Split Statistics +2025-04-22 17:29:03,378 - INFO - Timestamp: 2025-04-22 17:29:03 +2025-04-22 17:29:03,378 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 17:29:03,378 - INFO - Training set size: 15123 (91.58%) +2025-04-22 17:29:03,378 - INFO - Minimum Distance Statistics (km): +2025-04-22 17:29:03,378 - INFO - Mean: 4.7899 +2025-04-22 17:29:03,378 - INFO - Median: 4.2255 +2025-04-22 17:29:03,378 - INFO - Min: 0.3763 +2025-04-22 17:29:03,378 - INFO - Max: 18.8528 +2025-04-22 17:29:03,378 - INFO - Std: 2.8893 +2025-04-22 17:29:03,378 - INFO - OC Distribution Statistics: +2025-04-22 17:29:03,378 - INFO - Validation Mean: 17.3772 +2025-04-22 17:29:03,378 - INFO - Validation Std: 8.9271 +2025-04-22 17:29:03,378 - INFO - Training Mean: 23.0004 +2025-04-22 17:29:03,378 - INFO - Training Std: 20.3076 +2025-04-22 17:29:03,379 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 17:29:03,379 - INFO - KS Statistic: 0.1538 +2025-04-22 17:29:03,379 - INFO - P-value: 0.0000 +2025-04-22 17:29:03,379 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 17:29:03,379 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 17:29:05,036 - INFO - Validation and Training Set Split Statistics +2025-04-22 17:29:05,036 - INFO - Timestamp: 2025-04-22 17:29:05 +2025-04-22 17:29:05,036 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 17:29:05,036 - INFO - Training set size: 15123 (91.58%) +2025-04-22 17:29:05,036 - INFO - Minimum Distance Statistics (km): +2025-04-22 17:29:05,036 - INFO - Mean: 4.7899 +2025-04-22 17:29:05,036 - INFO - Median: 4.2255 +2025-04-22 17:29:05,036 - INFO - Min: 0.3763 +2025-04-22 17:29:05,036 - INFO - Max: 18.8528 +2025-04-22 17:29:05,036 - INFO - Std: 2.8893 +2025-04-22 17:29:05,036 - INFO - OC Distribution Statistics: +2025-04-22 17:29:05,036 - INFO - Validation Mean: 17.3772 +2025-04-22 17:29:05,036 - INFO - Validation Std: 8.9271 +2025-04-22 17:29:05,036 - INFO - Training Mean: 23.0004 +2025-04-22 17:29:05,036 - INFO - Training Std: 20.3076 +2025-04-22 17:29:05,036 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 17:29:05,036 - INFO - KS Statistic: 0.1538 +2025-04-22 17:29:05,036 - INFO - P-value: 0.0000 +2025-04-22 17:29:05,036 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 17:29:05,036 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 17:29:05,726 - INFO - Validation and Training Set Split Statistics +2025-04-22 17:29:05,726 - INFO - Timestamp: 2025-04-22 17:29:05 +2025-04-22 17:29:05,726 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 17:29:05,726 - INFO - Training set size: 15123 (91.58%) +2025-04-22 17:29:05,726 - INFO - Minimum Distance Statistics (km): +2025-04-22 17:29:05,726 - INFO - Mean: 4.7899 +2025-04-22 17:29:05,726 - INFO - Median: 4.2255 +2025-04-22 17:29:05,726 - INFO - Min: 0.3763 +2025-04-22 17:29:05,726 - INFO - Max: 18.8528 +2025-04-22 17:29:05,726 - INFO - Std: 2.8893 +2025-04-22 17:29:05,726 - INFO - OC Distribution Statistics: +2025-04-22 17:29:05,726 - INFO - Validation Mean: 17.3772 +2025-04-22 17:29:05,726 - INFO - Validation Std: 8.9271 +2025-04-22 17:29:05,726 - INFO - Training Mean: 23.0004 +2025-04-22 17:29:05,726 - INFO - Training Std: 20.3076 +2025-04-22 17:29:05,726 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 17:29:05,726 - INFO - KS Statistic: 0.1538 +2025-04-22 17:29:05,726 - INFO - P-value: 0.0000 +2025-04-22 17:29:05,726 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 17:29:05,726 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 17:43:58,659 - INFO - Validation and Training Set Split Statistics +2025-04-22 17:43:58,660 - INFO - Timestamp: 2025-04-22 17:43:58 +2025-04-22 17:43:58,660 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 17:43:58,660 - INFO - Training set size: 15123 (91.58%) +2025-04-22 17:43:58,660 - INFO - Minimum Distance Statistics (km): +2025-04-22 17:43:58,660 - INFO - Mean: 4.7899 +2025-04-22 17:43:58,660 - INFO - Median: 4.2255 +2025-04-22 17:43:58,660 - INFO - Min: 0.3763 +2025-04-22 17:43:58,660 - INFO - Max: 18.8528 +2025-04-22 17:43:58,660 - INFO - Std: 2.8893 +2025-04-22 17:43:58,660 - INFO - OC Distribution Statistics: +2025-04-22 17:43:58,660 - INFO - Validation Mean: 17.3772 +2025-04-22 17:43:58,660 - INFO - Validation Std: 8.9271 +2025-04-22 17:43:58,660 - INFO - Training Mean: 23.0004 +2025-04-22 17:43:58,660 - INFO - Training Std: 20.3076 +2025-04-22 17:43:58,660 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 17:43:58,660 - INFO - KS Statistic: 0.1538 +2025-04-22 17:43:58,660 - INFO - P-value: 0.0000 +2025-04-22 17:43:58,660 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 17:43:58,660 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 17:44:02,782 - INFO - Validation and Training Set Split Statistics +2025-04-22 17:44:02,782 - INFO - Timestamp: 2025-04-22 17:44:02 +2025-04-22 17:44:02,782 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 17:44:02,782 - INFO - Training set size: 15123 (91.58%) +2025-04-22 17:44:02,782 - INFO - Minimum Distance Statistics (km): +2025-04-22 17:44:02,782 - INFO - Mean: 4.7899 +2025-04-22 17:44:02,782 - INFO - Median: 4.2255 +2025-04-22 17:44:02,782 - INFO - Min: 0.3763 +2025-04-22 17:44:02,782 - INFO - Max: 18.8528 +2025-04-22 17:44:02,782 - INFO - Std: 2.8893 +2025-04-22 17:44:02,782 - INFO - OC Distribution Statistics: +2025-04-22 17:44:02,782 - INFO - Validation Mean: 17.3772 +2025-04-22 17:44:02,782 - INFO - Validation Std: 8.9271 +2025-04-22 17:44:02,782 - INFO - Training Mean: 23.0004 +2025-04-22 17:44:02,782 - INFO - Training Std: 20.3076 +2025-04-22 17:44:02,782 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 17:44:02,782 - INFO - KS Statistic: 0.1538 +2025-04-22 17:44:02,782 - INFO - P-value: 0.0000 +2025-04-22 17:44:02,782 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 17:44:02,782 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 17:44:10,985 - INFO - Validation and Training Set Split Statistics +2025-04-22 17:44:10,986 - INFO - Timestamp: 2025-04-22 17:44:10 +2025-04-22 17:44:10,986 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 17:44:10,986 - INFO - Training set size: 15123 (91.58%) +2025-04-22 17:44:10,986 - INFO - Minimum Distance Statistics (km): +2025-04-22 17:44:10,986 - INFO - Mean: 4.7899 +2025-04-22 17:44:10,986 - INFO - Median: 4.2255 +2025-04-22 17:44:10,986 - INFO - Min: 0.3763 +2025-04-22 17:44:10,986 - INFO - Max: 18.8528 +2025-04-22 17:44:10,986 - INFO - Std: 2.8893 +2025-04-22 17:44:10,986 - INFO - OC Distribution Statistics: +2025-04-22 17:44:10,986 - INFO - Validation Mean: 17.3772 +2025-04-22 17:44:10,986 - INFO - Validation Std: 8.9271 +2025-04-22 17:44:10,986 - INFO - Training Mean: 23.0004 +2025-04-22 17:44:10,986 - INFO - Training Std: 20.3076 +2025-04-22 17:44:10,986 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 17:44:10,986 - INFO - KS Statistic: 0.1538 +2025-04-22 17:44:10,986 - INFO - P-value: 0.0000 +2025-04-22 17:44:10,986 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 17:44:10,986 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 17:44:33,558 - INFO - Validation and Training Set Split Statistics +2025-04-22 17:44:33,558 - INFO - Timestamp: 2025-04-22 17:44:33 +2025-04-22 17:44:33,558 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 17:44:33,558 - INFO - Training set size: 15123 (91.58%) +2025-04-22 17:44:33,558 - INFO - Minimum Distance Statistics (km): +2025-04-22 17:44:33,558 - INFO - Mean: 4.7899 +2025-04-22 17:44:33,558 - INFO - Median: 4.2255 +2025-04-22 17:44:33,558 - INFO - Min: 0.3763 +2025-04-22 17:44:33,558 - INFO - Max: 18.8528 +2025-04-22 17:44:33,558 - INFO - Std: 2.8893 +2025-04-22 17:44:33,558 - INFO - OC Distribution Statistics: +2025-04-22 17:44:33,558 - INFO - Validation Mean: 17.3772 +2025-04-22 17:44:33,558 - INFO - Validation Std: 8.9271 +2025-04-22 17:44:33,558 - INFO - Training Mean: 23.0004 +2025-04-22 17:44:33,558 - INFO - Training Std: 20.3076 +2025-04-22 17:44:33,558 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 17:44:33,558 - INFO - KS Statistic: 0.1538 +2025-04-22 17:44:33,558 - INFO - P-value: 0.0000 +2025-04-22 17:44:33,558 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 17:44:33,558 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 18:38:10,813 - INFO - Validation and Training Set Split Statistics +2025-04-22 18:38:10,813 - INFO - Timestamp: 2025-04-22 18:38:10 +2025-04-22 18:38:10,813 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 18:38:10,813 - INFO - Training set size: 15123 (91.58%) +2025-04-22 18:38:10,813 - INFO - Minimum Distance Statistics (km): +2025-04-22 18:38:10,813 - INFO - Mean: 4.7899 +2025-04-22 18:38:10,813 - INFO - Median: 4.2255 +2025-04-22 18:38:10,813 - INFO - Min: 0.3763 +2025-04-22 18:38:10,814 - INFO - Max: 18.8528 +2025-04-22 18:38:10,814 - INFO - Std: 2.8893 +2025-04-22 18:38:10,814 - INFO - OC Distribution Statistics: +2025-04-22 18:38:10,814 - INFO - Validation Mean: 17.3772 +2025-04-22 18:38:10,814 - INFO - Validation Std: 8.9271 +2025-04-22 18:38:10,814 - INFO - Training Mean: 23.0004 +2025-04-22 18:38:10,814 - INFO - Training Std: 20.3076 +2025-04-22 18:38:10,814 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 18:38:10,814 - INFO - KS Statistic: 0.1538 +2025-04-22 18:38:10,814 - INFO - P-value: 0.0000 +2025-04-22 18:38:10,814 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 18:38:10,814 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 18:38:12,476 - INFO - Validation and Training Set Split Statistics +2025-04-22 18:38:12,476 - INFO - Timestamp: 2025-04-22 18:38:12 +2025-04-22 18:38:12,476 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 18:38:12,476 - INFO - Training set size: 15123 (91.58%) +2025-04-22 18:38:12,476 - INFO - Minimum Distance Statistics (km): +2025-04-22 18:38:12,476 - INFO - Mean: 4.7899 +2025-04-22 18:38:12,476 - INFO - Median: 4.2255 +2025-04-22 18:38:12,476 - INFO - Min: 0.3763 +2025-04-22 18:38:12,477 - INFO - Max: 18.8528 +2025-04-22 18:38:12,477 - INFO - Std: 2.8893 +2025-04-22 18:38:12,477 - INFO - OC Distribution Statistics: +2025-04-22 18:38:12,477 - INFO - Validation Mean: 17.3772 +2025-04-22 18:38:12,477 - INFO - Validation Std: 8.9271 +2025-04-22 18:38:12,477 - INFO - Training Mean: 23.0004 +2025-04-22 18:38:12,477 - INFO - Training Std: 20.3076 +2025-04-22 18:38:12,477 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 18:38:12,477 - INFO - KS Statistic: 0.1538 +2025-04-22 18:38:12,477 - INFO - P-value: 0.0000 +2025-04-22 18:38:12,477 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 18:38:12,477 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 18:38:12,769 - INFO - Validation and Training Set Split Statistics +2025-04-22 18:38:12,769 - INFO - Timestamp: 2025-04-22 18:38:12 +2025-04-22 18:38:12,769 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 18:38:12,769 - INFO - Training set size: 15123 (91.58%) +2025-04-22 18:38:12,769 - INFO - Minimum Distance Statistics (km): +2025-04-22 18:38:12,769 - INFO - Mean: 4.7899 +2025-04-22 18:38:12,769 - INFO - Median: 4.2255 +2025-04-22 18:38:12,769 - INFO - Min: 0.3763 +2025-04-22 18:38:12,769 - INFO - Max: 18.8528 +2025-04-22 18:38:12,769 - INFO - Std: 2.8893 +2025-04-22 18:38:12,769 - INFO - OC Distribution Statistics: +2025-04-22 18:38:12,769 - INFO - Validation Mean: 17.3772 +2025-04-22 18:38:12,769 - INFO - Validation Std: 8.9271 +2025-04-22 18:38:12,769 - INFO - Training Mean: 23.0004 +2025-04-22 18:38:12,769 - INFO - Training Std: 20.3076 +2025-04-22 18:38:12,769 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 18:38:12,769 - INFO - KS Statistic: 0.1538 +2025-04-22 18:38:12,769 - INFO - P-value: 0.0000 +2025-04-22 18:38:12,769 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 18:38:12,769 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 18:38:13,316 - INFO - Validation and Training Set Split Statistics +2025-04-22 18:38:13,317 - INFO - Timestamp: 2025-04-22 18:38:13 +2025-04-22 18:38:13,317 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 18:38:13,317 - INFO - Training set size: 15123 (91.58%) +2025-04-22 18:38:13,317 - INFO - Minimum Distance Statistics (km): +2025-04-22 18:38:13,317 - INFO - Mean: 4.7899 +2025-04-22 18:38:13,317 - INFO - Median: 4.2255 +2025-04-22 18:38:13,317 - INFO - Min: 0.3763 +2025-04-22 18:38:13,317 - INFO - Max: 18.8528 +2025-04-22 18:38:13,317 - INFO - Std: 2.8893 +2025-04-22 18:38:13,317 - INFO - OC Distribution Statistics: +2025-04-22 18:38:13,317 - INFO - Validation Mean: 17.3772 +2025-04-22 18:38:13,317 - INFO - Validation Std: 8.9271 +2025-04-22 18:38:13,317 - INFO - Training Mean: 23.0004 +2025-04-22 18:38:13,317 - INFO - Training Std: 20.3076 +2025-04-22 18:38:13,317 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 18:38:13,317 - INFO - KS Statistic: 0.1538 +2025-04-22 18:38:13,317 - INFO - P-value: 0.0000 +2025-04-22 18:38:13,317 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 18:38:13,317 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 18:38:15,684 - INFO - Validation and Training Set Split Statistics +2025-04-22 18:38:15,684 - INFO - Timestamp: 2025-04-22 18:38:15 +2025-04-22 18:38:15,684 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 18:38:15,684 - INFO - Training set size: 15123 (91.58%) +2025-04-22 18:38:15,684 - INFO - Minimum Distance Statistics (km): +2025-04-22 18:38:15,684 - INFO - Mean: 4.7899 +2025-04-22 18:38:15,684 - INFO - Median: 4.2255 +2025-04-22 18:38:15,684 - INFO - Min: 0.3763 +2025-04-22 18:38:15,684 - INFO - Max: 18.8528 +2025-04-22 18:38:15,684 - INFO - Std: 2.8893 +2025-04-22 18:38:15,684 - INFO - OC Distribution Statistics: +2025-04-22 18:38:15,684 - INFO - Validation Mean: 17.3772 +2025-04-22 18:38:15,684 - INFO - Validation Std: 8.9271 +2025-04-22 18:38:15,684 - INFO - Training Mean: 23.0004 +2025-04-22 18:38:15,684 - INFO - Training Std: 20.3076 +2025-04-22 18:38:15,684 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 18:38:15,684 - INFO - KS Statistic: 0.1538 +2025-04-22 18:38:15,684 - INFO - P-value: 0.0000 +2025-04-22 18:38:15,685 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 18:38:15,685 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 18:38:16,440 - INFO - Validation and Training Set Split Statistics +2025-04-22 18:38:16,440 - INFO - Timestamp: 2025-04-22 18:38:16 +2025-04-22 18:38:16,440 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 18:38:16,440 - INFO - Training set size: 15123 (91.58%) +2025-04-22 18:38:16,440 - INFO - Minimum Distance Statistics (km): +2025-04-22 18:38:16,440 - INFO - Mean: 4.7899 +2025-04-22 18:38:16,440 - INFO - Median: 4.2255 +2025-04-22 18:38:16,440 - INFO - Min: 0.3763 +2025-04-22 18:38:16,440 - INFO - Max: 18.8528 +2025-04-22 18:38:16,440 - INFO - Std: 2.8893 +2025-04-22 18:38:16,440 - INFO - OC Distribution Statistics: +2025-04-22 18:38:16,440 - INFO - Validation Mean: 17.3772 +2025-04-22 18:38:16,440 - INFO - Validation Std: 8.9271 +2025-04-22 18:38:16,440 - INFO - Training Mean: 23.0004 +2025-04-22 18:38:16,440 - INFO - Training Std: 20.3076 +2025-04-22 18:38:16,440 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 18:38:16,440 - INFO - KS Statistic: 0.1538 +2025-04-22 18:38:16,440 - INFO - P-value: 0.0000 +2025-04-22 18:38:16,440 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 18:38:16,440 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 18:38:18,063 - INFO - Validation and Training Set Split Statistics +2025-04-22 18:38:18,064 - INFO - Timestamp: 2025-04-22 18:38:18 +2025-04-22 18:38:18,064 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 18:38:18,064 - INFO - Training set size: 15123 (91.58%) +2025-04-22 18:38:18,064 - INFO - Minimum Distance Statistics (km): +2025-04-22 18:38:18,064 - INFO - Mean: 4.7899 +2025-04-22 18:38:18,064 - INFO - Median: 4.2255 +2025-04-22 18:38:18,064 - INFO - Min: 0.3763 +2025-04-22 18:38:18,064 - INFO - Max: 18.8528 +2025-04-22 18:38:18,064 - INFO - Std: 2.8893 +2025-04-22 18:38:18,064 - INFO - OC Distribution Statistics: +2025-04-22 18:38:18,064 - INFO - Validation Mean: 17.3772 +2025-04-22 18:38:18,064 - INFO - Validation Std: 8.9271 +2025-04-22 18:38:18,064 - INFO - Training Mean: 23.0004 +2025-04-22 18:38:18,064 - INFO - Training Std: 20.3076 +2025-04-22 18:38:18,064 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 18:38:18,064 - INFO - KS Statistic: 0.1538 +2025-04-22 18:38:18,064 - INFO - P-value: 0.0000 +2025-04-22 18:38:18,064 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 18:38:18,064 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 18:38:18,715 - INFO - Validation and Training Set Split Statistics +2025-04-22 18:38:18,715 - INFO - Timestamp: 2025-04-22 18:38:18 +2025-04-22 18:38:18,715 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 18:38:18,715 - INFO - Training set size: 15123 (91.58%) +2025-04-22 18:38:18,715 - INFO - Minimum Distance Statistics (km): +2025-04-22 18:38:18,715 - INFO - Mean: 4.7899 +2025-04-22 18:38:18,715 - INFO - Median: 4.2255 +2025-04-22 18:38:18,715 - INFO - Min: 0.3763 +2025-04-22 18:38:18,715 - INFO - Max: 18.8528 +2025-04-22 18:38:18,715 - INFO - Std: 2.8893 +2025-04-22 18:38:18,715 - INFO - OC Distribution Statistics: +2025-04-22 18:38:18,715 - INFO - Validation Mean: 17.3772 +2025-04-22 18:38:18,715 - INFO - Validation Std: 8.9271 +2025-04-22 18:38:18,715 - INFO - Training Mean: 23.0004 +2025-04-22 18:38:18,715 - INFO - Training Std: 20.3076 +2025-04-22 18:38:18,715 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 18:38:18,715 - INFO - KS Statistic: 0.1538 +2025-04-22 18:38:18,716 - INFO - P-value: 0.0000 +2025-04-22 18:38:18,716 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 18:38:18,716 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 18:58:40,681 - INFO - Validation and Training Set Split Statistics +2025-04-22 18:58:40,681 - INFO - Timestamp: 2025-04-22 18:58:40 +2025-04-22 18:58:40,681 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 18:58:40,681 - INFO - Training set size: 15123 (91.58%) +2025-04-22 18:58:40,681 - INFO - Minimum Distance Statistics (km): +2025-04-22 18:58:40,681 - INFO - Mean: 4.7899 +2025-04-22 18:58:40,681 - INFO - Median: 4.2255 +2025-04-22 18:58:40,681 - INFO - Min: 0.3763 +2025-04-22 18:58:40,681 - INFO - Max: 18.8528 +2025-04-22 18:58:40,681 - INFO - Std: 2.8893 +2025-04-22 18:58:40,681 - INFO - OC Distribution Statistics: +2025-04-22 18:58:40,681 - INFO - Validation Mean: 17.3772 +2025-04-22 18:58:40,681 - INFO - Validation Std: 8.9271 +2025-04-22 18:58:40,681 - INFO - Training Mean: 23.0004 +2025-04-22 18:58:40,681 - INFO - Training Std: 20.3076 +2025-04-22 18:58:40,681 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 18:58:40,681 - INFO - KS Statistic: 0.1538 +2025-04-22 18:58:40,681 - INFO - P-value: 0.0000 +2025-04-22 18:58:40,681 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 18:58:40,681 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 18:58:42,444 - INFO - Validation and Training Set Split Statistics +2025-04-22 18:58:42,444 - INFO - Timestamp: 2025-04-22 18:58:42 +2025-04-22 18:58:42,444 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 18:58:42,444 - INFO - Training set size: 15123 (91.58%) +2025-04-22 18:58:42,444 - INFO - Minimum Distance Statistics (km): +2025-04-22 18:58:42,444 - INFO - Mean: 4.7899 +2025-04-22 18:58:42,444 - INFO - Median: 4.2255 +2025-04-22 18:58:42,444 - INFO - Min: 0.3763 +2025-04-22 18:58:42,444 - INFO - Max: 18.8528 +2025-04-22 18:58:42,444 - INFO - Std: 2.8893 +2025-04-22 18:58:42,444 - INFO - OC Distribution Statistics: +2025-04-22 18:58:42,444 - INFO - Validation Mean: 17.3772 +2025-04-22 18:58:42,444 - INFO - Validation Std: 8.9271 +2025-04-22 18:58:42,444 - INFO - Training Mean: 23.0004 +2025-04-22 18:58:42,444 - INFO - Training Std: 20.3076 +2025-04-22 18:58:42,444 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 18:58:42,444 - INFO - KS Statistic: 0.1538 +2025-04-22 18:58:42,444 - INFO - P-value: 0.0000 +2025-04-22 18:58:42,444 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 18:58:42,444 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 18:58:57,515 - INFO - Validation and Training Set Split Statistics +2025-04-22 18:58:57,516 - INFO - Timestamp: 2025-04-22 18:58:57 +2025-04-22 18:58:57,516 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 18:58:57,516 - INFO - Training set size: 15123 (91.58%) +2025-04-22 18:58:57,516 - INFO - Minimum Distance Statistics (km): +2025-04-22 18:58:57,516 - INFO - Mean: 4.7899 +2025-04-22 18:58:57,516 - INFO - Median: 4.2255 +2025-04-22 18:58:57,516 - INFO - Min: 0.3763 +2025-04-22 18:58:57,516 - INFO - Max: 18.8528 +2025-04-22 18:58:57,516 - INFO - Std: 2.8893 +2025-04-22 18:58:57,516 - INFO - OC Distribution Statistics: +2025-04-22 18:58:57,516 - INFO - Validation Mean: 17.3772 +2025-04-22 18:58:57,516 - INFO - Validation Std: 8.9271 +2025-04-22 18:58:57,516 - INFO - Training Mean: 23.0004 +2025-04-22 18:58:57,516 - INFO - Training Std: 20.3076 +2025-04-22 18:58:57,516 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 18:58:57,516 - INFO - KS Statistic: 0.1538 +2025-04-22 18:58:57,516 - INFO - P-value: 0.0000 +2025-04-22 18:58:57,516 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 18:58:57,516 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 18:59:10,364 - INFO - Validation and Training Set Split Statistics +2025-04-22 18:59:10,364 - INFO - Timestamp: 2025-04-22 18:59:10 +2025-04-22 18:59:10,365 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 18:59:10,365 - INFO - Training set size: 15123 (91.58%) +2025-04-22 18:59:10,365 - INFO - Minimum Distance Statistics (km): +2025-04-22 18:59:10,365 - INFO - Mean: 4.7899 +2025-04-22 18:59:10,365 - INFO - Median: 4.2255 +2025-04-22 18:59:10,365 - INFO - Min: 0.3763 +2025-04-22 18:59:10,365 - INFO - Max: 18.8528 +2025-04-22 18:59:10,365 - INFO - Std: 2.8893 +2025-04-22 18:59:10,365 - INFO - OC Distribution Statistics: +2025-04-22 18:59:10,365 - INFO - Validation Mean: 17.3772 +2025-04-22 18:59:10,365 - INFO - Validation Std: 8.9271 +2025-04-22 18:59:10,365 - INFO - Training Mean: 23.0004 +2025-04-22 18:59:10,365 - INFO - Training Std: 20.3076 +2025-04-22 18:59:10,365 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 18:59:10,365 - INFO - KS Statistic: 0.1538 +2025-04-22 18:59:10,365 - INFO - P-value: 0.0000 +2025-04-22 18:59:10,365 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 18:59:10,365 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 19:46:54,630 - INFO - Validation and Training Set Split Statistics +2025-04-22 19:46:54,630 - INFO - Timestamp: 2025-04-22 19:46:54 +2025-04-22 19:46:54,630 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 19:46:54,630 - INFO - Training set size: 15123 (91.58%) +2025-04-22 19:46:54,630 - INFO - Minimum Distance Statistics (km): +2025-04-22 19:46:54,630 - INFO - Mean: 4.7899 +2025-04-22 19:46:54,630 - INFO - Median: 4.2255 +2025-04-22 19:46:54,630 - INFO - Min: 0.3763 +2025-04-22 19:46:54,630 - INFO - Max: 18.8528 +2025-04-22 19:46:54,630 - INFO - Std: 2.8893 +2025-04-22 19:46:54,630 - INFO - OC Distribution Statistics: +2025-04-22 19:46:54,630 - INFO - Validation Mean: 17.3772 +2025-04-22 19:46:54,630 - INFO - Validation Std: 8.9271 +2025-04-22 19:46:54,630 - INFO - Training Mean: 23.0004 +2025-04-22 19:46:54,630 - INFO - Training Std: 20.3076 +2025-04-22 19:46:54,630 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 19:46:54,630 - INFO - KS Statistic: 0.1538 +2025-04-22 19:46:54,630 - INFO - P-value: 0.0000 +2025-04-22 19:46:54,630 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 19:46:54,630 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 19:46:55,064 - INFO - Validation and Training Set Split Statistics +2025-04-22 19:46:55,064 - INFO - Timestamp: 2025-04-22 19:46:55 +2025-04-22 19:46:55,064 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 19:46:55,064 - INFO - Training set size: 15123 (91.58%) +2025-04-22 19:46:55,064 - INFO - Minimum Distance Statistics (km): +2025-04-22 19:46:55,064 - INFO - Mean: 4.7899 +2025-04-22 19:46:55,064 - INFO - Median: 4.2255 +2025-04-22 19:46:55,064 - INFO - Min: 0.3763 +2025-04-22 19:46:55,064 - INFO - Max: 18.8528 +2025-04-22 19:46:55,064 - INFO - Std: 2.8893 +2025-04-22 19:46:55,064 - INFO - OC Distribution Statistics: +2025-04-22 19:46:55,064 - INFO - Validation Mean: 17.3772 +2025-04-22 19:46:55,064 - INFO - Validation Std: 8.9271 +2025-04-22 19:46:55,064 - INFO - Training Mean: 23.0004 +2025-04-22 19:46:55,064 - INFO - Training Std: 20.3076 +2025-04-22 19:46:55,064 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 19:46:55,064 - INFO - KS Statistic: 0.1538 +2025-04-22 19:46:55,064 - INFO - P-value: 0.0000 +2025-04-22 19:46:55,064 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 19:46:55,064 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 19:46:56,141 - INFO - Validation and Training Set Split Statistics +2025-04-22 19:46:56,141 - INFO - Timestamp: 2025-04-22 19:46:56 +2025-04-22 19:46:56,141 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 19:46:56,141 - INFO - Training set size: 15123 (91.58%) +2025-04-22 19:46:56,141 - INFO - Minimum Distance Statistics (km): +2025-04-22 19:46:56,141 - INFO - Mean: 4.7899 +2025-04-22 19:46:56,141 - INFO - Median: 4.2255 +2025-04-22 19:46:56,141 - INFO - Min: 0.3763 +2025-04-22 19:46:56,141 - INFO - Max: 18.8528 +2025-04-22 19:46:56,141 - INFO - Std: 2.8893 +2025-04-22 19:46:56,141 - INFO - OC Distribution Statistics: +2025-04-22 19:46:56,141 - INFO - Validation Mean: 17.3772 +2025-04-22 19:46:56,141 - INFO - Validation Std: 8.9271 +2025-04-22 19:46:56,141 - INFO - Training Mean: 23.0004 +2025-04-22 19:46:56,141 - INFO - Training Std: 20.3076 +2025-04-22 19:46:56,141 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 19:46:56,141 - INFO - KS Statistic: 0.1538 +2025-04-22 19:46:56,141 - INFO - P-value: 0.0000 +2025-04-22 19:46:56,141 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 19:46:56,141 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 19:46:56,305 - INFO - Validation and Training Set Split Statistics +2025-04-22 19:46:56,305 - INFO - Timestamp: 2025-04-22 19:46:56 +2025-04-22 19:46:56,305 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 19:46:56,305 - INFO - Training set size: 15123 (91.58%) +2025-04-22 19:46:56,305 - INFO - Minimum Distance Statistics (km): +2025-04-22 19:46:56,305 - INFO - Mean: 4.7899 +2025-04-22 19:46:56,305 - INFO - Median: 4.2255 +2025-04-22 19:46:56,305 - INFO - Min: 0.3763 +2025-04-22 19:46:56,305 - INFO - Max: 18.8528 +2025-04-22 19:46:56,305 - INFO - Std: 2.8893 +2025-04-22 19:46:56,305 - INFO - OC Distribution Statistics: +2025-04-22 19:46:56,305 - INFO - Validation Mean: 17.3772 +2025-04-22 19:46:56,305 - INFO - Validation Std: 8.9271 +2025-04-22 19:46:56,305 - INFO - Training Mean: 23.0004 +2025-04-22 19:46:56,305 - INFO - Training Std: 20.3076 +2025-04-22 19:46:56,305 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 19:46:56,305 - INFO - KS Statistic: 0.1538 +2025-04-22 19:46:56,305 - INFO - P-value: 0.0000 +2025-04-22 19:46:56,305 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 19:46:56,305 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 19:46:59,481 - INFO - Validation and Training Set Split Statistics +2025-04-22 19:46:59,481 - INFO - Timestamp: 2025-04-22 19:46:59 +2025-04-22 19:46:59,481 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 19:46:59,481 - INFO - Training set size: 15123 (91.58%) +2025-04-22 19:46:59,481 - INFO - Minimum Distance Statistics (km): +2025-04-22 19:46:59,481 - INFO - Mean: 4.7899 +2025-04-22 19:46:59,481 - INFO - Median: 4.2255 +2025-04-22 19:46:59,481 - INFO - Min: 0.3763 +2025-04-22 19:46:59,481 - INFO - Max: 18.8528 +2025-04-22 19:46:59,481 - INFO - Std: 2.8893 +2025-04-22 19:46:59,481 - INFO - OC Distribution Statistics: +2025-04-22 19:46:59,481 - INFO - Validation Mean: 17.3772 +2025-04-22 19:46:59,481 - INFO - Validation Std: 8.9271 +2025-04-22 19:46:59,481 - INFO - Training Mean: 23.0004 +2025-04-22 19:46:59,481 - INFO - Training Std: 20.3076 +2025-04-22 19:46:59,481 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 19:46:59,481 - INFO - KS Statistic: 0.1538 +2025-04-22 19:46:59,481 - INFO - P-value: 0.0000 +2025-04-22 19:46:59,481 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 19:46:59,481 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 19:47:01,113 - INFO - Validation and Training Set Split Statistics +2025-04-22 19:47:01,113 - INFO - Timestamp: 2025-04-22 19:47:01 +2025-04-22 19:47:01,113 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 19:47:01,113 - INFO - Training set size: 15123 (91.58%) +2025-04-22 19:47:01,113 - INFO - Minimum Distance Statistics (km): +2025-04-22 19:47:01,113 - INFO - Mean: 4.7899 +2025-04-22 19:47:01,113 - INFO - Median: 4.2255 +2025-04-22 19:47:01,113 - INFO - Min: 0.3763 +2025-04-22 19:47:01,113 - INFO - Max: 18.8528 +2025-04-22 19:47:01,113 - INFO - Std: 2.8893 +2025-04-22 19:47:01,113 - INFO - OC Distribution Statistics: +2025-04-22 19:47:01,113 - INFO - Validation Mean: 17.3772 +2025-04-22 19:47:01,113 - INFO - Validation Std: 8.9271 +2025-04-22 19:47:01,113 - INFO - Training Mean: 23.0004 +2025-04-22 19:47:01,113 - INFO - Training Std: 20.3076 +2025-04-22 19:47:01,113 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 19:47:01,113 - INFO - KS Statistic: 0.1538 +2025-04-22 19:47:01,113 - INFO - P-value: 0.0000 +2025-04-22 19:47:01,113 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 19:47:01,113 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 19:47:01,604 - INFO - Validation and Training Set Split Statistics +2025-04-22 19:47:01,605 - INFO - Timestamp: 2025-04-22 19:47:01 +2025-04-22 19:47:01,605 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 19:47:01,605 - INFO - Training set size: 15123 (91.58%) +2025-04-22 19:47:01,605 - INFO - Minimum Distance Statistics (km): +2025-04-22 19:47:01,605 - INFO - Mean: 4.7899 +2025-04-22 19:47:01,605 - INFO - Median: 4.2255 +2025-04-22 19:47:01,605 - INFO - Min: 0.3763 +2025-04-22 19:47:01,605 - INFO - Max: 18.8528 +2025-04-22 19:47:01,605 - INFO - Std: 2.8893 +2025-04-22 19:47:01,605 - INFO - OC Distribution Statistics: +2025-04-22 19:47:01,605 - INFO - Validation Mean: 17.3772 +2025-04-22 19:47:01,605 - INFO - Validation Std: 8.9271 +2025-04-22 19:47:01,605 - INFO - Training Mean: 23.0004 +2025-04-22 19:47:01,605 - INFO - Training Std: 20.3076 +2025-04-22 19:47:01,605 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 19:47:01,605 - INFO - KS Statistic: 0.1538 +2025-04-22 19:47:01,605 - INFO - P-value: 0.0000 +2025-04-22 19:47:01,605 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 19:47:01,605 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 19:47:02,714 - INFO - Validation and Training Set Split Statistics +2025-04-22 19:47:02,714 - INFO - Timestamp: 2025-04-22 19:47:02 +2025-04-22 19:47:02,714 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 19:47:02,714 - INFO - Training set size: 15123 (91.58%) +2025-04-22 19:47:02,714 - INFO - Minimum Distance Statistics (km): +2025-04-22 19:47:02,714 - INFO - Mean: 4.7899 +2025-04-22 19:47:02,714 - INFO - Median: 4.2255 +2025-04-22 19:47:02,714 - INFO - Min: 0.3763 +2025-04-22 19:47:02,714 - INFO - Max: 18.8528 +2025-04-22 19:47:02,714 - INFO - Std: 2.8893 +2025-04-22 19:47:02,714 - INFO - OC Distribution Statistics: +2025-04-22 19:47:02,714 - INFO - Validation Mean: 17.3772 +2025-04-22 19:47:02,714 - INFO - Validation Std: 8.9271 +2025-04-22 19:47:02,714 - INFO - Training Mean: 23.0004 +2025-04-22 19:47:02,714 - INFO - Training Std: 20.3076 +2025-04-22 19:47:02,714 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 19:47:02,714 - INFO - KS Statistic: 0.1538 +2025-04-22 19:47:02,714 - INFO - P-value: 0.0000 +2025-04-22 19:47:02,714 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 19:47:02,714 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 20:57:13,403 - INFO - Validation and Training Set Split Statistics +2025-04-22 20:57:13,404 - INFO - Timestamp: 2025-04-22 20:57:13 +2025-04-22 20:57:13,404 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 20:57:13,404 - INFO - Training set size: 15123 (91.58%) +2025-04-22 20:57:13,404 - INFO - Minimum Distance Statistics (km): +2025-04-22 20:57:13,404 - INFO - Mean: 4.7899 +2025-04-22 20:57:13,404 - INFO - Median: 4.2255 +2025-04-22 20:57:13,404 - INFO - Min: 0.3763 +2025-04-22 20:57:13,404 - INFO - Max: 18.8528 +2025-04-22 20:57:13,404 - INFO - Std: 2.8893 +2025-04-22 20:57:13,404 - INFO - OC Distribution Statistics: +2025-04-22 20:57:13,404 - INFO - Validation Mean: 17.3772 +2025-04-22 20:57:13,404 - INFO - Validation Std: 8.9271 +2025-04-22 20:57:13,404 - INFO - Training Mean: 23.0004 +2025-04-22 20:57:13,404 - INFO - Training Std: 20.3076 +2025-04-22 20:57:13,404 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 20:57:13,404 - INFO - KS Statistic: 0.1538 +2025-04-22 20:57:13,404 - INFO - P-value: 0.0000 +2025-04-22 20:57:13,404 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 20:57:13,404 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 20:57:13,844 - INFO - Validation and Training Set Split Statistics +2025-04-22 20:57:13,844 - INFO - Timestamp: 2025-04-22 20:57:13 +2025-04-22 20:57:13,844 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 20:57:13,844 - INFO - Training set size: 15123 (91.58%) +2025-04-22 20:57:13,844 - INFO - Minimum Distance Statistics (km): +2025-04-22 20:57:13,844 - INFO - Mean: 4.7899 +2025-04-22 20:57:13,844 - INFO - Median: 4.2255 +2025-04-22 20:57:13,844 - INFO - Min: 0.3763 +2025-04-22 20:57:13,844 - INFO - Max: 18.8528 +2025-04-22 20:57:13,844 - INFO - Std: 2.8893 +2025-04-22 20:57:13,844 - INFO - OC Distribution Statistics: +2025-04-22 20:57:13,844 - INFO - Validation Mean: 17.3772 +2025-04-22 20:57:13,844 - INFO - Validation Std: 8.9271 +2025-04-22 20:57:13,844 - INFO - Training Mean: 23.0004 +2025-04-22 20:57:13,844 - INFO - Training Std: 20.3076 +2025-04-22 20:57:13,844 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 20:57:13,844 - INFO - KS Statistic: 0.1538 +2025-04-22 20:57:13,844 - INFO - P-value: 0.0000 +2025-04-22 20:57:13,844 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 20:57:13,844 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 20:57:14,474 - INFO - Validation and Training Set Split Statistics +2025-04-22 20:57:14,474 - INFO - Timestamp: 2025-04-22 20:57:14 +2025-04-22 20:57:14,474 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 20:57:14,474 - INFO - Training set size: 15123 (91.58%) +2025-04-22 20:57:14,474 - INFO - Minimum Distance Statistics (km): +2025-04-22 20:57:14,474 - INFO - Mean: 4.7899 +2025-04-22 20:57:14,474 - INFO - Median: 4.2255 +2025-04-22 20:57:14,474 - INFO - Min: 0.3763 +2025-04-22 20:57:14,474 - INFO - Max: 18.8528 +2025-04-22 20:57:14,474 - INFO - Std: 2.8893 +2025-04-22 20:57:14,474 - INFO - OC Distribution Statistics: +2025-04-22 20:57:14,474 - INFO - Validation Mean: 17.3772 +2025-04-22 20:57:14,474 - INFO - Validation Std: 8.9271 +2025-04-22 20:57:14,474 - INFO - Training Mean: 23.0004 +2025-04-22 20:57:14,474 - INFO - Training Std: 20.3076 +2025-04-22 20:57:14,474 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 20:57:14,474 - INFO - KS Statistic: 0.1538 +2025-04-22 20:57:14,474 - INFO - P-value: 0.0000 +2025-04-22 20:57:14,474 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 20:57:14,474 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 20:57:15,196 - INFO - Validation and Training Set Split Statistics +2025-04-22 20:57:15,196 - INFO - Timestamp: 2025-04-22 20:57:15 +2025-04-22 20:57:15,197 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 20:57:15,197 - INFO - Training set size: 15123 (91.58%) +2025-04-22 20:57:15,197 - INFO - Minimum Distance Statistics (km): +2025-04-22 20:57:15,197 - INFO - Mean: 4.7899 +2025-04-22 20:57:15,197 - INFO - Median: 4.2255 +2025-04-22 20:57:15,197 - INFO - Min: 0.3763 +2025-04-22 20:57:15,197 - INFO - Max: 18.8528 +2025-04-22 20:57:15,197 - INFO - Std: 2.8893 +2025-04-22 20:57:15,197 - INFO - OC Distribution Statistics: +2025-04-22 20:57:15,197 - INFO - Validation Mean: 17.3772 +2025-04-22 20:57:15,197 - INFO - Validation Std: 8.9271 +2025-04-22 20:57:15,197 - INFO - Training Mean: 23.0004 +2025-04-22 20:57:15,197 - INFO - Training Std: 20.3076 +2025-04-22 20:57:15,197 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 20:57:15,197 - INFO - KS Statistic: 0.1538 +2025-04-22 20:57:15,197 - INFO - P-value: 0.0000 +2025-04-22 20:57:15,197 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 20:57:15,197 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 20:57:16,786 - INFO - Validation and Training Set Split Statistics +2025-04-22 20:57:16,786 - INFO - Timestamp: 2025-04-22 20:57:16 +2025-04-22 20:57:16,786 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 20:57:16,786 - INFO - Training set size: 15123 (91.58%) +2025-04-22 20:57:16,786 - INFO - Minimum Distance Statistics (km): +2025-04-22 20:57:16,786 - INFO - Mean: 4.7899 +2025-04-22 20:57:16,786 - INFO - Median: 4.2255 +2025-04-22 20:57:16,786 - INFO - Min: 0.3763 +2025-04-22 20:57:16,786 - INFO - Max: 18.8528 +2025-04-22 20:57:16,786 - INFO - Std: 2.8893 +2025-04-22 20:57:16,786 - INFO - OC Distribution Statistics: +2025-04-22 20:57:16,786 - INFO - Validation Mean: 17.3772 +2025-04-22 20:57:16,786 - INFO - Validation Std: 8.9271 +2025-04-22 20:57:16,786 - INFO - Training Mean: 23.0004 +2025-04-22 20:57:16,786 - INFO - Training Std: 20.3076 +2025-04-22 20:57:16,786 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 20:57:16,786 - INFO - KS Statistic: 0.1538 +2025-04-22 20:57:16,786 - INFO - P-value: 0.0000 +2025-04-22 20:57:16,786 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 20:57:16,786 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 20:57:18,524 - INFO - Validation and Training Set Split Statistics +2025-04-22 20:57:18,524 - INFO - Timestamp: 2025-04-22 20:57:18 +2025-04-22 20:57:18,524 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 20:57:18,524 - INFO - Training set size: 15123 (91.58%) +2025-04-22 20:57:18,524 - INFO - Minimum Distance Statistics (km): +2025-04-22 20:57:18,524 - INFO - Mean: 4.7899 +2025-04-22 20:57:18,524 - INFO - Median: 4.2255 +2025-04-22 20:57:18,524 - INFO - Min: 0.3763 +2025-04-22 20:57:18,524 - INFO - Max: 18.8528 +2025-04-22 20:57:18,524 - INFO - Std: 2.8893 +2025-04-22 20:57:18,524 - INFO - OC Distribution Statistics: +2025-04-22 20:57:18,524 - INFO - Validation Mean: 17.3772 +2025-04-22 20:57:18,524 - INFO - Validation Std: 8.9271 +2025-04-22 20:57:18,524 - INFO - Training Mean: 23.0004 +2025-04-22 20:57:18,524 - INFO - Training Std: 20.3076 +2025-04-22 20:57:18,524 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 20:57:18,524 - INFO - KS Statistic: 0.1538 +2025-04-22 20:57:18,524 - INFO - P-value: 0.0000 +2025-04-22 20:57:18,524 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 20:57:18,524 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 20:57:18,962 - INFO - Validation and Training Set Split Statistics +2025-04-22 20:57:18,962 - INFO - Timestamp: 2025-04-22 20:57:18 +2025-04-22 20:57:18,962 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 20:57:18,962 - INFO - Training set size: 15123 (91.58%) +2025-04-22 20:57:18,962 - INFO - Minimum Distance Statistics (km): +2025-04-22 20:57:18,962 - INFO - Mean: 4.7899 +2025-04-22 20:57:18,962 - INFO - Median: 4.2255 +2025-04-22 20:57:18,962 - INFO - Min: 0.3763 +2025-04-22 20:57:18,962 - INFO - Max: 18.8528 +2025-04-22 20:57:18,962 - INFO - Std: 2.8893 +2025-04-22 20:57:18,962 - INFO - OC Distribution Statistics: +2025-04-22 20:57:18,962 - INFO - Validation Mean: 17.3772 +2025-04-22 20:57:18,962 - INFO - Validation Std: 8.9271 +2025-04-22 20:57:18,962 - INFO - Training Mean: 23.0004 +2025-04-22 20:57:18,962 - INFO - Training Std: 20.3076 +2025-04-22 20:57:18,962 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 20:57:18,962 - INFO - KS Statistic: 0.1538 +2025-04-22 20:57:18,962 - INFO - P-value: 0.0000 +2025-04-22 20:57:18,962 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 20:57:18,962 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 20:57:19,124 - INFO - Validation and Training Set Split Statistics +2025-04-22 20:57:19,124 - INFO - Timestamp: 2025-04-22 20:57:19 +2025-04-22 20:57:19,124 - INFO - Validation set size: 1391 (8.42%) +2025-04-22 20:57:19,124 - INFO - Training set size: 15123 (91.58%) +2025-04-22 20:57:19,124 - INFO - Minimum Distance Statistics (km): +2025-04-22 20:57:19,125 - INFO - Mean: 4.7899 +2025-04-22 20:57:19,125 - INFO - Median: 4.2255 +2025-04-22 20:57:19,125 - INFO - Min: 0.3763 +2025-04-22 20:57:19,125 - INFO - Max: 18.8528 +2025-04-22 20:57:19,125 - INFO - Std: 2.8893 +2025-04-22 20:57:19,125 - INFO - OC Distribution Statistics: +2025-04-22 20:57:19,125 - INFO - Validation Mean: 17.3772 +2025-04-22 20:57:19,125 - INFO - Validation Std: 8.9271 +2025-04-22 20:57:19,125 - INFO - Training Mean: 23.0004 +2025-04-22 20:57:19,125 - INFO - Training Std: 20.3076 +2025-04-22 20:57:19,125 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 20:57:19,125 - INFO - KS Statistic: 0.1538 +2025-04-22 20:57:19,125 - INFO - P-value: 0.0000 +2025-04-22 20:57:19,125 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 20:57:19,125 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:14:59,019 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:14:59,019 - INFO - Timestamp: 2025-04-23 15:14:59 +2025-04-23 15:14:59,019 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:14:59,019 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:14:59,019 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:14:59,019 - INFO - Mean: 4.7899 +2025-04-23 15:14:59,019 - INFO - Median: 4.2255 +2025-04-23 15:14:59,019 - INFO - Min: 0.3763 +2025-04-23 15:14:59,019 - INFO - Max: 18.8528 +2025-04-23 15:14:59,019 - INFO - Std: 2.8893 +2025-04-23 15:14:59,019 - INFO - OC Distribution Statistics: +2025-04-23 15:14:59,020 - INFO - Validation Mean: 17.3772 +2025-04-23 15:14:59,020 - INFO - Validation Std: 8.9271 +2025-04-23 15:14:59,020 - INFO - Training Mean: 23.0004 +2025-04-23 15:14:59,020 - INFO - Training Std: 20.3076 +2025-04-23 15:14:59,020 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:14:59,020 - INFO - KS Statistic: 0.1538 +2025-04-23 15:14:59,020 - INFO - P-value: 0.0000 +2025-04-23 15:14:59,020 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:14:59,020 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:14:59,931 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:14:59,931 - INFO - Timestamp: 2025-04-23 15:14:59 +2025-04-23 15:14:59,931 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:14:59,931 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:14:59,931 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:14:59,931 - INFO - Mean: 4.7899 +2025-04-23 15:14:59,931 - INFO - Median: 4.2255 +2025-04-23 15:14:59,931 - INFO - Min: 0.3763 +2025-04-23 15:14:59,931 - INFO - Max: 18.8528 +2025-04-23 15:14:59,931 - INFO - Std: 2.8893 +2025-04-23 15:14:59,931 - INFO - OC Distribution Statistics: +2025-04-23 15:14:59,931 - INFO - Validation Mean: 17.3772 +2025-04-23 15:14:59,931 - INFO - Validation Std: 8.9271 +2025-04-23 15:14:59,931 - INFO - Training Mean: 23.0004 +2025-04-23 15:14:59,931 - INFO - Training Std: 20.3076 +2025-04-23 15:14:59,931 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:14:59,931 - INFO - KS Statistic: 0.1538 +2025-04-23 15:14:59,932 - INFO - P-value: 0.0000 +2025-04-23 15:14:59,932 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:14:59,932 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:15:00,352 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:15:00,352 - INFO - Timestamp: 2025-04-23 15:15:00 +2025-04-23 15:15:00,352 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:15:00,352 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:15:00,352 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:15:00,352 - INFO - Mean: 4.7899 +2025-04-23 15:15:00,352 - INFO - Median: 4.2255 +2025-04-23 15:15:00,352 - INFO - Min: 0.3763 +2025-04-23 15:15:00,352 - INFO - Max: 18.8528 +2025-04-23 15:15:00,352 - INFO - Std: 2.8893 +2025-04-23 15:15:00,352 - INFO - OC Distribution Statistics: +2025-04-23 15:15:00,352 - INFO - Validation Mean: 17.3772 +2025-04-23 15:15:00,352 - INFO - Validation Std: 8.9271 +2025-04-23 15:15:00,352 - INFO - Training Mean: 23.0004 +2025-04-23 15:15:00,352 - INFO - Training Std: 20.3076 +2025-04-23 15:15:00,352 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:15:00,352 - INFO - KS Statistic: 0.1538 +2025-04-23 15:15:00,352 - INFO - P-value: 0.0000 +2025-04-23 15:15:00,352 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:15:00,352 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:15:00,576 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:15:00,576 - INFO - Timestamp: 2025-04-23 15:15:00 +2025-04-23 15:15:00,576 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:15:00,576 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:15:00,576 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:15:00,576 - INFO - Mean: 4.7899 +2025-04-23 15:15:00,576 - INFO - Median: 4.2255 +2025-04-23 15:15:00,576 - INFO - Min: 0.3763 +2025-04-23 15:15:00,576 - INFO - Max: 18.8528 +2025-04-23 15:15:00,576 - INFO - Std: 2.8893 +2025-04-23 15:15:00,576 - INFO - OC Distribution Statistics: +2025-04-23 15:15:00,576 - INFO - Validation Mean: 17.3772 +2025-04-23 15:15:00,576 - INFO - Validation Std: 8.9271 +2025-04-23 15:15:00,576 - INFO - Training Mean: 23.0004 +2025-04-23 15:15:00,576 - INFO - Training Std: 20.3076 +2025-04-23 15:15:00,576 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:15:00,576 - INFO - KS Statistic: 0.1538 +2025-04-23 15:15:00,576 - INFO - P-value: 0.0000 +2025-04-23 15:15:00,576 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:15:00,576 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:15:02,525 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:15:02,525 - INFO - Timestamp: 2025-04-23 15:15:02 +2025-04-23 15:15:02,525 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:15:02,525 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:15:02,525 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:15:02,525 - INFO - Mean: 4.7899 +2025-04-23 15:15:02,525 - INFO - Median: 4.2255 +2025-04-23 15:15:02,525 - INFO - Min: 0.3763 +2025-04-23 15:15:02,525 - INFO - Max: 18.8528 +2025-04-23 15:15:02,525 - INFO - Std: 2.8893 +2025-04-23 15:15:02,525 - INFO - OC Distribution Statistics: +2025-04-23 15:15:02,525 - INFO - Validation Mean: 17.3772 +2025-04-23 15:15:02,525 - INFO - Validation Std: 8.9271 +2025-04-23 15:15:02,525 - INFO - Training Mean: 23.0004 +2025-04-23 15:15:02,525 - INFO - Training Std: 20.3076 +2025-04-23 15:15:02,525 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:15:02,525 - INFO - KS Statistic: 0.1538 +2025-04-23 15:15:02,525 - INFO - P-value: 0.0000 +2025-04-23 15:15:02,525 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:15:02,525 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:15:04,682 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:15:04,682 - INFO - Timestamp: 2025-04-23 15:15:04 +2025-04-23 15:15:04,682 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:15:04,682 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:15:04,682 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:15:04,682 - INFO - Mean: 4.7899 +2025-04-23 15:15:04,682 - INFO - Median: 4.2255 +2025-04-23 15:15:04,682 - INFO - Min: 0.3763 +2025-04-23 15:15:04,682 - INFO - Max: 18.8528 +2025-04-23 15:15:04,682 - INFO - Std: 2.8893 +2025-04-23 15:15:04,682 - INFO - OC Distribution Statistics: +2025-04-23 15:15:04,682 - INFO - Validation Mean: 17.3772 +2025-04-23 15:15:04,682 - INFO - Validation Std: 8.9271 +2025-04-23 15:15:04,682 - INFO - Training Mean: 23.0004 +2025-04-23 15:15:04,682 - INFO - Training Std: 20.3076 +2025-04-23 15:15:04,682 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:15:04,682 - INFO - KS Statistic: 0.1538 +2025-04-23 15:15:04,682 - INFO - P-value: 0.0000 +2025-04-23 15:15:04,682 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:15:04,682 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:15:04,816 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:15:04,816 - INFO - Timestamp: 2025-04-23 15:15:04 +2025-04-23 15:15:04,816 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:15:04,816 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:15:04,816 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:15:04,816 - INFO - Mean: 4.7899 +2025-04-23 15:15:04,816 - INFO - Median: 4.2255 +2025-04-23 15:15:04,816 - INFO - Min: 0.3763 +2025-04-23 15:15:04,816 - INFO - Max: 18.8528 +2025-04-23 15:15:04,816 - INFO - Std: 2.8893 +2025-04-23 15:15:04,816 - INFO - OC Distribution Statistics: +2025-04-23 15:15:04,816 - INFO - Validation Mean: 17.3772 +2025-04-23 15:15:04,816 - INFO - Validation Std: 8.9271 +2025-04-23 15:15:04,816 - INFO - Training Mean: 23.0004 +2025-04-23 15:15:04,816 - INFO - Training Std: 20.3076 +2025-04-23 15:15:04,816 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:15:04,816 - INFO - KS Statistic: 0.1538 +2025-04-23 15:15:04,816 - INFO - P-value: 0.0000 +2025-04-23 15:15:04,816 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:15:04,816 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:15:04,864 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:15:04,864 - INFO - Timestamp: 2025-04-23 15:15:04 +2025-04-23 15:15:04,864 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:15:04,864 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:15:04,864 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:15:04,864 - INFO - Mean: 4.7899 +2025-04-23 15:15:04,864 - INFO - Median: 4.2255 +2025-04-23 15:15:04,864 - INFO - Min: 0.3763 +2025-04-23 15:15:04,864 - INFO - Max: 18.8528 +2025-04-23 15:15:04,864 - INFO - Std: 2.8893 +2025-04-23 15:15:04,864 - INFO - OC Distribution Statistics: +2025-04-23 15:15:04,864 - INFO - Validation Mean: 17.3772 +2025-04-23 15:15:04,864 - INFO - Validation Std: 8.9271 +2025-04-23 15:15:04,864 - INFO - Training Mean: 23.0004 +2025-04-23 15:15:04,864 - INFO - Training Std: 20.3076 +2025-04-23 15:15:04,864 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:15:04,865 - INFO - KS Statistic: 0.1538 +2025-04-23 15:15:04,865 - INFO - P-value: 0.0000 +2025-04-23 15:15:04,865 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:15:04,865 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:26:43,513 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:26:43,513 - INFO - Timestamp: 2025-04-23 15:26:43 +2025-04-23 15:26:43,513 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:26:43,513 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:26:43,513 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:26:43,513 - INFO - Mean: 4.7899 +2025-04-23 15:26:43,513 - INFO - Median: 4.2255 +2025-04-23 15:26:43,513 - INFO - Min: 0.3763 +2025-04-23 15:26:43,513 - INFO - Max: 18.8528 +2025-04-23 15:26:43,513 - INFO - Std: 2.8893 +2025-04-23 15:26:43,513 - INFO - OC Distribution Statistics: +2025-04-23 15:26:43,513 - INFO - Validation Mean: 17.3772 +2025-04-23 15:26:43,513 - INFO - Validation Std: 8.9271 +2025-04-23 15:26:43,514 - INFO - Training Mean: 23.0004 +2025-04-23 15:26:43,514 - INFO - Training Std: 20.3076 +2025-04-23 15:26:43,514 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:26:43,514 - INFO - KS Statistic: 0.1538 +2025-04-23 15:26:43,514 - INFO - P-value: 0.0000 +2025-04-23 15:26:43,514 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:26:43,514 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:26:45,764 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:26:45,765 - INFO - Timestamp: 2025-04-23 15:26:45 +2025-04-23 15:26:45,765 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:26:45,765 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:26:45,765 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:26:45,765 - INFO - Mean: 4.7899 +2025-04-23 15:26:45,765 - INFO - Median: 4.2255 +2025-04-23 15:26:45,765 - INFO - Min: 0.3763 +2025-04-23 15:26:45,765 - INFO - Max: 18.8528 +2025-04-23 15:26:45,765 - INFO - Std: 2.8893 +2025-04-23 15:26:45,765 - INFO - OC Distribution Statistics: +2025-04-23 15:26:45,765 - INFO - Validation Mean: 17.3772 +2025-04-23 15:26:45,765 - INFO - Validation Std: 8.9271 +2025-04-23 15:26:45,765 - INFO - Training Mean: 23.0004 +2025-04-23 15:26:45,765 - INFO - Training Std: 20.3076 +2025-04-23 15:26:45,765 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:26:45,765 - INFO - KS Statistic: 0.1538 +2025-04-23 15:26:45,765 - INFO - P-value: 0.0000 +2025-04-23 15:26:45,765 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:26:45,765 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:26:46,208 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:26:46,208 - INFO - Timestamp: 2025-04-23 15:26:46 +2025-04-23 15:26:46,208 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:26:46,208 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:26:46,208 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:26:46,208 - INFO - Mean: 4.7899 +2025-04-23 15:26:46,208 - INFO - Median: 4.2255 +2025-04-23 15:26:46,208 - INFO - Min: 0.3763 +2025-04-23 15:26:46,208 - INFO - Max: 18.8528 +2025-04-23 15:26:46,208 - INFO - Std: 2.8893 +2025-04-23 15:26:46,208 - INFO - OC Distribution Statistics: +2025-04-23 15:26:46,208 - INFO - Validation Mean: 17.3772 +2025-04-23 15:26:46,208 - INFO - Validation Std: 8.9271 +2025-04-23 15:26:46,208 - INFO - Training Mean: 23.0004 +2025-04-23 15:26:46,208 - INFO - Training Std: 20.3076 +2025-04-23 15:26:46,208 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:26:46,208 - INFO - KS Statistic: 0.1538 +2025-04-23 15:26:46,208 - INFO - P-value: 0.0000 +2025-04-23 15:26:46,208 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:26:46,208 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:26:46,269 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:26:46,269 - INFO - Timestamp: 2025-04-23 15:26:46 +2025-04-23 15:26:46,269 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:26:46,269 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:26:46,269 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:26:46,269 - INFO - Mean: 4.7899 +2025-04-23 15:26:46,269 - INFO - Median: 4.2255 +2025-04-23 15:26:46,269 - INFO - Min: 0.3763 +2025-04-23 15:26:46,269 - INFO - Max: 18.8528 +2025-04-23 15:26:46,269 - INFO - Std: 2.8893 +2025-04-23 15:26:46,269 - INFO - OC Distribution Statistics: +2025-04-23 15:26:46,269 - INFO - Validation Mean: 17.3772 +2025-04-23 15:26:46,269 - INFO - Validation Std: 8.9271 +2025-04-23 15:26:46,269 - INFO - Training Mean: 23.0004 +2025-04-23 15:26:46,269 - INFO - Training Std: 20.3076 +2025-04-23 15:26:46,269 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:26:46,269 - INFO - KS Statistic: 0.1538 +2025-04-23 15:26:46,269 - INFO - P-value: 0.0000 +2025-04-23 15:26:46,269 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:26:46,269 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:26:48,725 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:26:48,725 - INFO - Timestamp: 2025-04-23 15:26:48 +2025-04-23 15:26:48,725 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:26:48,725 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:26:48,725 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:26:48,725 - INFO - Mean: 4.7899 +2025-04-23 15:26:48,725 - INFO - Median: 4.2255 +2025-04-23 15:26:48,725 - INFO - Min: 0.3763 +2025-04-23 15:26:48,725 - INFO - Max: 18.8528 +2025-04-23 15:26:48,725 - INFO - Std: 2.8893 +2025-04-23 15:26:48,725 - INFO - OC Distribution Statistics: +2025-04-23 15:26:48,725 - INFO - Validation Mean: 17.3772 +2025-04-23 15:26:48,725 - INFO - Validation Std: 8.9271 +2025-04-23 15:26:48,725 - INFO - Training Mean: 23.0004 +2025-04-23 15:26:48,725 - INFO - Training Std: 20.3076 +2025-04-23 15:26:48,725 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:26:48,725 - INFO - KS Statistic: 0.1538 +2025-04-23 15:26:48,725 - INFO - P-value: 0.0000 +2025-04-23 15:26:48,725 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:26:48,725 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:26:49,583 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:26:49,583 - INFO - Timestamp: 2025-04-23 15:26:49 +2025-04-23 15:26:49,584 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:26:49,584 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:26:49,584 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:26:49,584 - INFO - Mean: 4.7899 +2025-04-23 15:26:49,584 - INFO - Median: 4.2255 +2025-04-23 15:26:49,584 - INFO - Min: 0.3763 +2025-04-23 15:26:49,584 - INFO - Max: 18.8528 +2025-04-23 15:26:49,584 - INFO - Std: 2.8893 +2025-04-23 15:26:49,584 - INFO - OC Distribution Statistics: +2025-04-23 15:26:49,584 - INFO - Validation Mean: 17.3772 +2025-04-23 15:26:49,584 - INFO - Validation Std: 8.9271 +2025-04-23 15:26:49,584 - INFO - Training Mean: 23.0004 +2025-04-23 15:26:49,584 - INFO - Training Std: 20.3076 +2025-04-23 15:26:49,584 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:26:49,584 - INFO - KS Statistic: 0.1538 +2025-04-23 15:26:49,584 - INFO - P-value: 0.0000 +2025-04-23 15:26:49,584 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:26:49,584 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:26:51,020 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:26:51,020 - INFO - Timestamp: 2025-04-23 15:26:51 +2025-04-23 15:26:51,020 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:26:51,020 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:26:51,020 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:26:51,020 - INFO - Mean: 4.7899 +2025-04-23 15:26:51,020 - INFO - Median: 4.2255 +2025-04-23 15:26:51,020 - INFO - Min: 0.3763 +2025-04-23 15:26:51,020 - INFO - Max: 18.8528 +2025-04-23 15:26:51,020 - INFO - Std: 2.8893 +2025-04-23 15:26:51,020 - INFO - OC Distribution Statistics: +2025-04-23 15:26:51,021 - INFO - Validation Mean: 17.3772 +2025-04-23 15:26:51,021 - INFO - Validation Std: 8.9271 +2025-04-23 15:26:51,021 - INFO - Training Mean: 23.0004 +2025-04-23 15:26:51,021 - INFO - Training Std: 20.3076 +2025-04-23 15:26:51,021 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:26:51,021 - INFO - KS Statistic: 0.1538 +2025-04-23 15:26:51,021 - INFO - P-value: 0.0000 +2025-04-23 15:26:51,021 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:26:51,021 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:26:51,215 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:26:51,215 - INFO - Timestamp: 2025-04-23 15:26:51 +2025-04-23 15:26:51,215 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:26:51,215 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:26:51,215 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:26:51,215 - INFO - Mean: 4.7899 +2025-04-23 15:26:51,215 - INFO - Median: 4.2255 +2025-04-23 15:26:51,215 - INFO - Min: 0.3763 +2025-04-23 15:26:51,215 - INFO - Max: 18.8528 +2025-04-23 15:26:51,215 - INFO - Std: 2.8893 +2025-04-23 15:26:51,215 - INFO - OC Distribution Statistics: +2025-04-23 15:26:51,215 - INFO - Validation Mean: 17.3772 +2025-04-23 15:26:51,215 - INFO - Validation Std: 8.9271 +2025-04-23 15:26:51,215 - INFO - Training Mean: 23.0004 +2025-04-23 15:26:51,215 - INFO - Training Std: 20.3076 +2025-04-23 15:26:51,215 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:26:51,215 - INFO - KS Statistic: 0.1538 +2025-04-23 15:26:51,215 - INFO - P-value: 0.0000 +2025-04-23 15:26:51,215 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:26:51,215 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:38:32,199 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:38:32,200 - INFO - Timestamp: 2025-04-23 15:38:32 +2025-04-23 15:38:32,200 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:38:32,200 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:38:32,200 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:38:32,200 - INFO - Mean: 4.7899 +2025-04-23 15:38:32,200 - INFO - Median: 4.2255 +2025-04-23 15:38:32,200 - INFO - Min: 0.3763 +2025-04-23 15:38:32,200 - INFO - Max: 18.8528 +2025-04-23 15:38:32,200 - INFO - Std: 2.8893 +2025-04-23 15:38:32,200 - INFO - OC Distribution Statistics: +2025-04-23 15:38:32,200 - INFO - Validation Mean: 17.3772 +2025-04-23 15:38:32,200 - INFO - Validation Std: 8.9271 +2025-04-23 15:38:32,200 - INFO - Training Mean: 23.0004 +2025-04-23 15:38:32,200 - INFO - Training Std: 20.3076 +2025-04-23 15:38:32,200 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:38:32,200 - INFO - KS Statistic: 0.1538 +2025-04-23 15:38:32,200 - INFO - P-value: 0.0000 +2025-04-23 15:38:32,200 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:38:32,200 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:38:33,362 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:38:33,363 - INFO - Timestamp: 2025-04-23 15:38:33 +2025-04-23 15:38:33,363 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:38:33,363 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:38:33,363 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:38:33,363 - INFO - Mean: 4.7899 +2025-04-23 15:38:33,363 - INFO - Median: 4.2255 +2025-04-23 15:38:33,363 - INFO - Min: 0.3763 +2025-04-23 15:38:33,363 - INFO - Max: 18.8528 +2025-04-23 15:38:33,363 - INFO - Std: 2.8893 +2025-04-23 15:38:33,363 - INFO - OC Distribution Statistics: +2025-04-23 15:38:33,363 - INFO - Validation Mean: 17.3772 +2025-04-23 15:38:33,363 - INFO - Validation Std: 8.9271 +2025-04-23 15:38:33,363 - INFO - Training Mean: 23.0004 +2025-04-23 15:38:33,363 - INFO - Training Std: 20.3076 +2025-04-23 15:38:33,363 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:38:33,363 - INFO - KS Statistic: 0.1538 +2025-04-23 15:38:33,363 - INFO - P-value: 0.0000 +2025-04-23 15:38:33,363 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:38:33,363 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:38:34,591 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:38:34,591 - INFO - Timestamp: 2025-04-23 15:38:34 +2025-04-23 15:38:34,591 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:38:34,591 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:38:34,591 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:38:34,591 - INFO - Mean: 4.7899 +2025-04-23 15:38:34,591 - INFO - Median: 4.2255 +2025-04-23 15:38:34,591 - INFO - Min: 0.3763 +2025-04-23 15:38:34,591 - INFO - Max: 18.8528 +2025-04-23 15:38:34,591 - INFO - Std: 2.8893 +2025-04-23 15:38:34,591 - INFO - OC Distribution Statistics: +2025-04-23 15:38:34,591 - INFO - Validation Mean: 17.3772 +2025-04-23 15:38:34,591 - INFO - Validation Std: 8.9271 +2025-04-23 15:38:34,591 - INFO - Training Mean: 23.0004 +2025-04-23 15:38:34,591 - INFO - Training Std: 20.3076 +2025-04-23 15:38:34,591 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:38:34,591 - INFO - KS Statistic: 0.1538 +2025-04-23 15:38:34,591 - INFO - P-value: 0.0000 +2025-04-23 15:38:34,591 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:38:34,591 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:38:35,037 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:38:35,037 - INFO - Timestamp: 2025-04-23 15:38:35 +2025-04-23 15:38:35,037 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:38:35,037 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:38:35,037 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:38:35,037 - INFO - Mean: 4.7899 +2025-04-23 15:38:35,037 - INFO - Median: 4.2255 +2025-04-23 15:38:35,037 - INFO - Min: 0.3763 +2025-04-23 15:38:35,037 - INFO - Max: 18.8528 +2025-04-23 15:38:35,037 - INFO - Std: 2.8893 +2025-04-23 15:38:35,037 - INFO - OC Distribution Statistics: +2025-04-23 15:38:35,037 - INFO - Validation Mean: 17.3772 +2025-04-23 15:38:35,037 - INFO - Validation Std: 8.9271 +2025-04-23 15:38:35,037 - INFO - Training Mean: 23.0004 +2025-04-23 15:38:35,037 - INFO - Training Std: 20.3076 +2025-04-23 15:38:35,037 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:38:35,037 - INFO - KS Statistic: 0.1538 +2025-04-23 15:38:35,038 - INFO - P-value: 0.0000 +2025-04-23 15:38:35,038 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:38:35,038 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:38:36,074 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:38:36,075 - INFO - Timestamp: 2025-04-23 15:38:36 +2025-04-23 15:38:36,075 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:38:36,075 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:38:36,075 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:38:36,075 - INFO - Mean: 4.7899 +2025-04-23 15:38:36,075 - INFO - Median: 4.2255 +2025-04-23 15:38:36,075 - INFO - Min: 0.3763 +2025-04-23 15:38:36,075 - INFO - Max: 18.8528 +2025-04-23 15:38:36,075 - INFO - Std: 2.8893 +2025-04-23 15:38:36,075 - INFO - OC Distribution Statistics: +2025-04-23 15:38:36,075 - INFO - Validation Mean: 17.3772 +2025-04-23 15:38:36,075 - INFO - Validation Std: 8.9271 +2025-04-23 15:38:36,075 - INFO - Training Mean: 23.0004 +2025-04-23 15:38:36,075 - INFO - Training Std: 20.3076 +2025-04-23 15:38:36,075 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:38:36,075 - INFO - KS Statistic: 0.1538 +2025-04-23 15:38:36,075 - INFO - P-value: 0.0000 +2025-04-23 15:38:36,075 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:38:36,075 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:38:37,609 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:38:37,609 - INFO - Timestamp: 2025-04-23 15:38:37 +2025-04-23 15:38:37,609 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:38:37,609 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:38:37,610 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:38:37,610 - INFO - Mean: 4.7899 +2025-04-23 15:38:37,610 - INFO - Median: 4.2255 +2025-04-23 15:38:37,610 - INFO - Min: 0.3763 +2025-04-23 15:38:37,610 - INFO - Max: 18.8528 +2025-04-23 15:38:37,610 - INFO - Std: 2.8893 +2025-04-23 15:38:37,610 - INFO - OC Distribution Statistics: +2025-04-23 15:38:37,610 - INFO - Validation Mean: 17.3772 +2025-04-23 15:38:37,610 - INFO - Validation Std: 8.9271 +2025-04-23 15:38:37,610 - INFO - Training Mean: 23.0004 +2025-04-23 15:38:37,610 - INFO - Training Std: 20.3076 +2025-04-23 15:38:37,610 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:38:37,610 - INFO - KS Statistic: 0.1538 +2025-04-23 15:38:37,610 - INFO - P-value: 0.0000 +2025-04-23 15:38:37,610 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:38:37,610 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:38:38,403 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:38:38,403 - INFO - Timestamp: 2025-04-23 15:38:38 +2025-04-23 15:38:38,403 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:38:38,403 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:38:38,403 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:38:38,403 - INFO - Mean: 4.7899 +2025-04-23 15:38:38,403 - INFO - Median: 4.2255 +2025-04-23 15:38:38,403 - INFO - Min: 0.3763 +2025-04-23 15:38:38,403 - INFO - Max: 18.8528 +2025-04-23 15:38:38,403 - INFO - Std: 2.8893 +2025-04-23 15:38:38,403 - INFO - OC Distribution Statistics: +2025-04-23 15:38:38,403 - INFO - Validation Mean: 17.3772 +2025-04-23 15:38:38,403 - INFO - Validation Std: 8.9271 +2025-04-23 15:38:38,403 - INFO - Training Mean: 23.0004 +2025-04-23 15:38:38,403 - INFO - Training Std: 20.3076 +2025-04-23 15:38:38,403 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:38:38,403 - INFO - KS Statistic: 0.1538 +2025-04-23 15:38:38,403 - INFO - P-value: 0.0000 +2025-04-23 15:38:38,403 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:38:38,403 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:38:40,665 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:38:40,665 - INFO - Timestamp: 2025-04-23 15:38:40 +2025-04-23 15:38:40,666 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:38:40,666 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:38:40,666 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:38:40,666 - INFO - Mean: 4.7899 +2025-04-23 15:38:40,666 - INFO - Median: 4.2255 +2025-04-23 15:38:40,666 - INFO - Min: 0.3763 +2025-04-23 15:38:40,666 - INFO - Max: 18.8528 +2025-04-23 15:38:40,666 - INFO - Std: 2.8893 +2025-04-23 15:38:40,666 - INFO - OC Distribution Statistics: +2025-04-23 15:38:40,666 - INFO - Validation Mean: 17.3772 +2025-04-23 15:38:40,666 - INFO - Validation Std: 8.9271 +2025-04-23 15:38:40,666 - INFO - Training Mean: 23.0004 +2025-04-23 15:38:40,666 - INFO - Training Std: 20.3076 +2025-04-23 15:38:40,666 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:38:40,666 - INFO - KS Statistic: 0.1538 +2025-04-23 15:38:40,666 - INFO - P-value: 0.0000 +2025-04-23 15:38:40,666 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:38:40,666 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:45:35,777 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:45:35,778 - INFO - Timestamp: 2025-04-23 15:45:35 +2025-04-23 15:45:35,778 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:45:35,778 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:45:35,778 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:45:35,778 - INFO - Mean: 4.7899 +2025-04-23 15:45:35,778 - INFO - Median: 4.2255 +2025-04-23 15:45:35,778 - INFO - Min: 0.3763 +2025-04-23 15:45:35,778 - INFO - Max: 18.8528 +2025-04-23 15:45:35,778 - INFO - Std: 2.8893 +2025-04-23 15:45:35,778 - INFO - OC Distribution Statistics: +2025-04-23 15:45:35,778 - INFO - Validation Mean: 17.3772 +2025-04-23 15:45:35,778 - INFO - Validation Std: 8.9271 +2025-04-23 15:45:35,778 - INFO - Training Mean: 23.0004 +2025-04-23 15:45:35,778 - INFO - Training Std: 20.3076 +2025-04-23 15:45:35,778 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:45:35,778 - INFO - KS Statistic: 0.1538 +2025-04-23 15:45:35,778 - INFO - P-value: 0.0000 +2025-04-23 15:45:35,778 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:45:35,778 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:45:36,901 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:45:36,902 - INFO - Timestamp: 2025-04-23 15:45:36 +2025-04-23 15:45:36,902 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:45:36,902 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:45:36,902 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:45:36,902 - INFO - Mean: 4.7899 +2025-04-23 15:45:36,902 - INFO - Median: 4.2255 +2025-04-23 15:45:36,902 - INFO - Min: 0.3763 +2025-04-23 15:45:36,902 - INFO - Max: 18.8528 +2025-04-23 15:45:36,902 - INFO - Std: 2.8893 +2025-04-23 15:45:36,902 - INFO - OC Distribution Statistics: +2025-04-23 15:45:36,902 - INFO - Validation Mean: 17.3772 +2025-04-23 15:45:36,902 - INFO - Validation Std: 8.9271 +2025-04-23 15:45:36,902 - INFO - Training Mean: 23.0004 +2025-04-23 15:45:36,902 - INFO - Training Std: 20.3076 +2025-04-23 15:45:36,902 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:45:36,902 - INFO - KS Statistic: 0.1538 +2025-04-23 15:45:36,902 - INFO - P-value: 0.0000 +2025-04-23 15:45:36,902 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:45:36,902 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:45:37,714 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:45:37,714 - INFO - Timestamp: 2025-04-23 15:45:37 +2025-04-23 15:45:37,714 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:45:37,714 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:45:37,714 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:45:37,714 - INFO - Mean: 4.7899 +2025-04-23 15:45:37,714 - INFO - Median: 4.2255 +2025-04-23 15:45:37,714 - INFO - Min: 0.3763 +2025-04-23 15:45:37,714 - INFO - Max: 18.8528 +2025-04-23 15:45:37,714 - INFO - Std: 2.8893 +2025-04-23 15:45:37,714 - INFO - OC Distribution Statistics: +2025-04-23 15:45:37,714 - INFO - Validation Mean: 17.3772 +2025-04-23 15:45:37,714 - INFO - Validation Std: 8.9271 +2025-04-23 15:45:37,714 - INFO - Training Mean: 23.0004 +2025-04-23 15:45:37,714 - INFO - Training Std: 20.3076 +2025-04-23 15:45:37,714 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:45:37,714 - INFO - KS Statistic: 0.1538 +2025-04-23 15:45:37,714 - INFO - P-value: 0.0000 +2025-04-23 15:45:37,714 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:45:37,714 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:45:38,092 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:45:38,092 - INFO - Timestamp: 2025-04-23 15:45:38 +2025-04-23 15:45:38,092 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:45:38,093 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:45:38,093 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:45:38,093 - INFO - Mean: 4.7899 +2025-04-23 15:45:38,093 - INFO - Median: 4.2255 +2025-04-23 15:45:38,093 - INFO - Min: 0.3763 +2025-04-23 15:45:38,093 - INFO - Max: 18.8528 +2025-04-23 15:45:38,093 - INFO - Std: 2.8893 +2025-04-23 15:45:38,093 - INFO - OC Distribution Statistics: +2025-04-23 15:45:38,093 - INFO - Validation Mean: 17.3772 +2025-04-23 15:45:38,093 - INFO - Validation Std: 8.9271 +2025-04-23 15:45:38,093 - INFO - Training Mean: 23.0004 +2025-04-23 15:45:38,093 - INFO - Training Std: 20.3076 +2025-04-23 15:45:38,093 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:45:38,093 - INFO - KS Statistic: 0.1538 +2025-04-23 15:45:38,093 - INFO - P-value: 0.0000 +2025-04-23 15:45:38,093 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:45:38,093 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:45:39,811 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:45:39,811 - INFO - Timestamp: 2025-04-23 15:45:39 +2025-04-23 15:45:39,811 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:45:39,811 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:45:39,811 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:45:39,811 - INFO - Mean: 4.7899 +2025-04-23 15:45:39,811 - INFO - Median: 4.2255 +2025-04-23 15:45:39,811 - INFO - Min: 0.3763 +2025-04-23 15:45:39,811 - INFO - Max: 18.8528 +2025-04-23 15:45:39,811 - INFO - Std: 2.8893 +2025-04-23 15:45:39,811 - INFO - OC Distribution Statistics: +2025-04-23 15:45:39,811 - INFO - Validation Mean: 17.3772 +2025-04-23 15:45:39,811 - INFO - Validation Std: 8.9271 +2025-04-23 15:45:39,811 - INFO - Training Mean: 23.0004 +2025-04-23 15:45:39,811 - INFO - Training Std: 20.3076 +2025-04-23 15:45:39,811 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:45:39,811 - INFO - KS Statistic: 0.1538 +2025-04-23 15:45:39,811 - INFO - P-value: 0.0000 +2025-04-23 15:45:39,811 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:45:39,811 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:45:41,091 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:45:41,091 - INFO - Timestamp: 2025-04-23 15:45:41 +2025-04-23 15:45:41,091 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:45:41,091 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:45:41,091 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:45:41,091 - INFO - Mean: 4.7899 +2025-04-23 15:45:41,091 - INFO - Median: 4.2255 +2025-04-23 15:45:41,091 - INFO - Min: 0.3763 +2025-04-23 15:45:41,091 - INFO - Max: 18.8528 +2025-04-23 15:45:41,092 - INFO - Std: 2.8893 +2025-04-23 15:45:41,092 - INFO - OC Distribution Statistics: +2025-04-23 15:45:41,092 - INFO - Validation Mean: 17.3772 +2025-04-23 15:45:41,092 - INFO - Validation Std: 8.9271 +2025-04-23 15:45:41,092 - INFO - Training Mean: 23.0004 +2025-04-23 15:45:41,092 - INFO - Training Std: 20.3076 +2025-04-23 15:45:41,092 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:45:41,092 - INFO - KS Statistic: 0.1538 +2025-04-23 15:45:41,092 - INFO - P-value: 0.0000 +2025-04-23 15:45:41,092 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:45:41,092 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:45:41,726 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:45:41,726 - INFO - Timestamp: 2025-04-23 15:45:41 +2025-04-23 15:45:41,726 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:45:41,726 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:45:41,726 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:45:41,726 - INFO - Mean: 4.7899 +2025-04-23 15:45:41,726 - INFO - Median: 4.2255 +2025-04-23 15:45:41,726 - INFO - Min: 0.3763 +2025-04-23 15:45:41,726 - INFO - Max: 18.8528 +2025-04-23 15:45:41,726 - INFO - Std: 2.8893 +2025-04-23 15:45:41,726 - INFO - OC Distribution Statistics: +2025-04-23 15:45:41,726 - INFO - Validation Mean: 17.3772 +2025-04-23 15:45:41,726 - INFO - Validation Std: 8.9271 +2025-04-23 15:45:41,726 - INFO - Training Mean: 23.0004 +2025-04-23 15:45:41,726 - INFO - Training Std: 20.3076 +2025-04-23 15:45:41,727 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:45:41,727 - INFO - KS Statistic: 0.1538 +2025-04-23 15:45:41,727 - INFO - P-value: 0.0000 +2025-04-23 15:45:41,727 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:45:41,727 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:45:45,625 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:45:45,626 - INFO - Timestamp: 2025-04-23 15:45:45 +2025-04-23 15:45:45,626 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:45:45,626 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:45:45,626 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:45:45,626 - INFO - Mean: 4.7899 +2025-04-23 15:45:45,626 - INFO - Median: 4.2255 +2025-04-23 15:45:45,626 - INFO - Min: 0.3763 +2025-04-23 15:45:45,626 - INFO - Max: 18.8528 +2025-04-23 15:45:45,626 - INFO - Std: 2.8893 +2025-04-23 15:45:45,626 - INFO - OC Distribution Statistics: +2025-04-23 15:45:45,626 - INFO - Validation Mean: 17.3772 +2025-04-23 15:45:45,626 - INFO - Validation Std: 8.9271 +2025-04-23 15:45:45,626 - INFO - Training Mean: 23.0004 +2025-04-23 15:45:45,626 - INFO - Training Std: 20.3076 +2025-04-23 15:45:45,626 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:45:45,626 - INFO - KS Statistic: 0.1538 +2025-04-23 15:45:45,626 - INFO - P-value: 0.0000 +2025-04-23 15:45:45,626 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:45:45,626 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:56:33,355 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:56:33,356 - INFO - Timestamp: 2025-04-23 15:56:33 +2025-04-23 15:56:33,356 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:56:33,356 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:56:33,356 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:56:33,356 - INFO - Mean: 4.7899 +2025-04-23 15:56:33,356 - INFO - Median: 4.2255 +2025-04-23 15:56:33,356 - INFO - Min: 0.3763 +2025-04-23 15:56:33,356 - INFO - Max: 18.8528 +2025-04-23 15:56:33,356 - INFO - Std: 2.8893 +2025-04-23 15:56:33,356 - INFO - OC Distribution Statistics: +2025-04-23 15:56:33,356 - INFO - Validation Mean: 17.3772 +2025-04-23 15:56:33,356 - INFO - Validation Std: 8.9271 +2025-04-23 15:56:33,356 - INFO - Training Mean: 23.0004 +2025-04-23 15:56:33,356 - INFO - Training Std: 20.3076 +2025-04-23 15:56:33,356 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:56:33,356 - INFO - KS Statistic: 0.1538 +2025-04-23 15:56:33,356 - INFO - P-value: 0.0000 +2025-04-23 15:56:33,356 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:56:33,356 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:56:34,088 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:56:34,088 - INFO - Timestamp: 2025-04-23 15:56:34 +2025-04-23 15:56:34,088 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:56:34,088 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:56:34,088 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:56:34,088 - INFO - Mean: 4.7899 +2025-04-23 15:56:34,088 - INFO - Median: 4.2255 +2025-04-23 15:56:34,088 - INFO - Min: 0.3763 +2025-04-23 15:56:34,088 - INFO - Max: 18.8528 +2025-04-23 15:56:34,088 - INFO - Std: 2.8893 +2025-04-23 15:56:34,088 - INFO - OC Distribution Statistics: +2025-04-23 15:56:34,088 - INFO - Validation Mean: 17.3772 +2025-04-23 15:56:34,088 - INFO - Validation Std: 8.9271 +2025-04-23 15:56:34,088 - INFO - Training Mean: 23.0004 +2025-04-23 15:56:34,088 - INFO - Training Std: 20.3076 +2025-04-23 15:56:34,088 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:56:34,088 - INFO - KS Statistic: 0.1538 +2025-04-23 15:56:34,088 - INFO - P-value: 0.0000 +2025-04-23 15:56:34,088 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:56:34,088 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:56:34,543 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:56:34,543 - INFO - Timestamp: 2025-04-23 15:56:34 +2025-04-23 15:56:34,543 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:56:34,543 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:56:34,543 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:56:34,543 - INFO - Mean: 4.7899 +2025-04-23 15:56:34,543 - INFO - Median: 4.2255 +2025-04-23 15:56:34,543 - INFO - Min: 0.3763 +2025-04-23 15:56:34,543 - INFO - Max: 18.8528 +2025-04-23 15:56:34,543 - INFO - Std: 2.8893 +2025-04-23 15:56:34,543 - INFO - OC Distribution Statistics: +2025-04-23 15:56:34,543 - INFO - Validation Mean: 17.3772 +2025-04-23 15:56:34,543 - INFO - Validation Std: 8.9271 +2025-04-23 15:56:34,543 - INFO - Training Mean: 23.0004 +2025-04-23 15:56:34,543 - INFO - Training Std: 20.3076 +2025-04-23 15:56:34,543 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:56:34,543 - INFO - KS Statistic: 0.1538 +2025-04-23 15:56:34,543 - INFO - P-value: 0.0000 +2025-04-23 15:56:34,543 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:56:34,543 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:56:35,318 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:56:35,318 - INFO - Timestamp: 2025-04-23 15:56:35 +2025-04-23 15:56:35,318 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:56:35,318 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:56:35,318 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:56:35,318 - INFO - Mean: 4.7899 +2025-04-23 15:56:35,318 - INFO - Median: 4.2255 +2025-04-23 15:56:35,318 - INFO - Min: 0.3763 +2025-04-23 15:56:35,318 - INFO - Max: 18.8528 +2025-04-23 15:56:35,318 - INFO - Std: 2.8893 +2025-04-23 15:56:35,318 - INFO - OC Distribution Statistics: +2025-04-23 15:56:35,318 - INFO - Validation Mean: 17.3772 +2025-04-23 15:56:35,318 - INFO - Validation Std: 8.9271 +2025-04-23 15:56:35,318 - INFO - Training Mean: 23.0004 +2025-04-23 15:56:35,318 - INFO - Training Std: 20.3076 +2025-04-23 15:56:35,318 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:56:35,318 - INFO - KS Statistic: 0.1538 +2025-04-23 15:56:35,318 - INFO - P-value: 0.0000 +2025-04-23 15:56:35,318 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:56:35,318 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:56:36,524 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:56:36,524 - INFO - Timestamp: 2025-04-23 15:56:36 +2025-04-23 15:56:36,524 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:56:36,524 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:56:36,525 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:56:36,525 - INFO - Mean: 4.7899 +2025-04-23 15:56:36,525 - INFO - Median: 4.2255 +2025-04-23 15:56:36,525 - INFO - Min: 0.3763 +2025-04-23 15:56:36,525 - INFO - Max: 18.8528 +2025-04-23 15:56:36,525 - INFO - Std: 2.8893 +2025-04-23 15:56:36,525 - INFO - OC Distribution Statistics: +2025-04-23 15:56:36,525 - INFO - Validation Mean: 17.3772 +2025-04-23 15:56:36,525 - INFO - Validation Std: 8.9271 +2025-04-23 15:56:36,525 - INFO - Training Mean: 23.0004 +2025-04-23 15:56:36,525 - INFO - Training Std: 20.3076 +2025-04-23 15:56:36,525 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:56:36,525 - INFO - KS Statistic: 0.1538 +2025-04-23 15:56:36,525 - INFO - P-value: 0.0000 +2025-04-23 15:56:36,525 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:56:36,525 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:56:38,737 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:56:38,737 - INFO - Timestamp: 2025-04-23 15:56:38 +2025-04-23 15:56:38,737 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:56:38,737 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:56:38,737 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:56:38,737 - INFO - Mean: 4.7899 +2025-04-23 15:56:38,737 - INFO - Median: 4.2255 +2025-04-23 15:56:38,737 - INFO - Min: 0.3763 +2025-04-23 15:56:38,737 - INFO - Max: 18.8528 +2025-04-23 15:56:38,737 - INFO - Std: 2.8893 +2025-04-23 15:56:38,737 - INFO - OC Distribution Statistics: +2025-04-23 15:56:38,737 - INFO - Validation Mean: 17.3772 +2025-04-23 15:56:38,737 - INFO - Validation Std: 8.9271 +2025-04-23 15:56:38,737 - INFO - Training Mean: 23.0004 +2025-04-23 15:56:38,737 - INFO - Training Std: 20.3076 +2025-04-23 15:56:38,737 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:56:38,737 - INFO - KS Statistic: 0.1538 +2025-04-23 15:56:38,737 - INFO - P-value: 0.0000 +2025-04-23 15:56:38,737 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:56:38,737 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:56:40,405 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:56:40,405 - INFO - Timestamp: 2025-04-23 15:56:40 +2025-04-23 15:56:40,405 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:56:40,405 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:56:40,405 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:56:40,405 - INFO - Mean: 4.7899 +2025-04-23 15:56:40,405 - INFO - Median: 4.2255 +2025-04-23 15:56:40,405 - INFO - Min: 0.3763 +2025-04-23 15:56:40,405 - INFO - Max: 18.8528 +2025-04-23 15:56:40,405 - INFO - Std: 2.8893 +2025-04-23 15:56:40,405 - INFO - OC Distribution Statistics: +2025-04-23 15:56:40,405 - INFO - Validation Mean: 17.3772 +2025-04-23 15:56:40,405 - INFO - Validation Std: 8.9271 +2025-04-23 15:56:40,405 - INFO - Training Mean: 23.0004 +2025-04-23 15:56:40,405 - INFO - Training Std: 20.3076 +2025-04-23 15:56:40,405 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:56:40,405 - INFO - KS Statistic: 0.1538 +2025-04-23 15:56:40,405 - INFO - P-value: 0.0000 +2025-04-23 15:56:40,405 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:56:40,405 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 15:56:41,408 - INFO - Validation and Training Set Split Statistics +2025-04-23 15:56:41,408 - INFO - Timestamp: 2025-04-23 15:56:41 +2025-04-23 15:56:41,408 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 15:56:41,408 - INFO - Training set size: 15123 (91.58%) +2025-04-23 15:56:41,408 - INFO - Minimum Distance Statistics (km): +2025-04-23 15:56:41,408 - INFO - Mean: 4.7899 +2025-04-23 15:56:41,408 - INFO - Median: 4.2255 +2025-04-23 15:56:41,408 - INFO - Min: 0.3763 +2025-04-23 15:56:41,408 - INFO - Max: 18.8528 +2025-04-23 15:56:41,408 - INFO - Std: 2.8893 +2025-04-23 15:56:41,408 - INFO - OC Distribution Statistics: +2025-04-23 15:56:41,408 - INFO - Validation Mean: 17.3772 +2025-04-23 15:56:41,408 - INFO - Validation Std: 8.9271 +2025-04-23 15:56:41,408 - INFO - Training Mean: 23.0004 +2025-04-23 15:56:41,408 - INFO - Training Std: 20.3076 +2025-04-23 15:56:41,408 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 15:56:41,408 - INFO - KS Statistic: 0.1538 +2025-04-23 15:56:41,408 - INFO - P-value: 0.0000 +2025-04-23 15:56:41,408 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 15:56:41,408 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:07:30,887 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:07:30,887 - INFO - Timestamp: 2025-04-23 16:07:30 +2025-04-23 16:07:30,887 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:07:30,887 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:07:30,887 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:07:30,887 - INFO - Mean: 4.7899 +2025-04-23 16:07:30,887 - INFO - Median: 4.2255 +2025-04-23 16:07:30,887 - INFO - Min: 0.3763 +2025-04-23 16:07:30,887 - INFO - Max: 18.8528 +2025-04-23 16:07:30,887 - INFO - Std: 2.8893 +2025-04-23 16:07:30,887 - INFO - OC Distribution Statistics: +2025-04-23 16:07:30,887 - INFO - Validation Mean: 17.3772 +2025-04-23 16:07:30,887 - INFO - Validation Std: 8.9271 +2025-04-23 16:07:30,887 - INFO - Training Mean: 23.0004 +2025-04-23 16:07:30,887 - INFO - Training Std: 20.3076 +2025-04-23 16:07:30,887 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:07:30,887 - INFO - KS Statistic: 0.1538 +2025-04-23 16:07:30,887 - INFO - P-value: 0.0000 +2025-04-23 16:07:30,887 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:07:30,887 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:07:31,538 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:07:31,538 - INFO - Timestamp: 2025-04-23 16:07:31 +2025-04-23 16:07:31,538 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:07:31,538 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:07:31,538 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:07:31,539 - INFO - Mean: 4.7899 +2025-04-23 16:07:31,539 - INFO - Median: 4.2255 +2025-04-23 16:07:31,539 - INFO - Min: 0.3763 +2025-04-23 16:07:31,539 - INFO - Max: 18.8528 +2025-04-23 16:07:31,539 - INFO - Std: 2.8893 +2025-04-23 16:07:31,539 - INFO - OC Distribution Statistics: +2025-04-23 16:07:31,539 - INFO - Validation Mean: 17.3772 +2025-04-23 16:07:31,539 - INFO - Validation Std: 8.9271 +2025-04-23 16:07:31,539 - INFO - Training Mean: 23.0004 +2025-04-23 16:07:31,539 - INFO - Training Std: 20.3076 +2025-04-23 16:07:31,539 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:07:31,539 - INFO - KS Statistic: 0.1538 +2025-04-23 16:07:31,539 - INFO - P-value: 0.0000 +2025-04-23 16:07:31,539 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:07:31,539 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:07:33,967 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:07:33,968 - INFO - Timestamp: 2025-04-23 16:07:33 +2025-04-23 16:07:33,968 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:07:33,968 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:07:33,968 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:07:33,968 - INFO - Mean: 4.7899 +2025-04-23 16:07:33,968 - INFO - Median: 4.2255 +2025-04-23 16:07:33,968 - INFO - Min: 0.3763 +2025-04-23 16:07:33,968 - INFO - Max: 18.8528 +2025-04-23 16:07:33,968 - INFO - Std: 2.8893 +2025-04-23 16:07:33,968 - INFO - OC Distribution Statistics: +2025-04-23 16:07:33,968 - INFO - Validation Mean: 17.3772 +2025-04-23 16:07:33,968 - INFO - Validation Std: 8.9271 +2025-04-23 16:07:33,968 - INFO - Training Mean: 23.0004 +2025-04-23 16:07:33,968 - INFO - Training Std: 20.3076 +2025-04-23 16:07:33,968 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:07:33,968 - INFO - KS Statistic: 0.1538 +2025-04-23 16:07:33,968 - INFO - P-value: 0.0000 +2025-04-23 16:07:33,968 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:07:33,968 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:07:34,844 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:07:34,844 - INFO - Timestamp: 2025-04-23 16:07:34 +2025-04-23 16:07:34,844 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:07:34,844 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:07:34,844 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:07:34,844 - INFO - Mean: 4.7899 +2025-04-23 16:07:34,844 - INFO - Median: 4.2255 +2025-04-23 16:07:34,844 - INFO - Min: 0.3763 +2025-04-23 16:07:34,844 - INFO - Max: 18.8528 +2025-04-23 16:07:34,844 - INFO - Std: 2.8893 +2025-04-23 16:07:34,844 - INFO - OC Distribution Statistics: +2025-04-23 16:07:34,844 - INFO - Validation Mean: 17.3772 +2025-04-23 16:07:34,844 - INFO - Validation Std: 8.9271 +2025-04-23 16:07:34,844 - INFO - Training Mean: 23.0004 +2025-04-23 16:07:34,844 - INFO - Training Std: 20.3076 +2025-04-23 16:07:34,844 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:07:34,844 - INFO - KS Statistic: 0.1538 +2025-04-23 16:07:34,845 - INFO - P-value: 0.0000 +2025-04-23 16:07:34,845 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:07:34,845 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:07:36,489 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:07:36,489 - INFO - Timestamp: 2025-04-23 16:07:36 +2025-04-23 16:07:36,489 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:07:36,489 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:07:36,489 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:07:36,489 - INFO - Mean: 4.7899 +2025-04-23 16:07:36,489 - INFO - Median: 4.2255 +2025-04-23 16:07:36,489 - INFO - Min: 0.3763 +2025-04-23 16:07:36,489 - INFO - Max: 18.8528 +2025-04-23 16:07:36,489 - INFO - Std: 2.8893 +2025-04-23 16:07:36,489 - INFO - OC Distribution Statistics: +2025-04-23 16:07:36,489 - INFO - Validation Mean: 17.3772 +2025-04-23 16:07:36,489 - INFO - Validation Std: 8.9271 +2025-04-23 16:07:36,489 - INFO - Training Mean: 23.0004 +2025-04-23 16:07:36,489 - INFO - Training Std: 20.3076 +2025-04-23 16:07:36,489 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:07:36,489 - INFO - KS Statistic: 0.1538 +2025-04-23 16:07:36,489 - INFO - P-value: 0.0000 +2025-04-23 16:07:36,489 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:07:36,489 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:07:37,129 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:07:37,129 - INFO - Timestamp: 2025-04-23 16:07:37 +2025-04-23 16:07:37,129 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:07:37,129 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:07:37,129 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:07:37,129 - INFO - Mean: 4.7899 +2025-04-23 16:07:37,129 - INFO - Median: 4.2255 +2025-04-23 16:07:37,129 - INFO - Min: 0.3763 +2025-04-23 16:07:37,129 - INFO - Max: 18.8528 +2025-04-23 16:07:37,129 - INFO - Std: 2.8893 +2025-04-23 16:07:37,129 - INFO - OC Distribution Statistics: +2025-04-23 16:07:37,129 - INFO - Validation Mean: 17.3772 +2025-04-23 16:07:37,129 - INFO - Validation Std: 8.9271 +2025-04-23 16:07:37,129 - INFO - Training Mean: 23.0004 +2025-04-23 16:07:37,129 - INFO - Training Std: 20.3076 +2025-04-23 16:07:37,129 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:07:37,129 - INFO - KS Statistic: 0.1538 +2025-04-23 16:07:37,129 - INFO - P-value: 0.0000 +2025-04-23 16:07:37,129 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:07:37,129 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:07:37,225 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:07:37,225 - INFO - Timestamp: 2025-04-23 16:07:37 +2025-04-23 16:07:37,225 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:07:37,225 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:07:37,225 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:07:37,225 - INFO - Mean: 4.7899 +2025-04-23 16:07:37,225 - INFO - Median: 4.2255 +2025-04-23 16:07:37,225 - INFO - Min: 0.3763 +2025-04-23 16:07:37,225 - INFO - Max: 18.8528 +2025-04-23 16:07:37,225 - INFO - Std: 2.8893 +2025-04-23 16:07:37,226 - INFO - OC Distribution Statistics: +2025-04-23 16:07:37,226 - INFO - Validation Mean: 17.3772 +2025-04-23 16:07:37,226 - INFO - Validation Std: 8.9271 +2025-04-23 16:07:37,226 - INFO - Training Mean: 23.0004 +2025-04-23 16:07:37,226 - INFO - Training Std: 20.3076 +2025-04-23 16:07:37,226 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:07:37,226 - INFO - KS Statistic: 0.1538 +2025-04-23 16:07:37,226 - INFO - P-value: 0.0000 +2025-04-23 16:07:37,226 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:07:37,226 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:07:37,869 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:07:37,870 - INFO - Timestamp: 2025-04-23 16:07:37 +2025-04-23 16:07:37,870 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:07:37,870 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:07:37,870 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:07:37,870 - INFO - Mean: 4.7899 +2025-04-23 16:07:37,870 - INFO - Median: 4.2255 +2025-04-23 16:07:37,870 - INFO - Min: 0.3763 +2025-04-23 16:07:37,870 - INFO - Max: 18.8528 +2025-04-23 16:07:37,870 - INFO - Std: 2.8893 +2025-04-23 16:07:37,870 - INFO - OC Distribution Statistics: +2025-04-23 16:07:37,870 - INFO - Validation Mean: 17.3772 +2025-04-23 16:07:37,870 - INFO - Validation Std: 8.9271 +2025-04-23 16:07:37,870 - INFO - Training Mean: 23.0004 +2025-04-23 16:07:37,870 - INFO - Training Std: 20.3076 +2025-04-23 16:07:37,870 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:07:37,870 - INFO - KS Statistic: 0.1538 +2025-04-23 16:07:37,870 - INFO - P-value: 0.0000 +2025-04-23 16:07:37,870 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:07:37,870 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:18:41,560 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:18:41,561 - INFO - Timestamp: 2025-04-23 16:18:41 +2025-04-23 16:18:41,561 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:18:41,561 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:18:41,561 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:18:41,561 - INFO - Mean: 4.7899 +2025-04-23 16:18:41,561 - INFO - Median: 4.2255 +2025-04-23 16:18:41,561 - INFO - Min: 0.3763 +2025-04-23 16:18:41,561 - INFO - Max: 18.8528 +2025-04-23 16:18:41,561 - INFO - Std: 2.8893 +2025-04-23 16:18:41,561 - INFO - OC Distribution Statistics: +2025-04-23 16:18:41,561 - INFO - Validation Mean: 17.3772 +2025-04-23 16:18:41,561 - INFO - Validation Std: 8.9271 +2025-04-23 16:18:41,561 - INFO - Training Mean: 23.0004 +2025-04-23 16:18:41,561 - INFO - Training Std: 20.3076 +2025-04-23 16:18:41,561 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:18:41,561 - INFO - KS Statistic: 0.1538 +2025-04-23 16:18:41,561 - INFO - P-value: 0.0000 +2025-04-23 16:18:41,561 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:18:41,561 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:18:43,308 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:18:43,308 - INFO - Timestamp: 2025-04-23 16:18:43 +2025-04-23 16:18:43,308 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:18:43,308 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:18:43,308 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:18:43,308 - INFO - Mean: 4.7899 +2025-04-23 16:18:43,308 - INFO - Median: 4.2255 +2025-04-23 16:18:43,308 - INFO - Min: 0.3763 +2025-04-23 16:18:43,308 - INFO - Max: 18.8528 +2025-04-23 16:18:43,308 - INFO - Std: 2.8893 +2025-04-23 16:18:43,308 - INFO - OC Distribution Statistics: +2025-04-23 16:18:43,308 - INFO - Validation Mean: 17.3772 +2025-04-23 16:18:43,308 - INFO - Validation Std: 8.9271 +2025-04-23 16:18:43,308 - INFO - Training Mean: 23.0004 +2025-04-23 16:18:43,308 - INFO - Training Std: 20.3076 +2025-04-23 16:18:43,308 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:18:43,308 - INFO - KS Statistic: 0.1538 +2025-04-23 16:18:43,308 - INFO - P-value: 0.0000 +2025-04-23 16:18:43,308 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:18:43,308 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:18:43,615 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:18:43,615 - INFO - Timestamp: 2025-04-23 16:18:43 +2025-04-23 16:18:43,615 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:18:43,615 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:18:43,615 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:18:43,615 - INFO - Mean: 4.7899 +2025-04-23 16:18:43,615 - INFO - Median: 4.2255 +2025-04-23 16:18:43,615 - INFO - Min: 0.3763 +2025-04-23 16:18:43,615 - INFO - Max: 18.8528 +2025-04-23 16:18:43,615 - INFO - Std: 2.8893 +2025-04-23 16:18:43,615 - INFO - OC Distribution Statistics: +2025-04-23 16:18:43,615 - INFO - Validation Mean: 17.3772 +2025-04-23 16:18:43,615 - INFO - Validation Std: 8.9271 +2025-04-23 16:18:43,615 - INFO - Training Mean: 23.0004 +2025-04-23 16:18:43,615 - INFO - Training Std: 20.3076 +2025-04-23 16:18:43,615 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:18:43,615 - INFO - KS Statistic: 0.1538 +2025-04-23 16:18:43,616 - INFO - P-value: 0.0000 +2025-04-23 16:18:43,616 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:18:43,616 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:18:43,895 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:18:43,895 - INFO - Timestamp: 2025-04-23 16:18:43 +2025-04-23 16:18:43,895 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:18:43,895 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:18:43,895 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:18:43,895 - INFO - Mean: 4.7899 +2025-04-23 16:18:43,895 - INFO - Median: 4.2255 +2025-04-23 16:18:43,895 - INFO - Min: 0.3763 +2025-04-23 16:18:43,895 - INFO - Max: 18.8528 +2025-04-23 16:18:43,895 - INFO - Std: 2.8893 +2025-04-23 16:18:43,895 - INFO - OC Distribution Statistics: +2025-04-23 16:18:43,895 - INFO - Validation Mean: 17.3772 +2025-04-23 16:18:43,895 - INFO - Validation Std: 8.9271 +2025-04-23 16:18:43,895 - INFO - Training Mean: 23.0004 +2025-04-23 16:18:43,895 - INFO - Training Std: 20.3076 +2025-04-23 16:18:43,895 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:18:43,895 - INFO - KS Statistic: 0.1538 +2025-04-23 16:18:43,895 - INFO - P-value: 0.0000 +2025-04-23 16:18:43,895 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:18:43,895 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:18:44,900 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:18:44,900 - INFO - Timestamp: 2025-04-23 16:18:44 +2025-04-23 16:18:44,900 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:18:44,900 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:18:44,900 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:18:44,900 - INFO - Mean: 4.7899 +2025-04-23 16:18:44,900 - INFO - Median: 4.2255 +2025-04-23 16:18:44,900 - INFO - Min: 0.3763 +2025-04-23 16:18:44,900 - INFO - Max: 18.8528 +2025-04-23 16:18:44,900 - INFO - Std: 2.8893 +2025-04-23 16:18:44,900 - INFO - OC Distribution Statistics: +2025-04-23 16:18:44,900 - INFO - Validation Mean: 17.3772 +2025-04-23 16:18:44,900 - INFO - Validation Std: 8.9271 +2025-04-23 16:18:44,900 - INFO - Training Mean: 23.0004 +2025-04-23 16:18:44,900 - INFO - Training Std: 20.3076 +2025-04-23 16:18:44,900 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:18:44,900 - INFO - KS Statistic: 0.1538 +2025-04-23 16:18:44,900 - INFO - P-value: 0.0000 +2025-04-23 16:18:44,900 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:18:44,900 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:18:45,301 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:18:45,302 - INFO - Timestamp: 2025-04-23 16:18:45 +2025-04-23 16:18:45,302 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:18:45,302 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:18:45,302 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:18:45,302 - INFO - Mean: 4.7899 +2025-04-23 16:18:45,302 - INFO - Median: 4.2255 +2025-04-23 16:18:45,302 - INFO - Min: 0.3763 +2025-04-23 16:18:45,302 - INFO - Max: 18.8528 +2025-04-23 16:18:45,302 - INFO - Std: 2.8893 +2025-04-23 16:18:45,302 - INFO - OC Distribution Statistics: +2025-04-23 16:18:45,302 - INFO - Validation Mean: 17.3772 +2025-04-23 16:18:45,302 - INFO - Validation Std: 8.9271 +2025-04-23 16:18:45,302 - INFO - Training Mean: 23.0004 +2025-04-23 16:18:45,302 - INFO - Training Std: 20.3076 +2025-04-23 16:18:45,302 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:18:45,302 - INFO - KS Statistic: 0.1538 +2025-04-23 16:18:45,302 - INFO - P-value: 0.0000 +2025-04-23 16:18:45,302 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:18:45,302 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:18:45,310 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:18:45,310 - INFO - Timestamp: 2025-04-23 16:18:45 +2025-04-23 16:18:45,310 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:18:45,310 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:18:45,310 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:18:45,310 - INFO - Mean: 4.7899 +2025-04-23 16:18:45,310 - INFO - Median: 4.2255 +2025-04-23 16:18:45,310 - INFO - Min: 0.3763 +2025-04-23 16:18:45,310 - INFO - Max: 18.8528 +2025-04-23 16:18:45,310 - INFO - Std: 2.8893 +2025-04-23 16:18:45,310 - INFO - OC Distribution Statistics: +2025-04-23 16:18:45,310 - INFO - Validation Mean: 17.3772 +2025-04-23 16:18:45,310 - INFO - Validation Std: 8.9271 +2025-04-23 16:18:45,310 - INFO - Training Mean: 23.0004 +2025-04-23 16:18:45,310 - INFO - Training Std: 20.3076 +2025-04-23 16:18:45,310 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:18:45,310 - INFO - KS Statistic: 0.1538 +2025-04-23 16:18:45,310 - INFO - P-value: 0.0000 +2025-04-23 16:18:45,310 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:18:45,310 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:18:46,343 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:18:46,344 - INFO - Timestamp: 2025-04-23 16:18:46 +2025-04-23 16:18:46,344 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:18:46,344 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:18:46,344 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:18:46,344 - INFO - Mean: 4.7899 +2025-04-23 16:18:46,344 - INFO - Median: 4.2255 +2025-04-23 16:18:46,344 - INFO - Min: 0.3763 +2025-04-23 16:18:46,344 - INFO - Max: 18.8528 +2025-04-23 16:18:46,344 - INFO - Std: 2.8893 +2025-04-23 16:18:46,344 - INFO - OC Distribution Statistics: +2025-04-23 16:18:46,344 - INFO - Validation Mean: 17.3772 +2025-04-23 16:18:46,344 - INFO - Validation Std: 8.9271 +2025-04-23 16:18:46,344 - INFO - Training Mean: 23.0004 +2025-04-23 16:18:46,344 - INFO - Training Std: 20.3076 +2025-04-23 16:18:46,344 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:18:46,344 - INFO - KS Statistic: 0.1538 +2025-04-23 16:18:46,344 - INFO - P-value: 0.0000 +2025-04-23 16:18:46,344 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:18:46,344 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:29:48,369 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:29:48,369 - INFO - Timestamp: 2025-04-23 16:29:48 +2025-04-23 16:29:48,370 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:29:48,370 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:29:48,370 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:29:48,370 - INFO - Mean: 4.7899 +2025-04-23 16:29:48,370 - INFO - Median: 4.2255 +2025-04-23 16:29:48,370 - INFO - Min: 0.3763 +2025-04-23 16:29:48,370 - INFO - Max: 18.8528 +2025-04-23 16:29:48,370 - INFO - Std: 2.8893 +2025-04-23 16:29:48,370 - INFO - OC Distribution Statistics: +2025-04-23 16:29:48,370 - INFO - Validation Mean: 17.3772 +2025-04-23 16:29:48,370 - INFO - Validation Std: 8.9271 +2025-04-23 16:29:48,370 - INFO - Training Mean: 23.0004 +2025-04-23 16:29:48,370 - INFO - Training Std: 20.3076 +2025-04-23 16:29:48,370 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:29:48,370 - INFO - KS Statistic: 0.1538 +2025-04-23 16:29:48,370 - INFO - P-value: 0.0000 +2025-04-23 16:29:48,370 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:29:48,370 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:29:48,749 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:29:48,749 - INFO - Timestamp: 2025-04-23 16:29:48 +2025-04-23 16:29:48,749 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:29:48,749 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:29:48,749 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:29:48,749 - INFO - Mean: 4.7899 +2025-04-23 16:29:48,749 - INFO - Median: 4.2255 +2025-04-23 16:29:48,749 - INFO - Min: 0.3763 +2025-04-23 16:29:48,749 - INFO - Max: 18.8528 +2025-04-23 16:29:48,749 - INFO - Std: 2.8893 +2025-04-23 16:29:48,749 - INFO - OC Distribution Statistics: +2025-04-23 16:29:48,749 - INFO - Validation Mean: 17.3772 +2025-04-23 16:29:48,749 - INFO - Validation Std: 8.9271 +2025-04-23 16:29:48,749 - INFO - Training Mean: 23.0004 +2025-04-23 16:29:48,749 - INFO - Training Std: 20.3076 +2025-04-23 16:29:48,749 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:29:48,749 - INFO - KS Statistic: 0.1538 +2025-04-23 16:29:48,749 - INFO - P-value: 0.0000 +2025-04-23 16:29:48,749 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:29:48,749 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:29:49,331 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:29:49,331 - INFO - Timestamp: 2025-04-23 16:29:49 +2025-04-23 16:29:49,331 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:29:49,331 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:29:49,331 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:29:49,331 - INFO - Mean: 4.7899 +2025-04-23 16:29:49,331 - INFO - Median: 4.2255 +2025-04-23 16:29:49,331 - INFO - Min: 0.3763 +2025-04-23 16:29:49,331 - INFO - Max: 18.8528 +2025-04-23 16:29:49,331 - INFO - Std: 2.8893 +2025-04-23 16:29:49,331 - INFO - OC Distribution Statistics: +2025-04-23 16:29:49,331 - INFO - Validation Mean: 17.3772 +2025-04-23 16:29:49,331 - INFO - Validation Std: 8.9271 +2025-04-23 16:29:49,331 - INFO - Training Mean: 23.0004 +2025-04-23 16:29:49,331 - INFO - Training Std: 20.3076 +2025-04-23 16:29:49,331 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:29:49,331 - INFO - KS Statistic: 0.1538 +2025-04-23 16:29:49,331 - INFO - P-value: 0.0000 +2025-04-23 16:29:49,331 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:29:49,331 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:29:50,800 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:29:50,800 - INFO - Timestamp: 2025-04-23 16:29:50 +2025-04-23 16:29:50,800 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:29:50,800 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:29:50,800 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:29:50,800 - INFO - Mean: 4.7899 +2025-04-23 16:29:50,800 - INFO - Median: 4.2255 +2025-04-23 16:29:50,800 - INFO - Min: 0.3763 +2025-04-23 16:29:50,801 - INFO - Max: 18.8528 +2025-04-23 16:29:50,801 - INFO - Std: 2.8893 +2025-04-23 16:29:50,801 - INFO - OC Distribution Statistics: +2025-04-23 16:29:50,801 - INFO - Validation Mean: 17.3772 +2025-04-23 16:29:50,801 - INFO - Validation Std: 8.9271 +2025-04-23 16:29:50,801 - INFO - Training Mean: 23.0004 +2025-04-23 16:29:50,801 - INFO - Training Std: 20.3076 +2025-04-23 16:29:50,801 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:29:50,801 - INFO - KS Statistic: 0.1538 +2025-04-23 16:29:50,801 - INFO - P-value: 0.0000 +2025-04-23 16:29:50,801 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:29:50,801 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:29:51,525 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:29:51,526 - INFO - Timestamp: 2025-04-23 16:29:51 +2025-04-23 16:29:51,526 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:29:51,526 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:29:51,526 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:29:51,526 - INFO - Mean: 4.7899 +2025-04-23 16:29:51,526 - INFO - Median: 4.2255 +2025-04-23 16:29:51,526 - INFO - Min: 0.3763 +2025-04-23 16:29:51,526 - INFO - Max: 18.8528 +2025-04-23 16:29:51,526 - INFO - Std: 2.8893 +2025-04-23 16:29:51,526 - INFO - OC Distribution Statistics: +2025-04-23 16:29:51,526 - INFO - Validation Mean: 17.3772 +2025-04-23 16:29:51,526 - INFO - Validation Std: 8.9271 +2025-04-23 16:29:51,526 - INFO - Training Mean: 23.0004 +2025-04-23 16:29:51,526 - INFO - Training Std: 20.3076 +2025-04-23 16:29:51,526 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:29:51,526 - INFO - KS Statistic: 0.1538 +2025-04-23 16:29:51,526 - INFO - P-value: 0.0000 +2025-04-23 16:29:51,526 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:29:51,526 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:29:51,589 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:29:51,589 - INFO - Timestamp: 2025-04-23 16:29:51 +2025-04-23 16:29:51,589 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:29:51,589 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:29:51,589 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:29:51,589 - INFO - Mean: 4.7899 +2025-04-23 16:29:51,589 - INFO - Median: 4.2255 +2025-04-23 16:29:51,589 - INFO - Min: 0.3763 +2025-04-23 16:29:51,589 - INFO - Max: 18.8528 +2025-04-23 16:29:51,589 - INFO - Std: 2.8893 +2025-04-23 16:29:51,589 - INFO - OC Distribution Statistics: +2025-04-23 16:29:51,589 - INFO - Validation Mean: 17.3772 +2025-04-23 16:29:51,589 - INFO - Validation Std: 8.9271 +2025-04-23 16:29:51,589 - INFO - Training Mean: 23.0004 +2025-04-23 16:29:51,589 - INFO - Training Std: 20.3076 +2025-04-23 16:29:51,589 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:29:51,589 - INFO - KS Statistic: 0.1538 +2025-04-23 16:29:51,589 - INFO - P-value: 0.0000 +2025-04-23 16:29:51,589 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:29:51,589 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:29:52,068 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:29:52,068 - INFO - Timestamp: 2025-04-23 16:29:52 +2025-04-23 16:29:52,068 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:29:52,068 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:29:52,068 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:29:52,068 - INFO - Mean: 4.7899 +2025-04-23 16:29:52,068 - INFO - Median: 4.2255 +2025-04-23 16:29:52,068 - INFO - Min: 0.3763 +2025-04-23 16:29:52,068 - INFO - Max: 18.8528 +2025-04-23 16:29:52,068 - INFO - Std: 2.8893 +2025-04-23 16:29:52,068 - INFO - OC Distribution Statistics: +2025-04-23 16:29:52,068 - INFO - Validation Mean: 17.3772 +2025-04-23 16:29:52,068 - INFO - Validation Std: 8.9271 +2025-04-23 16:29:52,068 - INFO - Training Mean: 23.0004 +2025-04-23 16:29:52,069 - INFO - Training Std: 20.3076 +2025-04-23 16:29:52,069 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:29:52,069 - INFO - KS Statistic: 0.1538 +2025-04-23 16:29:52,069 - INFO - P-value: 0.0000 +2025-04-23 16:29:52,069 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:29:52,069 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 16:29:53,756 - INFO - Validation and Training Set Split Statistics +2025-04-23 16:29:53,756 - INFO - Timestamp: 2025-04-23 16:29:53 +2025-04-23 16:29:53,756 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 16:29:53,757 - INFO - Training set size: 15123 (91.58%) +2025-04-23 16:29:53,757 - INFO - Minimum Distance Statistics (km): +2025-04-23 16:29:53,757 - INFO - Mean: 4.7899 +2025-04-23 16:29:53,757 - INFO - Median: 4.2255 +2025-04-23 16:29:53,757 - INFO - Min: 0.3763 +2025-04-23 16:29:53,757 - INFO - Max: 18.8528 +2025-04-23 16:29:53,757 - INFO - Std: 2.8893 +2025-04-23 16:29:53,757 - INFO - OC Distribution Statistics: +2025-04-23 16:29:53,757 - INFO - Validation Mean: 17.3772 +2025-04-23 16:29:53,757 - INFO - Validation Std: 8.9271 +2025-04-23 16:29:53,757 - INFO - Training Mean: 23.0004 +2025-04-23 16:29:53,757 - INFO - Training Std: 20.3076 +2025-04-23 16:29:53,757 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 16:29:53,757 - INFO - KS Statistic: 0.1538 +2025-04-23 16:29:53,757 - INFO - P-value: 0.0000 +2025-04-23 16:29:53,757 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 16:29:53,757 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:20:30,609 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:20:30,610 - INFO - Timestamp: 2025-04-23 23:20:30 +2025-04-23 23:20:30,610 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:20:30,610 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:20:30,610 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:20:30,610 - INFO - Mean: 4.7899 +2025-04-23 23:20:30,610 - INFO - Median: 4.2255 +2025-04-23 23:20:30,610 - INFO - Min: 0.3763 +2025-04-23 23:20:30,610 - INFO - Max: 18.8528 +2025-04-23 23:20:30,610 - INFO - Std: 2.8893 +2025-04-23 23:20:30,610 - INFO - OC Distribution Statistics: +2025-04-23 23:20:30,610 - INFO - Validation Mean: 17.3772 +2025-04-23 23:20:30,610 - INFO - Validation Std: 8.9271 +2025-04-23 23:20:30,610 - INFO - Training Mean: 23.0004 +2025-04-23 23:20:30,610 - INFO - Training Std: 20.3076 +2025-04-23 23:20:30,610 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:20:30,610 - INFO - KS Statistic: 0.1538 +2025-04-23 23:20:30,610 - INFO - P-value: 0.0000 +2025-04-23 23:20:30,610 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:20:30,610 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:20:33,624 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:20:33,624 - INFO - Timestamp: 2025-04-23 23:20:33 +2025-04-23 23:20:33,624 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:20:33,624 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:20:33,625 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:20:33,625 - INFO - Mean: 4.7899 +2025-04-23 23:20:33,625 - INFO - Median: 4.2255 +2025-04-23 23:20:33,625 - INFO - Min: 0.3763 +2025-04-23 23:20:33,625 - INFO - Max: 18.8528 +2025-04-23 23:20:33,625 - INFO - Std: 2.8893 +2025-04-23 23:20:33,625 - INFO - OC Distribution Statistics: +2025-04-23 23:20:33,625 - INFO - Validation Mean: 17.3772 +2025-04-23 23:20:33,625 - INFO - Validation Std: 8.9271 +2025-04-23 23:20:33,625 - INFO - Training Mean: 23.0004 +2025-04-23 23:20:33,625 - INFO - Training Std: 20.3076 +2025-04-23 23:20:33,625 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:20:33,625 - INFO - KS Statistic: 0.1538 +2025-04-23 23:20:33,625 - INFO - P-value: 0.0000 +2025-04-23 23:20:33,625 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:20:33,625 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:20:33,628 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:20:33,628 - INFO - Timestamp: 2025-04-23 23:20:33 +2025-04-23 23:20:33,628 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:20:33,628 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:20:33,628 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:20:33,628 - INFO - Mean: 4.7899 +2025-04-23 23:20:33,628 - INFO - Median: 4.2255 +2025-04-23 23:20:33,628 - INFO - Min: 0.3763 +2025-04-23 23:20:33,628 - INFO - Max: 18.8528 +2025-04-23 23:20:33,628 - INFO - Std: 2.8893 +2025-04-23 23:20:33,628 - INFO - OC Distribution Statistics: +2025-04-23 23:20:33,628 - INFO - Validation Mean: 17.3772 +2025-04-23 23:20:33,628 - INFO - Validation Std: 8.9271 +2025-04-23 23:20:33,628 - INFO - Training Mean: 23.0004 +2025-04-23 23:20:33,629 - INFO - Training Std: 20.3076 +2025-04-23 23:20:33,629 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:20:33,629 - INFO - KS Statistic: 0.1538 +2025-04-23 23:20:33,629 - INFO - P-value: 0.0000 +2025-04-23 23:20:33,629 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:20:33,629 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:20:33,939 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:20:33,939 - INFO - Timestamp: 2025-04-23 23:20:33 +2025-04-23 23:20:33,939 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:20:33,939 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:20:33,940 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:20:33,940 - INFO - Mean: 4.7899 +2025-04-23 23:20:33,940 - INFO - Median: 4.2255 +2025-04-23 23:20:33,940 - INFO - Min: 0.3763 +2025-04-23 23:20:33,940 - INFO - Max: 18.8528 +2025-04-23 23:20:33,940 - INFO - Std: 2.8893 +2025-04-23 23:20:33,940 - INFO - OC Distribution Statistics: +2025-04-23 23:20:33,940 - INFO - Validation Mean: 17.3772 +2025-04-23 23:20:33,940 - INFO - Validation Std: 8.9271 +2025-04-23 23:20:33,940 - INFO - Training Mean: 23.0004 +2025-04-23 23:20:33,940 - INFO - Training Std: 20.3076 +2025-04-23 23:20:33,940 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:20:33,940 - INFO - KS Statistic: 0.1538 +2025-04-23 23:20:33,940 - INFO - P-value: 0.0000 +2025-04-23 23:20:33,940 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:20:33,940 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:20:36,842 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:20:36,842 - INFO - Timestamp: 2025-04-23 23:20:36 +2025-04-23 23:20:36,842 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:20:36,842 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:20:36,842 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:20:36,842 - INFO - Mean: 4.7899 +2025-04-23 23:20:36,842 - INFO - Median: 4.2255 +2025-04-23 23:20:36,842 - INFO - Min: 0.3763 +2025-04-23 23:20:36,842 - INFO - Max: 18.8528 +2025-04-23 23:20:36,842 - INFO - Std: 2.8893 +2025-04-23 23:20:36,842 - INFO - OC Distribution Statistics: +2025-04-23 23:20:36,842 - INFO - Validation Mean: 17.3772 +2025-04-23 23:20:36,842 - INFO - Validation Std: 8.9271 +2025-04-23 23:20:36,842 - INFO - Training Mean: 23.0004 +2025-04-23 23:20:36,842 - INFO - Training Std: 20.3076 +2025-04-23 23:20:36,842 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:20:36,842 - INFO - KS Statistic: 0.1538 +2025-04-23 23:20:36,842 - INFO - P-value: 0.0000 +2025-04-23 23:20:36,842 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:20:36,842 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:20:36,913 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:20:36,913 - INFO - Timestamp: 2025-04-23 23:20:36 +2025-04-23 23:20:36,913 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:20:36,913 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:20:36,913 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:20:36,913 - INFO - Mean: 4.7899 +2025-04-23 23:20:36,913 - INFO - Median: 4.2255 +2025-04-23 23:20:36,913 - INFO - Min: 0.3763 +2025-04-23 23:20:36,913 - INFO - Max: 18.8528 +2025-04-23 23:20:36,913 - INFO - Std: 2.8893 +2025-04-23 23:20:36,913 - INFO - OC Distribution Statistics: +2025-04-23 23:20:36,913 - INFO - Validation Mean: 17.3772 +2025-04-23 23:20:36,913 - INFO - Validation Std: 8.9271 +2025-04-23 23:20:36,913 - INFO - Training Mean: 23.0004 +2025-04-23 23:20:36,913 - INFO - Training Std: 20.3076 +2025-04-23 23:20:36,913 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:20:36,913 - INFO - KS Statistic: 0.1538 +2025-04-23 23:20:36,913 - INFO - P-value: 0.0000 +2025-04-23 23:20:36,913 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:20:36,913 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:20:37,117 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:20:37,117 - INFO - Timestamp: 2025-04-23 23:20:37 +2025-04-23 23:20:37,117 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:20:37,117 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:20:37,117 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:20:37,117 - INFO - Mean: 4.7899 +2025-04-23 23:20:37,117 - INFO - Median: 4.2255 +2025-04-23 23:20:37,117 - INFO - Min: 0.3763 +2025-04-23 23:20:37,117 - INFO - Max: 18.8528 +2025-04-23 23:20:37,117 - INFO - Std: 2.8893 +2025-04-23 23:20:37,117 - INFO - OC Distribution Statistics: +2025-04-23 23:20:37,117 - INFO - Validation Mean: 17.3772 +2025-04-23 23:20:37,117 - INFO - Validation Std: 8.9271 +2025-04-23 23:20:37,117 - INFO - Training Mean: 23.0004 +2025-04-23 23:20:37,117 - INFO - Training Std: 20.3076 +2025-04-23 23:20:37,117 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:20:37,117 - INFO - KS Statistic: 0.1538 +2025-04-23 23:20:37,117 - INFO - P-value: 0.0000 +2025-04-23 23:20:37,117 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:20:37,117 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:20:37,155 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:20:37,155 - INFO - Timestamp: 2025-04-23 23:20:37 +2025-04-23 23:20:37,155 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:20:37,155 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:20:37,155 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:20:37,155 - INFO - Mean: 4.7899 +2025-04-23 23:20:37,155 - INFO - Median: 4.2255 +2025-04-23 23:20:37,155 - INFO - Min: 0.3763 +2025-04-23 23:20:37,155 - INFO - Max: 18.8528 +2025-04-23 23:20:37,155 - INFO - Std: 2.8893 +2025-04-23 23:20:37,155 - INFO - OC Distribution Statistics: +2025-04-23 23:20:37,155 - INFO - Validation Mean: 17.3772 +2025-04-23 23:20:37,155 - INFO - Validation Std: 8.9271 +2025-04-23 23:20:37,155 - INFO - Training Mean: 23.0004 +2025-04-23 23:20:37,155 - INFO - Training Std: 20.3076 +2025-04-23 23:20:37,155 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:20:37,155 - INFO - KS Statistic: 0.1538 +2025-04-23 23:20:37,155 - INFO - P-value: 0.0000 +2025-04-23 23:20:37,155 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:20:37,155 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:47:22,948 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:47:22,948 - INFO - Timestamp: 2025-04-23 23:47:22 +2025-04-23 23:47:22,948 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:47:22,948 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:47:22,948 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:47:22,948 - INFO - Mean: 4.7899 +2025-04-23 23:47:22,948 - INFO - Median: 4.2255 +2025-04-23 23:47:22,948 - INFO - Min: 0.3763 +2025-04-23 23:47:22,948 - INFO - Max: 18.8528 +2025-04-23 23:47:22,948 - INFO - Std: 2.8893 +2025-04-23 23:47:22,948 - INFO - OC Distribution Statistics: +2025-04-23 23:47:22,948 - INFO - Validation Mean: 17.3772 +2025-04-23 23:47:22,948 - INFO - Validation Std: 8.9271 +2025-04-23 23:47:22,948 - INFO - Training Mean: 23.0004 +2025-04-23 23:47:22,948 - INFO - Training Std: 20.3076 +2025-04-23 23:47:22,948 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:47:22,948 - INFO - KS Statistic: 0.1538 +2025-04-23 23:47:22,948 - INFO - P-value: 0.0000 +2025-04-23 23:47:22,949 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:47:22,949 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:47:24,534 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:47:24,535 - INFO - Timestamp: 2025-04-23 23:47:24 +2025-04-23 23:47:24,535 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:47:24,535 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:47:24,535 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:47:24,535 - INFO - Mean: 4.7899 +2025-04-23 23:47:24,535 - INFO - Median: 4.2255 +2025-04-23 23:47:24,535 - INFO - Min: 0.3763 +2025-04-23 23:47:24,535 - INFO - Max: 18.8528 +2025-04-23 23:47:24,535 - INFO - Std: 2.8893 +2025-04-23 23:47:24,535 - INFO - OC Distribution Statistics: +2025-04-23 23:47:24,535 - INFO - Validation Mean: 17.3772 +2025-04-23 23:47:24,535 - INFO - Validation Std: 8.9271 +2025-04-23 23:47:24,535 - INFO - Training Mean: 23.0004 +2025-04-23 23:47:24,535 - INFO - Training Std: 20.3076 +2025-04-23 23:47:24,535 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:47:24,535 - INFO - KS Statistic: 0.1538 +2025-04-23 23:47:24,535 - INFO - P-value: 0.0000 +2025-04-23 23:47:24,535 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:47:24,535 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:47:24,574 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:47:24,575 - INFO - Timestamp: 2025-04-23 23:47:24 +2025-04-23 23:47:24,575 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:47:24,575 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:47:24,575 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:47:24,575 - INFO - Mean: 4.7899 +2025-04-23 23:47:24,575 - INFO - Median: 4.2255 +2025-04-23 23:47:24,575 - INFO - Min: 0.3763 +2025-04-23 23:47:24,575 - INFO - Max: 18.8528 +2025-04-23 23:47:24,575 - INFO - Std: 2.8893 +2025-04-23 23:47:24,575 - INFO - OC Distribution Statistics: +2025-04-23 23:47:24,575 - INFO - Validation Mean: 17.3772 +2025-04-23 23:47:24,575 - INFO - Validation Std: 8.9271 +2025-04-23 23:47:24,575 - INFO - Training Mean: 23.0004 +2025-04-23 23:47:24,575 - INFO - Training Std: 20.3076 +2025-04-23 23:47:24,575 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:47:24,575 - INFO - KS Statistic: 0.1538 +2025-04-23 23:47:24,575 - INFO - P-value: 0.0000 +2025-04-23 23:47:24,575 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:47:24,575 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:47:26,290 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:47:26,290 - INFO - Timestamp: 2025-04-23 23:47:26 +2025-04-23 23:47:26,290 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:47:26,290 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:47:26,290 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:47:26,290 - INFO - Mean: 4.7899 +2025-04-23 23:47:26,290 - INFO - Median: 4.2255 +2025-04-23 23:47:26,290 - INFO - Min: 0.3763 +2025-04-23 23:47:26,290 - INFO - Max: 18.8528 +2025-04-23 23:47:26,290 - INFO - Std: 2.8893 +2025-04-23 23:47:26,290 - INFO - OC Distribution Statistics: +2025-04-23 23:47:26,290 - INFO - Validation Mean: 17.3772 +2025-04-23 23:47:26,290 - INFO - Validation Std: 8.9271 +2025-04-23 23:47:26,291 - INFO - Training Mean: 23.0004 +2025-04-23 23:47:26,291 - INFO - Training Std: 20.3076 +2025-04-23 23:47:26,291 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:47:26,291 - INFO - KS Statistic: 0.1538 +2025-04-23 23:47:26,291 - INFO - P-value: 0.0000 +2025-04-23 23:47:26,291 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:47:26,291 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:47:27,773 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:47:27,773 - INFO - Timestamp: 2025-04-23 23:47:27 +2025-04-23 23:47:27,773 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:47:27,773 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:47:27,773 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:47:27,773 - INFO - Mean: 4.7899 +2025-04-23 23:47:27,773 - INFO - Median: 4.2255 +2025-04-23 23:47:27,773 - INFO - Min: 0.3763 +2025-04-23 23:47:27,773 - INFO - Max: 18.8528 +2025-04-23 23:47:27,773 - INFO - Std: 2.8893 +2025-04-23 23:47:27,773 - INFO - OC Distribution Statistics: +2025-04-23 23:47:27,773 - INFO - Validation Mean: 17.3772 +2025-04-23 23:47:27,773 - INFO - Validation Std: 8.9271 +2025-04-23 23:47:27,773 - INFO - Training Mean: 23.0004 +2025-04-23 23:47:27,773 - INFO - Training Std: 20.3076 +2025-04-23 23:47:27,773 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:47:27,773 - INFO - KS Statistic: 0.1538 +2025-04-23 23:47:27,773 - INFO - P-value: 0.0000 +2025-04-23 23:47:27,773 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:47:27,773 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:47:27,893 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:47:27,893 - INFO - Timestamp: 2025-04-23 23:47:27 +2025-04-23 23:47:27,893 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:47:27,893 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:47:27,893 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:47:27,893 - INFO - Mean: 4.7899 +2025-04-23 23:47:27,893 - INFO - Median: 4.2255 +2025-04-23 23:47:27,893 - INFO - Min: 0.3763 +2025-04-23 23:47:27,893 - INFO - Max: 18.8528 +2025-04-23 23:47:27,893 - INFO - Std: 2.8893 +2025-04-23 23:47:27,893 - INFO - OC Distribution Statistics: +2025-04-23 23:47:27,893 - INFO - Validation Mean: 17.3772 +2025-04-23 23:47:27,893 - INFO - Validation Std: 8.9271 +2025-04-23 23:47:27,893 - INFO - Training Mean: 23.0004 +2025-04-23 23:47:27,893 - INFO - Training Std: 20.3076 +2025-04-23 23:47:27,893 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:47:27,893 - INFO - KS Statistic: 0.1538 +2025-04-23 23:47:27,893 - INFO - P-value: 0.0000 +2025-04-23 23:47:27,893 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:47:27,893 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:47:29,352 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:47:29,352 - INFO - Timestamp: 2025-04-23 23:47:29 +2025-04-23 23:47:29,352 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:47:29,352 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:47:29,352 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:47:29,352 - INFO - Mean: 4.7899 +2025-04-23 23:47:29,352 - INFO - Median: 4.2255 +2025-04-23 23:47:29,352 - INFO - Min: 0.3763 +2025-04-23 23:47:29,352 - INFO - Max: 18.8528 +2025-04-23 23:47:29,352 - INFO - Std: 2.8893 +2025-04-23 23:47:29,352 - INFO - OC Distribution Statistics: +2025-04-23 23:47:29,352 - INFO - Validation Mean: 17.3772 +2025-04-23 23:47:29,352 - INFO - Validation Std: 8.9271 +2025-04-23 23:47:29,352 - INFO - Training Mean: 23.0004 +2025-04-23 23:47:29,352 - INFO - Training Std: 20.3076 +2025-04-23 23:47:29,352 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:47:29,352 - INFO - KS Statistic: 0.1538 +2025-04-23 23:47:29,352 - INFO - P-value: 0.0000 +2025-04-23 23:47:29,352 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:47:29,352 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-23 23:47:31,212 - INFO - Validation and Training Set Split Statistics +2025-04-23 23:47:31,213 - INFO - Timestamp: 2025-04-23 23:47:31 +2025-04-23 23:47:31,213 - INFO - Validation set size: 1391 (8.42%) +2025-04-23 23:47:31,213 - INFO - Training set size: 15123 (91.58%) +2025-04-23 23:47:31,213 - INFO - Minimum Distance Statistics (km): +2025-04-23 23:47:31,213 - INFO - Mean: 4.7899 +2025-04-23 23:47:31,213 - INFO - Median: 4.2255 +2025-04-23 23:47:31,213 - INFO - Min: 0.3763 +2025-04-23 23:47:31,213 - INFO - Max: 18.8528 +2025-04-23 23:47:31,213 - INFO - Std: 2.8893 +2025-04-23 23:47:31,213 - INFO - OC Distribution Statistics: +2025-04-23 23:47:31,213 - INFO - Validation Mean: 17.3772 +2025-04-23 23:47:31,213 - INFO - Validation Std: 8.9271 +2025-04-23 23:47:31,213 - INFO - Training Mean: 23.0004 +2025-04-23 23:47:31,213 - INFO - Training Std: 20.3076 +2025-04-23 23:47:31,213 - INFO - Kolmogorov-Smirnov Test: +2025-04-23 23:47:31,213 - INFO - KS Statistic: 0.1538 +2025-04-23 23:47:31,213 - INFO - P-value: 0.0000 +2025-04-23 23:47:31,213 - INFO - Distribution Fit: Inverse Gamma +2025-04-23 23:47:31,213 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:14:08,528 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:14:08,528 - INFO - Timestamp: 2025-04-24 00:14:08 +2025-04-24 00:14:08,528 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:14:08,528 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:14:08,528 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:14:08,528 - INFO - Mean: 4.7899 +2025-04-24 00:14:08,528 - INFO - Median: 4.2255 +2025-04-24 00:14:08,528 - INFO - Min: 0.3763 +2025-04-24 00:14:08,528 - INFO - Max: 18.8528 +2025-04-24 00:14:08,528 - INFO - Std: 2.8893 +2025-04-24 00:14:08,528 - INFO - OC Distribution Statistics: +2025-04-24 00:14:08,529 - INFO - Validation Mean: 17.3772 +2025-04-24 00:14:08,529 - INFO - Validation Std: 8.9271 +2025-04-24 00:14:08,529 - INFO - Training Mean: 23.0004 +2025-04-24 00:14:08,529 - INFO - Training Std: 20.3076 +2025-04-24 00:14:08,529 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:14:08,529 - INFO - KS Statistic: 0.1538 +2025-04-24 00:14:08,529 - INFO - P-value: 0.0000 +2025-04-24 00:14:08,529 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:14:08,529 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:14:08,561 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:14:08,561 - INFO - Timestamp: 2025-04-24 00:14:08 +2025-04-24 00:14:08,561 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:14:08,561 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:14:08,561 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:14:08,561 - INFO - Mean: 4.7899 +2025-04-24 00:14:08,561 - INFO - Median: 4.2255 +2025-04-24 00:14:08,562 - INFO - Min: 0.3763 +2025-04-24 00:14:08,562 - INFO - Max: 18.8528 +2025-04-24 00:14:08,562 - INFO - Std: 2.8893 +2025-04-24 00:14:08,562 - INFO - OC Distribution Statistics: +2025-04-24 00:14:08,562 - INFO - Validation Mean: 17.3772 +2025-04-24 00:14:08,562 - INFO - Validation Std: 8.9271 +2025-04-24 00:14:08,562 - INFO - Training Mean: 23.0004 +2025-04-24 00:14:08,562 - INFO - Training Std: 20.3076 +2025-04-24 00:14:08,562 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:14:08,562 - INFO - KS Statistic: 0.1538 +2025-04-24 00:14:08,562 - INFO - P-value: 0.0000 +2025-04-24 00:14:08,562 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:14:08,562 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:14:09,127 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:14:09,127 - INFO - Timestamp: 2025-04-24 00:14:09 +2025-04-24 00:14:09,127 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:14:09,127 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:14:09,127 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:14:09,127 - INFO - Mean: 4.7899 +2025-04-24 00:14:09,127 - INFO - Median: 4.2255 +2025-04-24 00:14:09,127 - INFO - Min: 0.3763 +2025-04-24 00:14:09,127 - INFO - Max: 18.8528 +2025-04-24 00:14:09,127 - INFO - Std: 2.8893 +2025-04-24 00:14:09,127 - INFO - OC Distribution Statistics: +2025-04-24 00:14:09,127 - INFO - Validation Mean: 17.3772 +2025-04-24 00:14:09,127 - INFO - Validation Std: 8.9271 +2025-04-24 00:14:09,127 - INFO - Training Mean: 23.0004 +2025-04-24 00:14:09,127 - INFO - Training Std: 20.3076 +2025-04-24 00:14:09,127 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:14:09,127 - INFO - KS Statistic: 0.1538 +2025-04-24 00:14:09,127 - INFO - P-value: 0.0000 +2025-04-24 00:14:09,127 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:14:09,127 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:14:10,127 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:14:10,127 - INFO - Timestamp: 2025-04-24 00:14:10 +2025-04-24 00:14:10,127 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:14:10,127 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:14:10,127 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:14:10,127 - INFO - Mean: 4.7899 +2025-04-24 00:14:10,127 - INFO - Median: 4.2255 +2025-04-24 00:14:10,127 - INFO - Min: 0.3763 +2025-04-24 00:14:10,127 - INFO - Max: 18.8528 +2025-04-24 00:14:10,127 - INFO - Std: 2.8893 +2025-04-24 00:14:10,127 - INFO - OC Distribution Statistics: +2025-04-24 00:14:10,127 - INFO - Validation Mean: 17.3772 +2025-04-24 00:14:10,127 - INFO - Validation Std: 8.9271 +2025-04-24 00:14:10,127 - INFO - Training Mean: 23.0004 +2025-04-24 00:14:10,127 - INFO - Training Std: 20.3076 +2025-04-24 00:14:10,127 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:14:10,127 - INFO - KS Statistic: 0.1538 +2025-04-24 00:14:10,127 - INFO - P-value: 0.0000 +2025-04-24 00:14:10,127 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:14:10,128 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:14:10,928 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:14:10,928 - INFO - Timestamp: 2025-04-24 00:14:10 +2025-04-24 00:14:10,928 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:14:10,928 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:14:10,928 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:14:10,928 - INFO - Mean: 4.7899 +2025-04-24 00:14:10,928 - INFO - Median: 4.2255 +2025-04-24 00:14:10,928 - INFO - Min: 0.3763 +2025-04-24 00:14:10,928 - INFO - Max: 18.8528 +2025-04-24 00:14:10,928 - INFO - Std: 2.8893 +2025-04-24 00:14:10,928 - INFO - OC Distribution Statistics: +2025-04-24 00:14:10,928 - INFO - Validation Mean: 17.3772 +2025-04-24 00:14:10,928 - INFO - Validation Std: 8.9271 +2025-04-24 00:14:10,928 - INFO - Training Mean: 23.0004 +2025-04-24 00:14:10,928 - INFO - Training Std: 20.3076 +2025-04-24 00:14:10,928 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:14:10,928 - INFO - KS Statistic: 0.1538 +2025-04-24 00:14:10,928 - INFO - P-value: 0.0000 +2025-04-24 00:14:10,928 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:14:10,928 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:14:12,981 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:14:12,981 - INFO - Timestamp: 2025-04-24 00:14:12 +2025-04-24 00:14:12,981 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:14:12,981 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:14:12,981 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:14:12,981 - INFO - Mean: 4.7899 +2025-04-24 00:14:12,981 - INFO - Median: 4.2255 +2025-04-24 00:14:12,981 - INFO - Min: 0.3763 +2025-04-24 00:14:12,981 - INFO - Max: 18.8528 +2025-04-24 00:14:12,981 - INFO - Std: 2.8893 +2025-04-24 00:14:12,981 - INFO - OC Distribution Statistics: +2025-04-24 00:14:12,981 - INFO - Validation Mean: 17.3772 +2025-04-24 00:14:12,981 - INFO - Validation Std: 8.9271 +2025-04-24 00:14:12,981 - INFO - Training Mean: 23.0004 +2025-04-24 00:14:12,982 - INFO - Training Std: 20.3076 +2025-04-24 00:14:12,982 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:14:12,982 - INFO - KS Statistic: 0.1538 +2025-04-24 00:14:12,982 - INFO - P-value: 0.0000 +2025-04-24 00:14:12,982 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:14:12,982 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:14:13,271 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:14:13,271 - INFO - Timestamp: 2025-04-24 00:14:13 +2025-04-24 00:14:13,271 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:14:13,271 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:14:13,271 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:14:13,271 - INFO - Mean: 4.7899 +2025-04-24 00:14:13,271 - INFO - Median: 4.2255 +2025-04-24 00:14:13,271 - INFO - Min: 0.3763 +2025-04-24 00:14:13,271 - INFO - Max: 18.8528 +2025-04-24 00:14:13,271 - INFO - Std: 2.8893 +2025-04-24 00:14:13,271 - INFO - OC Distribution Statistics: +2025-04-24 00:14:13,271 - INFO - Validation Mean: 17.3772 +2025-04-24 00:14:13,271 - INFO - Validation Std: 8.9271 +2025-04-24 00:14:13,271 - INFO - Training Mean: 23.0004 +2025-04-24 00:14:13,271 - INFO - Training Std: 20.3076 +2025-04-24 00:14:13,271 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:14:13,271 - INFO - KS Statistic: 0.1538 +2025-04-24 00:14:13,271 - INFO - P-value: 0.0000 +2025-04-24 00:14:13,271 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:14:13,271 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:14:13,621 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:14:13,621 - INFO - Timestamp: 2025-04-24 00:14:13 +2025-04-24 00:14:13,621 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:14:13,621 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:14:13,621 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:14:13,621 - INFO - Mean: 4.7899 +2025-04-24 00:14:13,621 - INFO - Median: 4.2255 +2025-04-24 00:14:13,621 - INFO - Min: 0.3763 +2025-04-24 00:14:13,621 - INFO - Max: 18.8528 +2025-04-24 00:14:13,621 - INFO - Std: 2.8893 +2025-04-24 00:14:13,621 - INFO - OC Distribution Statistics: +2025-04-24 00:14:13,621 - INFO - Validation Mean: 17.3772 +2025-04-24 00:14:13,621 - INFO - Validation Std: 8.9271 +2025-04-24 00:14:13,621 - INFO - Training Mean: 23.0004 +2025-04-24 00:14:13,621 - INFO - Training Std: 20.3076 +2025-04-24 00:14:13,621 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:14:13,622 - INFO - KS Statistic: 0.1538 +2025-04-24 00:14:13,622 - INFO - P-value: 0.0000 +2025-04-24 00:14:13,622 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:14:13,622 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:41:12,077 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:41:12,078 - INFO - Timestamp: 2025-04-24 00:41:12 +2025-04-24 00:41:12,078 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:41:12,078 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:41:12,078 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:41:12,078 - INFO - Mean: 4.7899 +2025-04-24 00:41:12,078 - INFO - Median: 4.2255 +2025-04-24 00:41:12,078 - INFO - Min: 0.3763 +2025-04-24 00:41:12,078 - INFO - Max: 18.8528 +2025-04-24 00:41:12,078 - INFO - Std: 2.8893 +2025-04-24 00:41:12,078 - INFO - OC Distribution Statistics: +2025-04-24 00:41:12,078 - INFO - Validation Mean: 17.3772 +2025-04-24 00:41:12,078 - INFO - Validation Std: 8.9271 +2025-04-24 00:41:12,078 - INFO - Training Mean: 23.0004 +2025-04-24 00:41:12,078 - INFO - Training Std: 20.3076 +2025-04-24 00:41:12,078 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:41:12,078 - INFO - KS Statistic: 0.1538 +2025-04-24 00:41:12,078 - INFO - P-value: 0.0000 +2025-04-24 00:41:12,078 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:41:12,078 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:41:12,604 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:41:12,604 - INFO - Timestamp: 2025-04-24 00:41:12 +2025-04-24 00:41:12,604 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:41:12,604 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:41:12,604 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:41:12,604 - INFO - Mean: 4.7899 +2025-04-24 00:41:12,604 - INFO - Median: 4.2255 +2025-04-24 00:41:12,604 - INFO - Min: 0.3763 +2025-04-24 00:41:12,604 - INFO - Max: 18.8528 +2025-04-24 00:41:12,604 - INFO - Std: 2.8893 +2025-04-24 00:41:12,604 - INFO - OC Distribution Statistics: +2025-04-24 00:41:12,604 - INFO - Validation Mean: 17.3772 +2025-04-24 00:41:12,604 - INFO - Validation Std: 8.9271 +2025-04-24 00:41:12,604 - INFO - Training Mean: 23.0004 +2025-04-24 00:41:12,604 - INFO - Training Std: 20.3076 +2025-04-24 00:41:12,604 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:41:12,604 - INFO - KS Statistic: 0.1538 +2025-04-24 00:41:12,604 - INFO - P-value: 0.0000 +2025-04-24 00:41:12,604 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:41:12,604 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:41:14,203 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:41:14,203 - INFO - Timestamp: 2025-04-24 00:41:14 +2025-04-24 00:41:14,203 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:41:14,203 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:41:14,203 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:41:14,203 - INFO - Mean: 4.7899 +2025-04-24 00:41:14,203 - INFO - Median: 4.2255 +2025-04-24 00:41:14,203 - INFO - Min: 0.3763 +2025-04-24 00:41:14,203 - INFO - Max: 18.8528 +2025-04-24 00:41:14,203 - INFO - Std: 2.8893 +2025-04-24 00:41:14,203 - INFO - OC Distribution Statistics: +2025-04-24 00:41:14,203 - INFO - Validation Mean: 17.3772 +2025-04-24 00:41:14,203 - INFO - Validation Std: 8.9271 +2025-04-24 00:41:14,203 - INFO - Training Mean: 23.0004 +2025-04-24 00:41:14,203 - INFO - Training Std: 20.3076 +2025-04-24 00:41:14,203 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:41:14,203 - INFO - KS Statistic: 0.1538 +2025-04-24 00:41:14,203 - INFO - P-value: 0.0000 +2025-04-24 00:41:14,203 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:41:14,203 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:41:14,612 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:41:14,612 - INFO - Timestamp: 2025-04-24 00:41:14 +2025-04-24 00:41:14,612 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:41:14,612 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:41:14,612 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:41:14,612 - INFO - Mean: 4.7899 +2025-04-24 00:41:14,612 - INFO - Median: 4.2255 +2025-04-24 00:41:14,612 - INFO - Min: 0.3763 +2025-04-24 00:41:14,612 - INFO - Max: 18.8528 +2025-04-24 00:41:14,612 - INFO - Std: 2.8893 +2025-04-24 00:41:14,612 - INFO - OC Distribution Statistics: +2025-04-24 00:41:14,612 - INFO - Validation Mean: 17.3772 +2025-04-24 00:41:14,612 - INFO - Validation Std: 8.9271 +2025-04-24 00:41:14,612 - INFO - Training Mean: 23.0004 +2025-04-24 00:41:14,612 - INFO - Training Std: 20.3076 +2025-04-24 00:41:14,612 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:41:14,612 - INFO - KS Statistic: 0.1538 +2025-04-24 00:41:14,612 - INFO - P-value: 0.0000 +2025-04-24 00:41:14,612 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:41:14,612 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:41:14,711 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:41:14,711 - INFO - Timestamp: 2025-04-24 00:41:14 +2025-04-24 00:41:14,711 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:41:14,711 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:41:14,711 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:41:14,711 - INFO - Mean: 4.7899 +2025-04-24 00:41:14,711 - INFO - Median: 4.2255 +2025-04-24 00:41:14,711 - INFO - Min: 0.3763 +2025-04-24 00:41:14,711 - INFO - Max: 18.8528 +2025-04-24 00:41:14,711 - INFO - Std: 2.8893 +2025-04-24 00:41:14,711 - INFO - OC Distribution Statistics: +2025-04-24 00:41:14,712 - INFO - Validation Mean: 17.3772 +2025-04-24 00:41:14,712 - INFO - Validation Std: 8.9271 +2025-04-24 00:41:14,712 - INFO - Training Mean: 23.0004 +2025-04-24 00:41:14,712 - INFO - Training Std: 20.3076 +2025-04-24 00:41:14,712 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:41:14,712 - INFO - KS Statistic: 0.1538 +2025-04-24 00:41:14,712 - INFO - P-value: 0.0000 +2025-04-24 00:41:14,712 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:41:14,712 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:41:17,031 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:41:17,032 - INFO - Timestamp: 2025-04-24 00:41:17 +2025-04-24 00:41:17,032 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:41:17,032 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:41:17,032 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:41:17,032 - INFO - Mean: 4.7899 +2025-04-24 00:41:17,032 - INFO - Median: 4.2255 +2025-04-24 00:41:17,032 - INFO - Min: 0.3763 +2025-04-24 00:41:17,032 - INFO - Max: 18.8528 +2025-04-24 00:41:17,032 - INFO - Std: 2.8893 +2025-04-24 00:41:17,032 - INFO - OC Distribution Statistics: +2025-04-24 00:41:17,032 - INFO - Validation Mean: 17.3772 +2025-04-24 00:41:17,032 - INFO - Validation Std: 8.9271 +2025-04-24 00:41:17,032 - INFO - Training Mean: 23.0004 +2025-04-24 00:41:17,032 - INFO - Training Std: 20.3076 +2025-04-24 00:41:17,032 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:41:17,032 - INFO - KS Statistic: 0.1538 +2025-04-24 00:41:17,032 - INFO - P-value: 0.0000 +2025-04-24 00:41:17,032 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:41:17,032 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:41:18,341 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:41:18,342 - INFO - Timestamp: 2025-04-24 00:41:18 +2025-04-24 00:41:18,342 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:41:18,342 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:41:18,342 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:41:18,342 - INFO - Mean: 4.7899 +2025-04-24 00:41:18,342 - INFO - Median: 4.2255 +2025-04-24 00:41:18,342 - INFO - Min: 0.3763 +2025-04-24 00:41:18,342 - INFO - Max: 18.8528 +2025-04-24 00:41:18,342 - INFO - Std: 2.8893 +2025-04-24 00:41:18,342 - INFO - OC Distribution Statistics: +2025-04-24 00:41:18,342 - INFO - Validation Mean: 17.3772 +2025-04-24 00:41:18,343 - INFO - Validation Std: 8.9271 +2025-04-24 00:41:18,343 - INFO - Training Mean: 23.0004 +2025-04-24 00:41:18,343 - INFO - Training Std: 20.3076 +2025-04-24 00:41:18,343 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:41:18,343 - INFO - KS Statistic: 0.1538 +2025-04-24 00:41:18,343 - INFO - P-value: 0.0000 +2025-04-24 00:41:18,343 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:41:18,343 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 00:41:18,892 - INFO - Validation and Training Set Split Statistics +2025-04-24 00:41:18,892 - INFO - Timestamp: 2025-04-24 00:41:18 +2025-04-24 00:41:18,892 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 00:41:18,892 - INFO - Training set size: 15123 (91.58%) +2025-04-24 00:41:18,892 - INFO - Minimum Distance Statistics (km): +2025-04-24 00:41:18,892 - INFO - Mean: 4.7899 +2025-04-24 00:41:18,892 - INFO - Median: 4.2255 +2025-04-24 00:41:18,892 - INFO - Min: 0.3763 +2025-04-24 00:41:18,892 - INFO - Max: 18.8528 +2025-04-24 00:41:18,892 - INFO - Std: 2.8893 +2025-04-24 00:41:18,893 - INFO - OC Distribution Statistics: +2025-04-24 00:41:18,893 - INFO - Validation Mean: 17.3772 +2025-04-24 00:41:18,893 - INFO - Validation Std: 8.9271 +2025-04-24 00:41:18,893 - INFO - Training Mean: 23.0004 +2025-04-24 00:41:18,893 - INFO - Training Std: 20.3076 +2025-04-24 00:41:18,893 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 00:41:18,893 - INFO - KS Statistic: 0.1538 +2025-04-24 00:41:18,893 - INFO - P-value: 0.0000 +2025-04-24 00:41:18,893 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 00:41:18,893 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 01:08:21,987 - INFO - Validation and Training Set Split Statistics +2025-04-24 01:08:21,987 - INFO - Timestamp: 2025-04-24 01:08:21 +2025-04-24 01:08:21,988 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 01:08:21,988 - INFO - Training set size: 15123 (91.58%) +2025-04-24 01:08:21,988 - INFO - Minimum Distance Statistics (km): +2025-04-24 01:08:21,988 - INFO - Mean: 4.7899 +2025-04-24 01:08:21,988 - INFO - Median: 4.2255 +2025-04-24 01:08:21,988 - INFO - Min: 0.3763 +2025-04-24 01:08:21,988 - INFO - Max: 18.8528 +2025-04-24 01:08:21,988 - INFO - Std: 2.8893 +2025-04-24 01:08:21,988 - INFO - OC Distribution Statistics: +2025-04-24 01:08:21,988 - INFO - Validation Mean: 17.3772 +2025-04-24 01:08:21,988 - INFO - Validation Std: 8.9271 +2025-04-24 01:08:21,988 - INFO - Training Mean: 23.0004 +2025-04-24 01:08:21,988 - INFO - Training Std: 20.3076 +2025-04-24 01:08:21,988 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 01:08:21,988 - INFO - KS Statistic: 0.1538 +2025-04-24 01:08:21,988 - INFO - P-value: 0.0000 +2025-04-24 01:08:21,988 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 01:08:21,988 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 01:08:23,503 - INFO - Validation and Training Set Split Statistics +2025-04-24 01:08:23,503 - INFO - Timestamp: 2025-04-24 01:08:23 +2025-04-24 01:08:23,503 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 01:08:23,503 - INFO - Training set size: 15123 (91.58%) +2025-04-24 01:08:23,503 - INFO - Minimum Distance Statistics (km): +2025-04-24 01:08:23,503 - INFO - Mean: 4.7899 +2025-04-24 01:08:23,503 - INFO - Median: 4.2255 +2025-04-24 01:08:23,503 - INFO - Min: 0.3763 +2025-04-24 01:08:23,503 - INFO - Max: 18.8528 +2025-04-24 01:08:23,503 - INFO - Std: 2.8893 +2025-04-24 01:08:23,503 - INFO - OC Distribution Statistics: +2025-04-24 01:08:23,503 - INFO - Validation Mean: 17.3772 +2025-04-24 01:08:23,503 - INFO - Validation Std: 8.9271 +2025-04-24 01:08:23,503 - INFO - Training Mean: 23.0004 +2025-04-24 01:08:23,503 - INFO - Training Std: 20.3076 +2025-04-24 01:08:23,503 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 01:08:23,503 - INFO - KS Statistic: 0.1538 +2025-04-24 01:08:23,503 - INFO - P-value: 0.0000 +2025-04-24 01:08:23,503 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 01:08:23,503 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 01:08:23,583 - INFO - Validation and Training Set Split Statistics +2025-04-24 01:08:23,583 - INFO - Timestamp: 2025-04-24 01:08:23 +2025-04-24 01:08:23,583 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 01:08:23,583 - INFO - Training set size: 15123 (91.58%) +2025-04-24 01:08:23,583 - INFO - Minimum Distance Statistics (km): +2025-04-24 01:08:23,583 - INFO - Mean: 4.7899 +2025-04-24 01:08:23,583 - INFO - Median: 4.2255 +2025-04-24 01:08:23,583 - INFO - Min: 0.3763 +2025-04-24 01:08:23,583 - INFO - Max: 18.8528 +2025-04-24 01:08:23,583 - INFO - Std: 2.8893 +2025-04-24 01:08:23,583 - INFO - OC Distribution Statistics: +2025-04-24 01:08:23,583 - INFO - Validation Mean: 17.3772 +2025-04-24 01:08:23,583 - INFO - Validation Std: 8.9271 +2025-04-24 01:08:23,583 - INFO - Training Mean: 23.0004 +2025-04-24 01:08:23,583 - INFO - Training Std: 20.3076 +2025-04-24 01:08:23,583 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 01:08:23,583 - INFO - KS Statistic: 0.1538 +2025-04-24 01:08:23,583 - INFO - P-value: 0.0000 +2025-04-24 01:08:23,583 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 01:08:23,583 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 01:08:24,618 - INFO - Validation and Training Set Split Statistics +2025-04-24 01:08:24,618 - INFO - Timestamp: 2025-04-24 01:08:24 +2025-04-24 01:08:24,618 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 01:08:24,618 - INFO - Training set size: 15123 (91.58%) +2025-04-24 01:08:24,618 - INFO - Minimum Distance Statistics (km): +2025-04-24 01:08:24,618 - INFO - Mean: 4.7899 +2025-04-24 01:08:24,618 - INFO - Median: 4.2255 +2025-04-24 01:08:24,618 - INFO - Min: 0.3763 +2025-04-24 01:08:24,618 - INFO - Max: 18.8528 +2025-04-24 01:08:24,618 - INFO - Std: 2.8893 +2025-04-24 01:08:24,618 - INFO - OC Distribution Statistics: +2025-04-24 01:08:24,618 - INFO - Validation Mean: 17.3772 +2025-04-24 01:08:24,618 - INFO - Validation Std: 8.9271 +2025-04-24 01:08:24,618 - INFO - Training Mean: 23.0004 +2025-04-24 01:08:24,619 - INFO - Training Std: 20.3076 +2025-04-24 01:08:24,619 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 01:08:24,619 - INFO - KS Statistic: 0.1538 +2025-04-24 01:08:24,619 - INFO - P-value: 0.0000 +2025-04-24 01:08:24,619 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 01:08:24,619 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 01:08:24,804 - INFO - Validation and Training Set Split Statistics +2025-04-24 01:08:24,804 - INFO - Timestamp: 2025-04-24 01:08:24 +2025-04-24 01:08:24,804 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 01:08:24,804 - INFO - Training set size: 15123 (91.58%) +2025-04-24 01:08:24,804 - INFO - Minimum Distance Statistics (km): +2025-04-24 01:08:24,804 - INFO - Mean: 4.7899 +2025-04-24 01:08:24,804 - INFO - Median: 4.2255 +2025-04-24 01:08:24,805 - INFO - Min: 0.3763 +2025-04-24 01:08:24,805 - INFO - Max: 18.8528 +2025-04-24 01:08:24,805 - INFO - Std: 2.8893 +2025-04-24 01:08:24,805 - INFO - OC Distribution Statistics: +2025-04-24 01:08:24,805 - INFO - Validation Mean: 17.3772 +2025-04-24 01:08:24,805 - INFO - Validation Std: 8.9271 +2025-04-24 01:08:24,805 - INFO - Training Mean: 23.0004 +2025-04-24 01:08:24,805 - INFO - Training Std: 20.3076 +2025-04-24 01:08:24,805 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 01:08:24,805 - INFO - KS Statistic: 0.1538 +2025-04-24 01:08:24,805 - INFO - P-value: 0.0000 +2025-04-24 01:08:24,805 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 01:08:24,805 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 01:08:27,119 - INFO - Validation and Training Set Split Statistics +2025-04-24 01:08:27,119 - INFO - Timestamp: 2025-04-24 01:08:27 +2025-04-24 01:08:27,119 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 01:08:27,119 - INFO - Training set size: 15123 (91.58%) +2025-04-24 01:08:27,119 - INFO - Minimum Distance Statistics (km): +2025-04-24 01:08:27,119 - INFO - Mean: 4.7899 +2025-04-24 01:08:27,119 - INFO - Median: 4.2255 +2025-04-24 01:08:27,119 - INFO - Min: 0.3763 +2025-04-24 01:08:27,119 - INFO - Max: 18.8528 +2025-04-24 01:08:27,119 - INFO - Std: 2.8893 +2025-04-24 01:08:27,119 - INFO - OC Distribution Statistics: +2025-04-24 01:08:27,119 - INFO - Validation Mean: 17.3772 +2025-04-24 01:08:27,119 - INFO - Validation Std: 8.9271 +2025-04-24 01:08:27,119 - INFO - Training Mean: 23.0004 +2025-04-24 01:08:27,119 - INFO - Training Std: 20.3076 +2025-04-24 01:08:27,119 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 01:08:27,119 - INFO - KS Statistic: 0.1538 +2025-04-24 01:08:27,119 - INFO - P-value: 0.0000 +2025-04-24 01:08:27,119 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 01:08:27,119 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 01:08:27,908 - INFO - Validation and Training Set Split Statistics +2025-04-24 01:08:27,908 - INFO - Timestamp: 2025-04-24 01:08:27 +2025-04-24 01:08:27,908 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 01:08:27,909 - INFO - Training set size: 15123 (91.58%) +2025-04-24 01:08:27,909 - INFO - Minimum Distance Statistics (km): +2025-04-24 01:08:27,909 - INFO - Mean: 4.7899 +2025-04-24 01:08:27,909 - INFO - Median: 4.2255 +2025-04-24 01:08:27,909 - INFO - Min: 0.3763 +2025-04-24 01:08:27,909 - INFO - Max: 18.8528 +2025-04-24 01:08:27,909 - INFO - Std: 2.8893 +2025-04-24 01:08:27,909 - INFO - OC Distribution Statistics: +2025-04-24 01:08:27,909 - INFO - Validation Mean: 17.3772 +2025-04-24 01:08:27,909 - INFO - Validation Std: 8.9271 +2025-04-24 01:08:27,909 - INFO - Training Mean: 23.0004 +2025-04-24 01:08:27,909 - INFO - Training Std: 20.3076 +2025-04-24 01:08:27,909 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 01:08:27,909 - INFO - KS Statistic: 0.1538 +2025-04-24 01:08:27,909 - INFO - P-value: 0.0000 +2025-04-24 01:08:27,909 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 01:08:27,909 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 01:08:29,119 - INFO - Validation and Training Set Split Statistics +2025-04-24 01:08:29,119 - INFO - Timestamp: 2025-04-24 01:08:29 +2025-04-24 01:08:29,119 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 01:08:29,119 - INFO - Training set size: 15123 (91.58%) +2025-04-24 01:08:29,119 - INFO - Minimum Distance Statistics (km): +2025-04-24 01:08:29,119 - INFO - Mean: 4.7899 +2025-04-24 01:08:29,119 - INFO - Median: 4.2255 +2025-04-24 01:08:29,119 - INFO - Min: 0.3763 +2025-04-24 01:08:29,119 - INFO - Max: 18.8528 +2025-04-24 01:08:29,119 - INFO - Std: 2.8893 +2025-04-24 01:08:29,119 - INFO - OC Distribution Statistics: +2025-04-24 01:08:29,119 - INFO - Validation Mean: 17.3772 +2025-04-24 01:08:29,119 - INFO - Validation Std: 8.9271 +2025-04-24 01:08:29,119 - INFO - Training Mean: 23.0004 +2025-04-24 01:08:29,119 - INFO - Training Std: 20.3076 +2025-04-24 01:08:29,120 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 01:08:29,120 - INFO - KS Statistic: 0.1538 +2025-04-24 01:08:29,120 - INFO - P-value: 0.0000 +2025-04-24 01:08:29,120 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 01:08:29,120 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:03:32,680 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:03:32,680 - INFO - Timestamp: 2025-04-24 20:03:32 +2025-04-24 20:03:32,680 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:03:32,680 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:03:32,680 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:03:32,680 - INFO - Mean: 4.7899 +2025-04-24 20:03:32,680 - INFO - Median: 4.2255 +2025-04-24 20:03:32,680 - INFO - Min: 0.3763 +2025-04-24 20:03:32,680 - INFO - Max: 18.8528 +2025-04-24 20:03:32,681 - INFO - Std: 2.8893 +2025-04-24 20:03:32,681 - INFO - OC Distribution Statistics: +2025-04-24 20:03:32,681 - INFO - Validation Mean: 17.3772 +2025-04-24 20:03:32,681 - INFO - Validation Std: 8.9271 +2025-04-24 20:03:32,681 - INFO - Training Mean: 23.0004 +2025-04-24 20:03:32,681 - INFO - Training Std: 20.3076 +2025-04-24 20:03:32,681 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:03:32,681 - INFO - KS Statistic: 0.1538 +2025-04-24 20:03:32,681 - INFO - P-value: 0.0000 +2025-04-24 20:03:32,681 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:03:32,681 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:03:32,808 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:03:32,808 - INFO - Timestamp: 2025-04-24 20:03:32 +2025-04-24 20:03:32,808 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:03:32,808 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:03:32,808 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:03:32,808 - INFO - Mean: 4.7899 +2025-04-24 20:03:32,808 - INFO - Median: 4.2255 +2025-04-24 20:03:32,808 - INFO - Min: 0.3763 +2025-04-24 20:03:32,808 - INFO - Max: 18.8528 +2025-04-24 20:03:32,808 - INFO - Std: 2.8893 +2025-04-24 20:03:32,808 - INFO - OC Distribution Statistics: +2025-04-24 20:03:32,808 - INFO - Validation Mean: 17.3772 +2025-04-24 20:03:32,808 - INFO - Validation Std: 8.9271 +2025-04-24 20:03:32,808 - INFO - Training Mean: 23.0004 +2025-04-24 20:03:32,808 - INFO - Training Std: 20.3076 +2025-04-24 20:03:32,808 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:03:32,808 - INFO - KS Statistic: 0.1538 +2025-04-24 20:03:32,808 - INFO - P-value: 0.0000 +2025-04-24 20:03:32,808 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:03:32,809 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:03:33,317 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:03:33,318 - INFO - Timestamp: 2025-04-24 20:03:33 +2025-04-24 20:03:33,318 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:03:33,318 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:03:33,318 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:03:33,318 - INFO - Mean: 4.7899 +2025-04-24 20:03:33,318 - INFO - Median: 4.2255 +2025-04-24 20:03:33,318 - INFO - Min: 0.3763 +2025-04-24 20:03:33,318 - INFO - Max: 18.8528 +2025-04-24 20:03:33,318 - INFO - Std: 2.8893 +2025-04-24 20:03:33,318 - INFO - OC Distribution Statistics: +2025-04-24 20:03:33,318 - INFO - Validation Mean: 17.3772 +2025-04-24 20:03:33,318 - INFO - Validation Std: 8.9271 +2025-04-24 20:03:33,318 - INFO - Training Mean: 23.0004 +2025-04-24 20:03:33,318 - INFO - Training Std: 20.3076 +2025-04-24 20:03:33,318 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:03:33,318 - INFO - KS Statistic: 0.1538 +2025-04-24 20:03:33,318 - INFO - P-value: 0.0000 +2025-04-24 20:03:33,318 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:03:33,318 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:03:34,592 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:03:34,593 - INFO - Timestamp: 2025-04-24 20:03:34 +2025-04-24 20:03:34,593 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:03:34,593 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:03:34,593 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:03:34,593 - INFO - Mean: 4.7899 +2025-04-24 20:03:34,593 - INFO - Median: 4.2255 +2025-04-24 20:03:34,593 - INFO - Min: 0.3763 +2025-04-24 20:03:34,593 - INFO - Max: 18.8528 +2025-04-24 20:03:34,593 - INFO - Std: 2.8893 +2025-04-24 20:03:34,593 - INFO - OC Distribution Statistics: +2025-04-24 20:03:34,593 - INFO - Validation Mean: 17.3772 +2025-04-24 20:03:34,593 - INFO - Validation Std: 8.9271 +2025-04-24 20:03:34,593 - INFO - Training Mean: 23.0004 +2025-04-24 20:03:34,593 - INFO - Training Std: 20.3076 +2025-04-24 20:03:34,593 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:03:34,593 - INFO - KS Statistic: 0.1538 +2025-04-24 20:03:34,593 - INFO - P-value: 0.0000 +2025-04-24 20:03:34,593 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:03:34,593 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:05:03,854 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:05:03,854 - INFO - Timestamp: 2025-04-24 20:05:03 +2025-04-24 20:05:03,854 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:05:03,854 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:05:03,854 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:05:03,855 - INFO - Mean: 4.7899 +2025-04-24 20:05:03,855 - INFO - Median: 4.2255 +2025-04-24 20:05:03,855 - INFO - Min: 0.3763 +2025-04-24 20:05:03,855 - INFO - Max: 18.8528 +2025-04-24 20:05:03,855 - INFO - Std: 2.8893 +2025-04-24 20:05:03,855 - INFO - OC Distribution Statistics: +2025-04-24 20:05:03,855 - INFO - Validation Mean: 17.3772 +2025-04-24 20:05:03,855 - INFO - Validation Std: 8.9271 +2025-04-24 20:05:03,855 - INFO - Training Mean: 23.0004 +2025-04-24 20:05:03,855 - INFO - Training Std: 20.3076 +2025-04-24 20:05:03,855 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:05:03,855 - INFO - KS Statistic: 0.1538 +2025-04-24 20:05:03,855 - INFO - P-value: 0.0000 +2025-04-24 20:05:03,855 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:05:03,855 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:05:04,162 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:05:04,162 - INFO - Timestamp: 2025-04-24 20:05:04 +2025-04-24 20:05:04,162 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:05:04,162 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:05:04,162 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:05:04,162 - INFO - Mean: 4.7899 +2025-04-24 20:05:04,162 - INFO - Median: 4.2255 +2025-04-24 20:05:04,162 - INFO - Min: 0.3763 +2025-04-24 20:05:04,162 - INFO - Max: 18.8528 +2025-04-24 20:05:04,162 - INFO - Std: 2.8893 +2025-04-24 20:05:04,162 - INFO - OC Distribution Statistics: +2025-04-24 20:05:04,162 - INFO - Validation Mean: 17.3772 +2025-04-24 20:05:04,162 - INFO - Validation Std: 8.9271 +2025-04-24 20:05:04,162 - INFO - Training Mean: 23.0004 +2025-04-24 20:05:04,162 - INFO - Training Std: 20.3076 +2025-04-24 20:05:04,162 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:05:04,162 - INFO - KS Statistic: 0.1538 +2025-04-24 20:05:04,162 - INFO - P-value: 0.0000 +2025-04-24 20:05:04,162 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:05:04,162 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:05:04,424 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:05:04,425 - INFO - Timestamp: 2025-04-24 20:05:04 +2025-04-24 20:05:04,425 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:05:04,425 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:05:04,425 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:05:04,425 - INFO - Mean: 4.7899 +2025-04-24 20:05:04,425 - INFO - Median: 4.2255 +2025-04-24 20:05:04,425 - INFO - Min: 0.3763 +2025-04-24 20:05:04,425 - INFO - Max: 18.8528 +2025-04-24 20:05:04,425 - INFO - Std: 2.8893 +2025-04-24 20:05:04,425 - INFO - OC Distribution Statistics: +2025-04-24 20:05:04,425 - INFO - Validation Mean: 17.3772 +2025-04-24 20:05:04,425 - INFO - Validation Std: 8.9271 +2025-04-24 20:05:04,425 - INFO - Training Mean: 23.0004 +2025-04-24 20:05:04,425 - INFO - Training Std: 20.3076 +2025-04-24 20:05:04,425 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:05:04,425 - INFO - KS Statistic: 0.1538 +2025-04-24 20:05:04,425 - INFO - P-value: 0.0000 +2025-04-24 20:05:04,425 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:05:04,425 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:05:08,832 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:05:08,832 - INFO - Timestamp: 2025-04-24 20:05:08 +2025-04-24 20:05:08,832 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:05:08,832 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:05:08,832 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:05:08,832 - INFO - Mean: 4.7899 +2025-04-24 20:05:08,832 - INFO - Median: 4.2255 +2025-04-24 20:05:08,832 - INFO - Min: 0.3763 +2025-04-24 20:05:08,832 - INFO - Max: 18.8528 +2025-04-24 20:05:08,832 - INFO - Std: 2.8893 +2025-04-24 20:05:08,832 - INFO - OC Distribution Statistics: +2025-04-24 20:05:08,832 - INFO - Validation Mean: 17.3772 +2025-04-24 20:05:08,832 - INFO - Validation Std: 8.9271 +2025-04-24 20:05:08,832 - INFO - Training Mean: 23.0004 +2025-04-24 20:05:08,832 - INFO - Training Std: 20.3076 +2025-04-24 20:05:08,832 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:05:08,832 - INFO - KS Statistic: 0.1538 +2025-04-24 20:05:08,832 - INFO - P-value: 0.0000 +2025-04-24 20:05:08,832 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:05:08,832 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:06:33,741 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:06:33,742 - INFO - Timestamp: 2025-04-24 20:06:33 +2025-04-24 20:06:33,742 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:06:33,742 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:06:33,742 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:06:33,742 - INFO - Mean: 4.7899 +2025-04-24 20:06:33,742 - INFO - Median: 4.2255 +2025-04-24 20:06:33,742 - INFO - Min: 0.3763 +2025-04-24 20:06:33,742 - INFO - Max: 18.8528 +2025-04-24 20:06:33,742 - INFO - Std: 2.8893 +2025-04-24 20:06:33,742 - INFO - OC Distribution Statistics: +2025-04-24 20:06:33,742 - INFO - Validation Mean: 17.3772 +2025-04-24 20:06:33,742 - INFO - Validation Std: 8.9271 +2025-04-24 20:06:33,742 - INFO - Training Mean: 23.0004 +2025-04-24 20:06:33,742 - INFO - Training Std: 20.3076 +2025-04-24 20:06:33,742 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:06:33,742 - INFO - KS Statistic: 0.1538 +2025-04-24 20:06:33,742 - INFO - P-value: 0.0000 +2025-04-24 20:06:33,742 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:06:33,742 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:06:34,084 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:06:34,084 - INFO - Timestamp: 2025-04-24 20:06:34 +2025-04-24 20:06:34,084 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:06:34,084 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:06:34,084 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:06:34,084 - INFO - Mean: 4.7899 +2025-04-24 20:06:34,084 - INFO - Median: 4.2255 +2025-04-24 20:06:34,084 - INFO - Min: 0.3763 +2025-04-24 20:06:34,084 - INFO - Max: 18.8528 +2025-04-24 20:06:34,084 - INFO - Std: 2.8893 +2025-04-24 20:06:34,084 - INFO - OC Distribution Statistics: +2025-04-24 20:06:34,084 - INFO - Validation Mean: 17.3772 +2025-04-24 20:06:34,084 - INFO - Validation Std: 8.9271 +2025-04-24 20:06:34,084 - INFO - Training Mean: 23.0004 +2025-04-24 20:06:34,084 - INFO - Training Std: 20.3076 +2025-04-24 20:06:34,084 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:06:34,084 - INFO - KS Statistic: 0.1538 +2025-04-24 20:06:34,084 - INFO - P-value: 0.0000 +2025-04-24 20:06:34,084 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:06:34,084 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:06:34,372 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:06:34,372 - INFO - Timestamp: 2025-04-24 20:06:34 +2025-04-24 20:06:34,372 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:06:34,372 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:06:34,372 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:06:34,372 - INFO - Mean: 4.7899 +2025-04-24 20:06:34,372 - INFO - Median: 4.2255 +2025-04-24 20:06:34,372 - INFO - Min: 0.3763 +2025-04-24 20:06:34,372 - INFO - Max: 18.8528 +2025-04-24 20:06:34,372 - INFO - Std: 2.8893 +2025-04-24 20:06:34,372 - INFO - OC Distribution Statistics: +2025-04-24 20:06:34,372 - INFO - Validation Mean: 17.3772 +2025-04-24 20:06:34,372 - INFO - Validation Std: 8.9271 +2025-04-24 20:06:34,372 - INFO - Training Mean: 23.0004 +2025-04-24 20:06:34,372 - INFO - Training Std: 20.3076 +2025-04-24 20:06:34,372 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:06:34,372 - INFO - KS Statistic: 0.1538 +2025-04-24 20:06:34,372 - INFO - P-value: 0.0000 +2025-04-24 20:06:34,372 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:06:34,372 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:06:41,769 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:06:41,769 - INFO - Timestamp: 2025-04-24 20:06:41 +2025-04-24 20:06:41,769 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:06:41,769 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:06:41,769 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:06:41,770 - INFO - Mean: 4.7899 +2025-04-24 20:06:41,770 - INFO - Median: 4.2255 +2025-04-24 20:06:41,770 - INFO - Min: 0.3763 +2025-04-24 20:06:41,770 - INFO - Max: 18.8528 +2025-04-24 20:06:41,770 - INFO - Std: 2.8893 +2025-04-24 20:06:41,770 - INFO - OC Distribution Statistics: +2025-04-24 20:06:41,770 - INFO - Validation Mean: 17.3772 +2025-04-24 20:06:41,770 - INFO - Validation Std: 8.9271 +2025-04-24 20:06:41,770 - INFO - Training Mean: 23.0004 +2025-04-24 20:06:41,770 - INFO - Training Std: 20.3076 +2025-04-24 20:06:41,770 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:06:41,770 - INFO - KS Statistic: 0.1538 +2025-04-24 20:06:41,770 - INFO - P-value: 0.0000 +2025-04-24 20:06:41,770 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:06:41,770 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:08:04,142 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:08:04,142 - INFO - Timestamp: 2025-04-24 20:08:04 +2025-04-24 20:08:04,142 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:08:04,142 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:08:04,142 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:08:04,142 - INFO - Mean: 4.7899 +2025-04-24 20:08:04,142 - INFO - Median: 4.2255 +2025-04-24 20:08:04,142 - INFO - Min: 0.3763 +2025-04-24 20:08:04,142 - INFO - Max: 18.8528 +2025-04-24 20:08:04,142 - INFO - Std: 2.8893 +2025-04-24 20:08:04,142 - INFO - OC Distribution Statistics: +2025-04-24 20:08:04,142 - INFO - Validation Mean: 17.3772 +2025-04-24 20:08:04,142 - INFO - Validation Std: 8.9271 +2025-04-24 20:08:04,142 - INFO - Training Mean: 23.0004 +2025-04-24 20:08:04,142 - INFO - Training Std: 20.3076 +2025-04-24 20:08:04,142 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:08:04,142 - INFO - KS Statistic: 0.1538 +2025-04-24 20:08:04,142 - INFO - P-value: 0.0000 +2025-04-24 20:08:04,142 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:08:04,142 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:08:05,532 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:08:05,533 - INFO - Timestamp: 2025-04-24 20:08:05 +2025-04-24 20:08:05,533 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:08:05,533 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:08:05,533 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:08:05,533 - INFO - Mean: 4.7899 +2025-04-24 20:08:05,533 - INFO - Median: 4.2255 +2025-04-24 20:08:05,533 - INFO - Min: 0.3763 +2025-04-24 20:08:05,533 - INFO - Max: 18.8528 +2025-04-24 20:08:05,533 - INFO - Std: 2.8893 +2025-04-24 20:08:05,533 - INFO - OC Distribution Statistics: +2025-04-24 20:08:05,533 - INFO - Validation Mean: 17.3772 +2025-04-24 20:08:05,533 - INFO - Validation Std: 8.9271 +2025-04-24 20:08:05,533 - INFO - Training Mean: 23.0004 +2025-04-24 20:08:05,533 - INFO - Training Std: 20.3076 +2025-04-24 20:08:05,533 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:08:05,533 - INFO - KS Statistic: 0.1538 +2025-04-24 20:08:05,533 - INFO - P-value: 0.0000 +2025-04-24 20:08:05,533 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:08:05,533 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:08:05,724 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:08:05,724 - INFO - Timestamp: 2025-04-24 20:08:05 +2025-04-24 20:08:05,724 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:08:05,724 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:08:05,724 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:08:05,724 - INFO - Mean: 4.7899 +2025-04-24 20:08:05,724 - INFO - Median: 4.2255 +2025-04-24 20:08:05,724 - INFO - Min: 0.3763 +2025-04-24 20:08:05,724 - INFO - Max: 18.8528 +2025-04-24 20:08:05,724 - INFO - Std: 2.8893 +2025-04-24 20:08:05,724 - INFO - OC Distribution Statistics: +2025-04-24 20:08:05,724 - INFO - Validation Mean: 17.3772 +2025-04-24 20:08:05,724 - INFO - Validation Std: 8.9271 +2025-04-24 20:08:05,724 - INFO - Training Mean: 23.0004 +2025-04-24 20:08:05,724 - INFO - Training Std: 20.3076 +2025-04-24 20:08:05,724 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:08:05,724 - INFO - KS Statistic: 0.1538 +2025-04-24 20:08:05,724 - INFO - P-value: 0.0000 +2025-04-24 20:08:05,725 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:08:05,725 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:08:16,192 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:08:16,193 - INFO - Timestamp: 2025-04-24 20:08:16 +2025-04-24 20:08:16,193 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:08:16,193 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:08:16,193 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:08:16,193 - INFO - Mean: 4.7899 +2025-04-24 20:08:16,193 - INFO - Median: 4.2255 +2025-04-24 20:08:16,193 - INFO - Min: 0.3763 +2025-04-24 20:08:16,193 - INFO - Max: 18.8528 +2025-04-24 20:08:16,193 - INFO - Std: 2.8893 +2025-04-24 20:08:16,193 - INFO - OC Distribution Statistics: +2025-04-24 20:08:16,193 - INFO - Validation Mean: 17.3772 +2025-04-24 20:08:16,193 - INFO - Validation Std: 8.9271 +2025-04-24 20:08:16,193 - INFO - Training Mean: 23.0004 +2025-04-24 20:08:16,193 - INFO - Training Std: 20.3076 +2025-04-24 20:08:16,193 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:08:16,193 - INFO - KS Statistic: 0.1538 +2025-04-24 20:08:16,193 - INFO - P-value: 0.0000 +2025-04-24 20:08:16,193 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:08:16,193 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:09:35,126 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:09:35,127 - INFO - Timestamp: 2025-04-24 20:09:35 +2025-04-24 20:09:35,127 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:09:35,127 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:09:35,127 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:09:35,127 - INFO - Mean: 4.7899 +2025-04-24 20:09:35,127 - INFO - Median: 4.2255 +2025-04-24 20:09:35,127 - INFO - Min: 0.3763 +2025-04-24 20:09:35,127 - INFO - Max: 18.8528 +2025-04-24 20:09:35,127 - INFO - Std: 2.8893 +2025-04-24 20:09:35,127 - INFO - OC Distribution Statistics: +2025-04-24 20:09:35,127 - INFO - Validation Mean: 17.3772 +2025-04-24 20:09:35,127 - INFO - Validation Std: 8.9271 +2025-04-24 20:09:35,127 - INFO - Training Mean: 23.0004 +2025-04-24 20:09:35,127 - INFO - Training Std: 20.3076 +2025-04-24 20:09:35,127 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:09:35,127 - INFO - KS Statistic: 0.1538 +2025-04-24 20:09:35,127 - INFO - P-value: 0.0000 +2025-04-24 20:09:35,127 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:09:35,127 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:09:36,475 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:09:36,475 - INFO - Timestamp: 2025-04-24 20:09:36 +2025-04-24 20:09:36,475 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:09:36,475 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:09:36,475 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:09:36,475 - INFO - Mean: 4.7899 +2025-04-24 20:09:36,475 - INFO - Median: 4.2255 +2025-04-24 20:09:36,475 - INFO - Min: 0.3763 +2025-04-24 20:09:36,475 - INFO - Max: 18.8528 +2025-04-24 20:09:36,475 - INFO - Std: 2.8893 +2025-04-24 20:09:36,475 - INFO - OC Distribution Statistics: +2025-04-24 20:09:36,475 - INFO - Validation Mean: 17.3772 +2025-04-24 20:09:36,475 - INFO - Validation Std: 8.9271 +2025-04-24 20:09:36,475 - INFO - Training Mean: 23.0004 +2025-04-24 20:09:36,475 - INFO - Training Std: 20.3076 +2025-04-24 20:09:36,475 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:09:36,475 - INFO - KS Statistic: 0.1538 +2025-04-24 20:09:36,475 - INFO - P-value: 0.0000 +2025-04-24 20:09:36,475 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:09:36,475 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:09:36,677 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:09:36,677 - INFO - Timestamp: 2025-04-24 20:09:36 +2025-04-24 20:09:36,677 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:09:36,677 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:09:36,677 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:09:36,677 - INFO - Mean: 4.7899 +2025-04-24 20:09:36,677 - INFO - Median: 4.2255 +2025-04-24 20:09:36,677 - INFO - Min: 0.3763 +2025-04-24 20:09:36,677 - INFO - Max: 18.8528 +2025-04-24 20:09:36,677 - INFO - Std: 2.8893 +2025-04-24 20:09:36,677 - INFO - OC Distribution Statistics: +2025-04-24 20:09:36,677 - INFO - Validation Mean: 17.3772 +2025-04-24 20:09:36,677 - INFO - Validation Std: 8.9271 +2025-04-24 20:09:36,677 - INFO - Training Mean: 23.0004 +2025-04-24 20:09:36,677 - INFO - Training Std: 20.3076 +2025-04-24 20:09:36,677 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:09:36,677 - INFO - KS Statistic: 0.1538 +2025-04-24 20:09:36,677 - INFO - P-value: 0.0000 +2025-04-24 20:09:36,677 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:09:36,677 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:09:50,004 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:09:50,004 - INFO - Timestamp: 2025-04-24 20:09:50 +2025-04-24 20:09:50,004 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:09:50,004 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:09:50,004 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:09:50,004 - INFO - Mean: 4.7899 +2025-04-24 20:09:50,004 - INFO - Median: 4.2255 +2025-04-24 20:09:50,004 - INFO - Min: 0.3763 +2025-04-24 20:09:50,004 - INFO - Max: 18.8528 +2025-04-24 20:09:50,004 - INFO - Std: 2.8893 +2025-04-24 20:09:50,004 - INFO - OC Distribution Statistics: +2025-04-24 20:09:50,004 - INFO - Validation Mean: 17.3772 +2025-04-24 20:09:50,004 - INFO - Validation Std: 8.9271 +2025-04-24 20:09:50,004 - INFO - Training Mean: 23.0004 +2025-04-24 20:09:50,004 - INFO - Training Std: 20.3076 +2025-04-24 20:09:50,004 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:09:50,004 - INFO - KS Statistic: 0.1538 +2025-04-24 20:09:50,004 - INFO - P-value: 0.0000 +2025-04-24 20:09:50,004 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:09:50,004 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:16:08,822 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:16:08,822 - INFO - Timestamp: 2025-04-24 20:16:08 +2025-04-24 20:16:08,823 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:16:08,823 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:16:08,823 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:16:08,823 - INFO - Mean: 4.7899 +2025-04-24 20:16:08,823 - INFO - Median: 4.2255 +2025-04-24 20:16:08,823 - INFO - Min: 0.3763 +2025-04-24 20:16:08,823 - INFO - Max: 18.8528 +2025-04-24 20:16:08,823 - INFO - Std: 2.8893 +2025-04-24 20:16:08,823 - INFO - OC Distribution Statistics: +2025-04-24 20:16:08,823 - INFO - Validation Mean: 17.3772 +2025-04-24 20:16:08,823 - INFO - Validation Std: 8.9271 +2025-04-24 20:16:08,823 - INFO - Training Mean: 23.0004 +2025-04-24 20:16:08,823 - INFO - Training Std: 20.3076 +2025-04-24 20:16:08,823 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:16:08,823 - INFO - KS Statistic: 0.1538 +2025-04-24 20:16:08,823 - INFO - P-value: 0.0000 +2025-04-24 20:16:08,823 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:16:08,823 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:16:09,216 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:16:09,216 - INFO - Timestamp: 2025-04-24 20:16:09 +2025-04-24 20:16:09,216 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:16:09,216 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:16:09,216 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:16:09,216 - INFO - Mean: 4.7899 +2025-04-24 20:16:09,216 - INFO - Median: 4.2255 +2025-04-24 20:16:09,216 - INFO - Min: 0.3763 +2025-04-24 20:16:09,216 - INFO - Max: 18.8528 +2025-04-24 20:16:09,216 - INFO - Std: 2.8893 +2025-04-24 20:16:09,216 - INFO - OC Distribution Statistics: +2025-04-24 20:16:09,216 - INFO - Validation Mean: 17.3772 +2025-04-24 20:16:09,216 - INFO - Validation Std: 8.9271 +2025-04-24 20:16:09,216 - INFO - Training Mean: 23.0004 +2025-04-24 20:16:09,216 - INFO - Training Std: 20.3076 +2025-04-24 20:16:09,216 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:16:09,216 - INFO - KS Statistic: 0.1538 +2025-04-24 20:16:09,216 - INFO - P-value: 0.0000 +2025-04-24 20:16:09,217 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:16:09,217 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:16:13,778 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:16:13,778 - INFO - Timestamp: 2025-04-24 20:16:13 +2025-04-24 20:16:13,778 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:16:13,778 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:16:13,778 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:16:13,778 - INFO - Mean: 4.7899 +2025-04-24 20:16:13,778 - INFO - Median: 4.2255 +2025-04-24 20:16:13,778 - INFO - Min: 0.3763 +2025-04-24 20:16:13,778 - INFO - Max: 18.8528 +2025-04-24 20:16:13,778 - INFO - Std: 2.8893 +2025-04-24 20:16:13,778 - INFO - OC Distribution Statistics: +2025-04-24 20:16:13,778 - INFO - Validation Mean: 17.3772 +2025-04-24 20:16:13,778 - INFO - Validation Std: 8.9271 +2025-04-24 20:16:13,778 - INFO - Training Mean: 23.0004 +2025-04-24 20:16:13,779 - INFO - Training Std: 20.3076 +2025-04-24 20:16:13,779 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:16:13,779 - INFO - KS Statistic: 0.1538 +2025-04-24 20:16:13,779 - INFO - P-value: 0.0000 +2025-04-24 20:16:13,779 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:16:13,779 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:16:15,382 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:16:15,382 - INFO - Timestamp: 2025-04-24 20:16:15 +2025-04-24 20:16:15,382 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:16:15,382 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:16:15,382 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:16:15,382 - INFO - Mean: 4.7899 +2025-04-24 20:16:15,383 - INFO - Median: 4.2255 +2025-04-24 20:16:15,383 - INFO - Min: 0.3763 +2025-04-24 20:16:15,383 - INFO - Max: 18.8528 +2025-04-24 20:16:15,383 - INFO - Std: 2.8893 +2025-04-24 20:16:15,383 - INFO - OC Distribution Statistics: +2025-04-24 20:16:15,383 - INFO - Validation Mean: 17.3772 +2025-04-24 20:16:15,383 - INFO - Validation Std: 8.9271 +2025-04-24 20:16:15,383 - INFO - Training Mean: 23.0004 +2025-04-24 20:16:15,383 - INFO - Training Std: 20.3076 +2025-04-24 20:16:15,383 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:16:15,383 - INFO - KS Statistic: 0.1538 +2025-04-24 20:16:15,383 - INFO - P-value: 0.0000 +2025-04-24 20:16:15,383 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:16:15,383 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:51:09,000 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:51:09,000 - INFO - Timestamp: 2025-04-24 20:51:08 +2025-04-24 20:51:09,000 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:51:09,000 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:51:09,000 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:51:09,000 - INFO - Mean: 4.7899 +2025-04-24 20:51:09,001 - INFO - Median: 4.2255 +2025-04-24 20:51:09,001 - INFO - Min: 0.3763 +2025-04-24 20:51:09,001 - INFO - Max: 18.8528 +2025-04-24 20:51:09,001 - INFO - Std: 2.8893 +2025-04-24 20:51:09,001 - INFO - OC Distribution Statistics: +2025-04-24 20:51:09,001 - INFO - Validation Mean: 17.3772 +2025-04-24 20:51:09,001 - INFO - Validation Std: 8.9271 +2025-04-24 20:51:09,001 - INFO - Training Mean: 23.0004 +2025-04-24 20:51:09,001 - INFO - Training Std: 20.3076 +2025-04-24 20:51:09,001 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:51:09,001 - INFO - KS Statistic: 0.1538 +2025-04-24 20:51:09,001 - INFO - P-value: 0.0000 +2025-04-24 20:51:09,001 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:51:09,001 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:51:09,030 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:51:09,030 - INFO - Timestamp: 2025-04-24 20:51:09 +2025-04-24 20:51:09,030 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:51:09,030 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:51:09,030 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:51:09,030 - INFO - Mean: 4.7899 +2025-04-24 20:51:09,030 - INFO - Median: 4.2255 +2025-04-24 20:51:09,030 - INFO - Min: 0.3763 +2025-04-24 20:51:09,030 - INFO - Max: 18.8528 +2025-04-24 20:51:09,030 - INFO - Std: 2.8893 +2025-04-24 20:51:09,030 - INFO - OC Distribution Statistics: +2025-04-24 20:51:09,030 - INFO - Validation Mean: 17.3772 +2025-04-24 20:51:09,030 - INFO - Validation Std: 8.9271 +2025-04-24 20:51:09,030 - INFO - Training Mean: 23.0004 +2025-04-24 20:51:09,030 - INFO - Training Std: 20.3076 +2025-04-24 20:51:09,030 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:51:09,030 - INFO - KS Statistic: 0.1538 +2025-04-24 20:51:09,030 - INFO - P-value: 0.0000 +2025-04-24 20:51:09,030 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:51:09,030 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:51:09,434 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:51:09,435 - INFO - Timestamp: 2025-04-24 20:51:09 +2025-04-24 20:51:09,435 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:51:09,435 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:51:09,435 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:51:09,435 - INFO - Mean: 4.7899 +2025-04-24 20:51:09,435 - INFO - Median: 4.2255 +2025-04-24 20:51:09,435 - INFO - Min: 0.3763 +2025-04-24 20:51:09,435 - INFO - Max: 18.8528 +2025-04-24 20:51:09,435 - INFO - Std: 2.8893 +2025-04-24 20:51:09,435 - INFO - OC Distribution Statistics: +2025-04-24 20:51:09,435 - INFO - Validation Mean: 17.3772 +2025-04-24 20:51:09,435 - INFO - Validation Std: 8.9271 +2025-04-24 20:51:09,435 - INFO - Training Mean: 23.0004 +2025-04-24 20:51:09,435 - INFO - Training Std: 20.3076 +2025-04-24 20:51:09,435 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:51:09,435 - INFO - KS Statistic: 0.1538 +2025-04-24 20:51:09,435 - INFO - P-value: 0.0000 +2025-04-24 20:51:09,435 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:51:09,435 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 20:51:13,343 - INFO - Validation and Training Set Split Statistics +2025-04-24 20:51:13,343 - INFO - Timestamp: 2025-04-24 20:51:13 +2025-04-24 20:51:13,343 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 20:51:13,343 - INFO - Training set size: 15123 (91.58%) +2025-04-24 20:51:13,343 - INFO - Minimum Distance Statistics (km): +2025-04-24 20:51:13,344 - INFO - Mean: 4.7899 +2025-04-24 20:51:13,344 - INFO - Median: 4.2255 +2025-04-24 20:51:13,344 - INFO - Min: 0.3763 +2025-04-24 20:51:13,344 - INFO - Max: 18.8528 +2025-04-24 20:51:13,344 - INFO - Std: 2.8893 +2025-04-24 20:51:13,344 - INFO - OC Distribution Statistics: +2025-04-24 20:51:13,344 - INFO - Validation Mean: 17.3772 +2025-04-24 20:51:13,344 - INFO - Validation Std: 8.9271 +2025-04-24 20:51:13,344 - INFO - Training Mean: 23.0004 +2025-04-24 20:51:13,344 - INFO - Training Std: 20.3076 +2025-04-24 20:51:13,344 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 20:51:13,344 - INFO - KS Statistic: 0.1538 +2025-04-24 20:51:13,344 - INFO - P-value: 0.0000 +2025-04-24 20:51:13,344 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 20:51:13,344 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 21:26:57,643 - INFO - Validation and Training Set Split Statistics +2025-04-24 21:26:57,644 - INFO - Timestamp: 2025-04-24 21:26:57 +2025-04-24 21:26:57,644 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 21:26:57,644 - INFO - Training set size: 15123 (91.58%) +2025-04-24 21:26:57,644 - INFO - Minimum Distance Statistics (km): +2025-04-24 21:26:57,644 - INFO - Mean: 4.7899 +2025-04-24 21:26:57,644 - INFO - Median: 4.2255 +2025-04-24 21:26:57,644 - INFO - Min: 0.3763 +2025-04-24 21:26:57,644 - INFO - Max: 18.8528 +2025-04-24 21:26:57,644 - INFO - Std: 2.8893 +2025-04-24 21:26:57,644 - INFO - OC Distribution Statistics: +2025-04-24 21:26:57,644 - INFO - Validation Mean: 17.3772 +2025-04-24 21:26:57,644 - INFO - Validation Std: 8.9271 +2025-04-24 21:26:57,644 - INFO - Training Mean: 23.0004 +2025-04-24 21:26:57,644 - INFO - Training Std: 20.3076 +2025-04-24 21:26:57,644 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 21:26:57,644 - INFO - KS Statistic: 0.1538 +2025-04-24 21:26:57,644 - INFO - P-value: 0.0000 +2025-04-24 21:26:57,644 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 21:26:57,644 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 21:26:57,679 - INFO - Validation and Training Set Split Statistics +2025-04-24 21:26:57,679 - INFO - Timestamp: 2025-04-24 21:26:57 +2025-04-24 21:26:57,680 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 21:26:57,680 - INFO - Training set size: 15123 (91.58%) +2025-04-24 21:26:57,680 - INFO - Minimum Distance Statistics (km): +2025-04-24 21:26:57,680 - INFO - Mean: 4.7899 +2025-04-24 21:26:57,680 - INFO - Median: 4.2255 +2025-04-24 21:26:57,680 - INFO - Min: 0.3763 +2025-04-24 21:26:57,680 - INFO - Max: 18.8528 +2025-04-24 21:26:57,680 - INFO - Std: 2.8893 +2025-04-24 21:26:57,680 - INFO - OC Distribution Statistics: +2025-04-24 21:26:57,680 - INFO - Validation Mean: 17.3772 +2025-04-24 21:26:57,680 - INFO - Validation Std: 8.9271 +2025-04-24 21:26:57,680 - INFO - Training Mean: 23.0004 +2025-04-24 21:26:57,680 - INFO - Training Std: 20.3076 +2025-04-24 21:26:57,680 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 21:26:57,680 - INFO - KS Statistic: 0.1538 +2025-04-24 21:26:57,680 - INFO - P-value: 0.0000 +2025-04-24 21:26:57,680 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 21:26:57,680 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 21:26:58,228 - INFO - Validation and Training Set Split Statistics +2025-04-24 21:26:58,228 - INFO - Timestamp: 2025-04-24 21:26:58 +2025-04-24 21:26:58,228 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 21:26:58,228 - INFO - Training set size: 15123 (91.58%) +2025-04-24 21:26:58,228 - INFO - Minimum Distance Statistics (km): +2025-04-24 21:26:58,228 - INFO - Mean: 4.7899 +2025-04-24 21:26:58,228 - INFO - Median: 4.2255 +2025-04-24 21:26:58,228 - INFO - Min: 0.3763 +2025-04-24 21:26:58,228 - INFO - Max: 18.8528 +2025-04-24 21:26:58,228 - INFO - Std: 2.8893 +2025-04-24 21:26:58,228 - INFO - OC Distribution Statistics: +2025-04-24 21:26:58,228 - INFO - Validation Mean: 17.3772 +2025-04-24 21:26:58,228 - INFO - Validation Std: 8.9271 +2025-04-24 21:26:58,228 - INFO - Training Mean: 23.0004 +2025-04-24 21:26:58,228 - INFO - Training Std: 20.3076 +2025-04-24 21:26:58,228 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 21:26:58,228 - INFO - KS Statistic: 0.1538 +2025-04-24 21:26:58,228 - INFO - P-value: 0.0000 +2025-04-24 21:26:58,228 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 21:26:58,228 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 21:27:01,599 - INFO - Validation and Training Set Split Statistics +2025-04-24 21:27:01,599 - INFO - Timestamp: 2025-04-24 21:27:01 +2025-04-24 21:27:01,599 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 21:27:01,599 - INFO - Training set size: 15123 (91.58%) +2025-04-24 21:27:01,599 - INFO - Minimum Distance Statistics (km): +2025-04-24 21:27:01,599 - INFO - Mean: 4.7899 +2025-04-24 21:27:01,599 - INFO - Median: 4.2255 +2025-04-24 21:27:01,599 - INFO - Min: 0.3763 +2025-04-24 21:27:01,599 - INFO - Max: 18.8528 +2025-04-24 21:27:01,599 - INFO - Std: 2.8893 +2025-04-24 21:27:01,599 - INFO - OC Distribution Statistics: +2025-04-24 21:27:01,599 - INFO - Validation Mean: 17.3772 +2025-04-24 21:27:01,599 - INFO - Validation Std: 8.9271 +2025-04-24 21:27:01,599 - INFO - Training Mean: 23.0004 +2025-04-24 21:27:01,599 - INFO - Training Std: 20.3076 +2025-04-24 21:27:01,599 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 21:27:01,599 - INFO - KS Statistic: 0.1538 +2025-04-24 21:27:01,599 - INFO - P-value: 0.0000 +2025-04-24 21:27:01,599 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 21:27:01,599 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 22:03:04,038 - INFO - Validation and Training Set Split Statistics +2025-04-24 22:03:04,038 - INFO - Timestamp: 2025-04-24 22:03:04 +2025-04-24 22:03:04,038 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 22:03:04,038 - INFO - Training set size: 15123 (91.58%) +2025-04-24 22:03:04,038 - INFO - Minimum Distance Statistics (km): +2025-04-24 22:03:04,038 - INFO - Mean: 4.7899 +2025-04-24 22:03:04,038 - INFO - Median: 4.2255 +2025-04-24 22:03:04,038 - INFO - Min: 0.3763 +2025-04-24 22:03:04,038 - INFO - Max: 18.8528 +2025-04-24 22:03:04,038 - INFO - Std: 2.8893 +2025-04-24 22:03:04,038 - INFO - OC Distribution Statistics: +2025-04-24 22:03:04,038 - INFO - Validation Mean: 17.3772 +2025-04-24 22:03:04,038 - INFO - Validation Std: 8.9271 +2025-04-24 22:03:04,038 - INFO - Training Mean: 23.0004 +2025-04-24 22:03:04,038 - INFO - Training Std: 20.3076 +2025-04-24 22:03:04,038 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 22:03:04,038 - INFO - KS Statistic: 0.1538 +2025-04-24 22:03:04,038 - INFO - P-value: 0.0000 +2025-04-24 22:03:04,038 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 22:03:04,038 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 22:03:04,627 - INFO - Validation and Training Set Split Statistics +2025-04-24 22:03:04,627 - INFO - Timestamp: 2025-04-24 22:03:04 +2025-04-24 22:03:04,627 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 22:03:04,627 - INFO - Training set size: 15123 (91.58%) +2025-04-24 22:03:04,627 - INFO - Minimum Distance Statistics (km): +2025-04-24 22:03:04,627 - INFO - Mean: 4.7899 +2025-04-24 22:03:04,627 - INFO - Median: 4.2255 +2025-04-24 22:03:04,627 - INFO - Min: 0.3763 +2025-04-24 22:03:04,627 - INFO - Max: 18.8528 +2025-04-24 22:03:04,627 - INFO - Std: 2.8893 +2025-04-24 22:03:04,627 - INFO - OC Distribution Statistics: +2025-04-24 22:03:04,627 - INFO - Validation Mean: 17.3772 +2025-04-24 22:03:04,627 - INFO - Validation Std: 8.9271 +2025-04-24 22:03:04,627 - INFO - Training Mean: 23.0004 +2025-04-24 22:03:04,627 - INFO - Training Std: 20.3076 +2025-04-24 22:03:04,627 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 22:03:04,627 - INFO - KS Statistic: 0.1538 +2025-04-24 22:03:04,627 - INFO - P-value: 0.0000 +2025-04-24 22:03:04,627 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 22:03:04,627 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 22:03:05,427 - INFO - Validation and Training Set Split Statistics +2025-04-24 22:03:05,427 - INFO - Timestamp: 2025-04-24 22:03:05 +2025-04-24 22:03:05,427 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 22:03:05,427 - INFO - Training set size: 15123 (91.58%) +2025-04-24 22:03:05,427 - INFO - Minimum Distance Statistics (km): +2025-04-24 22:03:05,427 - INFO - Mean: 4.7899 +2025-04-24 22:03:05,427 - INFO - Median: 4.2255 +2025-04-24 22:03:05,427 - INFO - Min: 0.3763 +2025-04-24 22:03:05,427 - INFO - Max: 18.8528 +2025-04-24 22:03:05,427 - INFO - Std: 2.8893 +2025-04-24 22:03:05,427 - INFO - OC Distribution Statistics: +2025-04-24 22:03:05,427 - INFO - Validation Mean: 17.3772 +2025-04-24 22:03:05,427 - INFO - Validation Std: 8.9271 +2025-04-24 22:03:05,428 - INFO - Training Mean: 23.0004 +2025-04-24 22:03:05,428 - INFO - Training Std: 20.3076 +2025-04-24 22:03:05,428 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 22:03:05,428 - INFO - KS Statistic: 0.1538 +2025-04-24 22:03:05,428 - INFO - P-value: 0.0000 +2025-04-24 22:03:05,428 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 22:03:05,428 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 22:03:08,855 - INFO - Validation and Training Set Split Statistics +2025-04-24 22:03:08,855 - INFO - Timestamp: 2025-04-24 22:03:08 +2025-04-24 22:03:08,855 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 22:03:08,855 - INFO - Training set size: 15123 (91.58%) +2025-04-24 22:03:08,855 - INFO - Minimum Distance Statistics (km): +2025-04-24 22:03:08,855 - INFO - Mean: 4.7899 +2025-04-24 22:03:08,855 - INFO - Median: 4.2255 +2025-04-24 22:03:08,855 - INFO - Min: 0.3763 +2025-04-24 22:03:08,855 - INFO - Max: 18.8528 +2025-04-24 22:03:08,855 - INFO - Std: 2.8893 +2025-04-24 22:03:08,855 - INFO - OC Distribution Statistics: +2025-04-24 22:03:08,855 - INFO - Validation Mean: 17.3772 +2025-04-24 22:03:08,855 - INFO - Validation Std: 8.9271 +2025-04-24 22:03:08,855 - INFO - Training Mean: 23.0004 +2025-04-24 22:03:08,855 - INFO - Training Std: 20.3076 +2025-04-24 22:03:08,855 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 22:03:08,856 - INFO - KS Statistic: 0.1538 +2025-04-24 22:03:08,856 - INFO - P-value: 0.0000 +2025-04-24 22:03:08,856 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 22:03:08,856 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 22:38:56,333 - INFO - Validation and Training Set Split Statistics +2025-04-24 22:38:56,334 - INFO - Timestamp: 2025-04-24 22:38:56 +2025-04-24 22:38:56,334 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 22:38:56,334 - INFO - Training set size: 15123 (91.58%) +2025-04-24 22:38:56,334 - INFO - Minimum Distance Statistics (km): +2025-04-24 22:38:56,334 - INFO - Mean: 4.7899 +2025-04-24 22:38:56,334 - INFO - Median: 4.2255 +2025-04-24 22:38:56,334 - INFO - Min: 0.3763 +2025-04-24 22:38:56,334 - INFO - Max: 18.8528 +2025-04-24 22:38:56,334 - INFO - Std: 2.8893 +2025-04-24 22:38:56,334 - INFO - OC Distribution Statistics: +2025-04-24 22:38:56,334 - INFO - Validation Mean: 17.3772 +2025-04-24 22:38:56,334 - INFO - Validation Std: 8.9271 +2025-04-24 22:38:56,334 - INFO - Training Mean: 23.0004 +2025-04-24 22:38:56,334 - INFO - Training Std: 20.3076 +2025-04-24 22:38:56,334 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 22:38:56,334 - INFO - KS Statistic: 0.1538 +2025-04-24 22:38:56,334 - INFO - P-value: 0.0000 +2025-04-24 22:38:56,334 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 22:38:56,334 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 22:38:56,574 - INFO - Validation and Training Set Split Statistics +2025-04-24 22:38:56,574 - INFO - Timestamp: 2025-04-24 22:38:56 +2025-04-24 22:38:56,574 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 22:38:56,574 - INFO - Training set size: 15123 (91.58%) +2025-04-24 22:38:56,574 - INFO - Minimum Distance Statistics (km): +2025-04-24 22:38:56,574 - INFO - Mean: 4.7899 +2025-04-24 22:38:56,574 - INFO - Median: 4.2255 +2025-04-24 22:38:56,574 - INFO - Min: 0.3763 +2025-04-24 22:38:56,574 - INFO - Max: 18.8528 +2025-04-24 22:38:56,574 - INFO - Std: 2.8893 +2025-04-24 22:38:56,574 - INFO - OC Distribution Statistics: +2025-04-24 22:38:56,574 - INFO - Validation Mean: 17.3772 +2025-04-24 22:38:56,574 - INFO - Validation Std: 8.9271 +2025-04-24 22:38:56,574 - INFO - Training Mean: 23.0004 +2025-04-24 22:38:56,575 - INFO - Training Std: 20.3076 +2025-04-24 22:38:56,575 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 22:38:56,575 - INFO - KS Statistic: 0.1538 +2025-04-24 22:38:56,575 - INFO - P-value: 0.0000 +2025-04-24 22:38:56,575 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 22:38:56,575 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 22:38:56,967 - INFO - Validation and Training Set Split Statistics +2025-04-24 22:38:56,967 - INFO - Timestamp: 2025-04-24 22:38:56 +2025-04-24 22:38:56,967 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 22:38:56,967 - INFO - Training set size: 15123 (91.58%) +2025-04-24 22:38:56,967 - INFO - Minimum Distance Statistics (km): +2025-04-24 22:38:56,967 - INFO - Mean: 4.7899 +2025-04-24 22:38:56,967 - INFO - Median: 4.2255 +2025-04-24 22:38:56,967 - INFO - Min: 0.3763 +2025-04-24 22:38:56,967 - INFO - Max: 18.8528 +2025-04-24 22:38:56,967 - INFO - Std: 2.8893 +2025-04-24 22:38:56,967 - INFO - OC Distribution Statistics: +2025-04-24 22:38:56,967 - INFO - Validation Mean: 17.3772 +2025-04-24 22:38:56,967 - INFO - Validation Std: 8.9271 +2025-04-24 22:38:56,967 - INFO - Training Mean: 23.0004 +2025-04-24 22:38:56,967 - INFO - Training Std: 20.3076 +2025-04-24 22:38:56,967 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 22:38:56,967 - INFO - KS Statistic: 0.1538 +2025-04-24 22:38:56,967 - INFO - P-value: 0.0000 +2025-04-24 22:38:56,967 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 22:38:56,967 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-24 22:39:00,630 - INFO - Validation and Training Set Split Statistics +2025-04-24 22:39:00,630 - INFO - Timestamp: 2025-04-24 22:39:00 +2025-04-24 22:39:00,630 - INFO - Validation set size: 1391 (8.42%) +2025-04-24 22:39:00,630 - INFO - Training set size: 15123 (91.58%) +2025-04-24 22:39:00,630 - INFO - Minimum Distance Statistics (km): +2025-04-24 22:39:00,630 - INFO - Mean: 4.7899 +2025-04-24 22:39:00,630 - INFO - Median: 4.2255 +2025-04-24 22:39:00,630 - INFO - Min: 0.3763 +2025-04-24 22:39:00,630 - INFO - Max: 18.8528 +2025-04-24 22:39:00,630 - INFO - Std: 2.8893 +2025-04-24 22:39:00,630 - INFO - OC Distribution Statistics: +2025-04-24 22:39:00,630 - INFO - Validation Mean: 17.3772 +2025-04-24 22:39:00,630 - INFO - Validation Std: 8.9271 +2025-04-24 22:39:00,630 - INFO - Training Mean: 23.0004 +2025-04-24 22:39:00,630 - INFO - Training Std: 20.3076 +2025-04-24 22:39:00,630 - INFO - Kolmogorov-Smirnov Test: +2025-04-24 22:39:00,630 - INFO - KS Statistic: 0.1538 +2025-04-24 22:39:00,630 - INFO - P-value: 0.0000 +2025-04-24 22:39:00,630 - INFO - Distribution Fit: Inverse Gamma +2025-04-24 22:39:00,630 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 16:10:36,415 - INFO - Validation and Training Set Split Statistics +2025-04-25 16:10:36,416 - INFO - Timestamp: 2025-04-25 16:10:36 +2025-04-25 16:10:36,416 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 16:10:36,416 - INFO - Training set size: 15123 (91.58%) +2025-04-25 16:10:36,416 - INFO - Minimum Distance Statistics (km): +2025-04-25 16:10:36,416 - INFO - Mean: 4.7899 +2025-04-25 16:10:36,416 - INFO - Median: 4.2255 +2025-04-25 16:10:36,416 - INFO - Min: 0.3763 +2025-04-25 16:10:36,416 - INFO - Max: 18.8528 +2025-04-25 16:10:36,416 - INFO - Std: 2.8893 +2025-04-25 16:10:36,416 - INFO - OC Distribution Statistics: +2025-04-25 16:10:36,416 - INFO - Validation Mean: 17.3772 +2025-04-25 16:10:36,416 - INFO - Validation Std: 8.9271 +2025-04-25 16:10:36,416 - INFO - Training Mean: 23.0004 +2025-04-25 16:10:36,416 - INFO - Training Std: 20.3076 +2025-04-25 16:10:36,416 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 16:10:36,416 - INFO - KS Statistic: 0.1538 +2025-04-25 16:10:36,416 - INFO - P-value: 0.0000 +2025-04-25 16:10:36,416 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 16:10:36,416 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 16:10:36,522 - INFO - Validation and Training Set Split Statistics +2025-04-25 16:10:36,522 - INFO - Timestamp: 2025-04-25 16:10:36 +2025-04-25 16:10:36,522 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 16:10:36,522 - INFO - Training set size: 15123 (91.58%) +2025-04-25 16:10:36,522 - INFO - Minimum Distance Statistics (km): +2025-04-25 16:10:36,522 - INFO - Mean: 4.7899 +2025-04-25 16:10:36,522 - INFO - Median: 4.2255 +2025-04-25 16:10:36,522 - INFO - Min: 0.3763 +2025-04-25 16:10:36,522 - INFO - Max: 18.8528 +2025-04-25 16:10:36,522 - INFO - Std: 2.8893 +2025-04-25 16:10:36,522 - INFO - OC Distribution Statistics: +2025-04-25 16:10:36,522 - INFO - Validation Mean: 17.3772 +2025-04-25 16:10:36,522 - INFO - Validation Std: 8.9271 +2025-04-25 16:10:36,522 - INFO - Training Mean: 23.0004 +2025-04-25 16:10:36,522 - INFO - Training Std: 20.3076 +2025-04-25 16:10:36,522 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 16:10:36,522 - INFO - KS Statistic: 0.1538 +2025-04-25 16:10:36,523 - INFO - P-value: 0.0000 +2025-04-25 16:10:36,523 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 16:10:36,523 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 16:10:36,648 - INFO - Validation and Training Set Split Statistics +2025-04-25 16:10:36,648 - INFO - Timestamp: 2025-04-25 16:10:36 +2025-04-25 16:10:36,649 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 16:10:36,649 - INFO - Training set size: 15123 (91.58%) +2025-04-25 16:10:36,649 - INFO - Minimum Distance Statistics (km): +2025-04-25 16:10:36,649 - INFO - Mean: 4.7899 +2025-04-25 16:10:36,649 - INFO - Median: 4.2255 +2025-04-25 16:10:36,649 - INFO - Min: 0.3763 +2025-04-25 16:10:36,649 - INFO - Max: 18.8528 +2025-04-25 16:10:36,649 - INFO - Std: 2.8893 +2025-04-25 16:10:36,649 - INFO - OC Distribution Statistics: +2025-04-25 16:10:36,649 - INFO - Validation Mean: 17.3772 +2025-04-25 16:10:36,649 - INFO - Validation Std: 8.9271 +2025-04-25 16:10:36,649 - INFO - Training Mean: 23.0004 +2025-04-25 16:10:36,649 - INFO - Training Std: 20.3076 +2025-04-25 16:10:36,649 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 16:10:36,649 - INFO - KS Statistic: 0.1538 +2025-04-25 16:10:36,649 - INFO - P-value: 0.0000 +2025-04-25 16:10:36,649 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 16:10:36,649 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 16:10:38,023 - INFO - Validation and Training Set Split Statistics +2025-04-25 16:10:38,023 - INFO - Timestamp: 2025-04-25 16:10:38 +2025-04-25 16:10:38,023 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 16:10:38,023 - INFO - Training set size: 15123 (91.58%) +2025-04-25 16:10:38,023 - INFO - Minimum Distance Statistics (km): +2025-04-25 16:10:38,023 - INFO - Mean: 4.7899 +2025-04-25 16:10:38,023 - INFO - Median: 4.2255 +2025-04-25 16:10:38,023 - INFO - Min: 0.3763 +2025-04-25 16:10:38,023 - INFO - Max: 18.8528 +2025-04-25 16:10:38,023 - INFO - Std: 2.8893 +2025-04-25 16:10:38,023 - INFO - OC Distribution Statistics: +2025-04-25 16:10:38,023 - INFO - Validation Mean: 17.3772 +2025-04-25 16:10:38,023 - INFO - Validation Std: 8.9271 +2025-04-25 16:10:38,023 - INFO - Training Mean: 23.0004 +2025-04-25 16:10:38,023 - INFO - Training Std: 20.3076 +2025-04-25 16:10:38,023 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 16:10:38,023 - INFO - KS Statistic: 0.1538 +2025-04-25 16:10:38,023 - INFO - P-value: 0.0000 +2025-04-25 16:10:38,023 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 16:10:38,023 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 16:52:20,393 - INFO - Validation and Training Set Split Statistics +2025-04-25 16:52:20,393 - INFO - Timestamp: 2025-04-25 16:52:20 +2025-04-25 16:52:20,393 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 16:52:20,393 - INFO - Training set size: 15123 (91.58%) +2025-04-25 16:52:20,393 - INFO - Minimum Distance Statistics (km): +2025-04-25 16:52:20,393 - INFO - Mean: 4.7899 +2025-04-25 16:52:20,393 - INFO - Median: 4.2255 +2025-04-25 16:52:20,393 - INFO - Min: 0.3763 +2025-04-25 16:52:20,393 - INFO - Max: 18.8528 +2025-04-25 16:52:20,393 - INFO - Std: 2.8893 +2025-04-25 16:52:20,393 - INFO - OC Distribution Statistics: +2025-04-25 16:52:20,393 - INFO - Validation Mean: 17.3772 +2025-04-25 16:52:20,393 - INFO - Validation Std: 8.9271 +2025-04-25 16:52:20,393 - INFO - Training Mean: 23.0004 +2025-04-25 16:52:20,394 - INFO - Training Std: 20.3076 +2025-04-25 16:52:20,394 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 16:52:20,394 - INFO - KS Statistic: 0.1538 +2025-04-25 16:52:20,394 - INFO - P-value: 0.0000 +2025-04-25 16:52:20,394 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 16:52:20,394 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 16:52:20,640 - INFO - Validation and Training Set Split Statistics +2025-04-25 16:52:20,640 - INFO - Timestamp: 2025-04-25 16:52:20 +2025-04-25 16:52:20,640 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 16:52:20,640 - INFO - Training set size: 15123 (91.58%) +2025-04-25 16:52:20,640 - INFO - Minimum Distance Statistics (km): +2025-04-25 16:52:20,640 - INFO - Mean: 4.7899 +2025-04-25 16:52:20,640 - INFO - Median: 4.2255 +2025-04-25 16:52:20,640 - INFO - Min: 0.3763 +2025-04-25 16:52:20,640 - INFO - Max: 18.8528 +2025-04-25 16:52:20,640 - INFO - Std: 2.8893 +2025-04-25 16:52:20,640 - INFO - OC Distribution Statistics: +2025-04-25 16:52:20,640 - INFO - Validation Mean: 17.3772 +2025-04-25 16:52:20,640 - INFO - Validation Std: 8.9271 +2025-04-25 16:52:20,641 - INFO - Training Mean: 23.0004 +2025-04-25 16:52:20,641 - INFO - Training Std: 20.3076 +2025-04-25 16:52:20,641 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 16:52:20,641 - INFO - KS Statistic: 0.1538 +2025-04-25 16:52:20,641 - INFO - P-value: 0.0000 +2025-04-25 16:52:20,641 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 16:52:20,641 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 16:52:20,734 - INFO - Validation and Training Set Split Statistics +2025-04-25 16:52:20,734 - INFO - Timestamp: 2025-04-25 16:52:20 +2025-04-25 16:52:20,734 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 16:52:20,734 - INFO - Training set size: 15123 (91.58%) +2025-04-25 16:52:20,734 - INFO - Minimum Distance Statistics (km): +2025-04-25 16:52:20,734 - INFO - Mean: 4.7899 +2025-04-25 16:52:20,734 - INFO - Median: 4.2255 +2025-04-25 16:52:20,734 - INFO - Min: 0.3763 +2025-04-25 16:52:20,734 - INFO - Max: 18.8528 +2025-04-25 16:52:20,734 - INFO - Std: 2.8893 +2025-04-25 16:52:20,734 - INFO - OC Distribution Statistics: +2025-04-25 16:52:20,735 - INFO - Validation Mean: 17.3772 +2025-04-25 16:52:20,735 - INFO - Validation Std: 8.9271 +2025-04-25 16:52:20,735 - INFO - Training Mean: 23.0004 +2025-04-25 16:52:20,735 - INFO - Training Std: 20.3076 +2025-04-25 16:52:20,735 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 16:52:20,735 - INFO - KS Statistic: 0.1538 +2025-04-25 16:52:20,735 - INFO - P-value: 0.0000 +2025-04-25 16:52:20,735 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 16:52:20,735 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 16:52:24,768 - INFO - Validation and Training Set Split Statistics +2025-04-25 16:52:24,768 - INFO - Timestamp: 2025-04-25 16:52:24 +2025-04-25 16:52:24,768 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 16:52:24,768 - INFO - Training set size: 15123 (91.58%) +2025-04-25 16:52:24,768 - INFO - Minimum Distance Statistics (km): +2025-04-25 16:52:24,768 - INFO - Mean: 4.7899 +2025-04-25 16:52:24,768 - INFO - Median: 4.2255 +2025-04-25 16:52:24,768 - INFO - Min: 0.3763 +2025-04-25 16:52:24,768 - INFO - Max: 18.8528 +2025-04-25 16:52:24,768 - INFO - Std: 2.8893 +2025-04-25 16:52:24,768 - INFO - OC Distribution Statistics: +2025-04-25 16:52:24,768 - INFO - Validation Mean: 17.3772 +2025-04-25 16:52:24,768 - INFO - Validation Std: 8.9271 +2025-04-25 16:52:24,768 - INFO - Training Mean: 23.0004 +2025-04-25 16:52:24,768 - INFO - Training Std: 20.3076 +2025-04-25 16:52:24,768 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 16:52:24,768 - INFO - KS Statistic: 0.1538 +2025-04-25 16:52:24,768 - INFO - P-value: 0.0000 +2025-04-25 16:52:24,768 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 16:52:24,768 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 17:35:44,228 - INFO - Validation and Training Set Split Statistics +2025-04-25 17:35:44,228 - INFO - Timestamp: 2025-04-25 17:35:44 +2025-04-25 17:35:44,228 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 17:35:44,228 - INFO - Training set size: 15123 (91.58%) +2025-04-25 17:35:44,228 - INFO - Minimum Distance Statistics (km): +2025-04-25 17:35:44,228 - INFO - Mean: 4.7899 +2025-04-25 17:35:44,228 - INFO - Median: 4.2255 +2025-04-25 17:35:44,228 - INFO - Min: 0.3763 +2025-04-25 17:35:44,228 - INFO - Max: 18.8528 +2025-04-25 17:35:44,228 - INFO - Std: 2.8893 +2025-04-25 17:35:44,228 - INFO - OC Distribution Statistics: +2025-04-25 17:35:44,228 - INFO - Validation Mean: 17.3772 +2025-04-25 17:35:44,228 - INFO - Validation Std: 8.9271 +2025-04-25 17:35:44,228 - INFO - Training Mean: 23.0004 +2025-04-25 17:35:44,228 - INFO - Training Std: 20.3076 +2025-04-25 17:35:44,228 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 17:35:44,228 - INFO - KS Statistic: 0.1538 +2025-04-25 17:35:44,228 - INFO - P-value: 0.0000 +2025-04-25 17:35:44,228 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 17:35:44,228 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 17:35:45,017 - INFO - Validation and Training Set Split Statistics +2025-04-25 17:35:45,017 - INFO - Timestamp: 2025-04-25 17:35:45 +2025-04-25 17:35:45,017 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 17:35:45,017 - INFO - Training set size: 15123 (91.58%) +2025-04-25 17:35:45,017 - INFO - Minimum Distance Statistics (km): +2025-04-25 17:35:45,017 - INFO - Mean: 4.7899 +2025-04-25 17:35:45,017 - INFO - Median: 4.2255 +2025-04-25 17:35:45,017 - INFO - Min: 0.3763 +2025-04-25 17:35:45,017 - INFO - Max: 18.8528 +2025-04-25 17:35:45,017 - INFO - Std: 2.8893 +2025-04-25 17:35:45,017 - INFO - OC Distribution Statistics: +2025-04-25 17:35:45,017 - INFO - Validation Mean: 17.3772 +2025-04-25 17:35:45,017 - INFO - Validation Std: 8.9271 +2025-04-25 17:35:45,017 - INFO - Training Mean: 23.0004 +2025-04-25 17:35:45,017 - INFO - Training Std: 20.3076 +2025-04-25 17:35:45,017 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 17:35:45,017 - INFO - KS Statistic: 0.1538 +2025-04-25 17:35:45,017 - INFO - P-value: 0.0000 +2025-04-25 17:35:45,017 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 17:35:45,017 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 17:35:45,230 - INFO - Validation and Training Set Split Statistics +2025-04-25 17:35:45,230 - INFO - Timestamp: 2025-04-25 17:35:45 +2025-04-25 17:35:45,230 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 17:35:45,230 - INFO - Training set size: 15123 (91.58%) +2025-04-25 17:35:45,230 - INFO - Minimum Distance Statistics (km): +2025-04-25 17:35:45,230 - INFO - Mean: 4.7899 +2025-04-25 17:35:45,230 - INFO - Median: 4.2255 +2025-04-25 17:35:45,230 - INFO - Min: 0.3763 +2025-04-25 17:35:45,230 - INFO - Max: 18.8528 +2025-04-25 17:35:45,231 - INFO - Std: 2.8893 +2025-04-25 17:35:45,231 - INFO - OC Distribution Statistics: +2025-04-25 17:35:45,231 - INFO - Validation Mean: 17.3772 +2025-04-25 17:35:45,231 - INFO - Validation Std: 8.9271 +2025-04-25 17:35:45,231 - INFO - Training Mean: 23.0004 +2025-04-25 17:35:45,231 - INFO - Training Std: 20.3076 +2025-04-25 17:35:45,231 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 17:35:45,231 - INFO - KS Statistic: 0.1538 +2025-04-25 17:35:45,231 - INFO - P-value: 0.0000 +2025-04-25 17:35:45,231 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 17:35:45,231 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 17:35:48,926 - INFO - Validation and Training Set Split Statistics +2025-04-25 17:35:48,927 - INFO - Timestamp: 2025-04-25 17:35:48 +2025-04-25 17:35:48,927 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 17:35:48,927 - INFO - Training set size: 15123 (91.58%) +2025-04-25 17:35:48,927 - INFO - Minimum Distance Statistics (km): +2025-04-25 17:35:48,927 - INFO - Mean: 4.7899 +2025-04-25 17:35:48,927 - INFO - Median: 4.2255 +2025-04-25 17:35:48,927 - INFO - Min: 0.3763 +2025-04-25 17:35:48,927 - INFO - Max: 18.8528 +2025-04-25 17:35:48,927 - INFO - Std: 2.8893 +2025-04-25 17:35:48,927 - INFO - OC Distribution Statistics: +2025-04-25 17:35:48,927 - INFO - Validation Mean: 17.3772 +2025-04-25 17:35:48,927 - INFO - Validation Std: 8.9271 +2025-04-25 17:35:48,927 - INFO - Training Mean: 23.0004 +2025-04-25 17:35:48,927 - INFO - Training Std: 20.3076 +2025-04-25 17:35:48,927 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 17:35:48,927 - INFO - KS Statistic: 0.1538 +2025-04-25 17:35:48,927 - INFO - P-value: 0.0000 +2025-04-25 17:35:48,927 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 17:35:48,927 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 18:19:58,296 - INFO - Validation and Training Set Split Statistics +2025-04-25 18:19:58,296 - INFO - Timestamp: 2025-04-25 18:19:58 +2025-04-25 18:19:58,296 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 18:19:58,296 - INFO - Training set size: 15123 (91.58%) +2025-04-25 18:19:58,297 - INFO - Minimum Distance Statistics (km): +2025-04-25 18:19:58,297 - INFO - Mean: 4.7899 +2025-04-25 18:19:58,297 - INFO - Median: 4.2255 +2025-04-25 18:19:58,297 - INFO - Min: 0.3763 +2025-04-25 18:19:58,297 - INFO - Max: 18.8528 +2025-04-25 18:19:58,297 - INFO - Std: 2.8893 +2025-04-25 18:19:58,297 - INFO - OC Distribution Statistics: +2025-04-25 18:19:58,297 - INFO - Validation Mean: 17.3772 +2025-04-25 18:19:58,297 - INFO - Validation Std: 8.9271 +2025-04-25 18:19:58,297 - INFO - Training Mean: 23.0004 +2025-04-25 18:19:58,297 - INFO - Training Std: 20.3076 +2025-04-25 18:19:58,297 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 18:19:58,297 - INFO - KS Statistic: 0.1538 +2025-04-25 18:19:58,297 - INFO - P-value: 0.0000 +2025-04-25 18:19:58,297 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 18:19:58,297 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 18:19:58,801 - INFO - Validation and Training Set Split Statistics +2025-04-25 18:19:58,801 - INFO - Timestamp: 2025-04-25 18:19:58 +2025-04-25 18:19:58,801 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 18:19:58,801 - INFO - Training set size: 15123 (91.58%) +2025-04-25 18:19:58,801 - INFO - Minimum Distance Statistics (km): +2025-04-25 18:19:58,801 - INFO - Mean: 4.7899 +2025-04-25 18:19:58,801 - INFO - Median: 4.2255 +2025-04-25 18:19:58,801 - INFO - Min: 0.3763 +2025-04-25 18:19:58,801 - INFO - Max: 18.8528 +2025-04-25 18:19:58,801 - INFO - Std: 2.8893 +2025-04-25 18:19:58,801 - INFO - OC Distribution Statistics: +2025-04-25 18:19:58,801 - INFO - Validation Mean: 17.3772 +2025-04-25 18:19:58,801 - INFO - Validation Std: 8.9271 +2025-04-25 18:19:58,801 - INFO - Training Mean: 23.0004 +2025-04-25 18:19:58,801 - INFO - Training Std: 20.3076 +2025-04-25 18:19:58,801 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 18:19:58,802 - INFO - KS Statistic: 0.1538 +2025-04-25 18:19:58,802 - INFO - P-value: 0.0000 +2025-04-25 18:19:58,802 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 18:19:58,802 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 18:19:59,457 - INFO - Validation and Training Set Split Statistics +2025-04-25 18:19:59,457 - INFO - Timestamp: 2025-04-25 18:19:59 +2025-04-25 18:19:59,458 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 18:19:59,458 - INFO - Training set size: 15123 (91.58%) +2025-04-25 18:19:59,458 - INFO - Minimum Distance Statistics (km): +2025-04-25 18:19:59,458 - INFO - Mean: 4.7899 +2025-04-25 18:19:59,458 - INFO - Median: 4.2255 +2025-04-25 18:19:59,458 - INFO - Min: 0.3763 +2025-04-25 18:19:59,458 - INFO - Max: 18.8528 +2025-04-25 18:19:59,458 - INFO - Std: 2.8893 +2025-04-25 18:19:59,458 - INFO - OC Distribution Statistics: +2025-04-25 18:19:59,458 - INFO - Validation Mean: 17.3772 +2025-04-25 18:19:59,458 - INFO - Validation Std: 8.9271 +2025-04-25 18:19:59,458 - INFO - Training Mean: 23.0004 +2025-04-25 18:19:59,458 - INFO - Training Std: 20.3076 +2025-04-25 18:19:59,458 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 18:19:59,458 - INFO - KS Statistic: 0.1538 +2025-04-25 18:19:59,458 - INFO - P-value: 0.0000 +2025-04-25 18:19:59,458 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 18:19:59,458 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 18:20:03,188 - INFO - Validation and Training Set Split Statistics +2025-04-25 18:20:03,188 - INFO - Timestamp: 2025-04-25 18:20:03 +2025-04-25 18:20:03,188 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 18:20:03,188 - INFO - Training set size: 15123 (91.58%) +2025-04-25 18:20:03,188 - INFO - Minimum Distance Statistics (km): +2025-04-25 18:20:03,188 - INFO - Mean: 4.7899 +2025-04-25 18:20:03,188 - INFO - Median: 4.2255 +2025-04-25 18:20:03,188 - INFO - Min: 0.3763 +2025-04-25 18:20:03,188 - INFO - Max: 18.8528 +2025-04-25 18:20:03,188 - INFO - Std: 2.8893 +2025-04-25 18:20:03,188 - INFO - OC Distribution Statistics: +2025-04-25 18:20:03,188 - INFO - Validation Mean: 17.3772 +2025-04-25 18:20:03,188 - INFO - Validation Std: 8.9271 +2025-04-25 18:20:03,188 - INFO - Training Mean: 23.0004 +2025-04-25 18:20:03,188 - INFO - Training Std: 20.3076 +2025-04-25 18:20:03,188 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 18:20:03,188 - INFO - KS Statistic: 0.1538 +2025-04-25 18:20:03,189 - INFO - P-value: 0.0000 +2025-04-25 18:20:03,189 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 18:20:03,189 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 18:38:39,326 - INFO - Validation and Training Set Split Statistics +2025-04-25 18:38:39,326 - INFO - Timestamp: 2025-04-25 18:38:39 +2025-04-25 18:38:39,326 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 18:38:39,326 - INFO - Training set size: 15123 (91.58%) +2025-04-25 18:38:39,326 - INFO - Minimum Distance Statistics (km): +2025-04-25 18:38:39,326 - INFO - Mean: 4.7899 +2025-04-25 18:38:39,326 - INFO - Median: 4.2255 +2025-04-25 18:38:39,326 - INFO - Min: 0.3763 +2025-04-25 18:38:39,326 - INFO - Max: 18.8528 +2025-04-25 18:38:39,326 - INFO - Std: 2.8893 +2025-04-25 18:38:39,326 - INFO - OC Distribution Statistics: +2025-04-25 18:38:39,326 - INFO - Validation Mean: 17.3772 +2025-04-25 18:38:39,326 - INFO - Validation Std: 8.9271 +2025-04-25 18:38:39,326 - INFO - Training Mean: 23.0004 +2025-04-25 18:38:39,326 - INFO - Training Std: 20.3076 +2025-04-25 18:38:39,327 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 18:38:39,327 - INFO - KS Statistic: 0.1538 +2025-04-25 18:38:39,327 - INFO - P-value: 0.0000 +2025-04-25 18:38:39,327 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 18:38:39,327 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 18:38:39,677 - INFO - Validation and Training Set Split Statistics +2025-04-25 18:38:39,677 - INFO - Timestamp: 2025-04-25 18:38:39 +2025-04-25 18:38:39,677 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 18:38:39,677 - INFO - Training set size: 15123 (91.58%) +2025-04-25 18:38:39,677 - INFO - Minimum Distance Statistics (km): +2025-04-25 18:38:39,677 - INFO - Mean: 4.7899 +2025-04-25 18:38:39,677 - INFO - Median: 4.2255 +2025-04-25 18:38:39,677 - INFO - Min: 0.3763 +2025-04-25 18:38:39,677 - INFO - Max: 18.8528 +2025-04-25 18:38:39,677 - INFO - Std: 2.8893 +2025-04-25 18:38:39,677 - INFO - OC Distribution Statistics: +2025-04-25 18:38:39,677 - INFO - Validation Mean: 17.3772 +2025-04-25 18:38:39,677 - INFO - Validation Std: 8.9271 +2025-04-25 18:38:39,677 - INFO - Training Mean: 23.0004 +2025-04-25 18:38:39,677 - INFO - Training Std: 20.3076 +2025-04-25 18:38:39,677 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 18:38:39,677 - INFO - KS Statistic: 0.1538 +2025-04-25 18:38:39,677 - INFO - P-value: 0.0000 +2025-04-25 18:38:39,677 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 18:38:39,677 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 18:38:39,706 - INFO - Validation and Training Set Split Statistics +2025-04-25 18:38:39,706 - INFO - Timestamp: 2025-04-25 18:38:39 +2025-04-25 18:38:39,706 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 18:38:39,706 - INFO - Training set size: 15123 (91.58%) +2025-04-25 18:38:39,706 - INFO - Minimum Distance Statistics (km): +2025-04-25 18:38:39,706 - INFO - Mean: 4.7899 +2025-04-25 18:38:39,706 - INFO - Median: 4.2255 +2025-04-25 18:38:39,706 - INFO - Min: 0.3763 +2025-04-25 18:38:39,706 - INFO - Max: 18.8528 +2025-04-25 18:38:39,706 - INFO - Std: 2.8893 +2025-04-25 18:38:39,706 - INFO - OC Distribution Statistics: +2025-04-25 18:38:39,706 - INFO - Validation Mean: 17.3772 +2025-04-25 18:38:39,706 - INFO - Validation Std: 8.9271 +2025-04-25 18:38:39,706 - INFO - Training Mean: 23.0004 +2025-04-25 18:38:39,706 - INFO - Training Std: 20.3076 +2025-04-25 18:38:39,706 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 18:38:39,706 - INFO - KS Statistic: 0.1538 +2025-04-25 18:38:39,706 - INFO - P-value: 0.0000 +2025-04-25 18:38:39,706 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 18:38:39,706 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 18:38:41,190 - INFO - Validation and Training Set Split Statistics +2025-04-25 18:38:41,190 - INFO - Timestamp: 2025-04-25 18:38:41 +2025-04-25 18:38:41,190 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 18:38:41,190 - INFO - Training set size: 15123 (91.58%) +2025-04-25 18:38:41,190 - INFO - Minimum Distance Statistics (km): +2025-04-25 18:38:41,190 - INFO - Mean: 4.7899 +2025-04-25 18:38:41,190 - INFO - Median: 4.2255 +2025-04-25 18:38:41,190 - INFO - Min: 0.3763 +2025-04-25 18:38:41,190 - INFO - Max: 18.8528 +2025-04-25 18:38:41,190 - INFO - Std: 2.8893 +2025-04-25 18:38:41,190 - INFO - OC Distribution Statistics: +2025-04-25 18:38:41,190 - INFO - Validation Mean: 17.3772 +2025-04-25 18:38:41,190 - INFO - Validation Std: 8.9271 +2025-04-25 18:38:41,190 - INFO - Training Mean: 23.0004 +2025-04-25 18:38:41,190 - INFO - Training Std: 20.3076 +2025-04-25 18:38:41,190 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 18:38:41,191 - INFO - KS Statistic: 0.1538 +2025-04-25 18:38:41,191 - INFO - P-value: 0.0000 +2025-04-25 18:38:41,191 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 18:38:41,191 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 20:03:59,975 - INFO - Validation and Training Set Split Statistics +2025-04-25 20:03:59,975 - INFO - Timestamp: 2025-04-25 20:03:59 +2025-04-25 20:03:59,975 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 20:03:59,975 - INFO - Training set size: 15123 (91.58%) +2025-04-25 20:03:59,975 - INFO - Minimum Distance Statistics (km): +2025-04-25 20:03:59,975 - INFO - Mean: 4.7899 +2025-04-25 20:03:59,975 - INFO - Median: 4.2255 +2025-04-25 20:03:59,975 - INFO - Min: 0.3763 +2025-04-25 20:03:59,975 - INFO - Max: 18.8528 +2025-04-25 20:03:59,975 - INFO - Std: 2.8893 +2025-04-25 20:03:59,975 - INFO - OC Distribution Statistics: +2025-04-25 20:03:59,975 - INFO - Validation Mean: 17.3772 +2025-04-25 20:03:59,975 - INFO - Validation Std: 8.9271 +2025-04-25 20:03:59,975 - INFO - Training Mean: 23.0004 +2025-04-25 20:03:59,975 - INFO - Training Std: 20.3076 +2025-04-25 20:03:59,975 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 20:03:59,975 - INFO - KS Statistic: 0.1538 +2025-04-25 20:03:59,975 - INFO - P-value: 0.0000 +2025-04-25 20:03:59,975 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 20:03:59,975 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 20:04:00,487 - INFO - Validation and Training Set Split Statistics +2025-04-25 20:04:00,488 - INFO - Timestamp: 2025-04-25 20:04:00 +2025-04-25 20:04:00,488 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 20:04:00,488 - INFO - Training set size: 15123 (91.58%) +2025-04-25 20:04:00,488 - INFO - Minimum Distance Statistics (km): +2025-04-25 20:04:00,488 - INFO - Mean: 4.7899 +2025-04-25 20:04:00,488 - INFO - Median: 4.2255 +2025-04-25 20:04:00,488 - INFO - Min: 0.3763 +2025-04-25 20:04:00,488 - INFO - Max: 18.8528 +2025-04-25 20:04:00,488 - INFO - Std: 2.8893 +2025-04-25 20:04:00,488 - INFO - OC Distribution Statistics: +2025-04-25 20:04:00,488 - INFO - Validation Mean: 17.3772 +2025-04-25 20:04:00,488 - INFO - Validation Std: 8.9271 +2025-04-25 20:04:00,488 - INFO - Training Mean: 23.0004 +2025-04-25 20:04:00,488 - INFO - Training Std: 20.3076 +2025-04-25 20:04:00,488 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 20:04:00,488 - INFO - KS Statistic: 0.1538 +2025-04-25 20:04:00,488 - INFO - P-value: 0.0000 +2025-04-25 20:04:00,488 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 20:04:00,488 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 20:04:00,627 - INFO - Validation and Training Set Split Statistics +2025-04-25 20:04:00,627 - INFO - Timestamp: 2025-04-25 20:04:00 +2025-04-25 20:04:00,627 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 20:04:00,627 - INFO - Training set size: 15123 (91.58%) +2025-04-25 20:04:00,627 - INFO - Minimum Distance Statistics (km): +2025-04-25 20:04:00,627 - INFO - Mean: 4.7899 +2025-04-25 20:04:00,627 - INFO - Median: 4.2255 +2025-04-25 20:04:00,627 - INFO - Min: 0.3763 +2025-04-25 20:04:00,627 - INFO - Max: 18.8528 +2025-04-25 20:04:00,627 - INFO - Std: 2.8893 +2025-04-25 20:04:00,627 - INFO - OC Distribution Statistics: +2025-04-25 20:04:00,627 - INFO - Validation Mean: 17.3772 +2025-04-25 20:04:00,627 - INFO - Validation Std: 8.9271 +2025-04-25 20:04:00,627 - INFO - Training Mean: 23.0004 +2025-04-25 20:04:00,627 - INFO - Training Std: 20.3076 +2025-04-25 20:04:00,627 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 20:04:00,627 - INFO - KS Statistic: 0.1538 +2025-04-25 20:04:00,627 - INFO - P-value: 0.0000 +2025-04-25 20:04:00,627 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 20:04:00,627 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-25 20:04:04,325 - INFO - Validation and Training Set Split Statistics +2025-04-25 20:04:04,325 - INFO - Timestamp: 2025-04-25 20:04:04 +2025-04-25 20:04:04,325 - INFO - Validation set size: 1391 (8.42%) +2025-04-25 20:04:04,325 - INFO - Training set size: 15123 (91.58%) +2025-04-25 20:04:04,325 - INFO - Minimum Distance Statistics (km): +2025-04-25 20:04:04,325 - INFO - Mean: 4.7899 +2025-04-25 20:04:04,325 - INFO - Median: 4.2255 +2025-04-25 20:04:04,325 - INFO - Min: 0.3763 +2025-04-25 20:04:04,325 - INFO - Max: 18.8528 +2025-04-25 20:04:04,325 - INFO - Std: 2.8893 +2025-04-25 20:04:04,325 - INFO - OC Distribution Statistics: +2025-04-25 20:04:04,325 - INFO - Validation Mean: 17.3772 +2025-04-25 20:04:04,325 - INFO - Validation Std: 8.9271 +2025-04-25 20:04:04,325 - INFO - Training Mean: 23.0004 +2025-04-25 20:04:04,325 - INFO - Training Std: 20.3076 +2025-04-25 20:04:04,325 - INFO - Kolmogorov-Smirnov Test: +2025-04-25 20:04:04,325 - INFO - KS Statistic: 0.1538 +2025-04-25 20:04:04,325 - INFO - P-value: 0.0000 +2025-04-25 20:04:04,326 - INFO - Distribution Fit: Inverse Gamma +2025-04-25 20:04:04,326 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} diff --git a/Archive/2DCNN_output/run_1_split/split_stats.txt b/Archive/2DCNN_output/run_1_split/split_stats.txt new file mode 100644 index 0000000000000000000000000000000000000000..a9c90b4e2dcced197de0f0f2ec97da2a9a1d0442 --- /dev/null +++ b/Archive/2DCNN_output/run_1_split/split_stats.txt @@ -0,0 +1,20 @@ +Validation and Training Set Split Statistics +Timestamp: 2025-04-25 18:38:41 +Validation set size: 1391 (8.42%) +Training set size: 15123 (91.58%) +Minimum Distance Statistics (km): + Mean: 4.7899 + Median: 4.2255 + Min: 0.3763 + Max: 18.8528 + Std: 2.8893 +OC Distribution Statistics: + Validation Mean: 17.3772 + Validation Std: 8.9271 + Training Mean: 23.0004 + Training Std: 20.3076 +Kolmogorov-Smirnov Test: + KS Statistic: 0.1538 + P-value: 0.0000 +Distribution Fit: Inverse Gamma +Distribution 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newline at end of file diff --git a/Archive/2DCNN_output/run_3_split/final_training_df.parquet b/Archive/2DCNN_output/run_3_split/final_training_df.parquet new file mode 100644 index 0000000000000000000000000000000000000000..02b6e14e54e18e1328334a8246eb68e5b598375d --- /dev/null +++ b/Archive/2DCNN_output/run_3_split/final_training_df.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:de80e6cee5e25ac3d70f0d97861f1fd849adfa751edb3067a01e171bdfe0b7a4 +size 266234 diff --git a/Archive/2DCNN_output/run_3_split/final_validation_df.parquet b/Archive/2DCNN_output/run_3_split/final_validation_df.parquet new file mode 100644 index 0000000000000000000000000000000000000000..adf2848c6cce3904874b10fd7ee59d876a3c2a66 --- /dev/null +++ b/Archive/2DCNN_output/run_3_split/final_validation_df.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9a2930db9e40d8fac61520a0491c30e7124db75c992cbe98fcdcb7072a9bd5e1 +size 38672 diff --git a/Archive/2DCNN_output/run_3_split/split_stats.txt b/Archive/2DCNN_output/run_3_split/split_stats.txt new file mode 100644 index 0000000000000000000000000000000000000000..edbe2946abb5867840404ab65a8f36b1969b162f --- /dev/null +++ b/Archive/2DCNN_output/run_3_split/split_stats.txt @@ -0,0 +1,20 @@ +Validation and Training Set Split Statistics +Timestamp: 2025-04-25 17:35:48 +Validation set size: 1391 (8.42%) +Training set size: 15123 (91.58%) +Minimum Distance Statistics (km): + Mean: 4.7899 + Median: 4.2255 + Min: 0.3763 + Max: 18.8528 + Std: 2.8893 +OC Distribution Statistics: + Validation Mean: 17.3772 + Validation Std: 8.9271 + Training Mean: 23.0004 + Training Std: 20.3076 +Kolmogorov-Smirnov Test: + KS Statistic: 0.1538 + P-value: 0.0000 +Distribution Fit: Inverse Gamma +Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} \ No newline at end of file diff --git a/Archive/2DCNN_output/run_4_split/final_training_df.parquet b/Archive/2DCNN_output/run_4_split/final_training_df.parquet new file mode 100644 index 0000000000000000000000000000000000000000..02b6e14e54e18e1328334a8246eb68e5b598375d --- /dev/null +++ b/Archive/2DCNN_output/run_4_split/final_training_df.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:de80e6cee5e25ac3d70f0d97861f1fd849adfa751edb3067a01e171bdfe0b7a4 +size 266234 diff --git a/Archive/2DCNN_output/run_4_split/final_validation_df.parquet b/Archive/2DCNN_output/run_4_split/final_validation_df.parquet new file mode 100644 index 0000000000000000000000000000000000000000..adf2848c6cce3904874b10fd7ee59d876a3c2a66 --- /dev/null +++ b/Archive/2DCNN_output/run_4_split/final_validation_df.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9a2930db9e40d8fac61520a0491c30e7124db75c992cbe98fcdcb7072a9bd5e1 +size 38672 diff --git a/Archive/2DCNN_output/run_4_split/split_stats.txt b/Archive/2DCNN_output/run_4_split/split_stats.txt new file mode 100644 index 0000000000000000000000000000000000000000..ff19707e5eed26c23a90baa2dc742b9ccad67b29 --- /dev/null +++ b/Archive/2DCNN_output/run_4_split/split_stats.txt @@ -0,0 +1,20 @@ +Validation and Training Set Split Statistics +Timestamp: 2025-04-25 18:20:03 +Validation set size: 1391 (8.42%) +Training set size: 15123 (91.58%) +Minimum Distance Statistics (km): + Mean: 4.7899 + Median: 4.2255 + Min: 0.3763 + Max: 18.8528 + Std: 2.8893 +OC Distribution Statistics: + Validation Mean: 17.3772 + Validation Std: 8.9271 + Training Mean: 23.0004 + Training Std: 20.3076 +Kolmogorov-Smirnov Test: + KS Statistic: 0.1538 + P-value: 0.0000 +Distribution Fit: Inverse Gamma +Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} \ No newline at end of file diff --git a/Archive/2DCNN_output/run_5_split/final_training_df.parquet b/Archive/2DCNN_output/run_5_split/final_training_df.parquet new file mode 100644 index 0000000000000000000000000000000000000000..02b6e14e54e18e1328334a8246eb68e5b598375d --- /dev/null +++ b/Archive/2DCNN_output/run_5_split/final_training_df.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:de80e6cee5e25ac3d70f0d97861f1fd849adfa751edb3067a01e171bdfe0b7a4 +size 266234 diff --git a/Archive/2DCNN_output/run_5_split/final_validation_df.parquet b/Archive/2DCNN_output/run_5_split/final_validation_df.parquet new file mode 100644 index 0000000000000000000000000000000000000000..adf2848c6cce3904874b10fd7ee59d876a3c2a66 --- /dev/null +++ b/Archive/2DCNN_output/run_5_split/final_validation_df.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9a2930db9e40d8fac61520a0491c30e7124db75c992cbe98fcdcb7072a9bd5e1 +size 38672 diff --git a/Archive/2DCNN_output/run_5_split/split_stats.txt b/Archive/2DCNN_output/run_5_split/split_stats.txt new file mode 100644 index 0000000000000000000000000000000000000000..844d97047a68a4b4fa6a4d4004934415d349e8c2 --- /dev/null +++ b/Archive/2DCNN_output/run_5_split/split_stats.txt @@ -0,0 +1,20 @@ +Validation and Training Set Split Statistics +Timestamp: 2025-04-24 22:39:00 +Validation set size: 1391 (8.42%) +Training set size: 15123 (91.58%) +Minimum Distance Statistics (km): + Mean: 4.7899 + Median: 4.2255 + Min: 0.3763 + Max: 18.8528 + Std: 2.8893 +OC Distribution Statistics: + Validation Mean: 17.3772 + Validation Std: 8.9271 + Training Mean: 23.0004 + Training Std: 20.3076 +Kolmogorov-Smirnov Test: + KS Statistic: 0.1538 + P-value: 0.0000 +Distribution Fit: Inverse Gamma +Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} \ No newline at end of file diff --git a/Archive/2DCNN_output/split_stats.log b/Archive/2DCNN_output/split_stats.log new file mode 100644 index 0000000000000000000000000000000000000000..c6d9143d79951f7f88eeec652818bde4673fc819 --- /dev/null +++ b/Archive/2DCNN_output/split_stats.log @@ -0,0 +1,4088 @@ +2025-04-17 23:35:37,437 - INFO - Validation and Training Set Split Statistics +2025-04-17 23:35:37,437 - INFO - Timestamp: 2025-04-17 23:35:37 +2025-04-17 23:35:37,437 - INFO - Validation set size: 1331 (8.06%) +2025-04-17 23:35:37,437 - INFO - Training set size: 15183 (91.94%) +2025-04-17 23:35:37,437 - INFO - Minimum Distance Statistics (km): +2025-04-17 23:35:37,437 - INFO - Mean: 67.5872 +2025-04-17 23:35:37,437 - INFO - Median: 4.4525 +2025-04-17 23:35:37,437 - INFO - Min: 0.5501 +2025-04-17 23:35:37,437 - INFO - Max: 770.4951 +2025-04-17 23:35:37,437 - INFO - Std: 110.9976 +2025-04-17 23:35:37,438 - INFO - OC Distribution Statistics: +2025-04-17 23:35:37,438 - INFO - Validation Mean: 16.7134 +2025-04-17 23:35:37,438 - INFO - Validation Std: 7.5433 +2025-04-17 23:35:37,438 - INFO - Training Mean: 23.0364 +2025-04-17 23:35:37,438 - INFO - Training Std: 20.3104 +2025-04-17 23:35:37,438 - INFO - Kolmogorov-Smirnov Test: +2025-04-17 23:35:37,438 - INFO - KS Statistic: 0.2138 +2025-04-17 23:35:37,438 - INFO - P-value: 0.0000 +2025-04-17 23:35:37,438 - INFO - Distribution Fit: Inverse Gamma +2025-04-17 23:35:37,438 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-17 23:35:37,451 - INFO - Validation and Training Set Split Statistics +2025-04-17 23:35:37,451 - INFO - Timestamp: 2025-04-17 23:35:37 +2025-04-17 23:35:37,451 - INFO - Validation set size: 1323 (8.01%) +2025-04-17 23:35:37,451 - INFO - Training set size: 15191 (91.99%) +2025-04-17 23:35:37,451 - INFO - Minimum Distance Statistics (km): +2025-04-17 23:35:37,451 - INFO - Mean: 61.5245 +2025-04-17 23:35:37,451 - INFO - Median: 4.0444 +2025-04-17 23:35:37,451 - INFO - Min: 0.8608 +2025-04-17 23:35:37,451 - INFO - Max: 655.9249 +2025-04-17 23:35:37,451 - INFO - Std: 100.1963 +2025-04-17 23:35:37,451 - INFO - OC Distribution Statistics: +2025-04-17 23:35:37,451 - INFO - Validation Mean: 16.6113 +2025-04-17 23:35:37,451 - INFO - Validation Std: 8.3854 +2025-04-17 23:35:37,451 - INFO - Training Mean: 23.0420 +2025-04-17 23:35:37,451 - INFO - Training Std: 20.2748 +2025-04-17 23:35:37,451 - INFO - Kolmogorov-Smirnov Test: +2025-04-17 23:35:37,451 - INFO - KS Statistic: 0.2294 +2025-04-17 23:35:37,451 - INFO - P-value: 0.0000 +2025-04-17 23:35:37,451 - INFO - Distribution Fit: Inverse Gamma +2025-04-17 23:35:37,451 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-17 23:35:37,542 - INFO - Validation and Training Set Split Statistics +2025-04-17 23:35:37,542 - INFO - Timestamp: 2025-04-17 23:35:37 +2025-04-17 23:35:37,542 - INFO - Validation set size: 1403 (8.50%) +2025-04-17 23:35:37,542 - INFO - Training set size: 15111 (91.50%) +2025-04-17 23:35:37,542 - INFO - Minimum Distance Statistics (km): +2025-04-17 23:35:37,542 - INFO - Mean: 69.2417 +2025-04-17 23:35:37,542 - INFO - Median: 4.4771 +2025-04-17 23:35:37,542 - INFO - Min: 0.8728 +2025-04-17 23:35:37,542 - INFO - Max: 917.1429 +2025-04-17 23:35:37,542 - INFO - Std: 116.9276 +2025-04-17 23:35:37,542 - INFO - OC Distribution Statistics: +2025-04-17 23:35:37,542 - INFO - Validation Mean: 16.5406 +2025-04-17 23:35:37,542 - INFO - Validation Std: 7.4755 +2025-04-17 23:35:37,542 - INFO - Training Mean: 23.0826 +2025-04-17 23:35:37,542 - INFO - Training Std: 20.3446 +2025-04-17 23:35:37,542 - INFO - Kolmogorov-Smirnov Test: +2025-04-17 23:35:37,542 - INFO - KS Statistic: 0.2105 +2025-04-17 23:35:37,542 - INFO - P-value: 0.0000 +2025-04-17 23:35:37,542 - INFO - Distribution Fit: Inverse Gamma +2025-04-17 23:35:37,542 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-17 23:35:38,874 - INFO - Validation and Training Set Split Statistics +2025-04-17 23:35:38,874 - INFO - Timestamp: 2025-04-17 23:35:38 +2025-04-17 23:35:38,874 - INFO - Validation set size: 1399 (8.47%) +2025-04-17 23:35:38,874 - INFO - Training set size: 15115 (91.53%) +2025-04-17 23:35:38,874 - INFO - Minimum Distance Statistics (km): +2025-04-17 23:35:38,874 - INFO - Mean: 62.3076 +2025-04-17 23:35:38,874 - INFO - Median: 4.2475 +2025-04-17 23:35:38,874 - INFO - Min: 1.2156 +2025-04-17 23:35:38,874 - INFO - Max: 772.4342 +2025-04-17 23:35:38,874 - INFO - Std: 104.3936 +2025-04-17 23:35:38,874 - INFO - OC Distribution Statistics: +2025-04-17 23:35:38,874 - INFO - Validation Mean: 16.3481 +2025-04-17 23:35:38,874 - INFO - Validation Std: 7.7691 +2025-04-17 23:35:38,874 - INFO - Training Mean: 23.0987 +2025-04-17 23:35:38,874 - INFO - Training Std: 20.3266 +2025-04-17 23:35:38,874 - INFO - Kolmogorov-Smirnov Test: +2025-04-17 23:35:38,874 - INFO - KS Statistic: 0.2290 +2025-04-17 23:35:38,875 - INFO - P-value: 0.0000 +2025-04-17 23:35:38,875 - INFO - Distribution Fit: Inverse Gamma +2025-04-17 23:35:38,875 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-17 23:39:17,861 - INFO - Validation and Training Set Split Statistics +2025-04-17 23:39:17,861 - INFO - Timestamp: 2025-04-17 23:39:17 +2025-04-17 23:39:17,861 - INFO - Validation set size: 1653 (10.01%) +2025-04-17 23:39:17,862 - INFO - Training set size: 14861 (89.99%) +2025-04-17 23:39:17,862 - INFO - Minimum Distance Statistics (km): +2025-04-17 23:39:17,862 - INFO - Mean: 65.5188 +2025-04-17 23:39:17,862 - INFO - Median: 4.0444 +2025-04-17 23:39:17,862 - INFO - Min: 1.2329 +2025-04-17 23:39:17,862 - INFO - Max: 670.0468 +2025-04-17 23:39:17,862 - INFO - Std: 111.6473 +2025-04-17 23:39:17,862 - INFO - OC Distribution Statistics: +2025-04-17 23:39:17,862 - INFO - Validation Mean: 16.1213 +2025-04-17 23:39:17,862 - INFO - Validation Std: 7.5079 +2025-04-17 23:39:17,862 - INFO - Training Mean: 23.2393 +2025-04-17 23:39:17,862 - INFO - Training Std: 20.4572 +2025-04-17 23:39:17,862 - INFO - Kolmogorov-Smirnov Test: +2025-04-17 23:39:17,862 - INFO - KS Statistic: 0.2312 +2025-04-17 23:39:17,862 - INFO - P-value: 0.0000 +2025-04-17 23:39:17,862 - INFO - Distribution Fit: Inverse Gamma +2025-04-17 23:39:17,862 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-17 23:39:18,059 - INFO - Validation and Training Set Split Statistics +2025-04-17 23:39:18,059 - INFO - Timestamp: 2025-04-17 23:39:18 +2025-04-17 23:39:18,059 - INFO - Validation set size: 1610 (9.75%) +2025-04-17 23:39:18,059 - INFO - Training set size: 14904 (90.25%) +2025-04-17 23:39:18,059 - INFO - Minimum Distance Statistics (km): +2025-04-17 23:39:18,059 - INFO - Mean: 64.4754 +2025-04-17 23:39:18,059 - INFO - Median: 4.4051 +2025-04-17 23:39:18,059 - INFO - Min: 1.1288 +2025-04-17 23:39:18,059 - INFO - Max: 705.4078 +2025-04-17 23:39:18,059 - INFO - Std: 109.0241 +2025-04-17 23:39:18,059 - INFO - OC Distribution Statistics: +2025-04-17 23:39:18,059 - INFO - Validation Mean: 16.7787 +2025-04-17 23:39:18,059 - INFO - Validation Std: 7.5959 +2025-04-17 23:39:18,059 - INFO - Training Mean: 23.1477 +2025-04-17 23:39:18,059 - INFO - Training Std: 20.4551 +2025-04-17 23:39:18,059 - INFO - Kolmogorov-Smirnov Test: +2025-04-17 23:39:18,059 - INFO - KS Statistic: 0.1962 +2025-04-17 23:39:18,059 - INFO - P-value: 0.0000 +2025-04-17 23:39:18,059 - INFO - Distribution Fit: Inverse Gamma +2025-04-17 23:39:18,059 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 14:43:13,826 - INFO - Validation and Training Set Split Statistics +2025-04-18 14:43:13,827 - INFO - Timestamp: 2025-04-18 14:43:13 +2025-04-18 14:43:13,827 - INFO - Validation set size: 1353 (8.19%) +2025-04-18 14:43:13,827 - INFO - Training set size: 15161 (91.81%) +2025-04-18 14:43:13,827 - INFO - Minimum Distance Statistics (km): +2025-04-18 14:43:13,827 - INFO - Mean: 73.7387 +2025-04-18 14:43:13,827 - INFO - Median: 4.5729 +2025-04-18 14:43:13,827 - INFO - Min: 1.2156 +2025-04-18 14:43:13,827 - INFO - Max: 875.2369 +2025-04-18 14:43:13,827 - INFO - Std: 128.6116 +2025-04-18 14:43:13,827 - INFO - OC Distribution Statistics: +2025-04-18 14:43:13,827 - INFO - Validation Mean: 16.4650 +2025-04-18 14:43:13,827 - INFO - Validation Std: 7.5645 +2025-04-18 14:43:13,827 - INFO - Training Mean: 23.0678 +2025-04-18 14:43:13,827 - INFO - Training Std: 20.3139 +2025-04-18 14:43:13,827 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 14:43:13,827 - INFO - KS Statistic: 0.2261 +2025-04-18 14:43:13,827 - INFO - P-value: 0.0000 +2025-04-18 14:43:13,827 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 14:43:13,827 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 14:43:13,944 - INFO - Validation and Training Set Split Statistics +2025-04-18 14:43:13,944 - INFO - Timestamp: 2025-04-18 14:43:13 +2025-04-18 14:43:13,944 - INFO - Validation set size: 1439 (8.71%) +2025-04-18 14:43:13,944 - INFO - Training set size: 15075 (91.29%) +2025-04-18 14:43:13,944 - INFO - Minimum Distance Statistics (km): +2025-04-18 14:43:13,945 - INFO - Mean: 70.8929 +2025-04-18 14:43:13,945 - INFO - Median: 4.7793 +2025-04-18 14:43:13,945 - INFO - Min: 1.2065 +2025-04-18 14:43:13,945 - INFO - Max: 743.7101 +2025-04-18 14:43:13,945 - INFO - Std: 116.0711 +2025-04-18 14:43:13,945 - INFO - OC Distribution Statistics: +2025-04-18 14:43:13,945 - INFO - Validation Mean: 16.4603 +2025-04-18 14:43:13,945 - INFO - Validation Std: 7.4367 +2025-04-18 14:43:13,945 - INFO - Training Mean: 23.1059 +2025-04-18 14:43:13,945 - INFO - Training Std: 20.3620 +2025-04-18 14:43:13,945 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 14:43:13,945 - INFO - KS Statistic: 0.2061 +2025-04-18 14:43:13,945 - INFO - P-value: 0.0000 +2025-04-18 14:43:13,945 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 14:43:13,945 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 14:43:14,110 - INFO - Validation and Training Set Split Statistics +2025-04-18 14:43:14,111 - INFO - Timestamp: 2025-04-18 14:43:14 +2025-04-18 14:43:14,111 - INFO - Validation set size: 1683 (10.19%) +2025-04-18 14:43:14,111 - INFO - Training set size: 14831 (89.81%) +2025-04-18 14:43:14,111 - INFO - Minimum Distance Statistics (km): +2025-04-18 14:43:14,111 - INFO - Mean: 67.3265 +2025-04-18 14:43:14,111 - INFO - Median: 4.4045 +2025-04-18 14:43:14,111 - INFO - Min: 0.5501 +2025-04-18 14:43:14,111 - INFO - Max: 824.9624 +2025-04-18 14:43:14,111 - INFO - Std: 122.2018 +2025-04-18 14:43:14,111 - INFO - OC Distribution Statistics: +2025-04-18 14:43:14,111 - INFO - Validation Mean: 16.1725 +2025-04-18 14:43:14,111 - INFO - Validation Std: 6.9967 +2025-04-18 14:43:14,111 - INFO - Training Mean: 23.2479 +2025-04-18 14:43:14,111 - INFO - Training Std: 20.4952 +2025-04-18 14:43:14,111 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 14:43:14,111 - INFO - KS Statistic: 0.2364 +2025-04-18 14:43:14,111 - INFO - P-value: 0.0000 +2025-04-18 14:43:14,111 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 14:43:14,111 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 14:43:15,270 - INFO - Validation and Training Set Split Statistics +2025-04-18 14:43:15,271 - INFO - Timestamp: 2025-04-18 14:43:15 +2025-04-18 14:43:15,271 - INFO - Validation set size: 1356 (8.21%) +2025-04-18 14:43:15,271 - INFO - Training set size: 15158 (91.79%) +2025-04-18 14:43:15,271 - INFO - Minimum Distance Statistics (km): +2025-04-18 14:43:15,271 - INFO - Mean: 62.3766 +2025-04-18 14:43:15,271 - INFO - Median: 5.7139 +2025-04-18 14:43:15,271 - INFO - Min: 1.2102 +2025-04-18 14:43:15,271 - INFO - Max: 655.9249 +2025-04-18 14:43:15,271 - INFO - Std: 102.2543 +2025-04-18 14:43:15,271 - INFO - OC Distribution Statistics: +2025-04-18 14:43:15,271 - INFO - Validation Mean: 16.8138 +2025-04-18 14:43:15,271 - INFO - Validation Std: 7.9054 +2025-04-18 14:43:15,271 - INFO - Training Mean: 23.0379 +2025-04-18 14:43:15,271 - INFO - Training Std: 20.3137 +2025-04-18 14:43:15,271 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 14:43:15,271 - INFO - KS Statistic: 0.2101 +2025-04-18 14:43:15,271 - INFO - P-value: 0.0000 +2025-04-18 14:43:15,271 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 14:43:15,271 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:03:50,983 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:03:50,983 - INFO - Timestamp: 2025-04-18 15:03:50 +2025-04-18 15:03:50,983 - INFO - Validation set size: 1341 (8.12%) +2025-04-18 15:03:50,983 - INFO - Training set size: 15173 (91.88%) +2025-04-18 15:03:50,983 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:03:50,983 - INFO - Mean: 75.2167 +2025-04-18 15:03:50,983 - INFO - Median: 4.6944 +2025-04-18 15:03:50,983 - INFO - Min: 1.2102 +2025-04-18 15:03:50,983 - INFO - Max: 862.7749 +2025-04-18 15:03:50,983 - INFO - Std: 134.9127 +2025-04-18 15:03:50,983 - INFO - OC Distribution Statistics: +2025-04-18 15:03:50,983 - INFO - Validation Mean: 16.9569 +2025-04-18 15:03:50,983 - INFO - Validation Std: 8.0231 +2025-04-18 15:03:50,983 - INFO - Training Mean: 23.0191 +2025-04-18 15:03:50,983 - INFO - Training Std: 20.3059 +2025-04-18 15:03:50,983 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:03:50,983 - INFO - KS Statistic: 0.1889 +2025-04-18 15:03:50,983 - INFO - P-value: 0.0000 +2025-04-18 15:03:50,983 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:03:50,983 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:03:51,053 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:03:51,053 - INFO - Timestamp: 2025-04-18 15:03:51 +2025-04-18 15:03:51,053 - INFO - Validation set size: 1326 (8.03%) +2025-04-18 15:03:51,053 - INFO - Training set size: 15188 (91.97%) +2025-04-18 15:03:51,053 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:03:51,053 - INFO - Mean: 71.8188 +2025-04-18 15:03:51,053 - INFO - Median: 5.1405 +2025-04-18 15:03:51,053 - INFO - Min: 1.0979 +2025-04-18 15:03:51,053 - INFO - Max: 917.2720 +2025-04-18 15:03:51,053 - INFO - Std: 125.0276 +2025-04-18 15:03:51,053 - INFO - OC Distribution Statistics: +2025-04-18 15:03:51,053 - INFO - Validation Mean: 16.6084 +2025-04-18 15:03:51,053 - INFO - Validation Std: 8.4506 +2025-04-18 15:03:51,053 - INFO - Training Mean: 23.0435 +2025-04-18 15:03:51,053 - INFO - Training Std: 20.2738 +2025-04-18 15:03:51,053 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:03:51,053 - INFO - KS Statistic: 0.2166 +2025-04-18 15:03:51,053 - INFO - P-value: 0.0000 +2025-04-18 15:03:51,053 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:03:51,053 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:03:51,282 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:03:51,282 - INFO - Timestamp: 2025-04-18 15:03:51 +2025-04-18 15:03:51,282 - INFO - Validation set size: 1522 (9.22%) +2025-04-18 15:03:51,282 - INFO - Training set size: 14992 (90.78%) +2025-04-18 15:03:51,282 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:03:51,282 - INFO - Mean: 63.1285 +2025-04-18 15:03:51,282 - INFO - Median: 4.2432 +2025-04-18 15:03:51,282 - INFO - Min: 1.2413 +2025-04-18 15:03:51,283 - INFO - Max: 743.7101 +2025-04-18 15:03:51,283 - INFO - Std: 111.5914 +2025-04-18 15:03:51,283 - INFO - OC Distribution Statistics: +2025-04-18 15:03:51,283 - INFO - Validation Mean: 16.6223 +2025-04-18 15:03:51,283 - INFO - Validation Std: 8.1666 +2025-04-18 15:03:51,283 - INFO - Training Mean: 23.1262 +2025-04-18 15:03:51,283 - INFO - Training Std: 20.3817 +2025-04-18 15:03:51,283 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:03:51,283 - INFO - KS Statistic: 0.2056 +2025-04-18 15:03:51,283 - INFO - P-value: 0.0000 +2025-04-18 15:03:51,283 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:03:51,283 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:03:52,072 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:03:52,072 - INFO - Timestamp: 2025-04-18 15:03:52 +2025-04-18 15:03:52,073 - INFO - Validation set size: 1378 (8.34%) +2025-04-18 15:03:52,073 - INFO - Training set size: 15136 (91.66%) +2025-04-18 15:03:52,073 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:03:52,073 - INFO - Mean: 63.0268 +2025-04-18 15:03:52,073 - INFO - Median: 3.9697 +2025-04-18 15:03:52,073 - INFO - Min: 1.2102 +2025-04-18 15:03:52,073 - INFO - Max: 888.3558 +2025-04-18 15:03:52,073 - INFO - Std: 108.7678 +2025-04-18 15:03:52,073 - INFO - OC Distribution Statistics: +2025-04-18 15:03:52,073 - INFO - Validation Mean: 16.6062 +2025-04-18 15:03:52,073 - INFO - Validation Std: 7.8464 +2025-04-18 15:03:52,073 - INFO - Training Mean: 23.0658 +2025-04-18 15:03:52,073 - INFO - Training Std: 20.3210 +2025-04-18 15:03:52,073 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:03:52,073 - INFO - KS Statistic: 0.2268 +2025-04-18 15:03:52,073 - INFO - P-value: 0.0000 +2025-04-18 15:03:52,073 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:03:52,073 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:10:22,450 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:10:22,450 - INFO - Timestamp: 2025-04-18 15:10:22 +2025-04-18 15:10:22,450 - INFO - Validation set size: 1346 (8.15%) +2025-04-18 15:10:22,450 - INFO - Training set size: 15168 (91.85%) +2025-04-18 15:10:22,450 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:10:22,450 - INFO - Mean: 66.9449 +2025-04-18 15:10:22,450 - INFO - Median: 4.9468 +2025-04-18 15:10:22,450 - INFO - Min: 0.9118 +2025-04-18 15:10:22,450 - INFO - Max: 866.4875 +2025-04-18 15:10:22,450 - INFO - Std: 115.4222 +2025-04-18 15:10:22,450 - INFO - OC Distribution Statistics: +2025-04-18 15:10:22,450 - INFO - Validation Mean: 16.7242 +2025-04-18 15:10:22,450 - INFO - Validation Std: 7.5819 +2025-04-18 15:10:22,450 - INFO - Training Mean: 23.0417 +2025-04-18 15:10:22,450 - INFO - Training Std: 20.3171 +2025-04-18 15:10:22,450 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:10:22,450 - INFO - KS Statistic: 0.1961 +2025-04-18 15:10:22,450 - INFO - P-value: 0.0000 +2025-04-18 15:10:22,450 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:10:22,450 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:10:22,500 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:10:22,500 - INFO - Timestamp: 2025-04-18 15:10:22 +2025-04-18 15:10:22,500 - INFO - Validation set size: 1338 (8.10%) +2025-04-18 15:10:22,500 - INFO - Training set size: 15176 (91.90%) +2025-04-18 15:10:22,500 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:10:22,500 - INFO - Mean: 61.3665 +2025-04-18 15:10:22,500 - INFO - Median: 4.2475 +2025-04-18 15:10:22,500 - INFO - Min: 0.9102 +2025-04-18 15:10:22,501 - INFO - Max: 681.9863 +2025-04-18 15:10:22,501 - INFO - Std: 101.2781 +2025-04-18 15:10:22,501 - INFO - OC Distribution Statistics: +2025-04-18 15:10:22,501 - INFO - Validation Mean: 16.6207 +2025-04-18 15:10:22,501 - INFO - Validation Std: 7.6110 +2025-04-18 15:10:22,501 - INFO - Training Mean: 23.0475 +2025-04-18 15:10:22,501 - INFO - Training Std: 20.3092 +2025-04-18 15:10:22,501 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:10:22,501 - INFO - KS Statistic: 0.1925 +2025-04-18 15:10:22,501 - INFO - P-value: 0.0000 +2025-04-18 15:10:22,501 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:10:22,501 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:10:22,715 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:10:22,715 - INFO - Timestamp: 2025-04-18 15:10:22 +2025-04-18 15:10:22,715 - INFO - Validation set size: 1564 (9.47%) +2025-04-18 15:10:22,715 - INFO - Training set size: 14950 (90.53%) +2025-04-18 15:10:22,715 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:10:22,715 - INFO - Mean: 66.1598 +2025-04-18 15:10:22,715 - INFO - Median: 4.9468 +2025-04-18 15:10:22,715 - INFO - Min: 1.1596 +2025-04-18 15:10:22,715 - INFO - Max: 743.7101 +2025-04-18 15:10:22,715 - INFO - Std: 111.9169 +2025-04-18 15:10:22,715 - INFO - OC Distribution Statistics: +2025-04-18 15:10:22,715 - INFO - Validation Mean: 16.6511 +2025-04-18 15:10:22,715 - INFO - Validation Std: 7.8759 +2025-04-18 15:10:22,715 - INFO - Training Mean: 23.1415 +2025-04-18 15:10:22,715 - INFO - Training Std: 20.4157 +2025-04-18 15:10:22,715 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:10:22,715 - INFO - KS Statistic: 0.2153 +2025-04-18 15:10:22,715 - INFO - P-value: 0.0000 +2025-04-18 15:10:22,715 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:10:22,716 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:10:23,631 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:10:23,631 - INFO - Timestamp: 2025-04-18 15:10:23 +2025-04-18 15:10:23,631 - INFO - Validation set size: 1364 (8.26%) +2025-04-18 15:10:23,631 - INFO - Training set size: 15150 (91.74%) +2025-04-18 15:10:23,631 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:10:23,631 - INFO - Mean: 63.6852 +2025-04-18 15:10:23,631 - INFO - Median: 4.5760 +2025-04-18 15:10:23,631 - INFO - Min: 1.2156 +2025-04-18 15:10:23,631 - INFO - Max: 655.9249 +2025-04-18 15:10:23,631 - INFO - Std: 107.1517 +2025-04-18 15:10:23,631 - INFO - OC Distribution Statistics: +2025-04-18 15:10:23,631 - INFO - Validation Mean: 16.7873 +2025-04-18 15:10:23,631 - INFO - Validation Std: 7.9241 +2025-04-18 15:10:23,631 - INFO - Training Mean: 23.0435 +2025-04-18 15:10:23,631 - INFO - Training Std: 20.3164 +2025-04-18 15:10:23,631 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:10:23,631 - INFO - KS Statistic: 0.2067 +2025-04-18 15:10:23,631 - INFO - P-value: 0.0000 +2025-04-18 15:10:23,631 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:10:23,631 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:10:43,911 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 15:10:43,918 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 15:10:43,918 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 15:10:43,923 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 15:14:31,181 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:14:31,181 - INFO - Timestamp: 2025-04-18 15:14:31 +2025-04-18 15:14:31,181 - INFO - Validation set size: 1472 (8.91%) +2025-04-18 15:14:31,181 - INFO - Training set size: 15042 (91.09%) +2025-04-18 15:14:31,181 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:14:31,181 - INFO - Mean: 69.9561 +2025-04-18 15:14:31,181 - INFO - Median: 4.4466 +2025-04-18 15:14:31,181 - INFO - Min: 1.0979 +2025-04-18 15:14:31,181 - INFO - Max: 684.2079 +2025-04-18 15:14:31,181 - INFO - Std: 114.6520 +2025-04-18 15:14:31,181 - INFO - OC Distribution Statistics: +2025-04-18 15:14:31,181 - INFO - Validation Mean: 16.5085 +2025-04-18 15:14:31,181 - INFO - Validation Std: 7.6313 +2025-04-18 15:14:31,181 - INFO - Training Mean: 23.1157 +2025-04-18 15:14:31,181 - INFO - Training Std: 20.3734 +2025-04-18 15:14:31,181 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:14:31,181 - INFO - KS Statistic: 0.2007 +2025-04-18 15:14:31,181 - INFO - P-value: 0.0000 +2025-04-18 15:14:31,181 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:14:31,181 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:14:31,236 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:14:31,236 - INFO - Timestamp: 2025-04-18 15:14:31 +2025-04-18 15:14:31,236 - INFO - Validation set size: 1369 (8.29%) +2025-04-18 15:14:31,236 - INFO - Training set size: 15145 (91.71%) +2025-04-18 15:14:31,236 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:14:31,236 - INFO - Mean: 74.4415 +2025-04-18 15:14:31,236 - INFO - Median: 4.6421 +2025-04-18 15:14:31,236 - INFO - Min: 1.2757 +2025-04-18 15:14:31,236 - INFO - Max: 743.7101 +2025-04-18 15:14:31,237 - INFO - Std: 126.0333 +2025-04-18 15:14:31,237 - INFO - OC Distribution Statistics: +2025-04-18 15:14:31,237 - INFO - Validation Mean: 16.9034 +2025-04-18 15:14:31,237 - INFO - Validation Std: 8.3189 +2025-04-18 15:14:31,237 - INFO - Training Mean: 23.0351 +2025-04-18 15:14:31,237 - INFO - Training Std: 20.3078 +2025-04-18 15:14:31,237 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:14:31,237 - INFO - KS Statistic: 0.2032 +2025-04-18 15:14:31,237 - INFO - P-value: 0.0000 +2025-04-18 15:14:31,237 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:14:31,237 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:14:31,526 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:14:31,526 - INFO - Timestamp: 2025-04-18 15:14:31 +2025-04-18 15:14:31,526 - INFO - Validation set size: 1716 (10.39%) +2025-04-18 15:14:31,526 - INFO - Training set size: 14798 (89.61%) +2025-04-18 15:14:31,526 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:14:31,526 - INFO - Mean: 59.4272 +2025-04-18 15:14:31,526 - INFO - Median: 3.9792 +2025-04-18 15:14:31,526 - INFO - Min: 1.2757 +2025-04-18 15:14:31,526 - INFO - Max: 835.9450 +2025-04-18 15:14:31,526 - INFO - Std: 110.9034 +2025-04-18 15:14:31,526 - INFO - OC Distribution Statistics: +2025-04-18 15:14:31,526 - INFO - Validation Mean: 16.2456 +2025-04-18 15:14:31,526 - INFO - Validation Std: 8.1030 +2025-04-18 15:14:31,526 - INFO - Training Mean: 23.2552 +2025-04-18 15:14:31,526 - INFO - Training Std: 20.4683 +2025-04-18 15:14:31,526 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:14:31,526 - INFO - KS Statistic: 0.2491 +2025-04-18 15:14:31,526 - INFO - P-value: 0.0000 +2025-04-18 15:14:31,526 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:14:31,526 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:14:32,543 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:14:32,543 - INFO - Timestamp: 2025-04-18 15:14:32 +2025-04-18 15:14:32,543 - INFO - Validation set size: 1384 (8.38%) +2025-04-18 15:14:32,543 - INFO - Training set size: 15130 (91.62%) +2025-04-18 15:14:32,543 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:14:32,543 - INFO - Mean: 71.2738 +2025-04-18 15:14:32,544 - INFO - Median: 5.1405 +2025-04-18 15:14:32,544 - INFO - Min: 1.0475 +2025-04-18 15:14:32,544 - INFO - Max: 702.5656 +2025-04-18 15:14:32,544 - INFO - Std: 115.5604 +2025-04-18 15:14:32,544 - INFO - OC Distribution Statistics: +2025-04-18 15:14:32,544 - INFO - Validation Mean: 16.6075 +2025-04-18 15:14:32,544 - INFO - Validation Std: 7.4112 +2025-04-18 15:14:32,544 - INFO - Training Mean: 23.0682 +2025-04-18 15:14:32,544 - INFO - Training Std: 20.3390 +2025-04-18 15:14:32,544 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:14:32,544 - INFO - KS Statistic: 0.2121 +2025-04-18 15:14:32,544 - INFO - P-value: 0.0000 +2025-04-18 15:14:32,544 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:14:32,544 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:20:22,668 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:20:22,668 - INFO - Timestamp: 2025-04-18 15:20:22 +2025-04-18 15:20:22,668 - INFO - Validation set size: 1419 (8.59%) +2025-04-18 15:20:22,668 - INFO - Training set size: 15095 (91.41%) +2025-04-18 15:20:22,668 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:20:22,668 - INFO - Mean: 67.9319 +2025-04-18 15:20:22,668 - INFO - Median: 4.4648 +2025-04-18 15:20:22,668 - INFO - Min: 1.2953 +2025-04-18 15:20:22,668 - INFO - Max: 888.3558 +2025-04-18 15:20:22,668 - INFO - Std: 116.6976 +2025-04-18 15:20:22,668 - INFO - OC Distribution Statistics: +2025-04-18 15:20:22,668 - INFO - Validation Mean: 16.7797 +2025-04-18 15:20:22,669 - INFO - Validation Std: 8.8439 +2025-04-18 15:20:22,669 - INFO - Training Mean: 23.0670 +2025-04-18 15:20:22,669 - INFO - Training Std: 20.3083 +2025-04-18 15:20:22,669 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:20:22,669 - INFO - KS Statistic: 0.2169 +2025-04-18 15:20:22,669 - INFO - P-value: 0.0000 +2025-04-18 15:20:22,669 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:20:22,669 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:21:28,109 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:21:28,110 - INFO - Timestamp: 2025-04-18 15:21:28 +2025-04-18 15:21:28,110 - INFO - Validation set size: 1367 (8.28%) +2025-04-18 15:21:28,110 - INFO - Training set size: 15147 (91.72%) +2025-04-18 15:21:28,110 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:21:28,110 - INFO - Mean: 58.8530 +2025-04-18 15:21:28,110 - INFO - Median: 3.9947 +2025-04-18 15:21:28,110 - INFO - Min: 1.2329 +2025-04-18 15:21:28,110 - INFO - Max: 706.9584 +2025-04-18 15:21:28,110 - INFO - Std: 102.8604 +2025-04-18 15:21:28,110 - INFO - OC Distribution Statistics: +2025-04-18 15:21:28,110 - INFO - Validation Mean: 16.5017 +2025-04-18 15:21:28,110 - INFO - Validation Std: 7.5188 +2025-04-18 15:21:28,110 - INFO - Training Mean: 23.0705 +2025-04-18 15:21:28,110 - INFO - Training Std: 20.3236 +2025-04-18 15:21:28,110 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:21:28,110 - INFO - KS Statistic: 0.2009 +2025-04-18 15:21:28,110 - INFO - P-value: 0.0000 +2025-04-18 15:21:28,110 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:21:28,110 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:21:28,398 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:21:28,398 - INFO - Timestamp: 2025-04-18 15:21:28 +2025-04-18 15:21:28,398 - INFO - Validation set size: 1586 (9.60%) +2025-04-18 15:21:28,398 - INFO - Training set size: 14928 (90.40%) +2025-04-18 15:21:28,398 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:21:28,398 - INFO - Mean: 63.1921 +2025-04-18 15:21:28,398 - INFO - Median: 4.3338 +2025-04-18 15:21:28,398 - INFO - Min: 0.5501 +2025-04-18 15:21:28,398 - INFO - Max: 743.7101 +2025-04-18 15:21:28,398 - INFO - Std: 108.8464 +2025-04-18 15:21:28,398 - INFO - OC Distribution Statistics: +2025-04-18 15:21:28,398 - INFO - Validation Mean: 16.2383 +2025-04-18 15:21:28,398 - INFO - Validation Std: 7.5216 +2025-04-18 15:21:28,398 - INFO - Training Mean: 23.1949 +2025-04-18 15:21:28,398 - INFO - Training Std: 20.4268 +2025-04-18 15:21:28,398 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:21:28,398 - INFO - KS Statistic: 0.2376 +2025-04-18 15:21:28,398 - INFO - P-value: 0.0000 +2025-04-18 15:21:28,398 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:21:28,398 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:21:28,400 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:21:28,400 - INFO - Timestamp: 2025-04-18 15:21:28 +2025-04-18 15:21:28,400 - INFO - Validation set size: 1594 (9.65%) +2025-04-18 15:21:28,400 - INFO - Training set size: 14920 (90.35%) +2025-04-18 15:21:28,400 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:21:28,400 - INFO - Mean: 59.1872 +2025-04-18 15:21:28,400 - INFO - Median: 4.6106 +2025-04-18 15:21:28,400 - INFO - Min: 1.1319 +2025-04-18 15:21:28,400 - INFO - Max: 917.1429 +2025-04-18 15:21:28,400 - INFO - Std: 108.0063 +2025-04-18 15:21:28,400 - INFO - OC Distribution Statistics: +2025-04-18 15:21:28,400 - INFO - Validation Mean: 16.4074 +2025-04-18 15:21:28,400 - INFO - Validation Std: 7.6087 +2025-04-18 15:21:28,400 - INFO - Training Mean: 23.1806 +2025-04-18 15:21:28,400 - INFO - Training Std: 20.4335 +2025-04-18 15:21:28,400 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:21:28,400 - INFO - KS Statistic: 0.2518 +2025-04-18 15:21:28,400 - INFO - P-value: 0.0000 +2025-04-18 15:21:28,400 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:21:28,400 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:21:31,514 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:21:31,514 - INFO - Timestamp: 2025-04-18 15:21:31 +2025-04-18 15:21:31,515 - INFO - Validation set size: 1421 (8.60%) +2025-04-18 15:21:31,515 - INFO - Training set size: 15093 (91.40%) +2025-04-18 15:21:31,515 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:21:31,515 - INFO - Mean: 66.6956 +2025-04-18 15:21:31,515 - INFO - Median: 4.4333 +2025-04-18 15:21:31,515 - INFO - Min: 1.1596 +2025-04-18 15:21:31,515 - INFO - Max: 702.5656 +2025-04-18 15:21:31,515 - INFO - Std: 107.7838 +2025-04-18 15:21:31,515 - INFO - OC Distribution Statistics: +2025-04-18 15:21:31,515 - INFO - Validation Mean: 16.7504 +2025-04-18 15:21:31,515 - INFO - Validation Std: 8.6472 +2025-04-18 15:21:31,515 - INFO - Training Mean: 23.0706 +2025-04-18 15:21:31,515 - INFO - Training Std: 20.3164 +2025-04-18 15:21:31,515 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:21:31,515 - INFO - KS Statistic: 0.2020 +2025-04-18 15:21:31,515 - INFO - P-value: 0.0000 +2025-04-18 15:21:31,515 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:21:31,515 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:25:18,597 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:25:18,597 - INFO - Timestamp: 2025-04-18 15:25:18 +2025-04-18 15:25:18,597 - INFO - Validation set size: 1354 (8.20%) +2025-04-18 15:25:18,597 - INFO - Training set size: 15160 (91.80%) +2025-04-18 15:25:18,597 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:25:18,597 - INFO - Mean: 71.3887 +2025-04-18 15:25:18,597 - INFO - Median: 4.6353 +2025-04-18 15:25:18,597 - INFO - Min: 1.0311 +2025-04-18 15:25:18,597 - INFO - Max: 611.1372 +2025-04-18 15:25:18,597 - INFO - Std: 112.2513 +2025-04-18 15:25:18,597 - INFO - OC Distribution Statistics: +2025-04-18 15:25:18,597 - INFO - Validation Mean: 16.9238 +2025-04-18 15:25:18,597 - INFO - Validation Std: 7.8057 +2025-04-18 15:25:18,598 - INFO - Training Mean: 23.0272 +2025-04-18 15:25:18,598 - INFO - Training Std: 20.3191 +2025-04-18 15:25:18,598 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:25:18,598 - INFO - KS Statistic: 0.1815 +2025-04-18 15:25:18,598 - INFO - P-value: 0.0000 +2025-04-18 15:25:18,598 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:25:18,598 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:25:18,716 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:25:18,716 - INFO - Timestamp: 2025-04-18 15:25:18 +2025-04-18 15:25:18,716 - INFO - Validation set size: 1702 (10.31%) +2025-04-18 15:25:18,716 - INFO - Training set size: 14812 (89.69%) +2025-04-18 15:25:18,716 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:25:18,716 - INFO - Mean: 59.5275 +2025-04-18 15:25:18,716 - INFO - Median: 4.0399 +2025-04-18 15:25:18,716 - INFO - Min: 0.9954 +2025-04-18 15:25:18,716 - INFO - Max: 702.5656 +2025-04-18 15:25:18,716 - INFO - Std: 106.2774 +2025-04-18 15:25:18,716 - INFO - OC Distribution Statistics: +2025-04-18 15:25:18,717 - INFO - Validation Mean: 15.8182 +2025-04-18 15:25:18,717 - INFO - Validation Std: 6.8953 +2025-04-18 15:25:18,717 - INFO - Training Mean: 23.2976 +2025-04-18 15:25:18,717 - INFO - Training Std: 20.4947 +2025-04-18 15:25:18,717 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:25:18,717 - INFO - KS Statistic: 0.2453 +2025-04-18 15:25:18,717 - INFO - P-value: 0.0000 +2025-04-18 15:25:18,717 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:25:18,717 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:25:18,745 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:25:18,745 - INFO - Timestamp: 2025-04-18 15:25:18 +2025-04-18 15:25:18,745 - INFO - Validation set size: 1376 (8.33%) +2025-04-18 15:25:18,745 - INFO - Training set size: 15138 (91.67%) +2025-04-18 15:25:18,745 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:25:18,745 - INFO - Mean: 63.6830 +2025-04-18 15:25:18,745 - INFO - Median: 4.5095 +2025-04-18 15:25:18,745 - INFO - Min: 1.2329 +2025-04-18 15:25:18,745 - INFO - Max: 655.9249 +2025-04-18 15:25:18,745 - INFO - Std: 104.8118 +2025-04-18 15:25:18,745 - INFO - OC Distribution Statistics: +2025-04-18 15:25:18,745 - INFO - Validation Mean: 16.5371 +2025-04-18 15:25:18,745 - INFO - Validation Std: 8.0860 +2025-04-18 15:25:18,745 - INFO - Training Mean: 23.0712 +2025-04-18 15:25:18,745 - INFO - Training Std: 20.3095 +2025-04-18 15:25:18,746 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:25:18,746 - INFO - KS Statistic: 0.2159 +2025-04-18 15:25:18,746 - INFO - P-value: 0.0000 +2025-04-18 15:25:18,746 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:25:18,746 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:25:22,338 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:25:22,338 - INFO - Timestamp: 2025-04-18 15:25:22 +2025-04-18 15:25:22,338 - INFO - Validation set size: 1517 (9.19%) +2025-04-18 15:25:22,338 - INFO - Training set size: 14997 (90.81%) +2025-04-18 15:25:22,338 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:25:22,338 - INFO - Mean: 70.4973 +2025-04-18 15:25:22,339 - INFO - Median: 4.4819 +2025-04-18 15:25:22,339 - INFO - Min: 0.6775 +2025-04-18 15:25:22,339 - INFO - Max: 824.9624 +2025-04-18 15:25:22,339 - INFO - Std: 119.2387 +2025-04-18 15:25:22,339 - INFO - OC Distribution Statistics: +2025-04-18 15:25:22,339 - INFO - Validation Mean: 16.2559 +2025-04-18 15:25:22,339 - INFO - Validation Std: 6.8152 +2025-04-18 15:25:22,339 - INFO - Training Mean: 23.1611 +2025-04-18 15:25:22,339 - INFO - Training Std: 20.4172 +2025-04-18 15:25:22,339 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:25:22,339 - INFO - KS Statistic: 0.2153 +2025-04-18 15:25:22,339 - INFO - P-value: 0.0000 +2025-04-18 15:25:22,339 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:25:22,339 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:29:13,114 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:29:13,115 - INFO - Timestamp: 2025-04-18 15:29:13 +2025-04-18 15:29:13,115 - INFO - Validation set size: 1457 (8.82%) +2025-04-18 15:29:13,115 - INFO - Training set size: 15057 (91.18%) +2025-04-18 15:29:13,115 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:29:13,115 - INFO - Mean: 63.2782 +2025-04-18 15:29:13,115 - INFO - Median: 4.8123 +2025-04-18 15:29:13,115 - INFO - Min: 1.1349 +2025-04-18 15:29:13,115 - INFO - Max: 706.9584 +2025-04-18 15:29:13,115 - INFO - Std: 101.2026 +2025-04-18 15:29:13,115 - INFO - OC Distribution Statistics: +2025-04-18 15:29:13,115 - INFO - Validation Mean: 17.1622 +2025-04-18 15:29:13,115 - INFO - Validation Std: 8.6951 +2025-04-18 15:29:13,115 - INFO - Training Mean: 23.0459 +2025-04-18 15:29:13,115 - INFO - Training Std: 20.3439 +2025-04-18 15:29:13,115 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:29:13,115 - INFO - KS Statistic: 0.1759 +2025-04-18 15:29:13,115 - INFO - P-value: 0.0000 +2025-04-18 15:29:13,115 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:29:13,115 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:29:13,132 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:29:13,132 - INFO - Timestamp: 2025-04-18 15:29:13 +2025-04-18 15:29:13,132 - INFO - Validation set size: 1345 (8.14%) +2025-04-18 15:29:13,132 - INFO - Training set size: 15169 (91.86%) +2025-04-18 15:29:13,132 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:29:13,132 - INFO - Mean: 74.3515 +2025-04-18 15:29:13,132 - INFO - Median: 5.0148 +2025-04-18 15:29:13,132 - INFO - Min: 1.2156 +2025-04-18 15:29:13,132 - INFO - Max: 743.7101 +2025-04-18 15:29:13,132 - INFO - Std: 118.9878 +2025-04-18 15:29:13,132 - INFO - OC Distribution Statistics: +2025-04-18 15:29:13,132 - INFO - Validation Mean: 16.6440 +2025-04-18 15:29:13,133 - INFO - Validation Std: 8.6899 +2025-04-18 15:29:13,133 - INFO - Training Mean: 23.0484 +2025-04-18 15:29:13,133 - INFO - Training Std: 20.2750 +2025-04-18 15:29:13,133 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:29:13,133 - INFO - KS Statistic: 0.2094 +2025-04-18 15:29:13,133 - INFO - P-value: 0.0000 +2025-04-18 15:29:13,133 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:29:13,133 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:29:13,213 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:29:13,214 - INFO - Timestamp: 2025-04-18 15:29:13 +2025-04-18 15:29:13,214 - INFO - Validation set size: 1477 (8.94%) +2025-04-18 15:29:13,214 - INFO - Training set size: 15037 (91.06%) +2025-04-18 15:29:13,214 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:29:13,214 - INFO - Mean: 67.1675 +2025-04-18 15:29:13,214 - INFO - Median: 4.4771 +2025-04-18 15:29:13,214 - INFO - Min: 1.0311 +2025-04-18 15:29:13,214 - INFO - Max: 706.9584 +2025-04-18 15:29:13,214 - INFO - Std: 114.6588 +2025-04-18 15:29:13,214 - INFO - OC Distribution Statistics: +2025-04-18 15:29:13,214 - INFO - Validation Mean: 16.2004 +2025-04-18 15:29:13,214 - INFO - Validation Std: 7.5413 +2025-04-18 15:29:13,214 - INFO - Training Mean: 23.1482 +2025-04-18 15:29:13,214 - INFO - Training Std: 20.3692 +2025-04-18 15:29:13,214 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:29:13,214 - INFO - KS Statistic: 0.2355 +2025-04-18 15:29:13,214 - INFO - P-value: 0.0000 +2025-04-18 15:29:13,214 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:29:13,214 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:29:16,915 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:29:16,915 - INFO - Timestamp: 2025-04-18 15:29:16 +2025-04-18 15:29:16,915 - INFO - Validation set size: 1410 (8.54%) +2025-04-18 15:29:16,915 - INFO - Training set size: 15104 (91.46%) +2025-04-18 15:29:16,915 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:29:16,915 - INFO - Mean: 66.0084 +2025-04-18 15:29:16,915 - INFO - Median: 4.2696 +2025-04-18 15:29:16,915 - INFO - Min: 1.2065 +2025-04-18 15:29:16,915 - INFO - Max: 706.9584 +2025-04-18 15:29:16,915 - INFO - Std: 113.5589 +2025-04-18 15:29:16,915 - INFO - OC Distribution Statistics: +2025-04-18 15:29:16,915 - INFO - Validation Mean: 16.4014 +2025-04-18 15:29:16,915 - INFO - Validation Std: 7.8377 +2025-04-18 15:29:16,915 - INFO - Training Mean: 23.0986 +2025-04-18 15:29:16,915 - INFO - Training Std: 20.3313 +2025-04-18 15:29:16,915 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:29:16,915 - INFO - KS Statistic: 0.2209 +2025-04-18 15:29:16,915 - INFO - P-value: 0.0000 +2025-04-18 15:29:16,915 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:29:16,915 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:30:58,876 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:30:58,877 - INFO - Timestamp: 2025-04-18 15:30:58 +2025-04-18 15:30:58,877 - INFO - Validation set size: 1380 (8.36%) +2025-04-18 15:30:58,877 - INFO - Training set size: 15134 (91.64%) +2025-04-18 15:30:58,877 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:30:58,877 - INFO - Mean: 80.3861 +2025-04-18 15:30:58,877 - INFO - Median: 6.2705 +2025-04-18 15:30:58,877 - INFO - Min: 0.7391 +2025-04-18 15:30:58,877 - INFO - Max: 706.9584 +2025-04-18 15:30:58,877 - INFO - Std: 128.1257 +2025-04-18 15:30:58,877 - INFO - OC Distribution Statistics: +2025-04-18 15:30:58,877 - INFO - Validation Mean: 16.5516 +2025-04-18 15:30:58,877 - INFO - Validation Std: 7.6470 +2025-04-18 15:30:58,877 - INFO - Training Mean: 23.0716 +2025-04-18 15:30:58,877 - INFO - Training Std: 20.3274 +2025-04-18 15:30:58,877 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:30:58,877 - INFO - KS Statistic: 0.2095 +2025-04-18 15:30:58,877 - INFO - P-value: 0.0000 +2025-04-18 15:30:58,877 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:30:58,877 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:30:58,923 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:30:58,923 - INFO - Timestamp: 2025-04-18 15:30:58 +2025-04-18 15:30:58,923 - INFO - Validation set size: 1342 (8.13%) +2025-04-18 15:30:58,923 - INFO - Training set size: 15172 (91.87%) +2025-04-18 15:30:58,923 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:30:58,923 - INFO - Mean: 64.6317 +2025-04-18 15:30:58,923 - INFO - Median: 4.1551 +2025-04-18 15:30:58,923 - INFO - Min: 1.2497 +2025-04-18 15:30:58,923 - INFO - Max: 743.7101 +2025-04-18 15:30:58,923 - INFO - Std: 106.8977 +2025-04-18 15:30:58,923 - INFO - OC Distribution Statistics: +2025-04-18 15:30:58,923 - INFO - Validation Mean: 16.6169 +2025-04-18 15:30:58,923 - INFO - Validation Std: 7.6652 +2025-04-18 15:30:58,923 - INFO - Training Mean: 23.0495 +2025-04-18 15:30:58,924 - INFO - Training Std: 20.3093 +2025-04-18 15:30:58,924 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:30:58,924 - INFO - KS Statistic: 0.2022 +2025-04-18 15:30:58,924 - INFO - P-value: 0.0000 +2025-04-18 15:30:58,924 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:30:58,924 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:30:58,954 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:30:58,954 - INFO - Timestamp: 2025-04-18 15:30:58 +2025-04-18 15:30:58,954 - INFO - Validation set size: 1512 (9.16%) +2025-04-18 15:30:58,954 - INFO - Training set size: 15002 (90.84%) +2025-04-18 15:30:58,954 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:30:58,954 - INFO - Mean: 61.8134 +2025-04-18 15:30:58,954 - INFO - Median: 3.8932 +2025-04-18 15:30:58,954 - INFO - Min: 0.8608 +2025-04-18 15:30:58,954 - INFO - Max: 706.9584 +2025-04-18 15:30:58,954 - INFO - Std: 111.2271 +2025-04-18 15:30:58,954 - INFO - OC Distribution Statistics: +2025-04-18 15:30:58,954 - INFO - Validation Mean: 15.8840 +2025-04-18 15:30:58,954 - INFO - Validation Std: 7.3329 +2025-04-18 15:30:58,954 - INFO - Training Mean: 23.1963 +2025-04-18 15:30:58,954 - INFO - Training Std: 20.3835 +2025-04-18 15:30:58,954 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:30:58,954 - INFO - KS Statistic: 0.2387 +2025-04-18 15:30:58,954 - INFO - P-value: 0.0000 +2025-04-18 15:30:58,954 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:30:58,954 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:31:00,226 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:31:00,226 - INFO - Timestamp: 2025-04-18 15:31:00 +2025-04-18 15:31:00,226 - INFO - Validation set size: 1339 (8.11%) +2025-04-18 15:31:00,226 - INFO - Training set size: 15175 (91.89%) +2025-04-18 15:31:00,226 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:31:00,226 - INFO - Mean: 67.7648 +2025-04-18 15:31:00,226 - INFO - Median: 4.5882 +2025-04-18 15:31:00,226 - INFO - Min: 1.2065 +2025-04-18 15:31:00,226 - INFO - Max: 875.2369 +2025-04-18 15:31:00,226 - INFO - Std: 109.0972 +2025-04-18 15:31:00,226 - INFO - OC Distribution Statistics: +2025-04-18 15:31:00,226 - INFO - Validation Mean: 16.4324 +2025-04-18 15:31:00,226 - INFO - Validation Std: 8.1436 +2025-04-18 15:31:00,226 - INFO - Training Mean: 23.0645 +2025-04-18 15:31:00,226 - INFO - Training Std: 20.2861 +2025-04-18 15:31:00,226 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:31:00,226 - INFO - KS Statistic: 0.2118 +2025-04-18 15:31:00,226 - INFO - P-value: 0.0000 +2025-04-18 15:31:00,226 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:31:00,226 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:35:57,531 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:35:57,531 - INFO - Timestamp: 2025-04-18 15:35:57 +2025-04-18 15:35:57,531 - INFO - Validation set size: 1397 (8.46%) +2025-04-18 15:35:57,531 - INFO - Training set size: 15117 (91.54%) +2025-04-18 15:35:57,531 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:35:57,532 - INFO - Mean: 67.7220 +2025-04-18 15:35:57,532 - INFO - Median: 4.9468 +2025-04-18 15:35:57,532 - INFO - Min: 1.1288 +2025-04-18 15:35:57,532 - INFO - Max: 706.9584 +2025-04-18 15:35:57,532 - INFO - Std: 111.5792 +2025-04-18 15:35:57,532 - INFO - OC Distribution Statistics: +2025-04-18 15:35:57,532 - INFO - Validation Mean: 16.3217 +2025-04-18 15:35:57,532 - INFO - Validation Std: 7.3113 +2025-04-18 15:35:57,532 - INFO - Training Mean: 23.1002 +2025-04-18 15:35:57,532 - INFO - Training Std: 20.3405 +2025-04-18 15:35:57,532 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:35:57,532 - INFO - KS Statistic: 0.2256 +2025-04-18 15:35:57,532 - INFO - P-value: 0.0000 +2025-04-18 15:35:57,532 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:35:57,532 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:35:57,544 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:35:57,545 - INFO - Timestamp: 2025-04-18 15:35:57 +2025-04-18 15:35:57,545 - INFO - Validation set size: 1416 (8.57%) +2025-04-18 15:35:57,545 - INFO - Training set size: 15098 (91.43%) +2025-04-18 15:35:57,545 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:35:57,545 - INFO - Mean: 73.7994 +2025-04-18 15:35:57,545 - INFO - Median: 5.0148 +2025-04-18 15:35:57,545 - INFO - Min: 1.2329 +2025-04-18 15:35:57,545 - INFO - Max: 826.2195 +2025-04-18 15:35:57,545 - INFO - Std: 120.4977 +2025-04-18 15:35:57,545 - INFO - OC Distribution Statistics: +2025-04-18 15:35:57,545 - INFO - Validation Mean: 16.2362 +2025-04-18 15:35:57,545 - INFO - Validation Std: 7.3379 +2025-04-18 15:35:57,545 - INFO - Training Mean: 23.1168 +2025-04-18 15:35:57,545 - INFO - Training Std: 20.3466 +2025-04-18 15:35:57,545 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:35:57,545 - INFO - KS Statistic: 0.2394 +2025-04-18 15:35:57,545 - INFO - P-value: 0.0000 +2025-04-18 15:35:57,545 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:35:57,545 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:35:57,632 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:35:57,632 - INFO - Timestamp: 2025-04-18 15:35:57 +2025-04-18 15:35:57,632 - INFO - Validation set size: 1445 (8.75%) +2025-04-18 15:35:57,632 - INFO - Training set size: 15069 (91.25%) +2025-04-18 15:35:57,632 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:35:57,632 - INFO - Mean: 69.4642 +2025-04-18 15:35:57,632 - INFO - Median: 5.1561 +2025-04-18 15:35:57,632 - INFO - Min: 1.2930 +2025-04-18 15:35:57,632 - INFO - Max: 743.7101 +2025-04-18 15:35:57,632 - INFO - Std: 111.1529 +2025-04-18 15:35:57,632 - INFO - OC Distribution Statistics: +2025-04-18 15:35:57,632 - INFO - Validation Mean: 16.3164 +2025-04-18 15:35:57,632 - INFO - Validation Std: 7.3681 +2025-04-18 15:35:57,632 - INFO - Training Mean: 23.1223 +2025-04-18 15:35:57,632 - INFO - Training Std: 20.3629 +2025-04-18 15:35:57,632 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:35:57,632 - INFO - KS Statistic: 0.2266 +2025-04-18 15:35:57,633 - INFO - P-value: 0.0000 +2025-04-18 15:35:57,633 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:35:57,633 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:35:58,953 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:35:58,954 - INFO - Timestamp: 2025-04-18 15:35:58 +2025-04-18 15:35:58,954 - INFO - Validation set size: 1358 (8.22%) +2025-04-18 15:35:58,954 - INFO - Training set size: 15156 (91.78%) +2025-04-18 15:35:58,954 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:35:58,954 - INFO - Mean: 66.7757 +2025-04-18 15:35:58,954 - INFO - Median: 4.1395 +2025-04-18 15:35:58,954 - INFO - Min: 0.5501 +2025-04-18 15:35:58,954 - INFO - Max: 684.2079 +2025-04-18 15:35:58,954 - INFO - Std: 112.9998 +2025-04-18 15:35:58,954 - INFO - OC Distribution Statistics: +2025-04-18 15:35:58,954 - INFO - Validation Mean: 16.6157 +2025-04-18 15:35:58,954 - INFO - Validation Std: 7.7217 +2025-04-18 15:35:58,954 - INFO - Training Mean: 23.0564 +2025-04-18 15:35:58,954 - INFO - Training Std: 20.3155 +2025-04-18 15:35:58,954 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:35:58,954 - INFO - KS Statistic: 0.2085 +2025-04-18 15:35:58,954 - INFO - P-value: 0.0000 +2025-04-18 15:35:58,954 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:35:58,954 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:41:38,331 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:41:38,331 - INFO - Timestamp: 2025-04-18 15:41:38 +2025-04-18 15:41:38,331 - INFO - Validation set size: 1401 (8.48%) +2025-04-18 15:41:38,331 - INFO - Training set size: 15113 (91.52%) +2025-04-18 15:41:38,331 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:41:38,331 - INFO - Mean: 63.7994 +2025-04-18 15:41:38,331 - INFO - Median: 4.2800 +2025-04-18 15:41:38,331 - INFO - Min: 0.8394 +2025-04-18 15:41:38,331 - INFO - Max: 706.9584 +2025-04-18 15:41:38,331 - INFO - Std: 111.7712 +2025-04-18 15:41:38,331 - INFO - OC Distribution Statistics: +2025-04-18 15:41:38,331 - INFO - Validation Mean: 16.7041 +2025-04-18 15:41:38,332 - INFO - Validation Std: 7.6092 +2025-04-18 15:41:38,332 - INFO - Training Mean: 23.0666 +2025-04-18 15:41:38,332 - INFO - Training Std: 20.3438 +2025-04-18 15:41:38,332 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:41:38,332 - INFO - KS Statistic: 0.2023 +2025-04-18 15:41:38,332 - INFO - P-value: 0.0000 +2025-04-18 15:41:38,332 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:41:38,332 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:41:38,339 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:41:38,339 - INFO - Timestamp: 2025-04-18 15:41:38 +2025-04-18 15:41:38,339 - INFO - Validation set size: 1401 (8.48%) +2025-04-18 15:41:38,339 - INFO - Training set size: 15113 (91.52%) +2025-04-18 15:41:38,339 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:41:38,339 - INFO - Mean: 57.3466 +2025-04-18 15:41:38,339 - INFO - Median: 4.2711 +2025-04-18 15:41:38,339 - INFO - Min: 0.8394 +2025-04-18 15:41:38,339 - INFO - Max: 568.1189 +2025-04-18 15:41:38,339 - INFO - Std: 96.4976 +2025-04-18 15:41:38,339 - INFO - OC Distribution Statistics: +2025-04-18 15:41:38,339 - INFO - Validation Mean: 16.5147 +2025-04-18 15:41:38,339 - INFO - Validation Std: 8.0449 +2025-04-18 15:41:38,339 - INFO - Training Mean: 23.0841 +2025-04-18 15:41:38,339 - INFO - Training Std: 20.3227 +2025-04-18 15:41:38,339 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:41:38,339 - INFO - KS Statistic: 0.2147 +2025-04-18 15:41:38,340 - INFO - P-value: 0.0000 +2025-04-18 15:41:38,340 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:41:38,340 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:41:38,345 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:41:38,345 - INFO - Timestamp: 2025-04-18 15:41:38 +2025-04-18 15:41:38,346 - INFO - Validation set size: 1396 (8.45%) +2025-04-18 15:41:38,346 - INFO - Training set size: 15118 (91.55%) +2025-04-18 15:41:38,346 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:41:38,346 - INFO - Mean: 67.9865 +2025-04-18 15:41:38,346 - INFO - Median: 3.9336 +2025-04-18 15:41:38,346 - INFO - Min: 0.8728 +2025-04-18 15:41:38,346 - INFO - Max: 706.9761 +2025-04-18 15:41:38,346 - INFO - Std: 112.9361 +2025-04-18 15:41:38,346 - INFO - OC Distribution Statistics: +2025-04-18 15:41:38,346 - INFO - Validation Mean: 16.5554 +2025-04-18 15:41:38,346 - INFO - Validation Std: 7.5666 +2025-04-18 15:41:38,346 - INFO - Training Mean: 23.0782 +2025-04-18 15:41:38,346 - INFO - Training Std: 20.3384 +2025-04-18 15:41:38,346 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:41:38,346 - INFO - KS Statistic: 0.1814 +2025-04-18 15:41:38,346 - INFO - P-value: 0.0000 +2025-04-18 15:41:38,346 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:41:38,346 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:41:41,761 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:41:41,761 - INFO - Timestamp: 2025-04-18 15:41:41 +2025-04-18 15:41:41,761 - INFO - Validation set size: 1451 (8.79%) +2025-04-18 15:41:41,761 - INFO - Training set size: 15063 (91.21%) +2025-04-18 15:41:41,761 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:41:41,761 - INFO - Mean: 70.2438 +2025-04-18 15:41:41,761 - INFO - Median: 5.1680 +2025-04-18 15:41:41,761 - INFO - Min: 1.2904 +2025-04-18 15:41:41,761 - INFO - Max: 702.5656 +2025-04-18 15:41:41,761 - INFO - Std: 117.8176 +2025-04-18 15:41:41,761 - INFO - OC Distribution Statistics: +2025-04-18 15:41:41,761 - INFO - Validation Mean: 16.7662 +2025-04-18 15:41:41,761 - INFO - Validation Std: 7.8874 +2025-04-18 15:41:41,761 - INFO - Training Mean: 23.0817 +2025-04-18 15:41:41,761 - INFO - Training Std: 20.3612 +2025-04-18 15:41:41,761 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:41:41,761 - INFO - KS Statistic: 0.2083 +2025-04-18 15:41:41,761 - INFO - P-value: 0.0000 +2025-04-18 15:41:41,761 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:41:41,761 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:42:59,617 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:42:59,618 - INFO - Timestamp: 2025-04-18 15:42:59 +2025-04-18 15:42:59,618 - INFO - Validation set size: 1365 (8.27%) +2025-04-18 15:42:59,618 - INFO - Training set size: 15149 (91.73%) +2025-04-18 15:42:59,618 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:42:59,618 - INFO - Mean: 60.0379 +2025-04-18 15:42:59,618 - INFO - Median: 4.2475 +2025-04-18 15:42:59,618 - INFO - Min: 1.2156 +2025-04-18 15:42:59,618 - INFO - Max: 716.7388 +2025-04-18 15:42:59,618 - INFO - Std: 108.8180 +2025-04-18 15:42:59,618 - INFO - OC Distribution Statistics: +2025-04-18 15:42:59,618 - INFO - Validation Mean: 16.1991 +2025-04-18 15:42:59,618 - INFO - Validation Std: 7.2914 +2025-04-18 15:42:59,618 - INFO - Training Mean: 23.0969 +2025-04-18 15:42:59,618 - INFO - Training Std: 20.3211 +2025-04-18 15:42:59,618 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:42:59,618 - INFO - KS Statistic: 0.2252 +2025-04-18 15:42:59,618 - INFO - P-value: 0.0000 +2025-04-18 15:42:59,618 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:42:59,618 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:42:59,628 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:42:59,628 - INFO - Timestamp: 2025-04-18 15:42:59 +2025-04-18 15:42:59,628 - INFO - Validation set size: 1376 (8.33%) +2025-04-18 15:42:59,628 - INFO - Training set size: 15138 (91.67%) +2025-04-18 15:42:59,629 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:42:59,629 - INFO - Mean: 65.7316 +2025-04-18 15:42:59,629 - INFO - Median: 4.5055 +2025-04-18 15:42:59,629 - INFO - Min: 1.2497 +2025-04-18 15:42:59,629 - INFO - Max: 702.5656 +2025-04-18 15:42:59,629 - INFO - Std: 105.9243 +2025-04-18 15:42:59,629 - INFO - OC Distribution Statistics: +2025-04-18 15:42:59,629 - INFO - Validation Mean: 16.5399 +2025-04-18 15:42:59,629 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:42:59,629 - INFO - Validation Std: 7.8547 +2025-04-18 15:42:59,629 - INFO - Training Mean: 23.0710 +2025-04-18 15:42:59,629 - INFO - Timestamp: 2025-04-18 15:42:59 +2025-04-18 15:42:59,629 - INFO - Training Std: 20.3178 +2025-04-18 15:42:59,629 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:42:59,629 - INFO - Validation set size: 1373 (8.31%) +2025-04-18 15:42:59,629 - INFO - KS Statistic: 0.2078 +2025-04-18 15:42:59,629 - INFO - Training set size: 15141 (91.69%) +2025-04-18 15:42:59,629 - INFO - P-value: 0.0000 +2025-04-18 15:42:59,629 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:42:59,629 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:42:59,629 - INFO - Mean: 67.2629 +2025-04-18 15:42:59,629 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:42:59,629 - INFO - Median: 3.9951 +2025-04-18 15:42:59,629 - INFO - Min: 0.8124 +2025-04-18 15:42:59,629 - INFO - Max: 969.1721 +2025-04-18 15:42:59,629 - INFO - Std: 119.4022 +2025-04-18 15:42:59,629 - INFO - OC Distribution Statistics: +2025-04-18 15:42:59,629 - INFO - Validation Mean: 16.1670 +2025-04-18 15:42:59,629 - INFO - Validation Std: 7.4838 +2025-04-18 15:42:59,629 - INFO - Training Mean: 23.1035 +2025-04-18 15:42:59,629 - INFO - Training Std: 20.3178 +2025-04-18 15:42:59,629 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:42:59,629 - INFO - KS Statistic: 0.2352 +2025-04-18 15:42:59,629 - INFO - P-value: 0.0000 +2025-04-18 15:42:59,629 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:42:59,629 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:43:03,336 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:43:03,337 - INFO - Timestamp: 2025-04-18 15:43:03 +2025-04-18 15:43:03,337 - INFO - Validation set size: 1357 (8.22%) +2025-04-18 15:43:03,337 - INFO - Training set size: 15157 (91.78%) +2025-04-18 15:43:03,337 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:43:03,337 - INFO - Mean: 67.2747 +2025-04-18 15:43:03,337 - INFO - Median: 5.2639 +2025-04-18 15:43:03,337 - INFO - Min: 1.1093 +2025-04-18 15:43:03,337 - INFO - Max: 705.4078 +2025-04-18 15:43:03,337 - INFO - Std: 109.2795 +2025-04-18 15:43:03,337 - INFO - OC Distribution Statistics: +2025-04-18 15:43:03,337 - INFO - Validation Mean: 16.3436 +2025-04-18 15:43:03,337 - INFO - Validation Std: 7.4824 +2025-04-18 15:43:03,337 - INFO - Training Mean: 23.0804 +2025-04-18 15:43:03,337 - INFO - Training Std: 20.3151 +2025-04-18 15:43:03,337 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:43:03,337 - INFO - KS Statistic: 0.2260 +2025-04-18 15:43:03,337 - INFO - P-value: 0.0000 +2025-04-18 15:43:03,337 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:43:03,337 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:46:28,637 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:46:28,637 - INFO - Timestamp: 2025-04-18 15:46:28 +2025-04-18 15:46:28,637 - INFO - Validation set size: 1321 (8.00%) +2025-04-18 15:46:28,637 - INFO - Training set size: 15193 (92.00%) +2025-04-18 15:46:28,637 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:46:28,637 - INFO - Mean: 73.9158 +2025-04-18 15:46:28,637 - INFO - Median: 5.1163 +2025-04-18 15:46:28,638 - INFO - Min: 1.0890 +2025-04-18 15:46:28,638 - INFO - Max: 772.2996 +2025-04-18 15:46:28,638 - INFO - Std: 128.1837 +2025-04-18 15:46:28,638 - INFO - OC Distribution Statistics: +2025-04-18 15:46:28,638 - INFO - Validation Mean: 16.2878 +2025-04-18 15:46:28,638 - INFO - Validation Std: 7.6242 +2025-04-18 15:46:28,638 - INFO - Training Mean: 23.0692 +2025-04-18 15:46:28,638 - INFO - Training Std: 20.2907 +2025-04-18 15:46:28,638 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:46:28,638 - INFO - KS Statistic: 0.2282 +2025-04-18 15:46:28,638 - INFO - P-value: 0.0000 +2025-04-18 15:46:28,638 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:46:28,638 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:46:28,761 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:46:28,762 - INFO - Timestamp: 2025-04-18 15:46:28 +2025-04-18 15:46:28,762 - INFO - Validation set size: 1401 (8.48%) +2025-04-18 15:46:28,762 - INFO - Training set size: 15113 (91.52%) +2025-04-18 15:46:28,762 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:46:28,762 - INFO - Mean: 67.8538 +2025-04-18 15:46:28,762 - INFO - Median: 4.2475 +2025-04-18 15:46:28,762 - INFO - Min: 1.2102 +2025-04-18 15:46:28,762 - INFO - Max: 683.3575 +2025-04-18 15:46:28,762 - INFO - Std: 112.7597 +2025-04-18 15:46:28,762 - INFO - OC Distribution Statistics: +2025-04-18 15:46:28,762 - INFO - Validation Mean: 17.0448 +2025-04-18 15:46:28,762 - INFO - Validation Std: 9.0489 +2025-04-18 15:46:28,762 - INFO - Training Mean: 23.0350 +2025-04-18 15:46:28,762 - INFO - Training Std: 20.2987 +2025-04-18 15:46:28,762 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:46:28,762 - INFO - KS Statistic: 0.1805 +2025-04-18 15:46:28,762 - INFO - P-value: 0.0000 +2025-04-18 15:46:28,762 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:46:28,762 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:46:28,995 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:46:28,995 - INFO - Timestamp: 2025-04-18 15:46:28 +2025-04-18 15:46:28,995 - INFO - Validation set size: 1358 (8.22%) +2025-04-18 15:46:28,995 - INFO - Training set size: 15156 (91.78%) +2025-04-18 15:46:28,995 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:46:28,995 - INFO - Mean: 63.1505 +2025-04-18 15:46:28,995 - INFO - Median: 4.9468 +2025-04-18 15:46:28,995 - INFO - Min: 1.2102 +2025-04-18 15:46:28,995 - INFO - Max: 743.7101 +2025-04-18 15:46:28,995 - INFO - Std: 106.0905 +2025-04-18 15:46:28,995 - INFO - OC Distribution Statistics: +2025-04-18 15:46:28,995 - INFO - Validation Mean: 16.6314 +2025-04-18 15:46:28,995 - INFO - Validation Std: 8.0957 +2025-04-18 15:46:28,995 - INFO - Training Mean: 23.0550 +2025-04-18 15:46:28,995 - INFO - Training Std: 20.3029 +2025-04-18 15:46:28,995 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:46:28,995 - INFO - KS Statistic: 0.2273 +2025-04-18 15:46:28,995 - INFO - P-value: 0.0000 +2025-04-18 15:46:28,995 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:46:28,995 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:46:32,284 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:46:32,284 - INFO - Timestamp: 2025-04-18 15:46:32 +2025-04-18 15:46:32,284 - INFO - Validation set size: 1556 (9.42%) +2025-04-18 15:46:32,284 - INFO - Training set size: 14958 (90.58%) +2025-04-18 15:46:32,284 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:46:32,284 - INFO - Mean: 61.9242 +2025-04-18 15:46:32,284 - INFO - Median: 4.2800 +2025-04-18 15:46:32,284 - INFO - Min: 0.8728 +2025-04-18 15:46:32,284 - INFO - Max: 888.3558 +2025-04-18 15:46:32,284 - INFO - Std: 115.4214 +2025-04-18 15:46:32,284 - INFO - OC Distribution Statistics: +2025-04-18 15:46:32,284 - INFO - Validation Mean: 16.2635 +2025-04-18 15:46:32,285 - INFO - Validation Std: 7.6106 +2025-04-18 15:46:32,285 - INFO - Training Mean: 23.1783 +2025-04-18 15:46:32,285 - INFO - Training Std: 20.4089 +2025-04-18 15:46:32,285 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:46:32,285 - INFO - KS Statistic: 0.2246 +2025-04-18 15:46:32,285 - INFO - P-value: 0.0000 +2025-04-18 15:46:32,285 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:46:32,285 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:47:52,200 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:47:52,200 - INFO - Timestamp: 2025-04-18 15:47:52 +2025-04-18 15:47:52,200 - INFO - Validation set size: 1648 (9.98%) +2025-04-18 15:47:52,200 - INFO - Training set size: 14866 (90.02%) +2025-04-18 15:47:52,200 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:47:52,200 - INFO - Mean: 64.8596 +2025-04-18 15:47:52,200 - INFO - Median: 4.8122 +2025-04-18 15:47:52,200 - INFO - Min: 0.7102 +2025-04-18 15:47:52,200 - INFO - Max: 824.9624 +2025-04-18 15:47:52,200 - INFO - Std: 115.2012 +2025-04-18 15:47:52,200 - INFO - OC Distribution Statistics: +2025-04-18 15:47:52,200 - INFO - Validation Mean: 16.6000 +2025-04-18 15:47:52,200 - INFO - Validation Std: 7.8031 +2025-04-18 15:47:52,200 - INFO - Training Mean: 23.1838 +2025-04-18 15:47:52,200 - INFO - Training Std: 20.4602 +2025-04-18 15:47:52,200 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:47:52,200 - INFO - KS Statistic: 0.2205 +2025-04-18 15:47:52,200 - INFO - P-value: 0.0000 +2025-04-18 15:47:52,200 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:47:52,200 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:48:27,840 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:48:27,840 - INFO - Timestamp: 2025-04-18 15:48:27 +2025-04-18 15:48:27,840 - INFO - Validation set size: 1359 (8.23%) +2025-04-18 15:48:27,840 - INFO - Training set size: 15155 (91.77%) +2025-04-18 15:48:27,840 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:48:27,840 - INFO - Mean: 74.3186 +2025-04-18 15:48:27,840 - INFO - Median: 5.3115 +2025-04-18 15:48:27,840 - INFO - Min: 0.7391 +2025-04-18 15:48:27,840 - INFO - Max: 702.5656 +2025-04-18 15:48:27,840 - INFO - Std: 114.1516 +2025-04-18 15:48:27,840 - INFO - OC Distribution Statistics: +2025-04-18 15:48:27,840 - INFO - Validation Mean: 16.3188 +2025-04-18 15:48:27,840 - INFO - Validation Std: 8.0823 +2025-04-18 15:48:27,840 - INFO - Training Mean: 23.0835 +2025-04-18 15:48:27,840 - INFO - Training Std: 20.2948 +2025-04-18 15:48:27,841 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:48:27,841 - INFO - KS Statistic: 0.2195 +2025-04-18 15:48:27,841 - INFO - P-value: 0.0000 +2025-04-18 15:48:27,841 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:48:27,841 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:48:27,930 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:48:27,930 - INFO - Timestamp: 2025-04-18 15:48:27 +2025-04-18 15:48:27,930 - INFO - Validation set size: 1533 (9.28%) +2025-04-18 15:48:27,930 - INFO - Training set size: 14981 (90.72%) +2025-04-18 15:48:27,930 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:48:27,930 - INFO - Mean: 66.0607 +2025-04-18 15:48:27,930 - INFO - Median: 4.0579 +2025-04-18 15:48:27,930 - INFO - Min: 1.2156 +2025-04-18 15:48:27,930 - INFO - Max: 702.5656 +2025-04-18 15:48:27,930 - INFO - Std: 115.8890 +2025-04-18 15:48:27,930 - INFO - OC Distribution Statistics: +2025-04-18 15:48:27,930 - INFO - Validation Mean: 16.5777 +2025-04-18 15:48:27,930 - INFO - Validation Std: 7.5673 +2025-04-18 15:48:27,930 - INFO - Training Mean: 23.1356 +2025-04-18 15:48:27,930 - INFO - Training Std: 20.4094 +2025-04-18 15:48:27,930 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:48:27,931 - INFO - KS Statistic: 0.2020 +2025-04-18 15:48:27,931 - INFO - P-value: 0.0000 +2025-04-18 15:48:27,931 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:48:27,931 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:48:28,051 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:48:28,051 - INFO - Timestamp: 2025-04-18 15:48:28 +2025-04-18 15:48:28,051 - INFO - Validation set size: 1329 (8.05%) +2025-04-18 15:48:28,051 - INFO - Training set size: 15185 (91.95%) +2025-04-18 15:48:28,051 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:48:28,051 - INFO - Mean: 76.4400 +2025-04-18 15:48:28,051 - INFO - Median: 5.2233 +2025-04-18 15:48:28,051 - INFO - Min: 1.2329 +2025-04-18 15:48:28,051 - INFO - Max: 862.7749 +2025-04-18 15:48:28,051 - INFO - Std: 120.8211 +2025-04-18 15:48:28,051 - INFO - OC Distribution Statistics: +2025-04-18 15:48:28,051 - INFO - Validation Mean: 16.5611 +2025-04-18 15:48:28,051 - INFO - Validation Std: 7.6794 +2025-04-18 15:48:28,051 - INFO - Training Mean: 23.0489 +2025-04-18 15:48:28,051 - INFO - Training Std: 20.3007 +2025-04-18 15:48:28,051 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:48:28,051 - INFO - KS Statistic: 0.1911 +2025-04-18 15:48:28,051 - INFO - P-value: 0.0000 +2025-04-18 15:48:28,051 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:48:28,051 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:48:31,650 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:48:31,650 - INFO - Timestamp: 2025-04-18 15:48:31 +2025-04-18 15:48:31,650 - INFO - Validation set size: 1366 (8.27%) +2025-04-18 15:48:31,650 - INFO - Training set size: 15148 (91.73%) +2025-04-18 15:48:31,650 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:48:31,650 - INFO - Mean: 69.0572 +2025-04-18 15:48:31,650 - INFO - Median: 4.9257 +2025-04-18 15:48:31,650 - INFO - Min: 1.2065 +2025-04-18 15:48:31,651 - INFO - Max: 875.2369 +2025-04-18 15:48:31,651 - INFO - Std: 113.7556 +2025-04-18 15:48:31,651 - INFO - OC Distribution Statistics: +2025-04-18 15:48:31,651 - INFO - Validation Mean: 16.5174 +2025-04-18 15:48:31,651 - INFO - Validation Std: 7.7331 +2025-04-18 15:48:31,651 - INFO - Training Mean: 23.0687 +2025-04-18 15:48:31,651 - INFO - Training Std: 20.3163 +2025-04-18 15:48:31,651 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:48:31,651 - INFO - KS Statistic: 0.2298 +2025-04-18 15:48:31,651 - INFO - P-value: 0.0000 +2025-04-18 15:48:31,651 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:48:31,651 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:50:33,487 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:50:33,487 - INFO - Timestamp: 2025-04-18 15:50:33 +2025-04-18 15:50:33,487 - INFO - Validation set size: 1429 (8.65%) +2025-04-18 15:50:33,487 - INFO - Training set size: 15085 (91.35%) +2025-04-18 15:50:33,488 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:50:33,488 - INFO - Mean: 67.8345 +2025-04-18 15:50:33,488 - INFO - Median: 4.3353 +2025-04-18 15:50:33,488 - INFO - Min: 0.8124 +2025-04-18 15:50:33,488 - INFO - Max: 716.7632 +2025-04-18 15:50:33,488 - INFO - Std: 107.3489 +2025-04-18 15:50:33,488 - INFO - OC Distribution Statistics: +2025-04-18 15:50:33,488 - INFO - Validation Mean: 16.4167 +2025-04-18 15:50:33,488 - INFO - Validation Std: 7.4935 +2025-04-18 15:50:33,488 - INFO - Training Mean: 23.1056 +2025-04-18 15:50:33,488 - INFO - Training Std: 20.3535 +2025-04-18 15:50:33,488 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:50:33,488 - INFO - KS Statistic: 0.2090 +2025-04-18 15:50:33,488 - INFO - P-value: 0.0000 +2025-04-18 15:50:33,488 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:50:33,488 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:52:38,274 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:52:38,274 - INFO - Timestamp: 2025-04-18 15:52:38 +2025-04-18 15:52:38,274 - INFO - Validation set size: 1384 (8.38%) +2025-04-18 15:52:38,274 - INFO - Training set size: 15130 (91.62%) +2025-04-18 15:52:38,274 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:52:38,274 - INFO - Mean: 66.4916 +2025-04-18 15:52:38,274 - INFO - Median: 4.5330 +2025-04-18 15:52:38,274 - INFO - Min: 1.1596 +2025-04-18 15:52:38,274 - INFO - Max: 716.7388 +2025-04-18 15:52:38,274 - INFO - Std: 114.2065 +2025-04-18 15:52:38,274 - INFO - OC Distribution Statistics: +2025-04-18 15:52:38,274 - INFO - Validation Mean: 16.0310 +2025-04-18 15:52:38,274 - INFO - Validation Std: 7.2915 +2025-04-18 15:52:38,274 - INFO - Training Mean: 23.1210 +2025-04-18 15:52:38,274 - INFO - Training Std: 20.3254 +2025-04-18 15:52:38,274 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:52:38,274 - INFO - KS Statistic: 0.2454 +2025-04-18 15:52:38,274 - INFO - P-value: 0.0000 +2025-04-18 15:52:38,274 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:52:38,274 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:52:38,472 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:52:38,472 - INFO - Timestamp: 2025-04-18 15:52:38 +2025-04-18 15:52:38,472 - INFO - Validation set size: 1633 (9.89%) +2025-04-18 15:52:38,472 - INFO - Training set size: 14881 (90.11%) +2025-04-18 15:52:38,472 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:52:38,472 - INFO - Mean: 63.7003 +2025-04-18 15:52:38,472 - INFO - Median: 4.2196 +2025-04-18 15:52:38,472 - INFO - Min: 1.2065 +2025-04-18 15:52:38,472 - INFO - Max: 706.9761 +2025-04-18 15:52:38,472 - INFO - Std: 115.0030 +2025-04-18 15:52:38,472 - INFO - OC Distribution Statistics: +2025-04-18 15:52:38,472 - INFO - Validation Mean: 16.3809 +2025-04-18 15:52:38,472 - INFO - Validation Std: 7.5599 +2025-04-18 15:52:38,472 - INFO - Training Mean: 23.2012 +2025-04-18 15:52:38,472 - INFO - Training Std: 20.4546 +2025-04-18 15:52:38,472 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:52:38,472 - INFO - KS Statistic: 0.2158 +2025-04-18 15:52:38,472 - INFO - P-value: 0.0000 +2025-04-18 15:52:38,472 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:52:38,472 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:52:44,680 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:52:44,681 - INFO - Timestamp: 2025-04-18 15:52:44 +2025-04-18 15:52:44,681 - INFO - Validation set size: 1353 (8.19%) +2025-04-18 15:52:44,681 - INFO - Training set size: 15161 (91.81%) +2025-04-18 15:52:44,681 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:52:44,681 - INFO - Mean: 69.5571 +2025-04-18 15:52:44,681 - INFO - Median: 5.2498 +2025-04-18 15:52:44,681 - INFO - Min: 1.2773 +2025-04-18 15:52:44,681 - INFO - Max: 731.3502 +2025-04-18 15:52:44,681 - INFO - Std: 116.9491 +2025-04-18 15:52:44,681 - INFO - OC Distribution Statistics: +2025-04-18 15:52:44,681 - INFO - Validation Mean: 16.3098 +2025-04-18 15:52:44,681 - INFO - Validation Std: 7.7782 +2025-04-18 15:52:44,681 - INFO - Training Mean: 23.0816 +2025-04-18 15:52:44,681 - INFO - Training Std: 20.3022 +2025-04-18 15:52:44,681 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:52:44,681 - INFO - KS Statistic: 0.2206 +2025-04-18 15:52:44,681 - INFO - P-value: 0.0000 +2025-04-18 15:52:44,681 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:52:44,681 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:53:39,315 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:53:39,315 - INFO - Timestamp: 2025-04-18 15:53:39 +2025-04-18 15:53:39,315 - INFO - Validation set size: 1414 (8.56%) +2025-04-18 15:53:39,315 - INFO - Training set size: 15100 (91.44%) +2025-04-18 15:53:39,315 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:53:39,315 - INFO - Mean: 61.7352 +2025-04-18 15:53:39,315 - INFO - Median: 4.4155 +2025-04-18 15:53:39,315 - INFO - Min: 1.2156 +2025-04-18 15:53:39,315 - INFO - Max: 888.3558 +2025-04-18 15:53:39,315 - INFO - Std: 103.9586 +2025-04-18 15:53:39,315 - INFO - OC Distribution Statistics: +2025-04-18 15:53:39,315 - INFO - Validation Mean: 16.5828 +2025-04-18 15:53:39,315 - INFO - Validation Std: 8.1567 +2025-04-18 15:53:39,315 - INFO - Training Mean: 23.0834 +2025-04-18 15:53:39,315 - INFO - Training Std: 20.3270 +2025-04-18 15:53:39,315 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:53:39,315 - INFO - KS Statistic: 0.2160 +2025-04-18 15:53:39,315 - INFO - P-value: 0.0000 +2025-04-18 15:53:39,315 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:53:39,315 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:59:27,756 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:59:27,757 - INFO - Timestamp: 2025-04-18 15:59:27 +2025-04-18 15:59:27,757 - INFO - Validation set size: 1396 (8.45%) +2025-04-18 15:59:27,757 - INFO - Training set size: 15118 (91.55%) +2025-04-18 15:59:27,757 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:59:27,757 - INFO - Mean: 63.7399 +2025-04-18 15:59:27,757 - INFO - Median: 4.4990 +2025-04-18 15:59:27,757 - INFO - Min: 0.5501 +2025-04-18 15:59:27,757 - INFO - Max: 743.7101 +2025-04-18 15:59:27,757 - INFO - Std: 115.1618 +2025-04-18 15:59:27,757 - INFO - OC Distribution Statistics: +2025-04-18 15:59:27,757 - INFO - Validation Mean: 16.3106 +2025-04-18 15:59:27,757 - INFO - Validation Std: 6.9358 +2025-04-18 15:59:27,757 - INFO - Training Mean: 23.1008 +2025-04-18 15:59:27,757 - INFO - Training Std: 20.3517 +2025-04-18 15:59:27,757 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:59:27,757 - INFO - KS Statistic: 0.2079 +2025-04-18 15:59:27,757 - INFO - P-value: 0.0000 +2025-04-18 15:59:27,757 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:59:27,757 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:02:41,062 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:02:41,063 - INFO - Timestamp: 2025-04-18 16:02:41 +2025-04-18 16:02:41,063 - INFO - Validation set size: 1357 (8.22%) +2025-04-18 16:02:41,063 - INFO - Training set size: 15157 (91.78%) +2025-04-18 16:02:41,063 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:02:41,063 - INFO - Mean: 68.4550 +2025-04-18 16:02:41,063 - INFO - Median: 4.8053 +2025-04-18 16:02:41,063 - INFO - Min: 0.8608 +2025-04-18 16:02:41,063 - INFO - Max: 706.9584 +2025-04-18 16:02:41,063 - INFO - Std: 106.9077 +2025-04-18 16:02:41,063 - INFO - OC Distribution Statistics: +2025-04-18 16:02:41,063 - INFO - Validation Mean: 16.7360 +2025-04-18 16:02:41,063 - INFO - Validation Std: 7.8632 +2025-04-18 16:02:41,063 - INFO - Training Mean: 23.0452 +2025-04-18 16:02:41,063 - INFO - Training Std: 20.3135 +2025-04-18 16:02:41,063 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:02:41,063 - INFO - KS Statistic: 0.1789 +2025-04-18 16:02:41,063 - INFO - P-value: 0.0000 +2025-04-18 16:02:41,063 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:02:41,063 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:02:41,116 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:02:41,116 - INFO - Timestamp: 2025-04-18 16:02:41 +2025-04-18 16:02:41,116 - INFO - Validation set size: 1434 (8.68%) +2025-04-18 16:02:41,116 - INFO - Training set size: 15080 (91.32%) +2025-04-18 16:02:41,116 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:02:41,116 - INFO - Mean: 57.8223 +2025-04-18 16:02:41,116 - INFO - Median: 4.1444 +2025-04-18 16:02:41,116 - INFO - Min: 1.2156 +2025-04-18 16:02:41,116 - INFO - Max: 706.9761 +2025-04-18 16:02:41,116 - INFO - Std: 102.1467 +2025-04-18 16:02:41,116 - INFO - OC Distribution Statistics: +2025-04-18 16:02:41,116 - INFO - Validation Mean: 16.6871 +2025-04-18 16:02:41,116 - INFO - Validation Std: 8.4030 +2025-04-18 16:02:41,116 - INFO - Training Mean: 23.0821 +2025-04-18 16:02:41,116 - INFO - Training Std: 20.3306 +2025-04-18 16:02:41,116 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:02:41,116 - INFO - KS Statistic: 0.2154 +2025-04-18 16:02:41,116 - INFO - P-value: 0.0000 +2025-04-18 16:02:41,116 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:02:41,116 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:02:41,143 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:02:41,143 - INFO - Timestamp: 2025-04-18 16:02:41 +2025-04-18 16:02:41,143 - INFO - Validation set size: 1375 (8.33%) +2025-04-18 16:02:41,143 - INFO - Training set size: 15139 (91.67%) +2025-04-18 16:02:41,143 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:02:41,143 - INFO - Mean: 69.6497 +2025-04-18 16:02:41,144 - INFO - Median: 4.5217 +2025-04-18 16:02:41,144 - INFO - Min: 0.7371 +2025-04-18 16:02:41,144 - INFO - Max: 866.4875 +2025-04-18 16:02:41,144 - INFO - Std: 113.7329 +2025-04-18 16:02:41,144 - INFO - OC Distribution Statistics: +2025-04-18 16:02:41,144 - INFO - Validation Mean: 16.3136 +2025-04-18 16:02:41,144 - INFO - Validation Std: 7.3411 +2025-04-18 16:02:41,144 - INFO - Training Mean: 23.0911 +2025-04-18 16:02:41,144 - INFO - Training Std: 20.3280 +2025-04-18 16:02:41,144 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:02:41,144 - INFO - KS Statistic: 0.2096 +2025-04-18 16:02:41,144 - INFO - P-value: 0.0000 +2025-04-18 16:02:41,144 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:02:41,144 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:02:42,397 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:02:42,397 - INFO - Timestamp: 2025-04-18 16:02:42 +2025-04-18 16:02:42,397 - INFO - Validation set size: 1357 (8.22%) +2025-04-18 16:02:42,397 - INFO - Training set size: 15157 (91.78%) +2025-04-18 16:02:42,397 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:02:42,397 - INFO - Mean: 68.6785 +2025-04-18 16:02:42,398 - INFO - Median: 4.9468 +2025-04-18 16:02:42,398 - INFO - Min: 0.5501 +2025-04-18 16:02:42,398 - INFO - Max: 835.9450 +2025-04-18 16:02:42,398 - INFO - Std: 116.3259 +2025-04-18 16:02:42,398 - INFO - OC Distribution Statistics: +2025-04-18 16:02:42,398 - INFO - Validation Mean: 16.4440 +2025-04-18 16:02:42,398 - INFO - Validation Std: 8.4046 +2025-04-18 16:02:42,398 - INFO - Training Mean: 23.0714 +2025-04-18 16:02:42,398 - INFO - Training Std: 20.2858 +2025-04-18 16:02:42,398 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:02:42,398 - INFO - KS Statistic: 0.2261 +2025-04-18 16:02:42,398 - INFO - P-value: 0.0000 +2025-04-18 16:02:42,398 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:02:42,398 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:04:49,227 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:04:49,227 - INFO - Timestamp: 2025-04-18 16:04:49 +2025-04-18 16:04:49,227 - INFO - Validation set size: 1356 (8.21%) +2025-04-18 16:04:49,228 - INFO - Training set size: 15158 (91.79%) +2025-04-18 16:04:49,228 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:04:49,228 - INFO - Mean: 67.2133 +2025-04-18 16:04:49,228 - INFO - Median: 4.4057 +2025-04-18 16:04:49,228 - INFO - Min: 1.1342 +2025-04-18 16:04:49,228 - INFO - Max: 743.7101 +2025-04-18 16:04:49,228 - INFO - Std: 114.7430 +2025-04-18 16:04:49,228 - INFO - OC Distribution Statistics: +2025-04-18 16:04:49,228 - INFO - Validation Mean: 16.6284 +2025-04-18 16:04:49,228 - INFO - Validation Std: 8.2043 +2025-04-18 16:04:49,228 - INFO - Training Mean: 23.0544 +2025-04-18 16:04:49,228 - INFO - Training Std: 20.2979 +2025-04-18 16:04:49,228 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:04:49,228 - INFO - KS Statistic: 0.2040 +2025-04-18 16:04:49,228 - INFO - P-value: 0.0000 +2025-04-18 16:04:49,228 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:04:49,228 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:04:49,332 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:04:49,332 - INFO - Timestamp: 2025-04-18 16:04:49 +2025-04-18 16:04:49,332 - INFO - Validation set size: 1373 (8.31%) +2025-04-18 16:04:49,332 - INFO - Training set size: 15141 (91.69%) +2025-04-18 16:04:49,332 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:04:49,332 - INFO - Mean: 65.2476 +2025-04-18 16:04:49,332 - INFO - Median: 4.8053 +2025-04-18 16:04:49,332 - INFO - Min: 1.2065 +2025-04-18 16:04:49,332 - INFO - Max: 536.0955 +2025-04-18 16:04:49,332 - INFO - Std: 104.1849 +2025-04-18 16:04:49,332 - INFO - OC Distribution Statistics: +2025-04-18 16:04:49,333 - INFO - Validation Mean: 16.6298 +2025-04-18 16:04:49,333 - INFO - Validation Std: 9.1378 +2025-04-18 16:04:49,333 - INFO - Training Mean: 23.0615 +2025-04-18 16:04:49,333 - INFO - Training Std: 20.2702 +2025-04-18 16:04:49,333 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:04:49,333 - INFO - KS Statistic: 0.2161 +2025-04-18 16:04:49,333 - INFO - P-value: 0.0000 +2025-04-18 16:04:49,333 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:04:49,333 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:04:49,443 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:04:49,444 - INFO - Timestamp: 2025-04-18 16:04:49 +2025-04-18 16:04:49,444 - INFO - Validation set size: 1551 (9.39%) +2025-04-18 16:04:49,444 - INFO - Training set size: 14963 (90.61%) +2025-04-18 16:04:49,444 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:04:49,444 - INFO - Mean: 61.2389 +2025-04-18 16:04:49,444 - INFO - Median: 4.0758 +2025-04-18 16:04:49,444 - INFO - Min: 0.6067 +2025-04-18 16:04:49,444 - INFO - Max: 702.5656 +2025-04-18 16:04:49,444 - INFO - Std: 104.4332 +2025-04-18 16:04:49,444 - INFO - OC Distribution Statistics: +2025-04-18 16:04:49,444 - INFO - Validation Mean: 16.1777 +2025-04-18 16:04:49,444 - INFO - Validation Std: 8.1085 +2025-04-18 16:04:49,444 - INFO - Training Mean: 23.1849 +2025-04-18 16:04:49,444 - INFO - Training Std: 20.3834 +2025-04-18 16:04:49,444 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:04:49,444 - INFO - KS Statistic: 0.2385 +2025-04-18 16:04:49,444 - INFO - P-value: 0.0000 +2025-04-18 16:04:49,444 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:04:49,444 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:04:50,645 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:04:50,645 - INFO - Timestamp: 2025-04-18 16:04:50 +2025-04-18 16:04:50,645 - INFO - Validation set size: 1407 (8.52%) +2025-04-18 16:04:50,645 - INFO - Training set size: 15107 (91.48%) +2025-04-18 16:04:50,645 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:04:50,645 - INFO - Mean: 63.8077 +2025-04-18 16:04:50,645 - INFO - Median: 4.2696 +2025-04-18 16:04:50,645 - INFO - Min: 1.1288 +2025-04-18 16:04:50,645 - INFO - Max: 706.9584 +2025-04-18 16:04:50,645 - INFO - Std: 104.6971 +2025-04-18 16:04:50,645 - INFO - OC Distribution Statistics: +2025-04-18 16:04:50,645 - INFO - Validation Mean: 16.1754 +2025-04-18 16:04:50,645 - INFO - Validation Std: 7.2235 +2025-04-18 16:04:50,645 - INFO - Training Mean: 23.1183 +2025-04-18 16:04:50,645 - INFO - Training Std: 20.3439 +2025-04-18 16:04:50,646 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:04:50,646 - INFO - KS Statistic: 0.2336 +2025-04-18 16:04:50,646 - INFO - P-value: 0.0000 +2025-04-18 16:04:50,646 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:04:50,646 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:05:21,635 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:05:21,635 - INFO - Timestamp: 2025-04-18 16:05:21 +2025-04-18 16:05:21,635 - INFO - Validation set size: 1417 (8.58%) +2025-04-18 16:05:21,635 - INFO - Training set size: 15097 (91.42%) +2025-04-18 16:05:21,635 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:05:21,635 - INFO - Mean: 63.6246 +2025-04-18 16:05:21,635 - INFO - Median: 3.9211 +2025-04-18 16:05:21,635 - INFO - Min: 1.2329 +2025-04-18 16:05:21,635 - INFO - Max: 772.4342 +2025-04-18 16:05:21,635 - INFO - Std: 112.2672 +2025-04-18 16:05:21,635 - INFO - OC Distribution Statistics: +2025-04-18 16:05:21,635 - INFO - Validation Mean: 16.4550 +2025-04-18 16:05:21,635 - INFO - Validation Std: 7.5300 +2025-04-18 16:05:21,635 - INFO - Training Mean: 23.0967 +2025-04-18 16:05:21,635 - INFO - Training Std: 20.3473 +2025-04-18 16:05:21,635 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:05:21,635 - INFO - KS Statistic: 0.2280 +2025-04-18 16:05:21,635 - INFO - P-value: 0.0000 +2025-04-18 16:05:21,635 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:05:21,635 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:06:48,871 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:06:48,871 - INFO - Timestamp: 2025-04-18 16:06:48 +2025-04-18 16:06:48,871 - INFO - Validation set size: 1390 (8.42%) +2025-04-18 16:06:48,871 - INFO - Training set size: 15124 (91.58%) +2025-04-18 16:06:48,871 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:06:48,871 - INFO - Mean: 76.0727 +2025-04-18 16:06:48,871 - INFO - Median: 4.2800 +2025-04-18 16:06:48,871 - INFO - Min: 1.0475 +2025-04-18 16:06:48,871 - INFO - Max: 824.9624 +2025-04-18 16:06:48,871 - INFO - Std: 128.4156 +2025-04-18 16:06:48,871 - INFO - OC Distribution Statistics: +2025-04-18 16:06:48,871 - INFO - Validation Mean: 16.7020 +2025-04-18 16:06:48,871 - INFO - Validation Std: 7.3852 +2025-04-18 16:06:48,872 - INFO - Training Mean: 23.0621 +2025-04-18 16:06:48,872 - INFO - Training Std: 20.3457 +2025-04-18 16:06:48,872 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:06:48,872 - INFO - KS Statistic: 0.2013 +2025-04-18 16:06:48,872 - INFO - P-value: 0.0000 +2025-04-18 16:06:48,872 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:06:48,872 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:06:48,884 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:06:48,884 - INFO - Timestamp: 2025-04-18 16:06:48 +2025-04-18 16:06:48,884 - INFO - Validation set size: 1438 (8.71%) +2025-04-18 16:06:48,884 - INFO - Training set size: 15076 (91.29%) +2025-04-18 16:06:48,884 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:06:48,884 - INFO - Mean: 58.1511 +2025-04-18 16:06:48,884 - INFO - Median: 4.1787 +2025-04-18 16:06:48,884 - INFO - Min: 0.9397 +2025-04-18 16:06:48,884 - INFO - Max: 706.9584 +2025-04-18 16:06:48,884 - INFO - Std: 100.1386 +2025-04-18 16:06:48,884 - INFO - OC Distribution Statistics: +2025-04-18 16:06:48,884 - INFO - Validation Mean: 15.9973 +2025-04-18 16:06:48,884 - INFO - Validation Std: 7.1834 +2025-04-18 16:06:48,884 - INFO - Training Mean: 23.1496 +2025-04-18 16:06:48,884 - INFO - Training Std: 20.3552 +2025-04-18 16:06:48,884 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:06:48,884 - INFO - KS Statistic: 0.2647 +2025-04-18 16:06:48,884 - INFO - P-value: 0.0000 +2025-04-18 16:06:48,884 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:06:48,884 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:06:48,899 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:06:48,899 - INFO - Timestamp: 2025-04-18 16:06:48 +2025-04-18 16:06:48,899 - INFO - Validation set size: 1332 (8.07%) +2025-04-18 16:06:48,899 - INFO - Training set size: 15182 (91.93%) +2025-04-18 16:06:48,900 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:06:48,900 - INFO - Mean: 70.2489 +2025-04-18 16:06:48,900 - INFO - Median: 4.7721 +2025-04-18 16:06:48,900 - INFO - Min: 1.2329 +2025-04-18 16:06:48,900 - INFO - Max: 705.4078 +2025-04-18 16:06:48,900 - INFO - Std: 114.5421 +2025-04-18 16:06:48,900 - INFO - OC Distribution Statistics: +2025-04-18 16:06:48,900 - INFO - Validation Mean: 16.7494 +2025-04-18 16:06:48,900 - INFO - Validation Std: 7.9630 +2025-04-18 16:06:48,900 - INFO - Training Mean: 23.0337 +2025-04-18 16:06:48,900 - INFO - Training Std: 20.2979 +2025-04-18 16:06:48,900 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:06:48,900 - INFO - KS Statistic: 0.2068 +2025-04-18 16:06:48,900 - INFO - P-value: 0.0000 +2025-04-18 16:06:48,900 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:06:48,900 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:06:50,147 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:06:50,147 - INFO - Timestamp: 2025-04-18 16:06:50 +2025-04-18 16:06:50,147 - INFO - Validation set size: 1488 (9.01%) +2025-04-18 16:06:50,147 - INFO - Training set size: 15026 (90.99%) +2025-04-18 16:06:50,147 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:06:50,147 - INFO - Mean: 68.0496 +2025-04-18 16:06:50,147 - INFO - Median: 4.2491 +2025-04-18 16:06:50,147 - INFO - Min: 1.1496 +2025-04-18 16:06:50,147 - INFO - Max: 826.2195 +2025-04-18 16:06:50,147 - INFO - Std: 122.6871 +2025-04-18 16:06:50,147 - INFO - OC Distribution Statistics: +2025-04-18 16:06:50,147 - INFO - Validation Mean: 16.2816 +2025-04-18 16:06:50,147 - INFO - Validation Std: 7.6053 +2025-04-18 16:06:50,147 - INFO - Training Mean: 23.1452 +2025-04-18 16:06:50,147 - INFO - Training Std: 20.3752 +2025-04-18 16:06:50,147 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:06:50,147 - INFO - KS Statistic: 0.2246 +2025-04-18 16:06:50,147 - INFO - P-value: 0.0000 +2025-04-18 16:06:50,147 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:06:50,147 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:11:46,358 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:11:46,358 - INFO - Timestamp: 2025-04-18 16:11:46 +2025-04-18 16:11:46,358 - INFO - Validation set size: 1401 (8.48%) +2025-04-18 16:11:46,358 - INFO - Training set size: 15113 (91.52%) +2025-04-18 16:11:46,358 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:11:46,358 - INFO - Mean: 70.5843 +2025-04-18 16:11:46,358 - INFO - Median: 5.7863 +2025-04-18 16:11:46,358 - INFO - Min: 0.9954 +2025-04-18 16:11:46,358 - INFO - Max: 716.7388 +2025-04-18 16:11:46,358 - INFO - Std: 114.2087 +2025-04-18 16:11:46,358 - INFO - OC Distribution Statistics: +2025-04-18 16:11:46,358 - INFO - Validation Mean: 16.4248 +2025-04-18 16:11:46,358 - INFO - Validation Std: 7.3243 +2025-04-18 16:11:46,358 - INFO - Training Mean: 23.0925 +2025-04-18 16:11:46,358 - INFO - Training Std: 20.3452 +2025-04-18 16:11:46,358 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:11:46,358 - INFO - KS Statistic: 0.2151 +2025-04-18 16:11:46,358 - INFO - P-value: 0.0000 +2025-04-18 16:11:46,358 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:11:46,358 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:11:46,371 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:11:46,371 - INFO - Timestamp: 2025-04-18 16:11:46 +2025-04-18 16:11:46,371 - INFO - Validation set size: 1382 (8.37%) +2025-04-18 16:11:46,371 - INFO - Training set size: 15132 (91.63%) +2025-04-18 16:11:46,371 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:11:46,371 - INFO - Mean: 72.0578 +2025-04-18 16:11:46,371 - INFO - Median: 4.8053 +2025-04-18 16:11:46,371 - INFO - Min: 1.2156 +2025-04-18 16:11:46,371 - INFO - Max: 875.2369 +2025-04-18 16:11:46,371 - INFO - Std: 126.4705 +2025-04-18 16:11:46,371 - INFO - OC Distribution Statistics: +2025-04-18 16:11:46,371 - INFO - Validation Mean: 16.5085 +2025-04-18 16:11:46,371 - INFO - Validation Std: 7.5828 +2025-04-18 16:11:46,371 - INFO - Training Mean: 23.0764 +2025-04-18 16:11:46,371 - INFO - Training Std: 20.3293 +2025-04-18 16:11:46,371 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:11:46,371 - INFO - KS Statistic: 0.1988 +2025-04-18 16:11:46,371 - INFO - P-value: 0.0000 +2025-04-18 16:11:46,371 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:11:46,372 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:11:46,464 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:11:46,465 - INFO - Timestamp: 2025-04-18 16:11:46 +2025-04-18 16:11:46,465 - INFO - Validation set size: 1335 (8.08%) +2025-04-18 16:11:46,465 - INFO - Training set size: 15179 (91.92%) +2025-04-18 16:11:46,465 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:11:46,465 - INFO - Mean: 76.7901 +2025-04-18 16:11:46,465 - INFO - Median: 6.6017 +2025-04-18 16:11:46,465 - INFO - Min: 1.2247 +2025-04-18 16:11:46,465 - INFO - Max: 702.5656 +2025-04-18 16:11:46,465 - INFO - Std: 115.4648 +2025-04-18 16:11:46,465 - INFO - OC Distribution Statistics: +2025-04-18 16:11:46,465 - INFO - Validation Mean: 16.6051 +2025-04-18 16:11:46,465 - INFO - Validation Std: 8.0727 +2025-04-18 16:11:46,465 - INFO - Training Mean: 23.0476 +2025-04-18 16:11:46,465 - INFO - Training Std: 20.2916 +2025-04-18 16:11:46,465 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:11:46,465 - INFO - KS Statistic: 0.2008 +2025-04-18 16:11:46,465 - INFO - P-value: 0.0000 +2025-04-18 16:11:46,465 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:11:46,465 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:11:49,883 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:11:49,883 - INFO - Timestamp: 2025-04-18 16:11:49 +2025-04-18 16:11:49,883 - INFO - Validation set size: 1374 (8.32%) +2025-04-18 16:11:49,883 - INFO - Training set size: 15140 (91.68%) +2025-04-18 16:11:49,883 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:11:49,883 - INFO - Mean: 71.4760 +2025-04-18 16:11:49,883 - INFO - Median: 4.1787 +2025-04-18 16:11:49,883 - INFO - Min: 1.2102 +2025-04-18 16:11:49,883 - INFO - Max: 716.7388 +2025-04-18 16:11:49,883 - INFO - Std: 115.8624 +2025-04-18 16:11:49,883 - INFO - OC Distribution Statistics: +2025-04-18 16:11:49,883 - INFO - Validation Mean: 16.6569 +2025-04-18 16:11:49,883 - INFO - Validation Std: 7.9751 +2025-04-18 16:11:49,883 - INFO - Training Mean: 23.0595 +2025-04-18 16:11:49,883 - INFO - Training Std: 20.3159 +2025-04-18 16:11:49,883 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:11:49,883 - INFO - KS Statistic: 0.1928 +2025-04-18 16:11:49,883 - INFO - P-value: 0.0000 +2025-04-18 16:11:49,883 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:11:49,883 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:12:39,199 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:12:39,200 - INFO - Timestamp: 2025-04-18 16:12:39 +2025-04-18 16:12:39,200 - INFO - Validation set size: 1350 (8.17%) +2025-04-18 16:12:39,200 - INFO - Training set size: 15164 (91.83%) +2025-04-18 16:12:39,200 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:12:39,200 - INFO - Mean: 80.2886 +2025-04-18 16:12:39,200 - INFO - Median: 5.9497 +2025-04-18 16:12:39,200 - INFO - Min: 1.2953 +2025-04-18 16:12:39,200 - INFO - Max: 870.7197 +2025-04-18 16:12:39,200 - INFO - Std: 130.8739 +2025-04-18 16:12:39,200 - INFO - OC Distribution Statistics: +2025-04-18 16:12:39,200 - INFO - Validation Mean: 16.7095 +2025-04-18 16:12:39,200 - INFO - Validation Std: 7.6246 +2025-04-18 16:12:39,200 - INFO - Training Mean: 23.0447 +2025-04-18 16:12:39,200 - INFO - Training Std: 20.3173 +2025-04-18 16:12:39,200 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:12:39,200 - INFO - KS Statistic: 0.1888 +2025-04-18 16:12:39,200 - INFO - P-value: 0.0000 +2025-04-18 16:12:39,200 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:12:39,200 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:12:39,204 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:12:39,204 - INFO - Timestamp: 2025-04-18 16:12:39 +2025-04-18 16:12:39,204 - INFO - Validation set size: 1415 (8.57%) +2025-04-18 16:12:39,204 - INFO - Training set size: 15099 (91.43%) +2025-04-18 16:12:39,204 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:12:39,204 - INFO - Mean: 66.4911 +2025-04-18 16:12:39,204 - INFO - Median: 5.0828 +2025-04-18 16:12:39,204 - INFO - Min: 1.2156 +2025-04-18 16:12:39,204 - INFO - Max: 743.7101 +2025-04-18 16:12:39,204 - INFO - Std: 116.2826 +2025-04-18 16:12:39,204 - INFO - OC Distribution Statistics: +2025-04-18 16:12:39,204 - INFO - Validation Mean: 16.4056 +2025-04-18 16:12:39,204 - INFO - Validation Std: 7.3180 +2025-04-18 16:12:39,204 - INFO - Training Mean: 23.1004 +2025-04-18 16:12:39,204 - INFO - Training Std: 20.3520 +2025-04-18 16:12:39,204 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:12:39,204 - INFO - KS Statistic: 0.2111 +2025-04-18 16:12:39,204 - INFO - P-value: 0.0000 +2025-04-18 16:12:39,204 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:12:39,204 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:12:39,256 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:12:39,257 - INFO - Timestamp: 2025-04-18 16:12:39 +2025-04-18 16:12:39,257 - INFO - Validation set size: 1429 (8.65%) +2025-04-18 16:12:39,257 - INFO - Training set size: 15085 (91.35%) +2025-04-18 16:12:39,257 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:12:39,257 - INFO - Mean: 70.9552 +2025-04-18 16:12:39,257 - INFO - Median: 5.2639 +2025-04-18 16:12:39,257 - INFO - Min: 1.1288 +2025-04-18 16:12:39,257 - INFO - Max: 862.7749 +2025-04-18 16:12:39,257 - INFO - Std: 116.8624 +2025-04-18 16:12:39,257 - INFO - OC Distribution Statistics: +2025-04-18 16:12:39,257 - INFO - Validation Mean: 16.6827 +2025-04-18 16:12:39,257 - INFO - Validation Std: 7.7379 +2025-04-18 16:12:39,257 - INFO - Training Mean: 23.0804 +2025-04-18 16:12:39,257 - INFO - Training Std: 20.3530 +2025-04-18 16:12:39,257 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:12:39,257 - INFO - KS Statistic: 0.1936 +2025-04-18 16:12:39,257 - INFO - P-value: 0.0000 +2025-04-18 16:12:39,257 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:12:39,257 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:12:40,481 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:12:40,481 - INFO - Timestamp: 2025-04-18 16:12:40 +2025-04-18 16:12:40,481 - INFO - Validation set size: 1409 (8.53%) +2025-04-18 16:12:40,481 - INFO - Training set size: 15105 (91.47%) +2025-04-18 16:12:40,481 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:12:40,481 - INFO - Mean: 71.7305 +2025-04-18 16:12:40,481 - INFO - Median: 4.4771 +2025-04-18 16:12:40,481 - INFO - Min: 1.2156 +2025-04-18 16:12:40,481 - INFO - Max: 629.8500 +2025-04-18 16:12:40,481 - INFO - Std: 113.2923 +2025-04-18 16:12:40,481 - INFO - OC Distribution Statistics: +2025-04-18 16:12:40,481 - INFO - Validation Mean: 16.3843 +2025-04-18 16:12:40,481 - INFO - Validation Std: 7.7056 +2025-04-18 16:12:40,481 - INFO - Training Mean: 23.0998 +2025-04-18 16:12:40,481 - INFO - Training Std: 20.3350 +2025-04-18 16:12:40,481 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:12:40,481 - INFO - KS Statistic: 0.2356 +2025-04-18 16:12:40,481 - INFO - P-value: 0.0000 +2025-04-18 16:12:40,481 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:12:40,481 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:13:01,208 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 16:13:01,212 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 16:13:01,223 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 16:13:01,238 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 16:17:00,870 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:17:00,870 - INFO - Timestamp: 2025-04-18 16:17:00 +2025-04-18 16:17:00,870 - INFO - Validation set size: 1509 (9.14%) +2025-04-18 16:17:00,870 - INFO - Training set size: 15005 (90.86%) +2025-04-18 16:17:00,870 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:17:00,870 - INFO - Mean: 65.6381 +2025-04-18 16:17:00,870 - INFO - Median: 4.2447 +2025-04-18 16:17:00,870 - INFO - Min: 1.2065 +2025-04-18 16:17:00,870 - INFO - Max: 772.4342 +2025-04-18 16:17:00,870 - INFO - Std: 110.2966 +2025-04-18 16:17:00,870 - INFO - OC Distribution Statistics: +2025-04-18 16:17:00,870 - INFO - Validation Mean: 16.2764 +2025-04-18 16:17:00,870 - INFO - Validation Std: 8.3436 +2025-04-18 16:17:00,870 - INFO - Training Mean: 23.1554 +2025-04-18 16:17:00,870 - INFO - Training Std: 20.3566 +2025-04-18 16:17:00,870 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:17:00,870 - INFO - KS Statistic: 0.2480 +2025-04-18 16:17:00,870 - INFO - P-value: 0.0000 +2025-04-18 16:17:00,870 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:17:00,870 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:17:01,151 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:17:01,151 - INFO - Timestamp: 2025-04-18 16:17:01 +2025-04-18 16:17:01,151 - INFO - Validation set size: 1681 (10.18%) +2025-04-18 16:17:01,151 - INFO - Training set size: 14833 (89.82%) +2025-04-18 16:17:01,151 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:17:01,151 - INFO - Mean: 67.2822 +2025-04-18 16:17:01,151 - INFO - Median: 4.2340 +2025-04-18 16:17:01,151 - INFO - Min: 0.2243 +2025-04-18 16:17:01,151 - INFO - Max: 917.2720 +2025-04-18 16:17:01,151 - INFO - Std: 126.7617 +2025-04-18 16:17:01,151 - INFO - OC Distribution Statistics: +2025-04-18 16:17:01,151 - INFO - Validation Mean: 16.7041 +2025-04-18 16:17:01,151 - INFO - Validation Std: 8.6341 +2025-04-18 16:17:01,151 - INFO - Training Mean: 23.1867 +2025-04-18 16:17:01,151 - INFO - Training Std: 20.4432 +2025-04-18 16:17:01,151 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:17:01,151 - INFO - KS Statistic: 0.2222 +2025-04-18 16:17:01,151 - INFO - P-value: 0.0000 +2025-04-18 16:17:01,151 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:17:01,151 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:17:01,161 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:17:01,161 - INFO - Timestamp: 2025-04-18 16:17:01 +2025-04-18 16:17:01,161 - INFO - Validation set size: 1617 (9.79%) +2025-04-18 16:17:01,161 - INFO - Training set size: 14897 (90.21%) +2025-04-18 16:17:01,162 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:17:01,162 - INFO - Mean: 63.6569 +2025-04-18 16:17:01,162 - INFO - Median: 4.2447 +2025-04-18 16:17:01,162 - INFO - Min: 1.2773 +2025-04-18 16:17:01,162 - INFO - Max: 731.3502 +2025-04-18 16:17:01,162 - INFO - Std: 111.7212 +2025-04-18 16:17:01,162 - INFO - OC Distribution Statistics: +2025-04-18 16:17:01,162 - INFO - Validation Mean: 16.1731 +2025-04-18 16:17:01,162 - INFO - Validation Std: 7.2861 +2025-04-18 16:17:01,162 - INFO - Training Mean: 23.2164 +2025-04-18 16:17:01,162 - INFO - Training Std: 20.4495 +2025-04-18 16:17:01,162 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:17:01,162 - INFO - KS Statistic: 0.2243 +2025-04-18 16:17:01,162 - INFO - P-value: 0.0000 +2025-04-18 16:17:01,162 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:17:01,162 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:17:05,384 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:17:05,384 - INFO - Timestamp: 2025-04-18 16:17:05 +2025-04-18 16:17:05,384 - INFO - Validation set size: 1354 (8.20%) +2025-04-18 16:17:05,384 - INFO - Training set size: 15160 (91.80%) +2025-04-18 16:17:05,384 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:17:05,384 - INFO - Mean: 61.5738 +2025-04-18 16:17:05,384 - INFO - Median: 4.0399 +2025-04-18 16:17:05,384 - INFO - Min: 0.6775 +2025-04-18 16:17:05,384 - INFO - Max: 888.3558 +2025-04-18 16:17:05,384 - INFO - Std: 110.2425 +2025-04-18 16:17:05,384 - INFO - OC Distribution Statistics: +2025-04-18 16:17:05,384 - INFO - Validation Mean: 16.8783 +2025-04-18 16:17:05,384 - INFO - Validation Std: 8.2177 +2025-04-18 16:17:05,384 - INFO - Training Mean: 23.0313 +2025-04-18 16:17:05,384 - INFO - Training Std: 20.3034 +2025-04-18 16:17:05,384 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:17:05,384 - INFO - KS Statistic: 0.1855 +2025-04-18 16:17:05,384 - INFO - P-value: 0.0000 +2025-04-18 16:17:05,384 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:17:05,384 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:17:23,071 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 16:17:23,098 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 16:17:23,098 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 16:17:23,126 - INFO - Reducer buckets have been rebuilt in this iteration. +025-04-18 16:17:23,220 - INFO - Mean: 69.5470 +2025-04-18 16:17:23,220 - INFO - Median: 5.5358 +2025-04-18 16:17:23,220 - INFO - Min: 0.8394 +2025-04-18 16:17:23,220 - INFO - Max: 525.9404 +2025-04-18 16:17:23,220 - INFO - Std: 105.0715 +2025-04-18 16:17:23,220 - INFO - OC Distribution Statistics: +2025-04-18 16:17:23,220 - INFO - Validation Mean: 16.6859 +2025-04-18 16:17:23,220 - INFO - Validation Std: 7.9063 +2025-04-18 16:17:23,220 - INFO - Training Mean: 23.0564 +2025-04-18 16:17:23,220 - INFO - Training Std: 20.3187 +2025-04-18 16:17:23,220 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:17:23,220 - INFO - KS Statistic: 0.1932 +2025-04-18 16:17:23,220 - INFO - P-value: 0.0000 +2025-04-18 16:17:23,220 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:17:23,220 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:17:23,360 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:17:23,360 - INFO - Timestamp: 2025-04-18 16:17:23 +2025-04-18 16:17:23,360 - INFO - Validation set size: 1366 (8.27%) +2025-04-18 16:17:23,360 - INFO - Training set size: 15148 (91.73%) +2025-04-18 16:17:23,360 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:17:23,360 - INFO - Mean: 65.4585 +2025-04-18 16:17:23,360 - INFO - Median: 4.4688 +2025-04-18 16:17:23,360 - INFO - Min: 0.5501 +2025-04-18 16:17:23,360 - INFO - Max: 1106.1626 +2025-04-18 16:17:23,360 - INFO - Std: 117.2714 +2025-04-18 16:17:23,360 - INFO - OC Distribution Statistics: +2025-04-18 16:17:23,360 - INFO - Validation Mean: 16.2785 +2025-04-18 16:17:23,360 - INFO - Validation Std: 7.1648 +2025-04-18 16:17:23,360 - INFO - Training Mean: 23.0902 +2025-04-18 16:17:23,360 - INFO - Training Std: 20.3280 +2025-04-18 16:17:23,360 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:17:23,360 - INFO - KS Statistic: 0.2227 +2025-04-18 16:17:23,360 - INFO - P-value: 0.0000 +2025-04-18 16:17:23,360 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:17:23,361 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:17:23,436 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:17:23,437 - INFO - Timestamp: 2025-04-18 16:17:23 +2025-04-18 16:17:23,437 - INFO - Validation set size: 1369 (8.29%) +2025-04-18 16:17:23,437 - INFO - Training set size: 15145 (91.71%) +2025-04-18 16:17:23,437 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:17:23,437 - INFO - Mean: 71.5199 +2025-04-18 16:17:23,437 - INFO - Median: 4.7243 +2025-04-18 16:17:23,437 - INFO - Min: 1.2102 +2025-04-18 16:17:23,437 - INFO - Max: 705.4078 +2025-04-18 16:17:23,437 - INFO - Std: 111.9382 +2025-04-18 16:17:23,437 - INFO - OC Distribution Statistics: +2025-04-18 16:17:23,437 - INFO - Validation Mean: 16.7175 +2025-04-18 16:17:23,437 - INFO - Validation Std: 8.2533 +2025-04-18 16:17:23,437 - INFO - Training Mean: 23.0519 +2025-04-18 16:17:23,437 - INFO - Training Std: 20.3051 +2025-04-18 16:17:23,437 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:17:23,437 - INFO - KS Statistic: 0.2162 +2025-04-18 16:17:23,437 - INFO - P-value: 0.0000 +2025-04-18 16:17:23,437 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:17:23,437 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:17:27,277 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:17:27,277 - INFO - Timestamp: 2025-04-18 16:17:27 +2025-04-18 16:17:27,277 - INFO - Validation set size: 1364 (8.26%) +2025-04-18 16:17:27,277 - INFO - Training set size: 15150 (91.74%) +2025-04-18 16:17:27,277 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:17:27,277 - INFO - Mean: 65.5612 +2025-04-18 16:17:27,277 - INFO - Median: 5.0148 +2025-04-18 16:17:27,277 - INFO - Min: 1.0321 +2025-04-18 16:17:27,277 - INFO - Max: 706.9584 +2025-04-18 16:17:27,277 - INFO - Std: 105.3227 +2025-04-18 16:17:27,277 - INFO - OC Distribution Statistics: +2025-04-18 16:17:27,277 - INFO - Validation Mean: 16.8327 +2025-04-18 16:17:27,277 - INFO - Validation Std: 9.0139 +2025-04-18 16:17:27,277 - INFO - Training Mean: 23.0394 +2025-04-18 16:17:27,277 - INFO - Training Std: 20.2767 +2025-04-18 16:17:27,277 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:17:27,277 - INFO - KS Statistic: 0.1967 +2025-04-18 16:17:27,277 - INFO - P-value: 0.0000 +2025-04-18 16:17:27,278 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:17:27,278 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:26:16,187 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:26:16,187 - INFO - Timestamp: 2025-04-18 16:26:16 +2025-04-18 16:26:16,187 - INFO - Validation set size: 1428 (8.65%) +2025-04-18 16:26:16,187 - INFO - Training set size: 15086 (91.35%) +2025-04-18 16:26:16,187 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:26:16,187 - INFO - Mean: 58.5762 +2025-04-18 16:26:16,187 - INFO - Median: 4.6101 +2025-04-18 16:26:16,187 - INFO - Min: 0.5685 +2025-04-18 16:26:16,187 - INFO - Max: 706.9584 +2025-04-18 16:26:16,187 - INFO - Std: 99.0073 +2025-04-18 16:26:16,187 - INFO - OC Distribution Statistics: +2025-04-18 16:26:16,187 - INFO - Validation Mean: 16.5108 +2025-04-18 16:26:16,187 - INFO - Validation Std: 7.6082 +2025-04-18 16:26:16,187 - INFO - Training Mean: 23.0962 +2025-04-18 16:26:16,187 - INFO - Training Std: 20.3519 +2025-04-18 16:26:16,187 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:26:16,187 - INFO - KS Statistic: 0.1900 +2025-04-18 16:26:16,187 - INFO - P-value: 0.0000 +2025-04-18 16:26:16,187 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:26:16,187 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:26:16,239 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:26:16,239 - INFO - Timestamp: 2025-04-18 16:26:16 +2025-04-18 16:26:16,239 - INFO - Validation set size: 1572 (9.52%) +2025-04-18 16:26:16,239 - INFO - Training set size: 14942 (90.48%) +2025-04-18 16:26:16,239 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:26:16,239 - INFO - Mean: 62.5232 +2025-04-18 16:26:16,239 - INFO - Median: 3.9657 +2025-04-18 16:26:16,239 - INFO - Min: 0.5501 +2025-04-18 16:26:16,239 - INFO - Max: 706.9584 +2025-04-18 16:26:16,239 - INFO - Std: 114.7125 +2025-04-18 16:26:16,239 - INFO - OC Distribution Statistics: +2025-04-18 16:26:16,239 - INFO - Validation Mean: 15.9659 +2025-04-18 16:26:16,239 - INFO - Validation Std: 6.7394 +2025-04-18 16:26:16,239 - INFO - Training Mean: 23.2170 +2025-04-18 16:26:16,239 - INFO - Training Std: 20.4384 +2025-04-18 16:26:16,239 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:26:16,239 - INFO - KS Statistic: 0.2517 +2025-04-18 16:26:16,239 - INFO - P-value: 0.0000 +2025-04-18 16:26:16,239 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:26:16,239 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:26:16,513 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:26:16,514 - INFO - Timestamp: 2025-04-18 16:26:16 +2025-04-18 16:26:16,514 - INFO - Validation set size: 1587 (9.61%) +2025-04-18 16:26:16,514 - INFO - Training set size: 14927 (90.39%) +2025-04-18 16:26:16,514 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:26:16,514 - INFO - Mean: 62.4172 +2025-04-18 16:26:16,514 - INFO - Median: 4.0771 +2025-04-18 16:26:16,514 - INFO - Min: 0.5501 +2025-04-18 16:26:16,514 - INFO - Max: 702.5656 +2025-04-18 16:26:16,514 - INFO - Std: 107.4585 +2025-04-18 16:26:16,514 - INFO - OC Distribution Statistics: +2025-04-18 16:26:16,514 - INFO - Validation Mean: 16.5232 +2025-04-18 16:26:16,514 - INFO - Validation Std: 7.9223 +2025-04-18 16:26:16,514 - INFO - Training Mean: 23.1651 +2025-04-18 16:26:16,514 - INFO - Training Std: 20.4212 +2025-04-18 16:26:16,514 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:26:16,514 - INFO - KS Statistic: 0.2228 +2025-04-18 16:26:16,514 - INFO - P-value: 0.0000 +2025-04-18 16:26:16,514 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:26:16,514 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:26:19,852 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:26:19,852 - INFO - Timestamp: 2025-04-18 16:26:19 +2025-04-18 16:26:19,853 - INFO - Validation set size: 1352 (8.19%) +2025-04-18 16:26:19,853 - INFO - Training set size: 15162 (91.81%) +2025-04-18 16:26:19,853 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:26:19,853 - INFO - Mean: 67.8937 +2025-04-18 16:26:19,853 - INFO - Median: 4.9528 +2025-04-18 16:26:19,853 - INFO - Min: 0.5501 +2025-04-18 16:26:19,853 - INFO - Max: 835.9450 +2025-04-18 16:26:19,853 - INFO - Std: 114.1746 +2025-04-18 16:26:19,853 - INFO - OC Distribution Statistics: +2025-04-18 16:26:19,853 - INFO - Validation Mean: 16.7349 +2025-04-18 16:26:19,853 - INFO - Validation Std: 8.1017 +2025-04-18 16:26:19,853 - INFO - Training Mean: 23.0433 +2025-04-18 16:26:19,853 - INFO - Training Std: 20.3026 +2025-04-18 16:26:19,853 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:26:19,853 - INFO - KS Statistic: 0.1956 +2025-04-18 16:26:19,853 - INFO - P-value: 0.0000 +2025-04-18 16:26:19,853 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:26:19,853 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:29:51,342 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:29:51,343 - INFO - Timestamp: 2025-04-18 16:29:51 +2025-04-18 16:29:51,343 - INFO - Validation set size: 1478 (8.95%) +2025-04-18 16:29:51,343 - INFO - Training set size: 15036 (91.05%) +2025-04-18 16:29:51,343 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:29:51,343 - INFO - Mean: 67.6987 +2025-04-18 16:29:51,343 - INFO - Median: 4.5217 +2025-04-18 16:29:51,343 - INFO - Min: 1.2497 +2025-04-18 16:29:51,343 - INFO - Max: 1464.8564 +2025-04-18 16:29:51,343 - INFO - Std: 136.4539 +2025-04-18 16:29:51,343 - INFO - OC Distribution Statistics: +2025-04-18 16:29:51,343 - INFO - Validation Mean: 16.2052 +2025-04-18 16:29:51,343 - INFO - Validation Std: 7.8894 +2025-04-18 16:29:51,343 - INFO - Training Mean: 23.1482 +2025-04-18 16:29:51,343 - INFO - Training Std: 20.3569 +2025-04-18 16:29:51,343 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:29:51,343 - INFO - KS Statistic: 0.2520 +2025-04-18 16:29:51,343 - INFO - P-value: 0.0000 +2025-04-18 16:29:51,343 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:29:51,343 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:29:51,460 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:29:51,460 - INFO - Timestamp: 2025-04-18 16:29:51 +2025-04-18 16:29:51,460 - INFO - Validation set size: 1477 (8.94%) +2025-04-18 16:29:51,460 - INFO - Training set size: 15037 (91.06%) +2025-04-18 16:29:51,460 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:29:51,460 - INFO - Mean: 61.6623 +2025-04-18 16:29:51,460 - INFO - Median: 4.2983 +2025-04-18 16:29:51,460 - INFO - Min: 1.2065 +2025-04-18 16:29:51,460 - INFO - Max: 706.9584 +2025-04-18 16:29:51,460 - INFO - Std: 104.3658 +2025-04-18 16:29:51,460 - INFO - OC Distribution Statistics: +2025-04-18 16:29:51,460 - INFO - Validation Mean: 16.2788 +2025-04-18 16:29:51,460 - INFO - Validation Std: 7.6249 +2025-04-18 16:29:51,460 - INFO - Training Mean: 23.1405 +2025-04-18 16:29:51,460 - INFO - Training Std: 20.3688 +2025-04-18 16:29:51,460 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:29:51,460 - INFO - KS Statistic: 0.2367 +2025-04-18 16:29:51,460 - INFO - P-value: 0.0000 +2025-04-18 16:29:51,460 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:29:51,460 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:29:51,571 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:29:51,571 - INFO - Timestamp: 2025-04-18 16:29:51 +2025-04-18 16:29:51,571 - INFO - Validation set size: 1334 (8.08%) +2025-04-18 16:29:51,571 - INFO - Training set size: 15180 (91.92%) +2025-04-18 16:29:51,571 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:29:51,571 - INFO - Mean: 63.9166 +2025-04-18 16:29:51,571 - INFO - Median: 4.2231 +2025-04-18 16:29:51,571 - INFO - Min: 0.2243 +2025-04-18 16:29:51,571 - INFO - Max: 862.7749 +2025-04-18 16:29:51,571 - INFO - Std: 113.3849 +2025-04-18 16:29:51,571 - INFO - OC Distribution Statistics: +2025-04-18 16:29:51,571 - INFO - Validation Mean: 16.0843 +2025-04-18 16:29:51,571 - INFO - Validation Std: 8.1828 +2025-04-18 16:29:51,571 - INFO - Training Mean: 23.0929 +2025-04-18 16:29:51,571 - INFO - Training Std: 20.2720 +2025-04-18 16:29:51,571 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:29:51,571 - INFO - KS Statistic: 0.2350 +2025-04-18 16:29:51,571 - INFO - P-value: 0.0000 +2025-04-18 16:29:51,571 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:29:51,571 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:29:55,274 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:29:55,274 - INFO - Timestamp: 2025-04-18 16:29:55 +2025-04-18 16:29:55,274 - INFO - Validation set size: 1584 (9.59%) +2025-04-18 16:29:55,274 - INFO - Training set size: 14930 (90.41%) +2025-04-18 16:29:55,274 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:29:55,274 - INFO - Mean: 70.1075 +2025-04-18 16:29:55,274 - INFO - Median: 4.6741 +2025-04-18 16:29:55,274 - INFO - Min: 0.8608 +2025-04-18 16:29:55,274 - INFO - Max: 743.7101 +2025-04-18 16:29:55,274 - INFO - Std: 117.3476 +2025-04-18 16:29:55,274 - INFO - OC Distribution Statistics: +2025-04-18 16:29:55,275 - INFO - Validation Mean: 16.7274 +2025-04-18 16:29:55,275 - INFO - Validation Std: 7.9075 +2025-04-18 16:29:55,275 - INFO - Training Mean: 23.1421 +2025-04-18 16:29:55,275 - INFO - Training Std: 20.4273 +2025-04-18 16:29:55,275 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:29:55,275 - INFO - KS Statistic: 0.1998 +2025-04-18 16:29:55,275 - INFO - P-value: 0.0000 +2025-04-18 16:29:55,275 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:29:55,275 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:35:12,190 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:35:12,190 - INFO - Timestamp: 2025-04-18 16:35:12 +2025-04-18 16:35:12,190 - INFO - Validation set size: 1821 (11.03%) +2025-04-18 16:35:12,190 - INFO - Training set size: 14693 (88.97%) +2025-04-18 16:35:12,190 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:35:12,190 - INFO - Mean: 61.7488 +2025-04-18 16:35:12,190 - INFO - Median: 4.2015 +2025-04-18 16:35:12,190 - INFO - Min: 0.8124 +2025-04-18 16:35:12,190 - INFO - Max: 862.7749 +2025-04-18 16:35:12,190 - INFO - Std: 108.1195 +2025-04-18 16:35:12,190 - INFO - OC Distribution Statistics: +2025-04-18 16:35:12,190 - INFO - Validation Mean: 16.2353 +2025-04-18 16:35:12,190 - INFO - Validation Std: 7.9512 +2025-04-18 16:35:12,190 - INFO - Training Mean: 23.3065 +2025-04-18 16:35:12,190 - INFO - Training Std: 20.5282 +2025-04-18 16:35:12,190 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:35:12,190 - INFO - KS Statistic: 0.2458 +2025-04-18 16:35:12,190 - INFO - P-value: 0.0000 +2025-04-18 16:35:12,190 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:35:12,190 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:35:12,284 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:35:12,284 - INFO - Timestamp: 2025-04-18 16:35:12 +2025-04-18 16:35:12,284 - INFO - Validation set size: 1765 (10.69%) +2025-04-18 16:35:12,284 - INFO - Training set size: 14749 (89.31%) +2025-04-18 16:35:12,284 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:35:12,284 - INFO - Mean: 69.9083 +2025-04-18 16:35:12,284 - INFO - Median: 4.1551 +2025-04-18 16:35:12,284 - INFO - Min: 1.2156 +2025-04-18 16:35:12,284 - INFO - Max: 706.9584 +2025-04-18 16:35:12,285 - INFO - Std: 115.3333 +2025-04-18 16:35:12,285 - INFO - OC Distribution Statistics: +2025-04-18 16:35:12,285 - INFO - Validation Mean: 16.5191 +2025-04-18 16:35:12,285 - INFO - Validation Std: 7.6451 +2025-04-18 16:35:12,285 - INFO - Training Mean: 23.2457 +2025-04-18 16:35:12,285 - INFO - Training Std: 20.5250 +2025-04-18 16:35:12,285 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:35:12,285 - INFO - KS Statistic: 0.2360 +2025-04-18 16:35:12,285 - INFO - P-value: 0.0000 +2025-04-18 16:35:12,285 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:35:12,285 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:35:13,885 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:35:13,885 - INFO - Timestamp: 2025-04-18 16:35:13 +2025-04-18 16:35:13,885 - INFO - Validation set size: 2006 (12.15%) +2025-04-18 16:35:13,885 - INFO - Training set size: 14508 (87.85%) +2025-04-18 16:35:13,885 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:35:13,885 - INFO - Mean: 56.6849 +2025-04-18 16:35:13,885 - INFO - Median: 3.9947 +2025-04-18 16:35:13,885 - INFO - Min: 0.9118 +2025-04-18 16:35:13,885 - INFO - Max: 917.1429 +2025-04-18 16:35:13,885 - INFO - Std: 108.4160 +2025-04-18 16:35:13,885 - INFO - OC Distribution Statistics: +2025-04-18 16:35:13,885 - INFO - Validation Mean: 16.0076 +2025-04-18 16:35:13,885 - INFO - Validation Std: 6.7640 +2025-04-18 16:35:13,885 - INFO - Training Mean: 23.4282 +2025-04-18 16:35:13,885 - INFO - Training Std: 20.6708 +2025-04-18 16:35:13,885 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:35:13,885 - INFO - KS Statistic: 0.2508 +2025-04-18 16:35:13,885 - INFO - P-value: 0.0000 +2025-04-18 16:35:13,885 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:35:13,885 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:35:17,170 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:35:17,171 - INFO - Timestamp: 2025-04-18 16:35:17 +2025-04-18 16:35:17,171 - INFO - Validation set size: 1771 (10.72%) +2025-04-18 16:35:17,171 - INFO - Training set size: 14743 (89.28%) +2025-04-18 16:35:17,171 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:35:17,171 - INFO - Mean: 66.2550 +2025-04-18 16:35:17,171 - INFO - Median: 4.1551 +2025-04-18 16:35:17,171 - INFO - Min: 0.2243 +2025-04-18 16:35:17,171 - INFO - Max: 875.2369 +2025-04-18 16:35:17,171 - INFO - Std: 118.1794 +2025-04-18 16:35:17,171 - INFO - OC Distribution Statistics: +2025-04-18 16:35:17,171 - INFO - Validation Mean: 16.3156 +2025-04-18 16:35:17,171 - INFO - Validation Std: 7.4957 +2025-04-18 16:35:17,171 - INFO - Training Mean: 23.2729 +2025-04-18 16:35:17,171 - INFO - Training Std: 20.5266 +2025-04-18 16:35:17,171 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:35:17,171 - INFO - KS Statistic: 0.2413 +2025-04-18 16:35:17,171 - INFO - P-value: 0.0000 +2025-04-18 16:35:17,171 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:35:17,171 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:36:45,193 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:36:45,194 - INFO - Timestamp: 2025-04-18 16:36:45 +2025-04-18 16:36:45,194 - INFO - Validation set size: 1351 (8.18%) +2025-04-18 16:36:45,194 - INFO - Training set size: 15163 (91.82%) +2025-04-18 16:36:45,194 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:36:45,194 - INFO - Mean: 74.4000 +2025-04-18 16:36:45,194 - INFO - Median: 5.2919 +2025-04-18 16:36:45,194 - INFO - Min: 0.8728 +2025-04-18 16:36:45,194 - INFO - Max: 743.7101 +2025-04-18 16:36:45,194 - INFO - Std: 118.0866 +2025-04-18 16:36:45,194 - INFO - OC Distribution Statistics: +2025-04-18 16:36:45,194 - INFO - Validation Mean: 17.2318 +2025-04-18 16:36:45,194 - INFO - Validation Std: 8.5158 +2025-04-18 16:36:45,194 - INFO - Training Mean: 22.9986 +2025-04-18 16:36:45,194 - INFO - Training Std: 20.3002 +2025-04-18 16:36:45,194 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:36:45,194 - INFO - KS Statistic: 0.1849 +2025-04-18 16:36:45,194 - INFO - P-value: 0.0000 +2025-04-18 16:36:45,194 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:36:45,194 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:37:34,492 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:37:34,492 - INFO - Timestamp: 2025-04-18 16:37:34 +2025-04-18 16:37:34,492 - INFO - Validation set size: 1334 (8.08%) +2025-04-18 16:37:34,492 - INFO - Training set size: 15180 (91.92%) +2025-04-18 16:37:34,492 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:37:34,492 - INFO - Mean: 79.6319 +2025-04-18 16:37:34,492 - INFO - Median: 4.4085 +2025-04-18 16:37:34,492 - INFO - Min: 1.2497 +2025-04-18 16:37:34,492 - INFO - Max: 888.3558 +2025-04-18 16:37:34,492 - INFO - Std: 132.6563 +2025-04-18 16:37:34,492 - INFO - OC Distribution Statistics: +2025-04-18 16:37:34,492 - INFO - Validation Mean: 16.7780 +2025-04-18 16:37:34,492 - INFO - Validation Std: 7.7153 +2025-04-18 16:37:34,492 - INFO - Training Mean: 23.0320 +2025-04-18 16:37:34,492 - INFO - Training Std: 20.3080 +2025-04-18 16:37:34,492 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:37:34,493 - INFO - KS Statistic: 0.1991 +2025-04-18 16:37:34,493 - INFO - P-value: 0.0000 +2025-04-18 16:37:34,493 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:37:34,493 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:38:49,197 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:38:49,198 - INFO - Timestamp: 2025-04-18 16:38:49 +2025-04-18 16:38:49,198 - INFO - Validation set size: 1367 (8.28%) +2025-04-18 16:38:49,198 - INFO - Training set size: 15147 (91.72%) +2025-04-18 16:38:49,198 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:38:49,198 - INFO - Mean: 71.5342 +2025-04-18 16:38:49,198 - INFO - Median: 5.1561 +2025-04-18 16:38:49,198 - INFO - Min: 1.2329 +2025-04-18 16:38:49,198 - INFO - Max: 743.7101 +2025-04-18 16:38:49,198 - INFO - Std: 126.3606 +2025-04-18 16:38:49,198 - INFO - OC Distribution Statistics: +2025-04-18 16:38:49,198 - INFO - Validation Mean: 16.5933 +2025-04-18 16:38:49,198 - INFO - Validation Std: 7.9589 +2025-04-18 16:38:49,198 - INFO - Training Mean: 23.0623 +2025-04-18 16:38:49,198 - INFO - Training Std: 20.3112 +2025-04-18 16:38:49,198 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:38:49,198 - INFO - KS Statistic: 0.2336 +2025-04-18 16:38:49,198 - INFO - P-value: 0.0000 +2025-04-18 16:38:49,198 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:38:49,198 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:39:54,579 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:39:54,579 - INFO - Timestamp: 2025-04-18 16:39:54 +2025-04-18 16:39:54,579 - INFO - Validation set size: 1414 (8.56%) +2025-04-18 16:39:54,579 - INFO - Training set size: 15100 (91.44%) +2025-04-18 16:39:54,579 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:39:54,579 - INFO - Mean: 71.6048 +2025-04-18 16:39:54,579 - INFO - Median: 4.4155 +2025-04-18 16:39:54,579 - INFO - Min: 1.2065 +2025-04-18 16:39:54,579 - INFO - Max: 702.5656 +2025-04-18 16:39:54,579 - INFO - Std: 116.0422 +2025-04-18 16:39:54,579 - INFO - OC Distribution Statistics: +2025-04-18 16:39:54,579 - INFO - Validation Mean: 16.4340 +2025-04-18 16:39:54,579 - INFO - Validation Std: 7.4187 +2025-04-18 16:39:54,579 - INFO - Training Mean: 23.0973 +2025-04-18 16:39:54,579 - INFO - Training Std: 20.3490 +2025-04-18 16:39:54,579 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:39:54,580 - INFO - KS Statistic: 0.2292 +2025-04-18 16:39:54,580 - INFO - P-value: 0.0000 +2025-04-18 16:39:54,580 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:39:54,580 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:45:05,774 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:45:05,775 - INFO - Timestamp: 2025-04-18 16:45:05 +2025-04-18 16:45:05,775 - INFO - Validation set size: 1406 (8.51%) +2025-04-18 16:45:05,775 - INFO - Training set size: 15108 (91.49%) +2025-04-18 16:45:05,775 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:45:05,775 - INFO - Mean: 64.6334 +2025-04-18 16:45:05,775 - INFO - Median: 4.6741 +2025-04-18 16:45:05,775 - INFO - Min: 1.2156 +2025-04-18 16:45:05,775 - INFO - Max: 888.3558 +2025-04-18 16:45:05,775 - INFO - Std: 113.7317 +2025-04-18 16:45:05,775 - INFO - OC Distribution Statistics: +2025-04-18 16:45:05,775 - INFO - Validation Mean: 16.6495 +2025-04-18 16:45:05,775 - INFO - Validation Std: 7.9792 +2025-04-18 16:45:05,775 - INFO - Training Mean: 23.0737 +2025-04-18 16:45:05,775 - INFO - Training Std: 20.3316 +2025-04-18 16:45:05,775 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:45:05,775 - INFO - KS Statistic: 0.2045 +2025-04-18 16:45:05,775 - INFO - P-value: 0.0000 +2025-04-18 16:45:05,775 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:45:05,775 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} + +2025-04-18 16:45:01,449 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:45:01,449 - INFO - Timestamp: 2025-04-18 16:45:01 +2025-04-18 16:45:01,450 - INFO - Validation set size: 1807 (10.94%) +2025-04-18 16:45:01,450 - INFO - Training set size: 14707 (89.06%) +2025-04-18 16:45:01,450 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:45:01,450 - INFO - Mean: 69.9613 +2025-04-18 16:45:01,450 - INFO - Median: 4.1444 +2025-04-18 16:45:01,450 - INFO - Min: 0.9995 +2025-04-18 16:45:01,450 - INFO - Max: 731.3502 +2025-04-18 16:45:01,450 - INFO - Std: 123.0209 +2025-04-18 16:45:01,450 - INFO - OC Distribution Statistics: +2025-04-18 16:45:01,450 - INFO - Validation Mean: 16.0162 +2025-04-18 16:45:01,450 - INFO - Validation Std: 7.1114 +2025-04-18 16:45:01,450 - INFO - Training Mean: 23.3267 +2025-04-18 16:45:01,450 - INFO - Training Std: 20.5494 +2025-04-18 16:45:01,450 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:45:01,450 - INFO - KS Statistic: 0.2318 +2025-04-18 16:45:01,450 - INFO - P-value: 0.0000 +2025-04-18 16:45:01,450 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:45:01,450 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:45:02,824 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:45:02,824 - INFO - Timestamp: 2025-04-18 16:45:02 +2025-04-18 16:45:02,824 - INFO - Validation set size: 1935 (11.72%) +2025-04-18 16:45:02,824 - INFO - Training set size: 14579 (88.28%) +2025-04-18 16:45:02,824 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:45:02,824 - INFO - Mean: 61.2397 +2025-04-18 16:45:02,824 - INFO - Median: 3.7181 +2025-04-18 16:45:02,824 - INFO - Min: 0.2243 +2025-04-18 16:45:02,824 - INFO - Max: 1106.1626 +2025-04-18 16:45:02,824 - INFO - Std: 120.2164 +2025-04-18 16:45:02,824 - INFO - OC Distribution Statistics: +2025-04-18 16:45:02,824 - INFO - Validation Mean: 15.8006 +2025-04-18 16:45:02,824 - INFO - Validation Std: 6.3743 +2025-04-18 16:45:02,824 - INFO - Training Mean: 23.4195 +2025-04-18 16:45:02,825 - INFO - Training Std: 20.6388 +2025-04-18 16:45:02,825 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:45:02,825 - INFO - KS Statistic: 0.2664 +2025-04-18 16:45:02,825 - INFO - P-value: 0.0000 +2025-04-18 16:45:02,825 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:45:02,825 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:45:06,253 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:45:06,254 - INFO - Timestamp: 2025-04-18 16:45:06 +2025-04-18 16:45:06,254 - INFO - Validation set size: 1969 (11.92%) +2025-04-18 16:45:06,254 - INFO - Training set size: 14545 (88.08%) +2025-04-18 16:45:06,254 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:45:06,254 - INFO - Mean: 53.2139 +2025-04-18 16:45:06,254 - INFO - Median: 4.1573 +2025-04-18 16:45:06,254 - INFO - Min: 0.7391 +2025-04-18 16:45:06,254 - INFO - Max: 706.9584 +2025-04-18 16:45:06,254 - INFO - Std: 103.1695 +2025-04-18 16:45:06,254 - INFO - OC Distribution Statistics: +2025-04-18 16:45:06,254 - INFO - Validation Mean: 16.0688 +2025-04-18 16:45:06,254 - INFO - Validation Std: 7.1071 +2025-04-18 16:45:06,254 - INFO - Training Mean: 23.4010 +2025-04-18 16:45:06,254 - INFO - Training Std: 20.6381 +2025-04-18 16:45:06,254 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:45:06,254 - INFO - KS Statistic: 0.2439 +2025-04-18 16:45:06,254 - INFO - P-value: 0.0000 +2025-04-18 16:45:06,254 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:45:06,254 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:59:35,240 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:59:35,241 - INFO - Timestamp: 2025-04-18 16:59:35 +2025-04-18 16:59:35,241 - INFO - Validation set size: 1429 (8.65%) +2025-04-18 16:59:35,241 - INFO - Training set size: 15085 (91.35%) +2025-04-18 16:59:35,241 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:59:35,241 - INFO - Mean: 71.0968 +2025-04-18 16:59:35,241 - INFO - Median: 4.4045 +2025-04-18 16:59:35,241 - INFO - Min: 1.1220 +2025-04-18 16:59:35,241 - INFO - Max: 875.2369 +2025-04-18 16:59:35,241 - INFO - Std: 122.4200 +2025-04-18 16:59:35,241 - INFO - OC Distribution Statistics: +2025-04-18 16:59:35,241 - INFO - Validation Mean: 16.4064 +2025-04-18 16:59:35,241 - INFO - Validation Std: 7.6997 +2025-04-18 16:59:35,241 - INFO - Training Mean: 23.1066 +2025-04-18 16:59:35,241 - INFO - Training Std: 20.3459 +2025-04-18 16:59:35,241 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:59:35,241 - INFO - KS Statistic: 0.2147 +2025-04-18 16:59:35,241 - INFO - P-value: 0.0000 +2025-04-18 16:59:35,241 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:59:35,241 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:03:47,654 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:03:47,654 - INFO - Timestamp: 2025-04-18 17:03:47 +2025-04-18 17:03:47,654 - INFO - Validation set size: 1355 (8.21%) +2025-04-18 17:03:47,654 - INFO - Training set size: 15159 (91.79%) +2025-04-18 17:03:47,654 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:03:47,654 - INFO - Mean: 72.7273 +2025-04-18 17:03:47,654 - INFO - Median: 4.2460 +2025-04-18 17:03:47,654 - INFO - Min: 0.8608 +2025-04-18 17:03:47,654 - INFO - Max: 875.2369 +2025-04-18 17:03:47,654 - INFO - Std: 123.8177 +2025-04-18 17:03:47,654 - INFO - OC Distribution Statistics: +2025-04-18 17:03:47,654 - INFO - Validation Mean: 16.2181 +2025-04-18 17:03:47,655 - INFO - Validation Std: 7.4170 +2025-04-18 17:03:47,655 - INFO - Training Mean: 23.0907 +2025-04-18 17:03:47,655 - INFO - Training Std: 20.3125 +2025-04-18 17:03:47,655 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:03:47,655 - INFO - KS Statistic: 0.2305 +2025-04-18 17:03:47,655 - INFO - P-value: 0.0000 +2025-04-18 17:03:47,655 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:03:47,655 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:08:07,649 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:08:07,649 - INFO - Timestamp: 2025-04-18 17:08:07 +2025-04-18 17:08:07,649 - INFO - Validation set size: 1336 (8.09%) +2025-04-18 17:08:07,650 - INFO - Training set size: 15178 (91.91%) +2025-04-18 17:08:07,650 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:08:07,650 - INFO - Mean: 70.0682 +2025-04-18 17:08:07,650 - INFO - Median: 4.6081 +2025-04-18 17:08:07,650 - INFO - Min: 0.6724 +2025-04-18 17:08:07,650 - INFO - Max: 705.4078 +2025-04-18 17:08:07,650 - INFO - Std: 110.2824 +2025-04-18 17:08:07,650 - INFO - OC Distribution Statistics: +2025-04-18 17:08:07,650 - INFO - Validation Mean: 16.5392 +2025-04-18 17:08:07,650 - INFO - Validation Std: 7.1430 +2025-04-18 17:08:07,650 - INFO - Training Mean: 23.0538 +2025-04-18 17:08:07,650 - INFO - Training Std: 20.3209 +2025-04-18 17:08:07,650 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:08:07,650 - INFO - KS Statistic: 0.1867 +2025-04-18 17:08:07,650 - INFO - P-value: 0.0000 +2025-04-18 17:08:07,650 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:08:07,650 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:08:12,347 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:08:12,347 - INFO - Timestamp: 2025-04-18 17:08:12 +2025-04-18 17:08:12,347 - INFO - Validation set size: 1369 (8.29%) +2025-04-18 17:08:12,347 - INFO - Training set size: 15145 (91.71%) +2025-04-18 17:08:12,347 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:08:12,347 - INFO - Mean: 81.4957 +2025-04-18 17:08:12,347 - INFO - Median: 5.9124 +2025-04-18 17:08:12,347 - INFO - Min: 1.0731 +2025-04-18 17:08:12,347 - INFO - Max: 835.9450 +2025-04-18 17:08:12,347 - INFO - Std: 130.2841 +2025-04-18 17:08:12,347 - INFO - OC Distribution Statistics: +2025-04-18 17:08:12,347 - INFO - Validation Mean: 16.9040 +2025-04-18 17:08:12,347 - INFO - Validation Std: 8.2427 +2025-04-18 17:08:12,347 - INFO - Training Mean: 23.0350 +2025-04-18 17:08:12,347 - INFO - Training Std: 20.3106 +2025-04-18 17:08:12,347 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:08:12,347 - INFO - KS Statistic: 0.1751 +2025-04-18 17:08:12,347 - INFO - P-value: 0.0000 +2025-04-18 17:08:12,348 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:08:12,348 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:08:12,614 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:08:12,615 - INFO - Timestamp: 2025-04-18 17:08:12 +2025-04-18 17:08:12,615 - INFO - Validation set size: 1583 (9.59%) +2025-04-18 17:08:12,615 - INFO - Training set size: 14931 (90.41%) +2025-04-18 17:08:12,615 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:08:12,615 - INFO - Mean: 59.7879 +2025-04-18 17:08:12,615 - INFO - Median: 4.0472 +2025-04-18 17:08:12,615 - INFO - Min: 0.2726 +2025-04-18 17:08:12,615 - INFO - Max: 875.2369 +2025-04-18 17:08:12,615 - INFO - Std: 113.4589 +2025-04-18 17:08:12,615 - INFO - OC Distribution Statistics: +2025-04-18 17:08:12,615 - INFO - Validation Mean: 16.1504 +2025-04-18 17:08:12,615 - INFO - Validation Std: 7.4240 +2025-04-18 17:08:12,615 - INFO - Training Mean: 23.2028 +2025-04-18 17:08:12,615 - INFO - Training Std: 20.4258 +2025-04-18 17:08:12,615 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:08:12,615 - INFO - KS Statistic: 0.2363 +2025-04-18 17:08:12,615 - INFO - P-value: 0.0000 +2025-04-18 17:08:12,615 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:08:12,615 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:09:21,050 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:09:21,050 - INFO - Timestamp: 2025-04-18 17:09:21 +2025-04-18 17:09:21,050 - INFO - Validation set size: 1416 (8.57%) +2025-04-18 17:09:21,050 - INFO - Training set size: 15098 (91.43%) +2025-04-18 17:09:21,050 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:09:21,050 - INFO - Mean: 74.1000 +2025-04-18 17:09:21,051 - INFO - Median: 5.1747 +2025-04-18 17:09:21,051 - INFO - Min: 1.2757 +2025-04-18 17:09:21,051 - INFO - Max: 1106.1626 +2025-04-18 17:09:21,051 - INFO - Std: 130.5128 +2025-04-18 17:09:21,051 - INFO - OC Distribution Statistics: +2025-04-18 17:09:21,051 - INFO - Validation Mean: 16.2319 +2025-04-18 17:09:21,051 - INFO - Validation Std: 7.8786 +2025-04-18 17:09:21,051 - INFO - Training Mean: 23.1172 +2025-04-18 17:09:21,051 - INFO - Training Std: 20.3275 +2025-04-18 17:09:21,051 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:09:21,051 - INFO - KS Statistic: 0.2458 +2025-04-18 17:09:21,051 - INFO - P-value: 0.0000 +2025-04-18 17:09:21,051 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:09:21,051 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:09:21,055 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:09:21,055 - INFO - Timestamp: 2025-04-18 17:09:21 +2025-04-18 17:09:21,055 - INFO - Validation set size: 1395 (8.45%) +2025-04-18 17:09:21,055 - INFO - Training set size: 15119 (91.55%) +2025-04-18 17:09:21,055 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:09:21,055 - INFO - Mean: 71.1717 +2025-04-18 17:09:21,056 - INFO - Median: 4.6141 +2025-04-18 17:09:21,056 - INFO - Min: 1.2287 +2025-04-18 17:09:21,056 - INFO - Max: 743.7101 +2025-04-18 17:09:21,056 - INFO - Std: 119.7100 +2025-04-18 17:09:21,056 - INFO - OC Distribution Statistics: +2025-04-18 17:09:21,056 - INFO - Validation Mean: 16.6112 +2025-04-18 17:09:21,056 - INFO - Validation Std: 8.2181 +2025-04-18 17:09:21,056 - INFO - Training Mean: 23.0726 +2025-04-18 17:09:21,056 - INFO - Training Std: 20.3162 +2025-04-18 17:09:21,056 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:09:21,056 - INFO - KS Statistic: 0.1947 +2025-04-18 17:09:21,056 - INFO - P-value: 0.0000 +2025-04-18 17:09:21,056 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:09:21,056 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:09:21,131 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:09:21,131 - INFO - Timestamp: 2025-04-18 17:09:21 +2025-04-18 17:09:21,131 - INFO - Validation set size: 1424 (8.62%) +2025-04-18 17:09:21,131 - INFO - Training set size: 15090 (91.38%) +2025-04-18 17:09:21,131 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:09:21,131 - INFO - Mean: 73.3081 +2025-04-18 17:09:21,131 - INFO - Median: 4.7665 +2025-04-18 17:09:21,131 - INFO - Min: 1.2935 +2025-04-18 17:09:21,131 - INFO - Max: 706.9761 +2025-04-18 17:09:21,131 - INFO - Std: 121.4790 +2025-04-18 17:09:21,131 - INFO - OC Distribution Statistics: +2025-04-18 17:09:21,131 - INFO - Validation Mean: 16.5231 +2025-04-18 17:09:21,131 - INFO - Validation Std: 7.6766 +2025-04-18 17:09:21,131 - INFO - Training Mean: 23.0933 +2025-04-18 17:09:21,131 - INFO - Training Std: 20.3478 +2025-04-18 17:09:21,131 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:09:21,131 - INFO - KS Statistic: 0.2161 +2025-04-18 17:09:21,131 - INFO - P-value: 0.0000 +2025-04-18 17:09:21,131 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:09:21,131 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:09:22,849 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:09:22,849 - INFO - Timestamp: 2025-04-18 17:09:22 +2025-04-18 17:09:22,849 - INFO - Validation set size: 1434 (8.68%) +2025-04-18 17:09:22,849 - INFO - Training set size: 15080 (91.32%) +2025-04-18 17:09:22,849 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:09:22,849 - INFO - Mean: 73.0000 +2025-04-18 17:09:22,849 - INFO - Median: 5.1561 +2025-04-18 17:09:22,849 - INFO - Min: 1.4050 +2025-04-18 17:09:22,849 - INFO - Max: 702.5656 +2025-04-18 17:09:22,849 - INFO - Std: 118.4419 +2025-04-18 17:09:22,849 - INFO - OC Distribution Statistics: +2025-04-18 17:09:22,849 - INFO - Validation Mean: 16.9317 +2025-04-18 17:09:22,849 - INFO - Validation Std: 8.9079 +2025-04-18 17:09:22,849 - INFO - Training Mean: 23.0588 +2025-04-18 17:09:22,849 - INFO - Training Std: 20.3173 +2025-04-18 17:09:22,849 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:09:22,849 - INFO - KS Statistic: 0.2029 +2025-04-18 17:09:22,849 - INFO - P-value: 0.0000 +2025-04-18 17:09:22,849 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:09:22,849 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:10:28,267 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:10:28,267 - INFO - Timestamp: 2025-04-18 17:10:28 +2025-04-18 17:10:28,267 - INFO - Validation set size: 1345 (8.14%) +2025-04-18 17:10:28,267 - INFO - Training set size: 15169 (91.86%) +2025-04-18 17:10:28,267 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:10:28,267 - INFO - Mean: 69.0207 +2025-04-18 17:10:28,267 - INFO - Median: 4.8053 +2025-04-18 17:10:28,267 - INFO - Min: 0.7391 +2025-04-18 17:10:28,267 - INFO - Max: 835.9450 +2025-04-18 17:10:28,267 - INFO - Std: 113.8467 +2025-04-18 17:10:28,267 - INFO - OC Distribution Statistics: +2025-04-18 17:10:28,267 - INFO - Validation Mean: 17.0120 +2025-04-18 17:10:28,267 - INFO - Validation Std: 8.2766 +2025-04-18 17:10:28,267 - INFO - Training Mean: 23.0158 +2025-04-18 17:10:28,267 - INFO - Training Std: 20.3003 +2025-04-18 17:10:28,267 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:10:28,267 - INFO - KS Statistic: 0.1977 +2025-04-18 17:10:28,268 - INFO - P-value: 0.0000 +2025-04-18 17:10:28,268 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:10:28,268 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:10:28,458 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:10:28,459 - INFO - Timestamp: 2025-04-18 17:10:28 +2025-04-18 17:10:28,459 - INFO - Validation set size: 1486 (9.00%) +2025-04-18 17:10:28,459 - INFO - Training set size: 15028 (91.00%) +2025-04-18 17:10:28,459 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:10:28,459 - INFO - Mean: 72.0203 +2025-04-18 17:10:28,459 - INFO - Median: 4.6741 +2025-04-18 17:10:28,459 - INFO - Min: 1.4050 +2025-04-18 17:10:28,459 - INFO - Max: 917.1429 +2025-04-18 17:10:28,459 - INFO - Std: 121.8843 +2025-04-18 17:10:28,459 - INFO - OC Distribution Statistics: +2025-04-18 17:10:28,459 - INFO - Validation Mean: 17.1339 +2025-04-18 17:10:28,459 - INFO - Validation Std: 9.7188 +2025-04-18 17:10:28,459 - INFO - Training Mean: 23.0600 +2025-04-18 17:10:28,459 - INFO - Training Std: 20.3117 +2025-04-18 17:10:28,459 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:10:28,459 - INFO - KS Statistic: 0.2008 +2025-04-18 17:10:28,459 - INFO - P-value: 0.0000 +2025-04-18 17:10:28,459 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:10:28,459 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:10:28,475 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:10:28,475 - INFO - Timestamp: 2025-04-18 17:10:28 +2025-04-18 17:10:28,475 - INFO - Validation set size: 1577 (9.55%) +2025-04-18 17:10:28,475 - INFO - Training set size: 14937 (90.45%) +2025-04-18 17:10:28,475 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:10:28,475 - INFO - Mean: 70.9817 +2025-04-18 17:10:28,475 - INFO - Median: 4.9833 +2025-04-18 17:10:28,475 - INFO - Min: 1.4069 +2025-04-18 17:10:28,475 - INFO - Max: 731.3502 +2025-04-18 17:10:28,475 - INFO - Std: 117.2425 +2025-04-18 17:10:28,476 - INFO - OC Distribution Statistics: +2025-04-18 17:10:28,476 - INFO - Validation Mean: 16.5594 +2025-04-18 17:10:28,476 - INFO - Validation Std: 8.0690 +2025-04-18 17:10:28,476 - INFO - Training Mean: 23.1568 +2025-04-18 17:10:28,476 - INFO - Training Std: 20.4113 +2025-04-18 17:10:28,476 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:10:28,476 - INFO - KS Statistic: 0.1995 +2025-04-18 17:10:28,476 - INFO - P-value: 0.0000 +2025-04-18 17:10:28,476 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:10:28,476 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:10:29,985 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:10:29,985 - INFO - Timestamp: 2025-04-18 17:10:29 +2025-04-18 17:10:29,985 - INFO - Validation set size: 1541 (9.33%) +2025-04-18 17:10:29,985 - INFO - Training set size: 14973 (90.67%) +2025-04-18 17:10:29,985 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:10:29,985 - INFO - Mean: 58.9269 +2025-04-18 17:10:29,985 - INFO - Median: 4.0399 +2025-04-18 17:10:29,985 - INFO - Min: 1.4276 +2025-04-18 17:10:29,985 - INFO - Max: 622.3819 +2025-04-18 17:10:29,985 - INFO - Std: 105.0347 +2025-04-18 17:10:29,985 - INFO - OC Distribution Statistics: +2025-04-18 17:10:29,985 - INFO - Validation Mean: 16.4463 +2025-04-18 17:10:29,985 - INFO - Validation Std: 7.6112 +2025-04-18 17:10:29,985 - INFO - Training Mean: 23.1526 +2025-04-18 17:10:29,985 - INFO - Training Std: 20.4075 +2025-04-18 17:10:29,985 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:10:29,985 - INFO - KS Statistic: 0.2253 +2025-04-18 17:10:29,985 - INFO - P-value: 0.0000 +2025-04-18 17:10:29,985 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:10:29,985 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:12:52,105 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:12:52,106 - INFO - Timestamp: 2025-04-18 17:12:52 +2025-04-18 17:12:52,106 - INFO - Validation set size: 1328 (8.04%) +2025-04-18 17:12:52,106 - INFO - Training set size: 15186 (91.96%) +2025-04-18 17:12:52,106 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:12:52,106 - INFO - Mean: 79.3318 +2025-04-18 17:12:52,106 - INFO - Median: 5.6516 +2025-04-18 17:12:52,106 - INFO - Min: 0.8526 +2025-04-18 17:12:52,106 - INFO - Max: 772.2996 +2025-04-18 17:12:52,106 - INFO - Std: 129.8559 +2025-04-18 17:12:52,106 - INFO - OC Distribution Statistics: +2025-04-18 17:12:52,106 - INFO - Validation Mean: 16.4982 +2025-04-18 17:12:52,106 - INFO - Validation Std: 7.8546 +2025-04-18 17:12:52,106 - INFO - Training Mean: 23.0540 +2025-04-18 17:12:52,106 - INFO - Training Std: 20.2926 +2025-04-18 17:12:52,106 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:12:52,106 - INFO - KS Statistic: 0.2128 +2025-04-18 17:12:52,106 - INFO - P-value: 0.0000 +2025-04-18 17:12:52,106 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:12:52,106 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:12:52,171 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:12:52,171 - INFO - Timestamp: 2025-04-18 17:12:52 +2025-04-18 17:12:52,171 - INFO - Validation set size: 1332 (8.07%) +2025-04-18 17:12:52,171 - INFO - Training set size: 15182 (91.93%) +2025-04-18 17:12:52,171 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:12:52,171 - INFO - Mean: 71.4182 +2025-04-18 17:12:52,171 - INFO - Median: 5.0148 +2025-04-18 17:12:52,171 - INFO - Min: 1.0473 +2025-04-18 17:12:52,171 - INFO - Max: 772.4342 +2025-04-18 17:12:52,171 - INFO - Std: 122.2297 +2025-04-18 17:12:52,171 - INFO - OC Distribution Statistics: +2025-04-18 17:12:52,171 - INFO - Validation Mean: 16.6901 +2025-04-18 17:12:52,171 - INFO - Validation Std: 8.2695 +2025-04-18 17:12:52,171 - INFO - Training Mean: 23.0389 +2025-04-18 17:12:52,171 - INFO - Training Std: 20.2855 +2025-04-18 17:12:52,171 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:12:52,171 - INFO - KS Statistic: 0.2189 +2025-04-18 17:12:52,171 - INFO - P-value: 0.0000 +2025-04-18 17:12:52,171 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:12:52,171 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:12:52,402 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:12:52,402 - INFO - Timestamp: 2025-04-18 17:12:52 +2025-04-18 17:12:52,402 - INFO - Validation set size: 1408 (8.53%) +2025-04-18 17:12:52,402 - INFO - Training set size: 15106 (91.47%) +2025-04-18 17:12:52,402 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:12:52,402 - INFO - Mean: 69.3854 +2025-04-18 17:12:52,402 - INFO - Median: 5.1441 +2025-04-18 17:12:52,402 - INFO - Min: 1.4084 +2025-04-18 17:12:52,402 - INFO - Max: 693.6612 +2025-04-18 17:12:52,402 - INFO - Std: 107.7037 +2025-04-18 17:12:52,402 - INFO - OC Distribution Statistics: +2025-04-18 17:12:52,402 - INFO - Validation Mean: 16.7770 +2025-04-18 17:12:52,402 - INFO - Validation Std: 8.2163 +2025-04-18 17:12:52,402 - INFO - Training Mean: 23.0627 +2025-04-18 17:12:52,402 - INFO - Training Std: 20.3275 +2025-04-18 17:12:52,402 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:12:52,403 - INFO - KS Statistic: 0.1836 +2025-04-18 17:12:52,403 - INFO - P-value: 0.0000 +2025-04-18 17:12:52,403 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:12:52,403 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:12:53,939 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:12:53,939 - INFO - Timestamp: 2025-04-18 17:12:53 +2025-04-18 17:12:53,939 - INFO - Validation set size: 1623 (9.83%) +2025-04-18 17:12:53,939 - INFO - Training set size: 14891 (90.17%) +2025-04-18 17:12:53,939 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:12:53,939 - INFO - Mean: 58.5822 +2025-04-18 17:12:53,939 - INFO - Median: 3.9951 +2025-04-18 17:12:53,939 - INFO - Min: 1.4084 +2025-04-18 17:12:53,939 - INFO - Max: 824.9624 +2025-04-18 17:12:53,939 - INFO - Std: 110.1175 +2025-04-18 17:12:53,939 - INFO - OC Distribution Statistics: +2025-04-18 17:12:53,939 - INFO - Validation Mean: 16.2227 +2025-04-18 17:12:53,939 - INFO - Validation Std: 7.1368 +2025-04-18 17:12:53,939 - INFO - Training Mean: 23.2139 +2025-04-18 17:12:53,939 - INFO - Training Std: 20.4602 +2025-04-18 17:12:53,939 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:12:53,939 - INFO - KS Statistic: 0.2162 +2025-04-18 17:12:53,939 - INFO - P-value: 0.0000 +2025-04-18 17:12:53,939 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:12:53,939 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:23:26,015 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:23:26,015 - INFO - Timestamp: 2025-04-18 17:23:26 +2025-04-18 17:23:26,015 - INFO - Validation set size: 1629 (9.86%) +2025-04-18 17:23:26,015 - INFO - Training set size: 14885 (90.14%) +2025-04-18 17:23:26,015 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:23:26,015 - INFO - Mean: 69.0989 +2025-04-18 17:23:26,015 - INFO - Median: 4.5343 +2025-04-18 17:23:26,015 - INFO - Min: 1.0731 +2025-04-18 17:23:26,015 - INFO - Max: 866.4875 +2025-04-18 17:23:26,015 - INFO - Std: 124.2216 +2025-04-18 17:23:26,015 - INFO - OC Distribution Statistics: +2025-04-18 17:23:26,015 - INFO - Validation Mean: 16.7511 +2025-04-18 17:23:26,015 - INFO - Validation Std: 8.5174 +2025-04-18 17:23:26,015 - INFO - Training Mean: 23.1589 +2025-04-18 17:23:26,015 - INFO - Training Std: 20.4244 +2025-04-18 17:23:26,015 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:23:26,015 - INFO - KS Statistic: 0.2199 +2025-04-18 17:23:26,016 - INFO - P-value: 0.0000 +2025-04-18 17:23:26,016 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:23:26,016 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:23:26,068 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:23:26,068 - INFO - Timestamp: 2025-04-18 17:23:26 +2025-04-18 17:23:26,068 - INFO - Validation set size: 1667 (10.09%) +2025-04-18 17:23:26,068 - INFO - Training set size: 14847 (89.91%) +2025-04-18 17:23:26,068 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:23:26,068 - INFO - Mean: 66.1427 +2025-04-18 17:23:26,068 - INFO - Median: 4.6795 +2025-04-18 17:23:26,069 - INFO - Min: 1.1496 +2025-04-18 17:23:26,069 - INFO - Max: 888.3558 +2025-04-18 17:23:26,069 - INFO - Std: 119.0173 +2025-04-18 17:23:26,069 - INFO - OC Distribution Statistics: +2025-04-18 17:23:26,069 - INFO - Validation Mean: 16.4072 +2025-04-18 17:23:26,069 - INFO - Validation Std: 7.8502 +2025-04-18 17:23:26,069 - INFO - Training Mean: 23.2139 +2025-04-18 17:23:26,069 - INFO - Training Std: 20.4609 +2025-04-18 17:23:26,069 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:23:26,069 - INFO - KS Statistic: 0.2190 +2025-04-18 17:23:26,069 - INFO - P-value: 0.0000 +2025-04-18 17:23:26,069 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:23:26,069 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:23:26,124 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:23:26,125 - INFO - Timestamp: 2025-04-18 17:23:26 +2025-04-18 17:23:26,125 - INFO - Validation set size: 1468 (8.89%) +2025-04-18 17:23:26,125 - INFO - Training set size: 15046 (91.11%) +2025-04-18 17:23:26,125 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:23:26,125 - INFO - Mean: 73.8974 +2025-04-18 17:23:26,125 - INFO - Median: 4.5330 +2025-04-18 17:23:26,125 - INFO - Min: 1.0890 +2025-04-18 17:23:26,125 - INFO - Max: 835.9450 +2025-04-18 17:23:26,125 - INFO - Std: 116.2431 +2025-04-18 17:23:26,125 - INFO - OC Distribution Statistics: +2025-04-18 17:23:26,125 - INFO - Validation Mean: 16.7576 +2025-04-18 17:23:26,125 - INFO - Validation Std: 7.9183 +2025-04-18 17:23:26,125 - INFO - Training Mean: 23.0897 +2025-04-18 17:23:26,125 - INFO - Training Std: 20.3684 +2025-04-18 17:23:26,125 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:23:26,125 - INFO - KS Statistic: 0.2028 +2025-04-18 17:23:26,125 - INFO - P-value: 0.0000 +2025-04-18 17:23:26,125 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:23:26,125 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 17:23:27,268 - INFO - Validation and Training Set Split Statistics +2025-04-18 17:23:27,268 - INFO - Timestamp: 2025-04-18 17:23:27 +2025-04-18 17:23:27,268 - INFO - Validation set size: 1354 (8.20%) +2025-04-18 17:23:27,268 - INFO - Training set size: 15160 (91.80%) +2025-04-18 17:23:27,268 - INFO - Minimum Distance Statistics (km): +2025-04-18 17:23:27,268 - INFO - Mean: 59.2613 +2025-04-18 17:23:27,268 - INFO - Median: 4.4045 +2025-04-18 17:23:27,268 - INFO - Min: 0.7448 +2025-04-18 17:23:27,268 - INFO - Max: 772.2996 +2025-04-18 17:23:27,268 - INFO - Std: 102.7148 +2025-04-18 17:23:27,268 - INFO - OC Distribution Statistics: +2025-04-18 17:23:27,268 - INFO - Validation Mean: 16.5030 +2025-04-18 17:23:27,268 - INFO - Validation Std: 7.4248 +2025-04-18 17:23:27,268 - INFO - Training Mean: 23.0648 +2025-04-18 17:23:27,268 - INFO - Training Std: 20.3201 +2025-04-18 17:23:27,268 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 17:23:27,268 - INFO - KS Statistic: 0.2239 +2025-04-18 17:23:27,268 - INFO - P-value: 0.0000 +2025-04-18 17:23:27,268 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 17:23:27,268 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 18:09:37,671 - INFO - Validation and Training Set Split Statistics +2025-04-18 18:09:37,671 - INFO - Timestamp: 2025-04-18 18:09:37 +2025-04-18 18:09:37,671 - INFO - Validation set size: 1342 (8.13%) +2025-04-18 18:09:37,671 - INFO - Training set size: 15172 (91.87%) +2025-04-18 18:09:37,672 - INFO - Minimum Distance Statistics (km): +2025-04-18 18:09:37,672 - INFO - Mean: 77.3867 +2025-04-18 18:09:37,672 - INFO - Median: 6.2249 +2025-04-18 18:09:37,672 - INFO - Min: 1.4686 +2025-04-18 18:09:37,672 - INFO - Max: 917.1429 +2025-04-18 18:09:37,672 - INFO - Std: 121.2924 +2025-04-18 18:09:37,672 - INFO - OC Distribution Statistics: +2025-04-18 18:09:37,672 - INFO - Validation Mean: 16.7637 +2025-04-18 18:09:37,672 - INFO - Validation Std: 8.2927 +2025-04-18 18:09:37,672 - INFO - Training Mean: 23.0365 +2025-04-18 18:09:37,672 - INFO - Training Std: 20.2916 +2025-04-18 18:09:37,672 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 18:09:37,672 - INFO - KS Statistic: 0.2003 +2025-04-18 18:09:37,672 - INFO - P-value: 0.0000 +2025-04-18 18:09:37,672 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 18:09:37,672 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 18:09:37,676 - INFO - Validation and Training Set Split Statistics +2025-04-18 18:09:37,676 - INFO - Timestamp: 2025-04-18 18:09:37 +2025-04-18 18:09:37,676 - INFO - Validation set size: 1380 (8.36%) +2025-04-18 18:09:37,676 - INFO - Training set size: 15134 (91.64%) +2025-04-18 18:09:37,676 - INFO - Minimum Distance Statistics (km): +2025-04-18 18:09:37,676 - INFO - Mean: 63.4355 +2025-04-18 18:09:37,676 - INFO - Median: 5.1368 +2025-04-18 18:09:37,676 - INFO - Min: 1.3302 +2025-04-18 18:09:37,676 - INFO - Max: 743.7101 +2025-04-18 18:09:37,676 - INFO - Std: 102.6949 +2025-04-18 18:09:37,676 - INFO - OC Distribution Statistics: +2025-04-18 18:09:37,676 - INFO - Validation Mean: 16.6189 +2025-04-18 18:09:37,676 - INFO - Validation Std: 7.7921 +2025-04-18 18:09:37,676 - INFO - Training Mean: 23.0655 +2025-04-18 18:09:37,676 - INFO - Training Std: 20.3243 +2025-04-18 18:09:37,676 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 18:09:37,676 - INFO - KS Statistic: 0.1999 +2025-04-18 18:09:37,676 - INFO - P-value: 0.0000 +2025-04-18 18:09:37,676 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 18:09:37,676 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 18:09:37,946 - INFO - Validation and Training Set Split Statistics +2025-04-18 18:09:37,946 - INFO - Timestamp: 2025-04-18 18:09:37 +2025-04-18 18:09:37,946 - INFO - Validation set size: 1533 (9.28%) +2025-04-18 18:09:37,946 - INFO - Training set size: 14981 (90.72%) +2025-04-18 18:09:37,946 - INFO - Minimum Distance Statistics (km): +2025-04-18 18:09:37,946 - INFO - Mean: 69.1916 +2025-04-18 18:09:37,946 - INFO - Median: 4.0944 +2025-04-18 18:09:37,946 - INFO - Min: 1.4084 +2025-04-18 18:09:37,946 - INFO - Max: 917.2720 +2025-04-18 18:09:37,946 - INFO - Std: 121.5666 +2025-04-18 18:09:37,946 - INFO - OC Distribution Statistics: +2025-04-18 18:09:37,946 - INFO - Validation Mean: 16.3317 +2025-04-18 18:09:37,946 - INFO - Validation Std: 8.0145 +2025-04-18 18:09:37,946 - INFO - Training Mean: 23.1607 +2025-04-18 18:09:37,946 - INFO - Training Std: 20.3837 +2025-04-18 18:09:37,946 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 18:09:37,946 - INFO - KS Statistic: 0.2252 +2025-04-18 18:09:37,946 - INFO - P-value: 0.0000 +2025-04-18 18:09:37,946 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 18:09:37,946 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 18:09:40,964 - INFO - Validation and Training Set Split Statistics +2025-04-18 18:09:40,964 - INFO - Timestamp: 2025-04-18 18:09:40 +2025-04-18 18:09:40,964 - INFO - Validation set size: 1385 (8.39%) +2025-04-18 18:09:40,964 - INFO - Training set size: 15129 (91.61%) +2025-04-18 18:09:40,964 - INFO - Minimum Distance Statistics (km): +2025-04-18 18:09:40,964 - INFO - Mean: 74.0385 +2025-04-18 18:09:40,964 - INFO - Median: 5.5358 +2025-04-18 18:09:40,964 - INFO - Min: 1.4069 +2025-04-18 18:09:40,964 - INFO - Max: 917.1429 +2025-04-18 18:09:40,964 - INFO - Std: 115.0866 +2025-04-18 18:09:40,964 - INFO - OC Distribution Statistics: +2025-04-18 18:09:40,964 - INFO - Validation Mean: 16.4548 +2025-04-18 18:09:40,964 - INFO - Validation Std: 7.6117 +2025-04-18 18:09:40,964 - INFO - Training Mean: 23.0827 +2025-04-18 18:09:40,964 - INFO - Training Std: 20.3282 +2025-04-18 18:09:40,964 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 18:09:40,964 - INFO - KS Statistic: 0.1982 +2025-04-18 18:09:40,964 - INFO - P-value: 0.0000 +2025-04-18 18:09:40,964 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 18:09:40,964 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 18:13:51,678 - INFO - Validation and Training Set Split Statistics +2025-04-18 18:13:51,678 - INFO - Timestamp: 2025-04-18 18:13:51 +2025-04-18 18:13:51,679 - INFO - Validation set size: 1323 (8.01%) +2025-04-18 18:13:51,679 - INFO - Training set size: 15191 (91.99%) +2025-04-18 18:13:51,679 - INFO - Minimum Distance Statistics (km): +2025-04-18 18:13:51,679 - INFO - Mean: 71.3754 +2025-04-18 18:13:51,679 - INFO - Median: 4.5882 +2025-04-18 18:13:51,679 - INFO - Min: 1.2301 +2025-04-18 18:13:51,679 - INFO - Max: 917.1429 +2025-04-18 18:13:51,679 - INFO - Std: 117.1083 +2025-04-18 18:13:51,679 - INFO - OC Distribution Statistics: +2025-04-18 18:13:51,679 - INFO - Validation Mean: 16.8871 +2025-04-18 18:13:51,679 - INFO - Validation Std: 7.9076 +2025-04-18 18:13:51,679 - INFO - Training Mean: 23.0180 +2025-04-18 18:13:51,679 - INFO - Training Std: 20.2989 +2025-04-18 18:13:51,679 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 18:13:51,679 - INFO - KS Statistic: 0.1784 +2025-04-18 18:13:51,679 - INFO - P-value: 0.0000 +2025-04-18 18:13:51,679 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 18:13:51,679 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 18:13:51,701 - INFO - Validation and Training Set Split Statistics +2025-04-18 18:13:51,701 - INFO - Timestamp: 2025-04-18 18:13:51 +2025-04-18 18:13:51,701 - INFO - Validation set size: 1329 (8.05%) +2025-04-18 18:13:51,701 - INFO - Training set size: 15185 (91.95%) +2025-04-18 18:13:51,701 - INFO - Minimum Distance Statistics (km): +2025-04-18 18:13:51,701 - INFO - Mean: 65.7589 +2025-04-18 18:13:51,701 - INFO - Median: 4.4525 +2025-04-18 18:13:51,701 - INFO - Min: 0.8301 +2025-04-18 18:13:51,701 - INFO - Max: 556.5609 +2025-04-18 18:13:51,701 - INFO - Std: 105.4312 +2025-04-18 18:13:51,701 - INFO - OC Distribution Statistics: +2025-04-18 18:13:51,701 - INFO - Validation Mean: 16.3160 +2025-04-18 18:13:51,701 - INFO - Validation Std: 6.9767 +2025-04-18 18:13:51,701 - INFO - Training Mean: 23.0704 +2025-04-18 18:13:51,701 - INFO - Training Std: 20.3159 +2025-04-18 18:13:51,701 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 18:13:51,701 - INFO - KS Statistic: 0.2089 +2025-04-18 18:13:51,701 - INFO - P-value: 0.0000 +2025-04-18 18:13:51,701 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 18:13:51,701 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 18:13:51,962 - INFO - Validation and Training Set Split Statistics +2025-04-18 18:13:51,962 - INFO - Timestamp: 2025-04-18 18:13:51 +2025-04-18 18:13:51,962 - INFO - Validation set size: 1445 (8.75%) +2025-04-18 18:13:51,962 - INFO - Training set size: 15069 (91.25%) +2025-04-18 18:13:51,962 - INFO - Minimum Distance Statistics (km): +2025-04-18 18:13:51,963 - INFO - Mean: 69.0980 +2025-04-18 18:13:51,963 - INFO - Median: 5.0148 +2025-04-18 18:13:51,963 - INFO - Min: 1.1342 +2025-04-18 18:13:51,963 - INFO - Max: 743.7101 +2025-04-18 18:13:51,963 - INFO - Std: 116.0814 +2025-04-18 18:13:51,963 - INFO - OC Distribution Statistics: +2025-04-18 18:13:51,963 - INFO - Validation Mean: 16.2911 +2025-04-18 18:13:51,963 - INFO - Validation Std: 7.4607 +2025-04-18 18:13:51,963 - INFO - Training Mean: 23.1247 +2025-04-18 18:13:51,963 - INFO - Training Std: 20.3589 +2025-04-18 18:13:51,963 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 18:13:51,963 - INFO - KS Statistic: 0.2367 +2025-04-18 18:13:51,963 - INFO - P-value: 0.0000 +2025-04-18 18:13:51,963 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 18:13:51,963 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 18:13:54,764 - INFO - Validation and Training Set Split Statistics +2025-04-18 18:13:54,764 - INFO - Timestamp: 2025-04-18 18:13:54 +2025-04-18 18:13:54,764 - INFO - Validation set size: 1365 (8.27%) +2025-04-18 18:13:54,764 - INFO - Training set size: 15149 (91.73%) +2025-04-18 18:13:54,764 - INFO - Minimum Distance Statistics (km): +2025-04-18 18:13:54,764 - INFO - Mean: 60.6979 +2025-04-18 18:13:54,764 - INFO - Median: 4.2696 +2025-04-18 18:13:54,764 - INFO - Min: 0.5577 +2025-04-18 18:13:54,764 - INFO - Max: 888.3558 +2025-04-18 18:13:54,764 - INFO - Std: 105.2251 +2025-04-18 18:13:54,764 - INFO - OC Distribution Statistics: +2025-04-18 18:13:54,764 - INFO - Validation Mean: 16.5384 +2025-04-18 18:13:54,764 - INFO - Validation Std: 8.6807 +2025-04-18 18:13:54,764 - INFO - Training Mean: 23.0664 +2025-04-18 18:13:54,764 - INFO - Training Std: 20.2819 +2025-04-18 18:13:54,764 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 18:13:54,764 - INFO - KS Statistic: 0.2238 +2025-04-18 18:13:54,764 - INFO - P-value: 0.0000 +2025-04-18 18:13:54,764 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 18:13:54,764 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 18:27:29,907 - INFO - Validation and Training Set Split Statistics +2025-04-18 18:27:29,908 - INFO - Timestamp: 2025-04-18 18:27:29 +2025-04-18 18:27:29,908 - INFO - Validation set size: 1350 (8.17%) +2025-04-18 18:27:29,908 - INFO - Training set size: 15164 (91.83%) +2025-04-18 18:27:29,908 - INFO - Minimum Distance Statistics (km): +2025-04-18 18:27:29,908 - INFO - Mean: 71.6152 +2025-04-18 18:27:29,908 - INFO - Median: 5.6192 +2025-04-18 18:27:29,908 - INFO - Min: 1.1342 +2025-04-18 18:27:29,908 - INFO - Max: 705.4078 +2025-04-18 18:27:29,908 - INFO - Std: 113.7672 +2025-04-18 18:27:29,908 - INFO - OC Distribution Statistics: +2025-04-18 18:27:29,908 - INFO - Validation Mean: 16.6583 +2025-04-18 18:27:29,908 - INFO - Validation Std: 8.1481 +2025-04-18 18:27:29,908 - INFO - Training Mean: 23.0492 +2025-04-18 18:27:29,908 - INFO - Training Std: 20.2978 +2025-04-18 18:27:29,908 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 18:27:29,908 - INFO - KS Statistic: 0.1948 +2025-04-18 18:27:29,908 - INFO - P-value: 0.0000 +2025-04-18 18:27:29,908 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 18:27:29,908 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 18:27:29,912 - INFO - Validation and Training Set Split Statistics +2025-04-18 18:27:29,912 - INFO - Timestamp: 2025-04-18 18:27:29 +2025-04-18 18:27:29,913 - INFO - Validation set size: 1339 (8.11%) +2025-04-18 18:27:29,913 - INFO - Training set size: 15175 (91.89%) +2025-04-18 18:27:29,913 - INFO - Minimum Distance Statistics (km): +2025-04-18 18:27:29,913 - INFO - Mean: 70.1560 +2025-04-18 18:27:29,913 - INFO - Median: 4.8124 +2025-04-18 18:27:29,913 - INFO - Min: 0.2726 +2025-04-18 18:27:29,913 - INFO - Max: 706.9584 +2025-04-18 18:27:29,913 - INFO - Std: 109.3666 +2025-04-18 18:27:29,913 - INFO - OC Distribution Statistics: +2025-04-18 18:27:29,913 - INFO - Validation Mean: 17.2853 +2025-04-18 18:27:29,913 - INFO - Validation Std: 8.8363 +2025-04-18 18:27:29,913 - INFO - Training Mean: 22.9893 +2025-04-18 18:27:29,913 - INFO - Training Std: 20.2834 +2025-04-18 18:27:29,913 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 18:27:29,913 - INFO - KS Statistic: 0.1844 +2025-04-18 18:27:29,913 - INFO - P-value: 0.0000 +2025-04-18 18:27:29,913 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 18:27:29,913 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 18:27:30,224 - INFO - Validation and Training Set Split Statistics +2025-04-18 18:27:30,224 - INFO - Timestamp: 2025-04-18 18:27:30 +2025-04-18 18:27:30,224 - INFO - Validation set size: 1646 (9.97%) +2025-04-18 18:27:30,224 - INFO - Training set size: 14868 (90.03%) +2025-04-18 18:27:30,225 - INFO - Minimum Distance Statistics (km): +2025-04-18 18:27:30,225 - INFO - Mean: 65.9370 +2025-04-18 18:27:30,225 - INFO - Median: 3.9951 +2025-04-18 18:27:30,225 - INFO - Min: 1.0513 +2025-04-18 18:27:30,225 - INFO - Max: 1063.3892 +2025-04-18 18:27:30,225 - INFO - Std: 120.8974 +2025-04-18 18:27:30,225 - INFO - OC Distribution Statistics: +2025-04-18 18:27:30,225 - INFO - Validation Mean: 16.3045 +2025-04-18 18:27:30,225 - INFO - Validation Std: 7.8130 +2025-04-18 18:27:30,225 - INFO - Training Mean: 23.2156 +2025-04-18 18:27:30,225 - INFO - Training Std: 20.4480 +2025-04-18 18:27:30,225 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 18:27:30,225 - INFO - KS Statistic: 0.2269 +2025-04-18 18:27:30,225 - INFO - P-value: 0.0000 +2025-04-18 18:27:30,225 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 18:27:30,225 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 18:27:34,003 - INFO - Validation and Training Set Split Statistics +2025-04-18 18:27:34,003 - INFO - Timestamp: 2025-04-18 18:27:34 +2025-04-18 18:27:34,003 - INFO - Validation set size: 1445 (8.75%) +2025-04-18 18:27:34,003 - INFO - Training set size: 15069 (91.25%) +2025-04-18 18:27:34,003 - INFO - Minimum Distance Statistics (km): +2025-04-18 18:27:34,003 - INFO - Mean: 66.9987 +2025-04-18 18:27:34,003 - INFO - Median: 4.2196 +2025-04-18 18:27:34,003 - INFO - Min: 0.4578 +2025-04-18 18:27:34,003 - INFO - Max: 875.2369 +2025-04-18 18:27:34,003 - INFO - Std: 118.8080 +2025-04-18 18:27:34,003 - INFO - OC Distribution Statistics: +2025-04-18 18:27:34,003 - INFO - Validation Mean: 16.0740 +2025-04-18 18:27:34,003 - INFO - Validation Std: 7.4447 +2025-04-18 18:27:34,003 - INFO - Training Mean: 23.1456 +2025-04-18 18:27:34,003 - INFO - Training Std: 20.3523 +2025-04-18 18:27:34,003 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 18:27:34,004 - INFO - KS Statistic: 0.2490 +2025-04-18 18:27:34,004 - INFO - P-value: 0.0000 +2025-04-18 18:27:34,004 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 18:27:34,004 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 19:10:13,294 - INFO - Validation and Training Set Split Statistics +2025-04-18 19:10:13,295 - INFO - Timestamp: 2025-04-18 19:10:13 +2025-04-18 19:10:13,295 - INFO - Validation set size: 1337 (8.10%) +2025-04-18 19:10:13,295 - INFO - Training set size: 15177 (91.90%) +2025-04-18 19:10:13,295 - INFO - Minimum Distance Statistics (km): +2025-04-18 19:10:13,295 - INFO - Mean: 71.1383 +2025-04-18 19:10:13,295 - INFO - Median: 5.2639 +2025-04-18 19:10:13,295 - INFO - Min: 1.1596 +2025-04-18 19:10:13,295 - INFO - Max: 706.9584 +2025-04-18 19:10:13,295 - INFO - Std: 111.6745 +2025-04-18 19:10:13,295 - INFO - OC Distribution Statistics: +2025-04-18 19:10:13,295 - INFO - Validation Mean: 16.5845 +2025-04-18 19:10:13,295 - INFO - Validation Std: 8.3208 +2025-04-18 19:10:13,295 - INFO - Training Mean: 23.0503 +2025-04-18 19:10:13,295 - INFO - Training Std: 20.2832 +2025-04-18 19:10:13,295 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 19:10:13,295 - INFO - KS Statistic: 0.2282 +2025-04-18 19:10:13,295 - INFO - P-value: 0.0000 +2025-04-18 19:10:13,295 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 19:10:13,295 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 19:10:13,304 - INFO - Validation and Training Set Split Statistics +2025-04-18 19:10:13,304 - INFO - Timestamp: 2025-04-18 19:10:13 +2025-04-18 19:10:13,304 - INFO - Validation set size: 1323 (8.01%) +2025-04-18 19:10:13,304 - INFO - Training set size: 15191 (91.99%) +2025-04-18 19:10:13,304 - INFO - Minimum Distance Statistics (km): +2025-04-18 19:10:13,304 - INFO - Mean: 72.9187 +2025-04-18 19:10:13,304 - INFO - Median: 5.1703 +2025-04-18 19:10:13,304 - INFO - Min: 1.2287 +2025-04-18 19:10:13,304 - INFO - Max: 716.7388 +2025-04-18 19:10:13,304 - INFO - Std: 119.8451 +2025-04-18 19:10:13,304 - INFO - OC Distribution Statistics: +2025-04-18 19:10:13,304 - INFO - Validation Mean: 16.7282 +2025-04-18 19:10:13,304 - INFO - Validation Std: 8.1338 +2025-04-18 19:10:13,304 - INFO - Training Mean: 23.0318 +2025-04-18 19:10:13,304 - INFO - Training Std: 20.2869 +2025-04-18 19:10:13,304 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 19:10:13,305 - INFO - KS Statistic: 0.1882 +2025-04-18 19:10:13,305 - INFO - P-value: 0.0000 +2025-04-18 19:10:13,305 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 19:10:13,305 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 19:10:13,592 - INFO - Validation and Training Set Split Statistics +2025-04-18 19:10:13,592 - INFO - Timestamp: 2025-04-18 19:10:13 +2025-04-18 19:10:13,592 - INFO - Validation set size: 1520 (9.20%) +2025-04-18 19:10:13,592 - INFO - Training set size: 14994 (90.80%) +2025-04-18 19:10:13,592 - INFO - Minimum Distance Statistics (km): +2025-04-18 19:10:13,592 - INFO - Mean: 77.3432 +2025-04-18 19:10:13,592 - INFO - Median: 7.1086 +2025-04-18 19:10:13,592 - INFO - Min: 1.4084 +2025-04-18 19:10:13,592 - INFO - Max: 743.7101 +2025-04-18 19:10:13,592 - INFO - Std: 120.0433 +2025-04-18 19:10:13,592 - INFO - OC Distribution Statistics: +2025-04-18 19:10:13,592 - INFO - Validation Mean: 17.0299 +2025-04-18 19:10:13,592 - INFO - Validation Std: 8.5563 +2025-04-18 19:10:13,593 - INFO - Training Mean: 23.0840 +2025-04-18 19:10:13,593 - INFO - Training Std: 20.3772 +2025-04-18 19:10:13,593 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 19:10:13,593 - INFO - KS Statistic: 0.1845 +2025-04-18 19:10:13,593 - INFO - P-value: 0.0000 +2025-04-18 19:10:13,593 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 19:10:13,593 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 19:10:16,575 - INFO - Validation and Training Set Split Statistics +2025-04-18 19:10:16,575 - INFO - Timestamp: 2025-04-18 19:10:16 +2025-04-18 19:10:16,575 - INFO - Validation set size: 1353 (8.19%) +2025-04-18 19:10:16,575 - INFO - Training set size: 15161 (91.81%) +2025-04-18 19:10:16,575 - INFO - Minimum Distance Statistics (km): +2025-04-18 19:10:16,575 - INFO - Mean: 68.5383 +2025-04-18 19:10:16,575 - INFO - Median: 4.8464 +2025-04-18 19:10:16,575 - INFO - Min: 1.4172 +2025-04-18 19:10:16,575 - INFO - Max: 702.5656 +2025-04-18 19:10:16,575 - INFO - Std: 109.9814 +2025-04-18 19:10:16,575 - INFO - OC Distribution Statistics: +2025-04-18 19:10:16,575 - INFO - Validation Mean: 16.6545 +2025-04-18 19:10:16,575 - INFO - Validation Std: 7.7792 +2025-04-18 19:10:16,575 - INFO - Training Mean: 23.0508 +2025-04-18 19:10:16,575 - INFO - Training Std: 20.3121 +2025-04-18 19:10:16,575 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 19:10:16,576 - INFO - KS Statistic: 0.2182 +2025-04-18 19:10:16,576 - INFO - P-value: 0.0000 +2025-04-18 19:10:16,576 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 19:10:16,576 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 19:16:38,561 - INFO - Validation and Training Set Split Statistics +2025-04-18 19:16:38,562 - INFO - Timestamp: 2025-04-18 19:16:38 +2025-04-18 19:16:38,562 - INFO - Validation set size: 1341 (8.12%) +2025-04-18 19:16:38,562 - INFO - Training set size: 15173 (91.88%) +2025-04-18 19:16:38,562 - INFO - Minimum Distance Statistics (km): +2025-04-18 19:16:38,562 - INFO - Mean: 75.8392 +2025-04-18 19:16:38,562 - INFO - Median: 5.8265 +2025-04-18 19:16:38,562 - INFO - Min: 0.9397 +2025-04-18 19:16:38,562 - INFO - Max: 629.8500 +2025-04-18 19:16:38,562 - INFO - Std: 111.4309 +2025-04-18 19:16:38,562 - INFO - OC Distribution Statistics: +2025-04-18 19:16:38,562 - INFO - Validation Mean: 17.0647 +2025-04-18 19:16:38,562 - INFO - Validation Std: 8.2780 +2025-04-18 19:16:38,562 - INFO - Training Mean: 23.0095 +2025-04-18 19:16:38,562 - INFO - Training Std: 20.2996 +2025-04-18 19:16:38,562 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 19:16:38,562 - INFO - KS Statistic: 0.1986 +2025-04-18 19:16:38,562 - INFO - P-value: 0.0000 +2025-04-18 19:16:38,562 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 19:16:38,562 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 19:16:38,805 - INFO - Validation and Training Set Split Statistics +2025-04-18 19:16:38,805 - INFO - Timestamp: 2025-04-18 19:16:38 +2025-04-18 19:16:38,805 - INFO - Validation set size: 1604 (9.71%) +2025-04-18 19:16:38,806 - INFO - Training set size: 14910 (90.29%) +2025-04-18 19:16:38,806 - INFO - Minimum Distance Statistics (km): +2025-04-18 19:16:38,806 - INFO - Mean: 61.0627 +2025-04-18 19:16:38,806 - INFO - Median: 4.4155 +2025-04-18 19:16:38,806 - INFO - Min: 0.6775 +2025-04-18 19:16:38,806 - INFO - Max: 1106.1626 +2025-04-18 19:16:38,806 - INFO - Std: 113.6513 +2025-04-18 19:16:38,806 - INFO - OC Distribution Statistics: +2025-04-18 19:16:38,806 - INFO - Validation Mean: 15.9675 +2025-04-18 19:16:38,806 - INFO - Validation Std: 7.6008 +2025-04-18 19:16:38,806 - INFO - Training Mean: 23.2324 +2025-04-18 19:16:38,806 - INFO - Training Std: 20.4227 +2025-04-18 19:16:38,806 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 19:16:38,806 - INFO - KS Statistic: 0.2347 +2025-04-18 19:16:38,806 - INFO - P-value: 0.0000 +2025-04-18 19:16:38,806 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 19:16:38,806 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 19:16:38,818 - INFO - Validation and Training Set Split Statistics +2025-04-18 19:16:38,818 - INFO - Timestamp: 2025-04-18 19:16:38 +2025-04-18 19:16:38,818 - INFO - Validation set size: 1496 (9.06%) +2025-04-18 19:16:38,818 - INFO - Training set size: 15018 (90.94%) +2025-04-18 19:16:38,818 - INFO - Minimum Distance Statistics (km): +2025-04-18 19:16:38,818 - INFO - Mean: 70.7323 +2025-04-18 19:16:38,818 - INFO - Median: 4.4780 +2025-04-18 19:16:38,818 - INFO - Min: 0.4760 +2025-04-18 19:16:38,818 - INFO - Max: 743.7101 +2025-04-18 19:16:38,818 - INFO - Std: 115.0411 +2025-04-18 19:16:38,818 - INFO - OC Distribution Statistics: +2025-04-18 19:16:38,818 - INFO - Validation Mean: 16.6440 +2025-04-18 19:16:38,818 - INFO - Validation Std: 7.6834 +2025-04-18 19:16:38,818 - INFO - Training Mean: 23.1128 +2025-04-18 19:16:38,818 - INFO - Training Std: 20.3881 +2025-04-18 19:16:38,818 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 19:16:38,818 - INFO - KS Statistic: 0.2136 +2025-04-18 19:16:38,818 - INFO - P-value: 0.0000 +2025-04-18 19:16:38,818 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 19:16:38,818 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 19:16:41,795 - INFO - Validation and Training Set Split Statistics +2025-04-18 19:16:41,795 - INFO - Timestamp: 2025-04-18 19:16:41 +2025-04-18 19:16:41,795 - INFO - Validation set size: 1490 (9.02%) +2025-04-18 19:16:41,795 - INFO - Training set size: 15024 (90.98%) +2025-04-18 19:16:41,795 - INFO - Minimum Distance Statistics (km): +2025-04-18 19:16:41,795 - INFO - Mean: 83.1922 +2025-04-18 19:16:41,795 - INFO - Median: 7.0484 +2025-04-18 19:16:41,795 - INFO - Min: 0.8042 +2025-04-18 19:16:41,795 - INFO - Max: 835.9450 +2025-04-18 19:16:41,795 - INFO - Std: 129.0362 +2025-04-18 19:16:41,795 - INFO - OC Distribution Statistics: +2025-04-18 19:16:41,795 - INFO - Validation Mean: 16.9893 +2025-04-18 19:16:41,795 - INFO - Validation Std: 8.5686 +2025-04-18 19:16:41,795 - INFO - Training Mean: 23.0760 +2025-04-18 19:16:41,795 - INFO - Training Std: 20.3606 +2025-04-18 19:16:41,795 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 19:16:41,795 - INFO - KS Statistic: 0.1950 +2025-04-18 19:16:41,795 - INFO - P-value: 0.0000 +2025-04-18 19:16:41,795 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 19:16:41,795 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 19:33:32,468 - INFO - Validation and Training Set Split Statistics +2025-04-18 19:33:32,468 - INFO - Timestamp: 2025-04-18 19:33:32 +2025-04-18 19:33:32,468 - INFO - Validation set size: 1381 (8.36%) +2025-04-18 19:33:32,468 - INFO - Training set size: 15133 (91.64%) +2025-04-18 19:33:32,468 - INFO - Minimum Distance Statistics (km): +2025-04-18 19:33:32,468 - INFO - Mean: 68.2667 +2025-04-18 19:33:32,468 - INFO - Median: 4.5236 +2025-04-18 19:33:32,468 - INFO - Min: 1.4234 +2025-04-18 19:33:32,468 - INFO - Max: 743.7101 +2025-04-18 19:33:32,468 - INFO - Std: 114.0883 +2025-04-18 19:33:32,468 - INFO - OC Distribution Statistics: +2025-04-18 19:33:32,468 - INFO - Validation Mean: 16.8419 +2025-04-18 19:33:32,468 - INFO - Validation Std: 7.3407 +2025-04-18 19:33:32,468 - INFO - Training Mean: 23.0456 +2025-04-18 19:33:32,468 - INFO - Training Std: 20.3465 +2025-04-18 19:33:32,468 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 19:33:32,468 - INFO - KS Statistic: 0.1932 +2025-04-18 19:33:32,468 - INFO - P-value: 0.0000 +2025-04-18 19:33:32,468 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 19:33:32,468 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 19:33:33,024 - INFO - Validation and Training Set Split Statistics +2025-04-18 19:33:33,024 - INFO - Timestamp: 2025-04-18 19:33:33 +2025-04-18 19:33:33,024 - INFO - Validation set size: 1474 (8.93%) +2025-04-18 19:33:33,024 - INFO - Training set size: 15040 (91.07%) +2025-04-18 19:33:33,024 - INFO - Minimum Distance Statistics (km): +2025-04-18 19:33:33,024 - INFO - Mean: 61.7601 +2025-04-18 19:33:33,024 - INFO - Median: 4.2506 +2025-04-18 19:33:33,024 - INFO - Min: 0.7448 +2025-04-18 19:33:33,024 - INFO - Max: 702.5656 +2025-04-18 19:33:33,024 - INFO - Std: 107.6505 +2025-04-18 19:33:33,024 - INFO - OC Distribution Statistics: +2025-04-18 19:33:33,024 - INFO - Validation Mean: 16.9204 +2025-04-18 19:33:33,024 - INFO - Validation Std: 8.4858 +2025-04-18 19:33:33,024 - INFO - Training Mean: 23.0762 +2025-04-18 19:33:33,024 - INFO - Training Std: 20.3540 +2025-04-18 19:33:33,024 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 19:33:33,024 - INFO - KS Statistic: 0.2089 +2025-04-18 19:33:33,024 - INFO - P-value: 0.0000 +2025-04-18 19:33:33,024 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 19:33:33,024 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 19:33:33,045 - INFO - Validation and Training Set Split Statistics +2025-04-18 19:33:33,046 - INFO - Timestamp: 2025-04-18 19:33:33 +2025-04-18 19:33:33,046 - INFO - Validation set size: 1543 (9.34%) +2025-04-18 19:33:33,046 - INFO - Training set size: 14971 (90.66%) +2025-04-18 19:33:33,046 - INFO - Minimum Distance Statistics (km): +2025-04-18 19:33:33,046 - INFO - Mean: 63.0291 +2025-04-18 19:33:33,046 - INFO - Median: 4.5217 +2025-04-18 19:33:33,046 - INFO - Min: 0.5724 +2025-04-18 19:33:33,046 - INFO - Max: 862.7749 +2025-04-18 19:33:33,046 - INFO - Std: 106.2630 +2025-04-18 19:33:33,046 - INFO - OC Distribution Statistics: +2025-04-18 19:33:33,046 - INFO - Validation Mean: 16.5114 +2025-04-18 19:33:33,046 - INFO - Validation Std: 8.0674 +2025-04-18 19:33:33,046 - INFO - Training Mean: 23.1468 +2025-04-18 19:33:33,046 - INFO - Training Std: 20.3926 +2025-04-18 19:33:33,046 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 19:33:33,046 - INFO - KS Statistic: 0.2099 +2025-04-18 19:33:33,046 - INFO - P-value: 0.0000 +2025-04-18 19:33:33,046 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 19:33:33,046 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 19:33:36,937 - INFO - Validation and Training Set Split Statistics +2025-04-18 19:33:36,937 - INFO - Timestamp: 2025-04-18 19:33:36 +2025-04-18 19:33:36,937 - INFO - Validation set size: 1345 (8.14%) +2025-04-18 19:33:36,937 - INFO - Training set size: 15169 (91.86%) +2025-04-18 19:33:36,938 - INFO - Minimum Distance Statistics (km): +2025-04-18 19:33:36,938 - INFO - Mean: 62.7330 +2025-04-18 19:33:36,938 - INFO - Median: 5.1441 +2025-04-18 19:33:36,938 - INFO - Min: 1.3302 +2025-04-18 19:33:36,938 - INFO - Max: 706.9584 +2025-04-18 19:33:36,938 - INFO - Std: 104.7094 +2025-04-18 19:33:36,938 - INFO - OC Distribution Statistics: +2025-04-18 19:33:36,938 - INFO - Validation Mean: 16.5375 +2025-04-18 19:33:36,938 - INFO - Validation Std: 8.4978 +2025-04-18 19:33:36,938 - INFO - Training Mean: 23.0578 +2025-04-18 19:33:36,938 - INFO - Training Std: 20.2792 +2025-04-18 19:33:36,938 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 19:33:36,938 - INFO - KS Statistic: 0.2257 +2025-04-18 19:33:36,938 - INFO - P-value: 0.0000 +2025-04-18 19:33:36,938 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 19:33:36,938 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 20:12:43,949 - INFO - Validation and Training Set Split Statistics +2025-04-18 20:12:43,949 - INFO - Timestamp: 2025-04-18 20:12:43 +2025-04-18 20:12:43,950 - INFO - Validation set size: 1388 (8.40%) +2025-04-18 20:12:43,950 - INFO - Training set size: 15126 (91.60%) +2025-04-18 20:12:43,950 - INFO - Minimum Distance Statistics (km): +2025-04-18 20:12:43,950 - INFO - Mean: 65.9200 +2025-04-18 20:12:43,950 - INFO - Median: 4.9468 +2025-04-18 20:12:43,950 - INFO - Min: 1.4050 +2025-04-18 20:12:43,950 - INFO - Max: 743.7101 +2025-04-18 20:12:43,950 - INFO - Std: 111.1447 +2025-04-18 20:12:43,950 - INFO - OC Distribution Statistics: +2025-04-18 20:12:43,950 - INFO - Validation Mean: 16.3650 +2025-04-18 20:12:43,950 - INFO - Validation Std: 8.4386 +2025-04-18 20:12:43,950 - INFO - Training Mean: 23.0922 +2025-04-18 20:12:43,950 - INFO - Training Std: 20.2971 +2025-04-18 20:12:43,950 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 20:12:43,950 - INFO - KS Statistic: 0.2315 +2025-04-18 20:12:43,950 - INFO - P-value: 0.0000 +2025-04-18 20:12:43,950 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 20:12:43,950 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 20:12:43,957 - INFO - Validation and Training Set Split Statistics +2025-04-18 20:12:43,957 - INFO - Timestamp: 2025-04-18 20:12:43 +2025-04-18 20:12:43,957 - INFO - Validation set size: 1363 (8.25%) +2025-04-18 20:12:43,957 - INFO - Training set size: 15151 (91.75%) +2025-04-18 20:12:43,957 - INFO - Minimum Distance Statistics (km): +2025-04-18 20:12:43,957 - INFO - Mean: 76.9583 +2025-04-18 20:12:43,957 - INFO - Median: 7.0467 +2025-04-18 20:12:43,957 - INFO - Min: 1.4390 +2025-04-18 20:12:43,957 - INFO - Max: 743.7101 +2025-04-18 20:12:43,957 - INFO - Std: 113.9067 +2025-04-18 20:12:43,957 - INFO - OC Distribution Statistics: +2025-04-18 20:12:43,957 - INFO - Validation Mean: 17.1780 +2025-04-18 20:12:43,957 - INFO - Validation Std: 8.4317 +2025-04-18 20:12:43,957 - INFO - Training Mean: 23.0080 +2025-04-18 20:12:43,957 - INFO - Training Std: 20.3079 +2025-04-18 20:12:43,957 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 20:12:43,957 - INFO - KS Statistic: 0.1720 +2025-04-18 20:12:43,957 - INFO - P-value: 0.0000 +2025-04-18 20:12:43,957 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 20:12:43,957 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 20:12:44,236 - INFO - Validation and Training Set Split Statistics +2025-04-18 20:12:44,236 - INFO - Timestamp: 2025-04-18 20:12:44 +2025-04-18 20:12:44,236 - INFO - Validation set size: 1624 (9.83%) +2025-04-18 20:12:44,236 - INFO - Training set size: 14890 (90.17%) +2025-04-18 20:12:44,236 - INFO - Minimum Distance Statistics (km): +2025-04-18 20:12:44,236 - INFO - Mean: 61.2050 +2025-04-18 20:12:44,236 - INFO - Median: 4.2696 +2025-04-18 20:12:44,236 - INFO - Min: 1.1596 +2025-04-18 20:12:44,236 - INFO - Max: 807.4973 +2025-04-18 20:12:44,236 - INFO - Std: 110.2523 +2025-04-18 20:12:44,236 - INFO - OC Distribution Statistics: +2025-04-18 20:12:44,236 - INFO - Validation Mean: 16.4669 +2025-04-18 20:12:44,236 - INFO - Validation Std: 8.0346 +2025-04-18 20:12:44,236 - INFO - Training Mean: 23.1877 +2025-04-18 20:12:44,236 - INFO - Training Std: 20.4333 +2025-04-18 20:12:44,236 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 20:12:44,236 - INFO - KS Statistic: 0.2116 +2025-04-18 20:12:44,236 - INFO - P-value: 0.0000 +2025-04-18 20:12:44,236 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 20:12:44,236 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 20:12:47,475 - INFO - Validation and Training Set Split Statistics +2025-04-18 20:12:47,475 - INFO - Timestamp: 2025-04-18 20:12:47 +2025-04-18 20:12:47,476 - INFO - Validation set size: 1580 (9.57%) +2025-04-18 20:12:47,476 - INFO - Training set size: 14934 (90.43%) +2025-04-18 20:12:47,476 - INFO - Minimum Distance Statistics (km): +2025-04-18 20:12:47,476 - INFO - Mean: 66.7112 +2025-04-18 20:12:47,476 - INFO - Median: 4.2800 +2025-04-18 20:12:47,476 - INFO - Min: 1.0890 +2025-04-18 20:12:47,476 - INFO - Max: 743.7101 +2025-04-18 20:12:47,476 - INFO - Std: 109.0218 +2025-04-18 20:12:47,476 - INFO - OC Distribution Statistics: +2025-04-18 20:12:47,476 - INFO - Validation Mean: 16.4280 +2025-04-18 20:12:47,476 - INFO - Validation Std: 7.1673 +2025-04-18 20:12:47,476 - INFO - Training Mean: 23.1720 +2025-04-18 20:12:47,476 - INFO - Training Std: 20.4439 +2025-04-18 20:12:47,476 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 20:12:47,476 - INFO - KS Statistic: 0.2246 +2025-04-18 20:12:47,476 - INFO - P-value: 0.0000 +2025-04-18 20:12:47,476 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 20:12:47,476 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 20:20:43,018 - INFO - Validation and Training Set Split Statistics +2025-04-18 20:20:43,019 - INFO - Timestamp: 2025-04-18 20:20:43 +2025-04-18 20:20:43,019 - INFO - Validation set size: 1349 (8.17%) +2025-04-18 20:20:43,019 - INFO - Training set size: 15165 (91.83%) +2025-04-18 20:20:43,019 - INFO - Minimum Distance Statistics (km): +2025-04-18 20:20:43,019 - INFO - Mean: 72.8366 +2025-04-18 20:20:43,019 - INFO - Median: 5.0465 +2025-04-18 20:20:43,019 - INFO - Min: 1.4084 +2025-04-18 20:20:43,019 - INFO - Max: 772.2996 +2025-04-18 20:20:43,019 - INFO - Std: 116.3353 +2025-04-18 20:20:43,019 - INFO - OC Distribution Statistics: +2025-04-18 20:20:43,019 - INFO - Validation Mean: 16.6823 +2025-04-18 20:20:43,019 - INFO - Validation Std: 8.0765 +2025-04-18 20:20:43,019 - INFO - Training Mean: 23.0467 +2025-04-18 20:20:43,019 - INFO - Training Std: 20.3005 +2025-04-18 20:20:43,019 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 20:20:43,019 - INFO - KS Statistic: 0.1949 +2025-04-18 20:20:43,019 - INFO - P-value: 0.0000 +2025-04-18 20:20:43,019 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 20:20:43,019 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 20:20:43,030 - INFO - Validation and Training Set Split Statistics +2025-04-18 20:20:43,030 - INFO - Timestamp: 2025-04-18 20:20:43 +2025-04-18 20:20:43,030 - INFO - Validation set size: 1508 (9.13%) +2025-04-18 20:20:43,030 - INFO - Training set size: 15006 (90.87%) +2025-04-18 20:20:43,030 - INFO - Minimum Distance Statistics (km): +2025-04-18 20:20:43,030 - INFO - Mean: 63.4316 +2025-04-18 20:20:43,030 - INFO - Median: 4.8388 +2025-04-18 20:20:43,030 - INFO - Min: 0.6477 +2025-04-18 20:20:43,030 - INFO - Max: 706.9761 +2025-04-18 20:20:43,031 - INFO - Std: 108.5671 +2025-04-18 20:20:43,031 - INFO - OC Distribution Statistics: +2025-04-18 20:20:43,031 - INFO - Validation Mean: 15.9359 +2025-04-18 20:20:43,031 - INFO - Validation Std: 6.7535 +2025-04-18 20:20:43,031 - INFO - Training Mean: 23.1891 +2025-04-18 20:20:43,031 - INFO - Training Std: 20.4034 +2025-04-18 20:20:43,031 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 20:20:43,031 - INFO - KS Statistic: 0.2335 +2025-04-18 20:20:43,031 - INFO - P-value: 0.0000 +2025-04-18 20:20:43,031 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 20:20:43,031 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 20:20:43,274 - INFO - Validation and Training Set Split Statistics +2025-04-18 20:20:43,274 - INFO - Timestamp: 2025-04-18 20:20:43 +2025-04-18 20:20:43,274 - INFO - Validation set size: 1495 (9.05%) +2025-04-18 20:20:43,274 - INFO - Training set size: 15019 (90.95%) +2025-04-18 20:20:43,274 - INFO - Minimum Distance Statistics (km): +2025-04-18 20:20:43,274 - INFO - Mean: 62.1199 +2025-04-18 20:20:43,274 - INFO - Median: 4.0399 +2025-04-18 20:20:43,274 - INFO - Min: 1.0473 +2025-04-18 20:20:43,274 - INFO - Max: 569.3741 +2025-04-18 20:20:43,274 - INFO - Std: 105.2322 +2025-04-18 20:20:43,274 - INFO - OC Distribution Statistics: +2025-04-18 20:20:43,274 - INFO - Validation Mean: 16.2262 +2025-04-18 20:20:43,274 - INFO - Validation Std: 8.0299 +2025-04-18 20:20:43,274 - INFO - Training Mean: 23.1539 +2025-04-18 20:20:43,274 - INFO - Training Std: 20.3606 +2025-04-18 20:20:43,274 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 20:20:43,274 - INFO - KS Statistic: 0.2268 +2025-04-18 20:20:43,274 - INFO - P-value: 0.0000 +2025-04-18 20:20:43,274 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 20:20:43,274 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 20:20:46,378 - INFO - Validation and Training Set Split Statistics +2025-04-18 20:20:46,378 - INFO - Timestamp: 2025-04-18 20:20:46 +2025-04-18 20:20:46,378 - INFO - Validation set size: 1511 (9.15%) +2025-04-18 20:20:46,378 - INFO - Training set size: 15003 (90.85%) +2025-04-18 20:20:46,378 - INFO - Minimum Distance Statistics (km): +2025-04-18 20:20:46,378 - INFO - Mean: 66.3553 +2025-04-18 20:20:46,378 - INFO - Median: 4.7619 +2025-04-18 20:20:46,378 - INFO - Min: 1.4084 +2025-04-18 20:20:46,378 - INFO - Max: 826.2195 +2025-04-18 20:20:46,378 - INFO - Std: 114.4899 +2025-04-18 20:20:46,378 - INFO - OC Distribution Statistics: +2025-04-18 20:20:46,378 - INFO - Validation Mean: 16.5707 +2025-04-18 20:20:46,378 - INFO - Validation Std: 8.0408 +2025-04-18 20:20:46,378 - INFO - Training Mean: 23.1266 +2025-04-18 20:20:46,378 - INFO - Training Std: 20.3796 +2025-04-18 20:20:46,378 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 20:20:46,378 - INFO - KS Statistic: 0.1887 +2025-04-18 20:20:46,378 - INFO - P-value: 0.0000 +2025-04-18 20:20:46,378 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 20:20:46,378 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 20:40:50,822 - INFO - Validation and Training Set Split Statistics +2025-04-18 20:40:50,823 - INFO - Timestamp: 2025-04-18 20:40:50 +2025-04-18 20:40:50,823 - INFO - Validation set size: 1522 (9.22%) +2025-04-18 20:40:50,823 - INFO - Training set size: 14992 (90.78%) +2025-04-18 20:40:50,823 - INFO - Minimum Distance Statistics (km): +2025-04-18 20:40:50,823 - INFO - Mean: 66.8060 +2025-04-18 20:40:50,823 - INFO - Median: 4.8592 +2025-04-18 20:40:50,823 - INFO - Min: 0.9954 +2025-04-18 20:40:50,823 - INFO - Max: 702.5656 +2025-04-18 20:40:50,823 - INFO - Std: 106.6051 +2025-04-18 20:40:50,823 - INFO - OC Distribution Statistics: +2025-04-18 20:40:50,823 - INFO - Validation Mean: 16.2911 +2025-04-18 20:40:50,823 - INFO - Validation Std: 7.8281 +2025-04-18 20:40:50,823 - INFO - Training Mean: 23.1598 +2025-04-18 20:40:50,823 - INFO - Training Std: 20.3842 +2025-04-18 20:40:50,823 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 20:40:50,823 - INFO - KS Statistic: 0.2278 +2025-04-18 20:40:50,823 - INFO - P-value: 0.0000 +2025-04-18 20:40:50,823 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 20:40:50,823 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 20:40:50,826 - INFO - Validation and Training Set Split Statistics +2025-04-18 20:40:50,826 - INFO - Timestamp: 2025-04-18 20:40:50 +2025-04-18 20:40:50,826 - INFO - Validation set size: 1556 (9.42%) +2025-04-18 20:40:50,826 - INFO - Training set size: 14958 (90.58%) +2025-04-18 20:40:50,826 - INFO - Minimum Distance Statistics (km): +2025-04-18 20:40:50,826 - INFO - Mean: 64.8082 +2025-04-18 20:40:50,826 - INFO - Median: 4.2711 +2025-04-18 20:40:50,826 - INFO - Min: 1.2935 +2025-04-18 20:40:50,826 - INFO - Max: 862.7749 +2025-04-18 20:40:50,826 - INFO - Std: 109.5535 +2025-04-18 20:40:50,826 - INFO - OC Distribution Statistics: +2025-04-18 20:40:50,826 - INFO - Validation Mean: 16.4466 +2025-04-18 20:40:50,826 - INFO - Validation Std: 7.6707 +2025-04-18 20:40:50,826 - INFO - Training Mean: 23.1593 +2025-04-18 20:40:50,826 - INFO - Training Std: 20.4129 +2025-04-18 20:40:50,826 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 20:40:50,826 - INFO - KS Statistic: 0.2231 +2025-04-18 20:40:50,826 - INFO - P-value: 0.0000 +2025-04-18 20:40:50,826 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 20:40:50,826 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 20:40:50,857 - INFO - Validation and Training Set Split Statistics +2025-04-18 20:40:50,857 - INFO - Timestamp: 2025-04-18 20:40:50 +2025-04-18 20:40:50,857 - INFO - Validation set size: 1415 (8.57%) +2025-04-18 20:40:50,857 - INFO - Training set size: 15099 (91.43%) +2025-04-18 20:40:50,857 - INFO - Minimum Distance Statistics (km): +2025-04-18 20:40:50,857 - INFO - Mean: 68.7512 +2025-04-18 20:40:50,857 - INFO - Median: 4.2711 +2025-04-18 20:40:50,857 - INFO - Min: 0.8301 +2025-04-18 20:40:50,857 - INFO - Max: 743.7101 +2025-04-18 20:40:50,857 - INFO - Std: 112.2860 +2025-04-18 20:40:50,857 - INFO - OC Distribution Statistics: +2025-04-18 20:40:50,857 - INFO - Validation Mean: 16.5282 +2025-04-18 20:40:50,857 - INFO - Validation Std: 8.1773 +2025-04-18 20:40:50,857 - INFO - Training Mean: 23.0889 +2025-04-18 20:40:50,857 - INFO - Training Std: 20.3251 +2025-04-18 20:40:50,857 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 20:40:50,857 - INFO - KS Statistic: 0.2059 +2025-04-18 20:40:50,857 - INFO - P-value: 0.0000 +2025-04-18 20:40:50,857 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 20:40:50,857 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 20:40:55,031 - INFO - Validation and Training Set Split Statistics +2025-04-18 20:40:55,031 - INFO - Timestamp: 2025-04-18 20:40:55 +2025-04-18 20:40:55,031 - INFO - Validation set size: 1515 (9.17%) +2025-04-18 20:40:55,031 - INFO - Training set size: 14999 (90.83%) +2025-04-18 20:40:55,031 - INFO - Minimum Distance Statistics (km): +2025-04-18 20:40:55,031 - INFO - Mean: 65.7181 +2025-04-18 20:40:55,031 - INFO - Median: 4.5760 +2025-04-18 20:40:55,031 - INFO - Min: 1.4443 +2025-04-18 20:40:55,031 - INFO - Max: 743.7101 +2025-04-18 20:40:55,031 - INFO - Std: 113.8858 +2025-04-18 20:40:55,031 - INFO - OC Distribution Statistics: +2025-04-18 20:40:55,031 - INFO - Validation Mean: 16.1281 +2025-04-18 20:40:55,031 - INFO - Validation Std: 6.9612 +2025-04-18 20:40:55,031 - INFO - Training Mean: 23.1731 +2025-04-18 20:40:55,031 - INFO - Training Std: 20.4068 +2025-04-18 20:40:55,031 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 20:40:55,031 - INFO - KS Statistic: 0.2280 +2025-04-18 20:40:55,031 - INFO - P-value: 0.0000 +2025-04-18 20:40:55,031 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 20:40:55,031 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:16:38,117 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:16:38,117 - INFO - Timestamp: 2025-04-18 21:16:38 +2025-04-18 21:16:38,117 - INFO - Validation set size: 1385 (8.39%) +2025-04-18 21:16:38,117 - INFO - Training set size: 15129 (91.61%) +2025-04-18 21:16:38,118 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:16:38,118 - INFO - Mean: 66.9400 +2025-04-18 21:16:38,118 - INFO - Median: 5.0148 +2025-04-18 21:16:38,118 - INFO - Min: 1.0321 +2025-04-18 21:16:38,118 - INFO - Max: 706.9584 +2025-04-18 21:16:38,118 - INFO - Std: 111.6080 +2025-04-18 21:16:38,118 - INFO - OC Distribution Statistics: +2025-04-18 21:16:38,118 - INFO - Validation Mean: 16.2507 +2025-04-18 21:16:38,118 - INFO - Validation Std: 7.3516 +2025-04-18 21:16:38,118 - INFO - Training Mean: 23.1013 +2025-04-18 21:16:38,118 - INFO - Training Std: 20.3308 +2025-04-18 21:16:38,118 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:16:38,118 - INFO - KS Statistic: 0.2288 +2025-04-18 21:16:38,118 - INFO - P-value: 0.0000 +2025-04-18 21:16:38,118 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:16:38,118 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:16:38,397 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:16:38,397 - INFO - Timestamp: 2025-04-18 21:16:38 +2025-04-18 21:16:38,397 - INFO - Validation set size: 1464 (8.87%) +2025-04-18 21:16:38,397 - INFO - Training set size: 15050 (91.13%) +2025-04-18 21:16:38,397 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:16:38,397 - INFO - Mean: 67.1233 +2025-04-18 21:16:38,397 - INFO - Median: 4.5217 +2025-04-18 21:16:38,397 - INFO - Min: 1.1342 +2025-04-18 21:16:38,397 - INFO - Max: 706.9584 +2025-04-18 21:16:38,397 - INFO - Std: 108.5857 +2025-04-18 21:16:38,397 - INFO - OC Distribution Statistics: +2025-04-18 21:16:38,397 - INFO - Validation Mean: 16.6321 +2025-04-18 21:16:38,397 - INFO - Validation Std: 8.0841 +2025-04-18 21:16:38,397 - INFO - Training Mean: 23.1002 +2025-04-18 21:16:38,397 - INFO - Training Std: 20.3562 +2025-04-18 21:16:38,397 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:16:38,397 - INFO - KS Statistic: 0.2130 +2025-04-18 21:16:38,397 - INFO - P-value: 0.0000 +2025-04-18 21:16:38,397 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:16:38,397 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:16:38,482 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:16:38,482 - INFO - Timestamp: 2025-04-18 21:16:38 +2025-04-18 21:16:38,482 - INFO - Validation set size: 1511 (9.15%) +2025-04-18 21:16:38,482 - INFO - Training set size: 15003 (90.85%) +2025-04-18 21:16:38,482 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:16:38,482 - INFO - Mean: 65.8284 +2025-04-18 21:16:38,482 - INFO - Median: 4.6741 +2025-04-18 21:16:38,482 - INFO - Min: 0.7391 +2025-04-18 21:16:38,483 - INFO - Max: 553.9996 +2025-04-18 21:16:38,483 - INFO - Std: 106.5957 +2025-04-18 21:16:38,483 - INFO - OC Distribution Statistics: +2025-04-18 21:16:38,483 - INFO - Validation Mean: 16.2341 +2025-04-18 21:16:38,483 - INFO - Validation Std: 7.8874 +2025-04-18 21:16:38,483 - INFO - Training Mean: 23.1605 +2025-04-18 21:16:38,483 - INFO - Training Std: 20.3744 +2025-04-18 21:16:38,483 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:16:38,483 - INFO - KS Statistic: 0.2280 +2025-04-18 21:16:38,483 - INFO - P-value: 0.0000 +2025-04-18 21:16:38,483 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:16:38,483 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:16:41,427 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:16:41,427 - INFO - Timestamp: 2025-04-18 21:16:41 +2025-04-18 21:16:41,427 - INFO - Validation set size: 1399 (8.47%) +2025-04-18 21:16:41,427 - INFO - Training set size: 15115 (91.53%) +2025-04-18 21:16:41,427 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:16:41,427 - INFO - Mean: 70.5918 +2025-04-18 21:16:41,427 - INFO - Median: 5.5898 +2025-04-18 21:16:41,427 - INFO - Min: 1.4069 +2025-04-18 21:16:41,427 - INFO - Max: 743.7101 +2025-04-18 21:16:41,428 - INFO - Std: 117.4564 +2025-04-18 21:16:41,428 - INFO - OC Distribution Statistics: +2025-04-18 21:16:41,428 - INFO - Validation Mean: 16.7433 +2025-04-18 21:16:41,428 - INFO - Validation Std: 7.9768 +2025-04-18 21:16:41,428 - INFO - Training Mean: 23.0621 +2025-04-18 21:16:41,428 - INFO - Training Std: 20.3309 +2025-04-18 21:16:41,428 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:16:41,428 - INFO - KS Statistic: 0.1807 +2025-04-18 21:16:41,428 - INFO - P-value: 0.0000 +2025-04-18 21:16:41,428 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:16:41,428 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:26:34,193 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:26:34,193 - INFO - Timestamp: 2025-04-18 21:26:34 +2025-04-18 21:26:34,193 - INFO - Validation set size: 1350 (8.17%) +2025-04-18 21:26:34,193 - INFO - Training set size: 15164 (91.83%) +2025-04-18 21:26:34,193 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:26:34,193 - INFO - Mean: 63.1505 +2025-04-18 21:26:34,193 - INFO - Median: 4.7243 +2025-04-18 21:26:34,193 - INFO - Min: 0.8728 +2025-04-18 21:26:34,193 - INFO - Max: 917.1429 +2025-04-18 21:26:34,193 - INFO - Std: 103.7537 +2025-04-18 21:26:34,193 - INFO - OC Distribution Statistics: +2025-04-18 21:26:34,193 - INFO - Validation Mean: 16.2188 +2025-04-18 21:26:34,193 - INFO - Validation Std: 7.0033 +2025-04-18 21:26:34,193 - INFO - Training Mean: 23.0884 +2025-04-18 21:26:34,193 - INFO - Training Std: 20.3231 +2025-04-18 21:26:34,193 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:26:34,193 - INFO - KS Statistic: 0.2162 +2025-04-18 21:26:34,193 - INFO - P-value: 0.0000 +2025-04-18 21:26:34,193 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:26:34,193 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:26:34,205 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:26:34,205 - INFO - Timestamp: 2025-04-18 21:26:34 +2025-04-18 21:26:34,205 - INFO - Validation set size: 1392 (8.43%) +2025-04-18 21:26:34,205 - INFO - Training set size: 15122 (91.57%) +2025-04-18 21:26:34,205 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:26:34,205 - INFO - Mean: 75.0230 +2025-04-18 21:26:34,205 - INFO - Median: 5.1783 +2025-04-18 21:26:34,205 - INFO - Min: 1.4390 +2025-04-18 21:26:34,205 - INFO - Max: 716.7388 +2025-04-18 21:26:34,205 - INFO - Std: 121.4301 +2025-04-18 21:26:34,205 - INFO - OC Distribution Statistics: +2025-04-18 21:26:34,205 - INFO - Validation Mean: 16.8606 +2025-04-18 21:26:34,205 - INFO - Validation Std: 8.7714 +2025-04-18 21:26:34,205 - INFO - Training Mean: 23.0484 +2025-04-18 21:26:34,206 - INFO - Training Std: 20.3005 +2025-04-18 21:26:34,206 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:26:34,206 - INFO - KS Statistic: 0.2125 +2025-04-18 21:26:34,206 - INFO - P-value: 0.0000 +2025-04-18 21:26:34,206 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:26:34,206 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:26:34,489 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:26:34,489 - INFO - Timestamp: 2025-04-18 21:26:34 +2025-04-18 21:26:34,489 - INFO - Validation set size: 1634 (9.89%) +2025-04-18 21:26:34,489 - INFO - Training set size: 14880 (90.11%) +2025-04-18 21:26:34,489 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:26:34,489 - INFO - Mean: 66.2423 +2025-04-18 21:26:34,489 - INFO - Median: 4.2506 +2025-04-18 21:26:34,489 - INFO - Min: 0.2726 +2025-04-18 21:26:34,489 - INFO - Max: 706.9584 +2025-04-18 21:26:34,490 - INFO - Std: 114.9774 +2025-04-18 21:26:34,490 - INFO - OC Distribution Statistics: +2025-04-18 21:26:34,490 - INFO - Validation Mean: 16.5479 +2025-04-18 21:26:34,490 - INFO - Validation Std: 7.7213 +2025-04-18 21:26:34,490 - INFO - Training Mean: 23.1833 +2025-04-18 21:26:34,490 - INFO - Training Std: 20.4545 +2025-04-18 21:26:34,490 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:26:34,490 - INFO - KS Statistic: 0.2105 +2025-04-18 21:26:34,490 - INFO - P-value: 0.0000 +2025-04-18 21:26:34,490 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:26:34,490 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:26:37,616 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:26:37,617 - INFO - Timestamp: 2025-04-18 21:26:37 +2025-04-18 21:26:37,617 - INFO - Validation set size: 1386 (8.39%) +2025-04-18 21:26:37,617 - INFO - Training set size: 15128 (91.61%) +2025-04-18 21:26:37,617 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:26:37,617 - INFO - Mean: 76.4381 +2025-04-18 21:26:37,617 - INFO - Median: 4.9468 +2025-04-18 21:26:37,617 - INFO - Min: 1.4498 +2025-04-18 21:26:37,617 - INFO - Max: 743.7101 +2025-04-18 21:26:37,617 - INFO - Std: 127.3312 +2025-04-18 21:26:37,617 - INFO - OC Distribution Statistics: +2025-04-18 21:26:37,617 - INFO - Validation Mean: 16.4723 +2025-04-18 21:26:37,617 - INFO - Validation Std: 7.9569 +2025-04-18 21:26:37,617 - INFO - Training Mean: 23.0815 +2025-04-18 21:26:37,617 - INFO - Training Std: 20.3172 +2025-04-18 21:26:37,617 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:26:37,617 - INFO - KS Statistic: 0.2024 +2025-04-18 21:26:37,617 - INFO - P-value: 0.0000 +2025-04-18 21:26:37,617 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:26:37,617 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:46:36,298 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:46:36,299 - INFO - Timestamp: 2025-04-18 21:46:36 +2025-04-18 21:46:36,299 - INFO - Validation set size: 1361 (8.24%) +2025-04-18 21:46:36,299 - INFO - Training set size: 15153 (91.76%) +2025-04-18 21:46:36,299 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:46:36,299 - INFO - Mean: 67.0895 +2025-04-18 21:46:36,299 - INFO - Median: 4.2711 +2025-04-18 21:46:36,299 - INFO - Min: 0.8526 +2025-04-18 21:46:36,299 - INFO - Max: 772.2996 +2025-04-18 21:46:36,299 - INFO - Std: 113.3802 +2025-04-18 21:46:36,299 - INFO - OC Distribution Statistics: +2025-04-18 21:46:36,299 - INFO - Validation Mean: 16.8145 +2025-04-18 21:46:36,299 - INFO - Validation Std: 9.1466 +2025-04-18 21:46:36,299 - INFO - Training Mean: 23.0398 +2025-04-18 21:46:36,299 - INFO - Training Std: 20.2694 +2025-04-18 21:46:36,299 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:46:36,299 - INFO - KS Statistic: 0.2045 +2025-04-18 21:46:36,299 - INFO - P-value: 0.0000 +2025-04-18 21:46:36,299 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:46:36,299 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:46:36,321 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:46:36,321 - INFO - Timestamp: 2025-04-18 21:46:36 +2025-04-18 21:46:36,321 - INFO - Validation set size: 1371 (8.30%) +2025-04-18 21:46:36,321 - INFO - Training set size: 15143 (91.70%) +2025-04-18 21:46:36,321 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:46:36,321 - INFO - Mean: 69.4529 +2025-04-18 21:46:36,321 - INFO - Median: 4.4778 +2025-04-18 21:46:36,321 - INFO - Min: 1.4390 +2025-04-18 21:46:36,321 - INFO - Max: 655.9249 +2025-04-18 21:46:36,321 - INFO - Std: 109.2851 +2025-04-18 21:46:36,321 - INFO - OC Distribution Statistics: +2025-04-18 21:46:36,321 - INFO - Validation Mean: 16.3454 +2025-04-18 21:46:36,321 - INFO - Validation Std: 6.8884 +2025-04-18 21:46:36,321 - INFO - Training Mean: 23.0864 +2025-04-18 21:46:36,321 - INFO - Training Std: 20.3413 +2025-04-18 21:46:36,321 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:46:36,321 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:46:36,321 - INFO - KS Statistic: 0.2030 +2025-04-18 21:46:36,321 - INFO - Timestamp: 2025-04-18 21:46:36 +2025-04-18 21:46:36,322 - INFO - P-value: 0.0000 +2025-04-18 21:46:36,322 - INFO - Validation set size: 1373 (8.31%) +2025-04-18 21:46:36,322 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:46:36,322 - INFO - Training set size: 15141 (91.69%) +2025-04-18 21:46:36,322 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:46:36,322 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:46:36,322 - INFO - Mean: 63.9778 +2025-04-18 21:46:36,322 - INFO - Median: 4.1787 +2025-04-18 21:46:36,322 - INFO - Min: 0.8608 +2025-04-18 21:46:36,322 - INFO - Max: 706.9584 +2025-04-18 21:46:36,322 - INFO - Std: 111.6995 +2025-04-18 21:46:36,322 - INFO - OC Distribution Statistics: +2025-04-18 21:46:36,322 - INFO - Validation Mean: 16.0209 +2025-04-18 21:46:36,322 - INFO - Validation Std: 6.6049 +2025-04-18 21:46:36,322 - INFO - Training Mean: 23.1167 +2025-04-18 21:46:36,322 - INFO - Training Std: 20.3408 +2025-04-18 21:46:36,322 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:46:36,322 - INFO - KS Statistic: 0.2347 +2025-04-18 21:46:36,322 - INFO - P-value: 0.0000 +2025-04-18 21:46:36,322 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:46:36,322 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:46:37,987 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:46:37,987 - INFO - Timestamp: 2025-04-18 21:46:37 +2025-04-18 21:46:37,987 - INFO - Validation set size: 1400 (8.48%) +2025-04-18 21:46:37,987 - INFO - Training set size: 15114 (91.52%) +2025-04-18 21:46:37,987 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:46:37,987 - INFO - Mean: 66.6408 +2025-04-18 21:46:37,987 - INFO - Median: 5.2985 +2025-04-18 21:46:37,987 - INFO - Min: 0.8124 +2025-04-18 21:46:37,987 - INFO - Max: 702.5656 +2025-04-18 21:46:37,987 - INFO - Std: 105.8343 +2025-04-18 21:46:37,987 - INFO - OC Distribution Statistics: +2025-04-18 21:46:37,987 - INFO - Validation Mean: 16.5708 +2025-04-18 21:46:37,987 - INFO - Validation Std: 7.7640 +2025-04-18 21:46:37,987 - INFO - Training Mean: 23.0785 +2025-04-18 21:46:37,987 - INFO - Training Std: 20.3340 +2025-04-18 21:46:37,987 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:46:37,987 - INFO - KS Statistic: 0.2162 +2025-04-18 21:46:37,987 - INFO - P-value: 0.0000 +2025-04-18 21:46:37,987 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:46:37,987 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:49:33,485 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:49:33,485 - INFO - Timestamp: 2025-04-18 21:49:33 +2025-04-18 21:49:33,485 - INFO - Validation set size: 1327 (8.04%) +2025-04-18 21:49:33,485 - INFO - Training set size: 15187 (91.96%) +2025-04-18 21:49:33,485 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:49:33,485 - INFO - Mean: 70.4556 +2025-04-18 21:49:33,485 - INFO - Median: 6.0896 +2025-04-18 21:49:33,485 - INFO - Min: 1.1496 +2025-04-18 21:49:33,485 - INFO - Max: 743.7101 +2025-04-18 21:49:33,485 - INFO - Std: 109.8304 +2025-04-18 21:49:33,485 - INFO - OC Distribution Statistics: +2025-04-18 21:49:33,485 - INFO - Validation Mean: 16.4694 +2025-04-18 21:49:33,485 - INFO - Validation Std: 7.4702 +2025-04-18 21:49:33,485 - INFO - Training Mean: 23.0561 +2025-04-18 21:49:33,485 - INFO - Training Std: 20.3040 +2025-04-18 21:49:33,485 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:49:33,485 - INFO - KS Statistic: 0.1978 +2025-04-18 21:49:33,485 - INFO - P-value: 0.0000 +2025-04-18 21:49:33,485 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:49:33,485 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:49:33,623 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:49:33,623 - INFO - Timestamp: 2025-04-18 21:49:33 +2025-04-18 21:49:33,623 - INFO - Validation set size: 1626 (9.85%) +2025-04-18 21:49:33,623 - INFO - Training set size: 14888 (90.15%) +2025-04-18 21:49:33,623 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:49:33,623 - INFO - Mean: 63.3688 +2025-04-18 21:49:33,623 - INFO - Median: 3.9730 +2025-04-18 21:49:33,623 - INFO - Min: 1.4069 +2025-04-18 21:49:33,623 - INFO - Max: 917.1429 +2025-04-18 21:49:33,623 - INFO - Std: 113.0496 +2025-04-18 21:49:33,623 - INFO - OC Distribution Statistics: +2025-04-18 21:49:33,623 - INFO - Validation Mean: 16.3212 +2025-04-18 21:49:33,623 - INFO - Validation Std: 7.9009 +2025-04-18 21:49:33,623 - INFO - Training Mean: 23.2045 +2025-04-18 21:49:33,623 - INFO - Training Std: 20.4347 +2025-04-18 21:49:33,623 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:49:33,623 - INFO - KS Statistic: 0.2200 +2025-04-18 21:49:33,623 - INFO - P-value: 0.0000 +2025-04-18 21:49:33,623 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:49:33,624 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:49:33,847 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:49:33,847 - INFO - Timestamp: 2025-04-18 21:49:33 +2025-04-18 21:49:33,847 - INFO - Validation set size: 1493 (9.04%) +2025-04-18 21:49:33,847 - INFO - Training set size: 15021 (90.96%) +2025-04-18 21:49:33,847 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:49:33,847 - INFO - Mean: 71.0321 +2025-04-18 21:49:33,847 - INFO - Median: 4.2475 +2025-04-18 21:49:33,847 - INFO - Min: 1.0176 +2025-04-18 21:49:33,847 - INFO - Max: 888.3558 +2025-04-18 21:49:33,847 - INFO - Std: 123.9143 +2025-04-18 21:49:33,848 - INFO - OC Distribution Statistics: +2025-04-18 21:49:33,848 - INFO - Validation Mean: 16.3366 +2025-04-18 21:49:33,848 - INFO - Validation Std: 7.4742 +2025-04-18 21:49:33,848 - INFO - Training Mean: 23.1421 +2025-04-18 21:49:33,848 - INFO - Training Std: 20.3844 +2025-04-18 21:49:33,848 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:49:33,848 - INFO - KS Statistic: 0.2207 +2025-04-18 21:49:33,848 - INFO - P-value: 0.0000 +2025-04-18 21:49:33,848 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:49:33,848 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:49:38,254 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:49:38,254 - INFO - Timestamp: 2025-04-18 21:49:38 +2025-04-18 21:49:38,254 - INFO - Validation set size: 1348 (8.16%) +2025-04-18 21:49:38,254 - INFO - Training set size: 15166 (91.84%) +2025-04-18 21:49:38,254 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:49:38,254 - INFO - Mean: 72.6235 +2025-04-18 21:49:38,254 - INFO - Median: 5.4365 +2025-04-18 21:49:38,254 - INFO - Min: 1.0321 +2025-04-18 21:49:38,254 - INFO - Max: 705.4078 +2025-04-18 21:49:38,254 - INFO - Std: 109.9102 +2025-04-18 21:49:38,254 - INFO - OC Distribution Statistics: +2025-04-18 21:49:38,254 - INFO - Validation Mean: 16.8375 +2025-04-18 21:49:38,254 - INFO - Validation Std: 7.8924 +2025-04-18 21:49:38,254 - INFO - Training Mean: 23.0325 +2025-04-18 21:49:38,254 - INFO - Training Std: 20.3107 +2025-04-18 21:49:38,254 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:49:38,254 - INFO - KS Statistic: 0.1996 +2025-04-18 21:49:38,254 - INFO - P-value: 0.0000 +2025-04-18 21:49:38,254 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:49:38,254 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:50:37,565 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:50:37,565 - INFO - Timestamp: 2025-04-18 22:50:37 +2025-04-18 22:50:37,565 - INFO - Validation set size: 1350 (8.17%) +2025-04-18 22:50:37,565 - INFO - Training set size: 15164 (91.83%) +2025-04-18 22:50:37,565 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:50:37,565 - INFO - Mean: 73.4987 +2025-04-18 22:50:37,565 - INFO - Median: 6.7121 +2025-04-18 22:50:37,565 - INFO - Min: 1.4498 +2025-04-18 22:50:37,565 - INFO - Max: 824.9624 +2025-04-18 22:50:37,565 - INFO - Std: 126.9345 +2025-04-18 22:50:37,565 - INFO - OC Distribution Statistics: +2025-04-18 22:50:37,565 - INFO - Validation Mean: 16.6360 +2025-04-18 22:50:37,565 - INFO - Validation Std: 8.5103 +2025-04-18 22:50:37,565 - INFO - Training Mean: 23.0512 +2025-04-18 22:50:37,565 - INFO - Training Std: 20.2840 +2025-04-18 22:50:37,565 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:50:37,565 - INFO - KS Statistic: 0.2141 +2025-04-18 22:50:37,566 - INFO - P-value: 0.0000 +2025-04-18 22:50:37,566 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:50:37,566 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:50:37,811 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:50:37,811 - INFO - Timestamp: 2025-04-18 22:50:37 +2025-04-18 22:50:37,811 - INFO - Validation set size: 1539 (9.32%) +2025-04-18 22:50:37,812 - INFO - Training set size: 14975 (90.68%) +2025-04-18 22:50:37,812 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:50:37,812 - INFO - Mean: 70.0543 +2025-04-18 22:50:37,812 - INFO - Median: 4.5729 +2025-04-18 22:50:37,812 - INFO - Min: 0.8728 +2025-04-18 22:50:37,812 - INFO - Max: 888.3558 +2025-04-18 22:50:37,812 - INFO - Std: 121.9694 +2025-04-18 22:50:37,812 - INFO - OC Distribution Statistics: +2025-04-18 22:50:37,812 - INFO - Validation Mean: 16.3785 +2025-04-18 22:50:37,812 - INFO - Validation Std: 7.8748 +2025-04-18 22:50:37,812 - INFO - Training Mean: 23.1586 +2025-04-18 22:50:37,812 - INFO - Training Std: 20.3939 +2025-04-18 22:50:37,812 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:50:37,812 - INFO - KS Statistic: 0.2272 +2025-04-18 22:50:37,812 - INFO - P-value: 0.0000 +2025-04-18 22:50:37,812 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:50:37,812 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:50:37,813 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:50:37,813 - INFO - Timestamp: 2025-04-18 22:50:37 +2025-04-18 22:50:37,813 - INFO - Validation set size: 1475 (8.93%) +2025-04-18 22:50:37,813 - INFO - Training set size: 15039 (91.07%) +2025-04-18 22:50:37,813 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:50:37,813 - INFO - Mean: 67.6643 +2025-04-18 22:50:37,813 - INFO - Median: 5.1703 +2025-04-18 22:50:37,813 - INFO - Min: 1.4084 +2025-04-18 22:50:37,813 - INFO - Max: 706.9584 +2025-04-18 22:50:37,813 - INFO - Std: 108.3090 +2025-04-18 22:50:37,813 - INFO - OC Distribution Statistics: +2025-04-18 22:50:37,813 - INFO - Validation Mean: 16.5023 +2025-04-18 22:50:37,813 - INFO - Validation Std: 7.7395 +2025-04-18 22:50:37,813 - INFO - Training Mean: 23.1177 +2025-04-18 22:50:37,813 - INFO - Training Std: 20.3708 +2025-04-18 22:50:37,813 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:50:37,813 - INFO - KS Statistic: 0.2107 +2025-04-18 22:50:37,813 - INFO - P-value: 0.0000 +2025-04-18 22:50:37,813 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:50:37,813 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:50:40,606 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:50:40,606 - INFO - Timestamp: 2025-04-18 22:50:40 +2025-04-18 22:50:40,606 - INFO - Validation set size: 1375 (8.33%) +2025-04-18 22:50:40,606 - INFO - Training set size: 15139 (91.67%) +2025-04-18 22:50:40,606 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:50:40,606 - INFO - Mean: 73.5805 +2025-04-18 22:50:40,606 - INFO - Median: 5.9386 +2025-04-18 22:50:40,606 - INFO - Min: 0.8301 +2025-04-18 22:50:40,606 - INFO - Max: 862.7749 +2025-04-18 22:50:40,607 - INFO - Std: 119.7348 +2025-04-18 22:50:40,607 - INFO - OC Distribution Statistics: +2025-04-18 22:50:40,607 - INFO - Validation Mean: 16.5214 +2025-04-18 22:50:40,607 - INFO - Validation Std: 7.7762 +2025-04-18 22:50:40,607 - INFO - Training Mean: 23.0722 +2025-04-18 22:50:40,607 - INFO - Training Std: 20.3195 +2025-04-18 22:50:40,607 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:50:40,607 - INFO - KS Statistic: 0.2048 +2025-04-18 22:50:40,607 - INFO - P-value: 0.0000 +2025-04-18 22:50:40,607 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:50:40,607 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 23:55:55,979 - INFO - Validation and Training Set Split Statistics +2025-04-18 23:55:55,979 - INFO - Timestamp: 2025-04-18 23:55:55 +2025-04-18 23:55:55,979 - INFO - Validation set size: 1359 (8.23%) +2025-04-18 23:55:55,979 - INFO - Training set size: 15155 (91.77%) +2025-04-18 23:55:55,979 - INFO - Minimum Distance Statistics (km): +2025-04-18 23:55:55,980 - INFO - Mean: 75.0450 +2025-04-18 23:55:55,980 - INFO - Median: 4.2506 +2025-04-18 23:55:55,980 - INFO - Min: 0.4760 +2025-04-18 23:55:55,980 - INFO - Max: 743.7101 +2025-04-18 23:55:55,980 - INFO - Std: 130.3314 +2025-04-18 23:55:55,980 - INFO - OC Distribution Statistics: +2025-04-18 23:55:55,980 - INFO - Validation Mean: 16.1754 +2025-04-18 23:55:55,980 - INFO - Validation Std: 7.6564 +2025-04-18 23:55:55,980 - INFO - Training Mean: 23.0963 +2025-04-18 23:55:55,980 - INFO - Training Std: 20.3052 +2025-04-18 23:55:55,980 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 23:55:55,980 - INFO - KS Statistic: 0.2427 +2025-04-18 23:55:55,980 - INFO - P-value: 0.0000 +2025-04-18 23:55:55,980 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 23:55:55,980 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 23:55:56,271 - INFO - Validation and Training Set Split Statistics +2025-04-18 23:55:56,271 - INFO - Timestamp: 2025-04-18 23:55:56 +2025-04-18 23:55:56,271 - INFO - Validation set size: 1497 (9.07%) +2025-04-18 23:55:56,271 - INFO - Training set size: 15017 (90.93%) +2025-04-18 23:55:56,271 - INFO - Minimum Distance Statistics (km): +2025-04-18 23:55:56,271 - INFO - Mean: 64.3923 +2025-04-18 23:55:56,271 - INFO - Median: 4.6106 +2025-04-18 23:55:56,271 - INFO - Min: 1.0513 +2025-04-18 23:55:56,271 - INFO - Max: 706.9584 +2025-04-18 23:55:56,271 - INFO - Std: 109.2621 +2025-04-18 23:55:56,271 - INFO - OC Distribution Statistics: +2025-04-18 23:55:56,271 - INFO - Validation Mean: 16.4310 +2025-04-18 23:55:56,272 - INFO - Validation Std: 8.3065 +2025-04-18 23:55:56,272 - INFO - Training Mean: 23.1345 +2025-04-18 23:55:56,272 - INFO - Training Std: 20.3574 +2025-04-18 23:55:56,272 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 23:55:56,272 - INFO - KS Statistic: 0.2210 +2025-04-18 23:55:56,272 - INFO - P-value: 0.0000 +2025-04-18 23:55:56,272 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 23:55:56,272 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 23:55:56,297 - INFO - Validation and Training Set Split Statistics +2025-04-18 23:55:56,297 - INFO - Timestamp: 2025-04-18 23:55:56 +2025-04-18 23:55:56,297 - INFO - Validation set size: 1627 (9.85%) +2025-04-18 23:55:56,297 - INFO - Training set size: 14887 (90.15%) +2025-04-18 23:55:56,297 - INFO - Minimum Distance Statistics (km): +2025-04-18 23:55:56,297 - INFO - Mean: 65.5985 +2025-04-18 23:55:56,297 - INFO - Median: 4.3609 +2025-04-18 23:55:56,297 - INFO - Min: 0.9118 +2025-04-18 23:55:56,297 - INFO - Max: 917.1429 +2025-04-18 23:55:56,297 - INFO - Std: 112.4981 +2025-04-18 23:55:56,297 - INFO - OC Distribution Statistics: +2025-04-18 23:55:56,297 - INFO - Validation Mean: 16.7269 +2025-04-18 23:55:56,298 - INFO - Validation Std: 7.7510 +2025-04-18 23:55:56,298 - INFO - Training Mean: 23.1606 +2025-04-18 23:55:56,298 - INFO - Training Std: 20.4559 +2025-04-18 23:55:56,298 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 23:55:56,298 - INFO - KS Statistic: 0.2058 +2025-04-18 23:55:56,298 - INFO - P-value: 0.0000 +2025-04-18 23:55:56,298 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 23:55:56,298 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 23:55:59,507 - INFO - Validation and Training Set Split Statistics +2025-04-18 23:55:59,507 - INFO - Timestamp: 2025-04-18 23:55:59 +2025-04-18 23:55:59,507 - INFO - Validation set size: 1560 (9.45%) +2025-04-18 23:55:59,507 - INFO - Training set size: 14954 (90.55%) +2025-04-18 23:55:59,507 - INFO - Minimum Distance Statistics (km): +2025-04-18 23:55:59,507 - INFO - Mean: 66.0324 +2025-04-18 23:55:59,507 - INFO - Median: 4.2696 +2025-04-18 23:55:59,507 - INFO - Min: 0.2726 +2025-04-18 23:55:59,507 - INFO - Max: 875.2369 +2025-04-18 23:55:59,507 - INFO - Std: 118.1101 +2025-04-18 23:55:59,507 - INFO - OC Distribution Statistics: +2025-04-18 23:55:59,507 - INFO - Validation Mean: 16.1742 +2025-04-18 23:55:59,507 - INFO - Validation Std: 7.3406 +2025-04-18 23:55:59,507 - INFO - Training Mean: 23.1895 +2025-04-18 23:55:59,507 - INFO - Training Std: 20.4180 +2025-04-18 23:55:59,507 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 23:55:59,507 - INFO - KS Statistic: 0.2303 +2025-04-18 23:55:59,507 - INFO - P-value: 0.0000 +2025-04-18 23:55:59,507 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 23:55:59,507 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 01:02:08,357 - INFO - Validation and Training Set Split Statistics +2025-04-19 01:02:08,357 - INFO - Timestamp: 2025-04-19 01:02:08 +2025-04-19 01:02:08,357 - INFO - Validation set size: 1348 (8.16%) +2025-04-19 01:02:08,357 - INFO - Training set size: 15166 (91.84%) +2025-04-19 01:02:08,357 - INFO - Minimum Distance Statistics (km): +2025-04-19 01:02:08,357 - INFO - Mean: 75.4306 +2025-04-19 01:02:08,357 - INFO - Median: 5.3998 +2025-04-19 01:02:08,357 - INFO - Min: 0.8728 +2025-04-19 01:02:08,357 - INFO - Max: 706.9584 +2025-04-19 01:02:08,357 - INFO - Std: 119.4003 +2025-04-19 01:02:08,357 - INFO - OC Distribution Statistics: +2025-04-19 01:02:08,357 - INFO - Validation Mean: 16.6775 +2025-04-19 01:02:08,357 - INFO - Validation Std: 8.3334 +2025-04-19 01:02:08,357 - INFO - Training Mean: 23.0467 +2025-04-19 01:02:08,357 - INFO - Training Std: 20.2907 +2025-04-19 01:02:08,357 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 01:02:08,357 - INFO - KS Statistic: 0.2157 +2025-04-19 01:02:08,357 - INFO - P-value: 0.0000 +2025-04-19 01:02:08,357 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 01:02:08,357 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 01:02:08,610 - INFO - Validation and Training Set Split Statistics +2025-04-19 01:02:08,611 - INFO - Timestamp: 2025-04-19 01:02:08 +2025-04-19 01:02:08,611 - INFO - Validation set size: 1553 (9.40%) +2025-04-19 01:02:08,611 - INFO - Training set size: 14961 (90.60%) +2025-04-19 01:02:08,611 - INFO - Minimum Distance Statistics (km): +2025-04-19 01:02:08,611 - INFO - Mean: 63.4477 +2025-04-19 01:02:08,611 - INFO - Median: 4.2506 +2025-04-19 01:02:08,611 - INFO - Min: 1.2301 +2025-04-19 01:02:08,611 - INFO - Max: 706.9584 +2025-04-19 01:02:08,611 - INFO - Std: 105.7616 +2025-04-19 01:02:08,611 - INFO - OC Distribution Statistics: +2025-04-19 01:02:08,611 - INFO - Validation Mean: 17.1074 +2025-04-19 01:02:08,611 - INFO - Validation Std: 8.8341 +2025-04-19 01:02:08,611 - INFO - Training Mean: 23.0893 +2025-04-19 01:02:08,611 - INFO - Training Std: 20.3838 +2025-04-19 01:02:08,611 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 01:02:08,611 - INFO - KS Statistic: 0.1942 +2025-04-19 01:02:08,611 - INFO - P-value: 0.0000 +2025-04-19 01:02:08,611 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 01:02:08,611 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 01:02:08,630 - INFO - Validation and Training Set Split Statistics +2025-04-19 01:02:08,630 - INFO - Timestamp: 2025-04-19 01:02:08 +2025-04-19 01:02:08,630 - INFO - Validation set size: 1558 (9.43%) +2025-04-19 01:02:08,630 - INFO - Training set size: 14956 (90.57%) +2025-04-19 01:02:08,630 - INFO - Minimum Distance Statistics (km): +2025-04-19 01:02:08,630 - INFO - Mean: 65.6344 +2025-04-19 01:02:08,630 - INFO - Median: 4.6658 +2025-04-19 01:02:08,630 - INFO - Min: 1.1596 +2025-04-19 01:02:08,630 - INFO - Max: 1106.1626 +2025-04-19 01:02:08,630 - INFO - Std: 121.6471 +2025-04-19 01:02:08,630 - INFO - OC Distribution Statistics: +2025-04-19 01:02:08,630 - INFO - Validation Mean: 16.3167 +2025-04-19 01:02:08,630 - INFO - Validation Std: 7.8312 +2025-04-19 01:02:08,630 - INFO - Training Mean: 23.1737 +2025-04-19 01:02:08,630 - INFO - Training Std: 20.4031 +2025-04-19 01:02:08,630 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 01:02:08,630 - INFO - KS Statistic: 0.2126 +2025-04-19 01:02:08,630 - INFO - P-value: 0.0000 +2025-04-19 01:02:08,630 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 01:02:08,630 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 01:02:11,669 - INFO - Validation and Training Set Split Statistics +2025-04-19 01:02:11,669 - INFO - Timestamp: 2025-04-19 01:02:11 +2025-04-19 01:02:11,669 - INFO - Validation set size: 1328 (8.04%) +2025-04-19 01:02:11,669 - INFO - Training set size: 15186 (91.96%) +2025-04-19 01:02:11,669 - INFO - Minimum Distance Statistics (km): +2025-04-19 01:02:11,669 - INFO - Mean: 74.4644 +2025-04-19 01:02:11,669 - INFO - Median: 5.1703 +2025-04-19 01:02:11,669 - INFO - Min: 1.0731 +2025-04-19 01:02:11,669 - INFO - Max: 835.9450 +2025-04-19 01:02:11,669 - INFO - Std: 123.6892 +2025-04-19 01:02:11,669 - INFO - OC Distribution Statistics: +2025-04-19 01:02:11,669 - INFO - Validation Mean: 16.3466 +2025-04-19 01:02:11,669 - INFO - Validation Std: 7.5143 +2025-04-19 01:02:11,669 - INFO - Training Mean: 23.0672 +2025-04-19 01:02:11,669 - INFO - Training Std: 20.2995 +2025-04-19 01:02:11,669 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 01:02:11,669 - INFO - KS Statistic: 0.2142 +2025-04-19 01:02:11,669 - INFO - P-value: 0.0000 +2025-04-19 01:02:11,669 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 01:02:11,669 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 02:10:08,953 - INFO - Validation and Training Set Split Statistics +2025-04-19 02:10:08,953 - INFO - Timestamp: 2025-04-19 02:10:08 +2025-04-19 02:10:08,953 - INFO - Validation set size: 1347 (8.16%) +2025-04-19 02:10:08,953 - INFO - Training set size: 15167 (91.84%) +2025-04-19 02:10:08,953 - INFO - Minimum Distance Statistics (km): +2025-04-19 02:10:08,953 - INFO - Mean: 80.8698 +2025-04-19 02:10:08,953 - INFO - Median: 7.3378 +2025-04-19 02:10:08,953 - INFO - Min: 1.4234 +2025-04-19 02:10:08,953 - INFO - Max: 862.7749 +2025-04-19 02:10:08,953 - INFO - Std: 124.8923 +2025-04-19 02:10:08,953 - INFO - OC Distribution Statistics: +2025-04-19 02:10:08,954 - INFO - Validation Mean: 16.8305 +2025-04-19 02:10:08,954 - INFO - Validation Std: 8.0516 +2025-04-19 02:10:08,954 - INFO - Training Mean: 23.0327 +2025-04-19 02:10:08,954 - INFO - Training Std: 20.3045 +2025-04-19 02:10:08,954 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 02:10:08,954 - INFO - KS Statistic: 0.2057 +2025-04-19 02:10:08,954 - INFO - P-value: 0.0000 +2025-04-19 02:10:08,954 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 02:10:08,954 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 02:10:09,259 - INFO - Validation and Training Set Split Statistics +2025-04-19 02:10:09,260 - INFO - Timestamp: 2025-04-19 02:10:09 +2025-04-19 02:10:09,260 - INFO - Validation set size: 1489 (9.02%) +2025-04-19 02:10:09,260 - INFO - Training set size: 15025 (90.98%) +2025-04-19 02:10:09,260 - INFO - Minimum Distance Statistics (km): +2025-04-19 02:10:09,260 - INFO - Mean: 66.4970 +2025-04-19 02:10:09,260 - INFO - Median: 4.5420 +2025-04-19 02:10:09,260 - INFO - Min: 1.0321 +2025-04-19 02:10:09,260 - INFO - Max: 702.5656 +2025-04-19 02:10:09,260 - INFO - Std: 111.0426 +2025-04-19 02:10:09,260 - INFO - OC Distribution Statistics: +2025-04-19 02:10:09,260 - INFO - Validation Mean: 17.2994 +2025-04-19 02:10:09,260 - INFO - Validation Std: 9.4379 +2025-04-19 02:10:09,260 - INFO - Training Mean: 23.0448 +2025-04-19 02:10:09,260 - INFO - Training Std: 20.3309 +2025-04-19 02:10:09,260 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 02:10:09,260 - INFO - KS Statistic: 0.1953 +2025-04-19 02:10:09,260 - INFO - P-value: 0.0000 +2025-04-19 02:10:09,260 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 02:10:09,260 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 02:10:09,287 - INFO - Validation and Training Set Split Statistics +2025-04-19 02:10:09,287 - INFO - Timestamp: 2025-04-19 02:10:09 +2025-04-19 02:10:09,287 - INFO - Validation set size: 1525 (9.23%) +2025-04-19 02:10:09,287 - INFO - Training set size: 14989 (90.77%) +2025-04-19 02:10:09,287 - INFO - Minimum Distance Statistics (km): +2025-04-19 02:10:09,287 - INFO - Mean: 65.6982 +2025-04-19 02:10:09,287 - INFO - Median: 4.5420 +2025-04-19 02:10:09,287 - INFO - Min: 1.1342 +2025-04-19 02:10:09,287 - INFO - Max: 875.2369 +2025-04-19 02:10:09,287 - INFO - Std: 111.1896 +2025-04-19 02:10:09,287 - INFO - OC Distribution Statistics: +2025-04-19 02:10:09,287 - INFO - Validation Mean: 16.6001 +2025-04-19 02:10:09,287 - INFO - Validation Std: 8.4145 +2025-04-19 02:10:09,287 - INFO - Training Mean: 23.1298 +2025-04-19 02:10:09,287 - INFO - Training Std: 20.3722 +2025-04-19 02:10:09,287 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 02:10:09,287 - INFO - KS Statistic: 0.2021 +2025-04-19 02:10:09,287 - INFO - P-value: 0.0000 +2025-04-19 02:10:09,287 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 02:10:09,287 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 02:10:12,799 - INFO - Validation and Training Set Split Statistics +2025-04-19 02:10:12,799 - INFO - Timestamp: 2025-04-19 02:10:12 +2025-04-19 02:10:12,799 - INFO - Validation set size: 1500 (9.08%) +2025-04-19 02:10:12,799 - INFO - Training set size: 15014 (90.92%) +2025-04-19 02:10:12,799 - INFO - Minimum Distance Statistics (km): +2025-04-19 02:10:12,799 - INFO - Mean: 68.9110 +2025-04-19 02:10:12,799 - INFO - Median: 4.4333 +2025-04-19 02:10:12,799 - INFO - Min: 0.6477 +2025-04-19 02:10:12,799 - INFO - Max: 1106.1626 +2025-04-19 02:10:12,799 - INFO - Std: 122.5878 +2025-04-19 02:10:12,799 - INFO - OC Distribution Statistics: +2025-04-19 02:10:12,799 - INFO - Validation Mean: 16.6652 +2025-04-19 02:10:12,799 - INFO - Validation Std: 8.2240 +2025-04-19 02:10:12,799 - INFO - Training Mean: 23.1124 +2025-04-19 02:10:12,799 - INFO - Training Std: 20.3698 +2025-04-19 02:10:12,800 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 02:10:12,800 - INFO - KS Statistic: 0.2100 +2025-04-19 02:10:12,800 - INFO - P-value: 0.0000 +2025-04-19 02:10:12,800 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 02:10:12,800 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} diff --git a/Archive/2DCNN_output/split_stats.txt b/Archive/2DCNN_output/split_stats.txt new file mode 100644 index 0000000000000000000000000000000000000000..25590216b6228cec346a7d37b83878040a72e555 --- /dev/null +++ b/Archive/2DCNN_output/split_stats.txt @@ -0,0 +1,20 @@ +Validation and Training Set Split Statistics +Timestamp: 2025-04-19 02:10:12 +Validation set size: 1500 (9.08%) +Training set size: 15014 (90.92%) +Minimum Distance Statistics (km): + Mean: 68.9110 + Median: 4.4333 + Min: 0.6477 + Max: 1106.1626 + Std: 122.5878 +OC Distribution Statistics: + Validation Mean: 16.6652 + Validation Std: 8.2240 + Training Mean: 23.1124 + Training Std: 20.3698 +Kolmogorov-Smirnov Test: + KS Statistic: 0.2100 + P-value: 0.0000 +Distribution Fit: Inverse Gamma +Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} \ No newline at end of file diff --git a/Archive/2DCNN_output/training_metrics_2dcnn_2fc53563.txt b/Archive/2DCNN_output/training_metrics_2dcnn_2fc53563.txt new file mode 100644 index 0000000000000000000000000000000000000000..3dbc71db05d9f5f976e9129127121d0c573b773e --- /dev/null +++ b/Archive/2DCNN_output/training_metrics_2dcnn_2fc53563.txt @@ -0,0 +1,93 @@ +Training Metrics and Configuration +================================================== + +Command Line Arguments: +------------------------------ +lr: 0.0001 +loss_type: mse +loss_alpha: 0.5 +target_transform: none +use_validation: True +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +use_gpu: True +distance_threshold: 1.2 +target_fraction: 0.75 +num_runs: 5 + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-3dcnn + Run Name: run_1 + Run ID: orxi6cd2 + +Run 2: + Project: socmapping-3dcnn + Run Name: run_2 + Run ID: e0kt1ey9 + +Run 3: + Project: socmapping-3dcnn + Run Name: run_3 + Run ID: t5w78np2 + +Run 4: + Project: socmapping-3dcnn + Run Name: run_4 + Run ID: n3i3eeyc + +Run 5: + Project: socmapping-3dcnn + Run Name: run_5 + Run ID: x98ynva9 + +Average Metrics Across Runs: +------------------------------ +train_loss: 248017.4175 +val_loss: 8138.4948 +correlation: -0.0238 +r_squared: 0.0063 +mse: 8187.3147 +rmse: 63.2010 +mae: 53.1739 +rpiq: 0.1408 +output_std: 35.7520 +target_std: 7.7157 + +Average Min Distance Statistics: +------------------------------ +avg_mean: 68.6176 +std_mean: 2.0287 +avg_median: 4.6139 +std_median: 0.3469 +avg_min: 0.9606 +std_min: 0.2373 +avg_max: 765.9144 +std_max: 54.6408 +avg_std: 115.0789 +std_std: 5.6506 + +Best Metrics Across Runs: +------------------------------ +r_squared: + Mean: 0.0159 + Std: 0.0095 + Values: ['0.0262', '0.0196', '0.0244', '0.0053', '0.0039'] + +mae: + Mean: 151.1558 + Std: 179.5241 + Values: ['106.2557', '25.8127', '10.3092', '500.5969', '112.8047'] + +rmse: + Mean: 161.3058 + Std: 178.2395 + Values: ['108.1145', '33.7113', '18.3430', '506.0625', '140.2975'] + +rpiq: + Mean: 0.1140 + Std: 0.1097 + Values: ['0.0453', '0.1780', '0.2998', '0.0101', '0.0365'] + diff --git a/Archive/2DCNN_output/training_metrics_2dcnn_d8ee39fd.txt b/Archive/2DCNN_output/training_metrics_2dcnn_d8ee39fd.txt new file mode 100644 index 0000000000000000000000000000000000000000..77746b4f4e6857cabca615e92d370388b9869e80 --- /dev/null +++ b/Archive/2DCNN_output/training_metrics_2dcnn_d8ee39fd.txt @@ -0,0 +1,93 @@ +Training Metrics and Configuration +================================================== + +Command Line Arguments: +------------------------------ +lr: 0.0001 +loss_type: composite_l2 +loss_alpha: 0.5 +target_transform: normalize +use_validation: True +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +use_gpu: True +distance_threshold: 1.2 +target_fraction: 0.75 +num_runs: 5 + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-3dcnn + Run Name: run_1 + Run ID: xfz0c862 + +Run 2: + Project: socmapping-3dcnn + Run Name: run_2 + Run ID: lotigc14 + +Run 3: + Project: socmapping-3dcnn + Run Name: run_3 + Run ID: 3d9rgxqa + +Run 4: + Project: socmapping-3dcnn + Run Name: run_4 + Run ID: u60w82ll + +Run 5: + Project: socmapping-3dcnn + Run Name: run_5 + Run ID: 722ajzn8 + +Average Metrics Across Runs: +------------------------------ +train_loss: 12282.9357 +val_loss: 85.4270 +correlation: -0.0078 +r_squared: 0.0014 +mse: 37698.5721 +rmse: 52.0181 +mae: 34.2225 +rpiq: 0.4996 +output_std: 38.1053 +target_std: 7.6048 + +Average Min Distance Statistics: +------------------------------ +avg_mean: 70.0261 +std_mean: 2.8291 +avg_median: 4.4696 +std_median: 0.1537 +avg_min: 1.0155 +std_min: 0.2142 +avg_max: 809.3605 +std_max: 86.1754 +avg_std: 117.2588 +std_std: 5.1429 + +Best Metrics Across Runs: +------------------------------ +r_squared: + Mean: 0.0041 + Std: 0.0024 + Values: ['0.0021', '0.0034', '0.0086', '0.0041', '0.0022'] + +mae: + Mean: 175.2670 + Std: 273.1694 + Values: ['8.7859', '8.9112', '137.2913', '712.4717', '8.8751'] + +rmse: + Mean: 291.8748 + Std: 448.1107 + Values: ['9.9293', '10.3324', '262.7961', '1166.4501', '9.8660'] + +rpiq: + Mean: 0.3313 + Std: 0.2648 + Values: ['0.4910', '0.5130', '0.0198', '0.0045', '0.6284'] + diff --git a/Archive/2DCNN_output/training_metrics_2dcnn_eb77f30c.txt b/Archive/2DCNN_output/training_metrics_2dcnn_eb77f30c.txt new file mode 100644 index 0000000000000000000000000000000000000000..4dc85c69124a286eb9427ec97666f8d6766c48b9 --- /dev/null +++ b/Archive/2DCNN_output/training_metrics_2dcnn_eb77f30c.txt @@ -0,0 +1,93 @@ +Training Metrics and Configuration +================================================== + +Command Line Arguments: +------------------------------ +lr: 0.0001 +loss_type: mse +loss_alpha: 0.5 +target_transform: none +use_validation: True +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +use_gpu: True +distance_threshold: 1.2 +target_fraction: 0.75 +num_runs: 5 + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-3dcnn + Run Name: run_1 + Run ID: eaubstsl + +Run 2: + Project: socmapping-3dcnn + Run Name: run_2 + Run ID: soete5x4 + +Run 3: + Project: socmapping-3dcnn + Run Name: run_3 + Run ID: 86p6lyox + +Run 4: + Project: socmapping-3dcnn + Run Name: run_4 + Run ID: d9rbfj8o + +Run 5: + Project: socmapping-3dcnn + Run Name: run_5 + Run ID: luq9nw28 + +Average Metrics Across Runs: +------------------------------ +train_loss: 80885.4460 +val_loss: 2598.8070 +correlation: -0.0212 +r_squared: 0.0032 +mse: 2554.5706 +rmse: 31.0612 +mae: 25.9229 +rpiq: 0.3595 +output_std: 15.6366 +target_std: 7.6585 + +Average Min Distance Statistics: +------------------------------ +avg_mean: 64.1301 +std_mean: 1.6269 +avg_median: 4.4935 +std_median: 0.3292 +avg_min: 0.9069 +std_min: 0.2735 +avg_max: 799.9916 +std_max: 51.4370 +avg_std: 111.4922 +std_std: 4.1222 + +Best Metrics Across Runs: +------------------------------ +r_squared: + Mean: 0.0112 + Std: 0.0095 + Values: ['0.0092', '0.0040', '0.0097', '0.0296', '0.0037'] + +mae: + Mean: 27.9371 + Std: 20.5006 + Values: ['6.2632', '66.9002', '21.9714', '24.9011', '19.6493'] + +rmse: + Mean: 36.5254 + Std: 20.4305 + Values: ['9.8742', '68.6032', '33.6278', '48.2257', '22.2962'] + +rpiq: + Mean: 0.2235 + Std: 0.1698 + Values: ['0.5469', '0.0743', '0.1576', '0.1099', '0.2287'] + diff --git a/Archive/2DCNN_output/training_summary_1a91a72f.txt b/Archive/2DCNN_output/training_summary_1a91a72f.txt new file mode 100644 index 0000000000000000000000000000000000000000..da94ddc5c0c94613741c8dcdd2b4178f9c636aa9 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_1a91a72f.txt @@ -0,0 +1,37 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 200 +loss_alpha: 0.8 +no_validation: True +loss_type: mse +target_transform: none +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 1 +project_name: socmapping-2DCNN +use_validation: False +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_87ce + ID: r8oxkczk + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 466.8532 +val_loss_avg: 0.0000 + +Validation was disabled. No validation metrics available. + diff --git a/Archive/2DCNN_output/training_summary_275c5dc4.txt b/Archive/2DCNN_output/training_summary_275c5dc4.txt new file mode 100644 index 0000000000000000000000000000000000000000..cfafedee987c71d849e561caadd38cb50e03a4c9 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_275c5dc4.txt @@ -0,0 +1,67 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0004 +epochs: 250 +loss_alpha: 0.8 +no_validation: False +loss_type: mse +target_transform: normalize +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 2 +project_name: socmapping-2DCNN +no_save: False +use_validation: True +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_e0bf + ID: tal62wyq + Best Val R²: N/A + +Run 2: + Project: socmapping-2dcnn + Name: run_2_cbea + ID: vdgixyar + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 8031.4759 +val_loss_avg: 8.9016 +val_r_squared: 0.0201 +val_rpiq: 0.3679 + +Average Min Distance Statistics (Validation Sets): +------------------------------ +avg_mean: 4.7899 +std_mean: 0.0000 +avg_median: 4.2255 +std_median: 0.0000 +avg_min: 0.3763 +std_min: 0.0000 +avg_max: 18.8528 +std_max: 0.0000 +avg_std: 2.8893 +std_std: 0.0000 + +Best Validation Metrics Aggregated Across Runs: +------------------------------ +val_r_squared: No finite values available. + +val_rmse: No finite values available. + +val_mae: No finite values available. + +val_rpiq: No finite values available. + diff --git a/Archive/2DCNN_output/training_summary_3040bb94.txt b/Archive/2DCNN_output/training_summary_3040bb94.txt new file mode 100644 index 0000000000000000000000000000000000000000..ea9c00e5f66d66e29959e26bb47b3a91e8e4d9d6 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_3040bb94.txt @@ -0,0 +1,38 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 70 +loss_alpha: 0.8 +no_validation: True +loss_type: mse +target_transform: none +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 1 +project_name: socmapping-2DCNN +no_save: False +use_validation: False +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_6e70 + ID: tv14bxzf + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 245.7013 +val_loss_avg: 0.0000 + +Validation was disabled. No validation metrics available. + diff --git a/Archive/2DCNN_output/training_summary_410ce729.txt b/Archive/2DCNN_output/training_summary_410ce729.txt new file mode 100644 index 0000000000000000000000000000000000000000..44f4db38d80e50b6721e2e19512138b42347aa89 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_410ce729.txt @@ -0,0 +1,37 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 200 +loss_alpha: 0.8 +no_validation: True +loss_type: mse +target_transform: normalize +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 1 +project_name: socmapping-2DCNN +use_validation: False +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_714d + ID: mzc58dg7 + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 0.3896 +val_loss_avg: 0.0000 + +Validation was disabled. No validation metrics available. + diff --git a/Archive/2DCNN_output/training_summary_488daa11.txt b/Archive/2DCNN_output/training_summary_488daa11.txt new file mode 100644 index 0000000000000000000000000000000000000000..2098e2a703a4a69312725fac88f4c0de436d5aac --- /dev/null +++ b/Archive/2DCNN_output/training_summary_488daa11.txt @@ -0,0 +1,38 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 16 +loss_alpha: 0.8 +no_validation: True +loss_type: mse +target_transform: log +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 1 +project_name: socmapping-2DCNN +no_save: False +use_validation: False +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_fc27 + ID: 594zvzo4 + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 0.6295 +val_loss_avg: 0.0000 + +Validation was disabled. No validation metrics available. + diff --git a/Archive/2DCNN_output/training_summary_585469a4.txt b/Archive/2DCNN_output/training_summary_585469a4.txt new file mode 100644 index 0000000000000000000000000000000000000000..b114be55885b79894a50d289cb97a3863580c0f8 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_585469a4.txt @@ -0,0 +1,38 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 120 +loss_alpha: 0.8 +no_validation: True +loss_type: mse +target_transform: none +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 1 +project_name: socmapping-2DCNN +no_save: False +use_validation: False +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_ae46 + ID: ysh9tqle + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 217.6991 +val_loss_avg: 0.0000 + +Validation was disabled. No validation metrics available. + diff --git a/Archive/2DCNN_output/training_summary_5932d813.txt b/Archive/2DCNN_output/training_summary_5932d813.txt new file mode 100644 index 0000000000000000000000000000000000000000..94feed5ff71fbc764c58b1593472e06f1157b9a0 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_5932d813.txt @@ -0,0 +1,38 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 120 +loss_alpha: 0.8 +no_validation: True +loss_type: composite_l2 +target_transform: none +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 1 +project_name: socmapping-2DCNN +no_save: False +use_validation: False +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_80b6 + ID: g2hu4zry + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 816.4915 +val_loss_avg: 0.0000 + +Validation was disabled. No validation metrics available. + diff --git a/Archive/2DCNN_output/training_summary_594e7e3c.txt b/Archive/2DCNN_output/training_summary_594e7e3c.txt new file mode 100644 index 0000000000000000000000000000000000000000..f6dd89311ef57b2efdfdc7dab516a8c778642a13 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_594e7e3c.txt @@ -0,0 +1,84 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 200 +loss_alpha: 0.8 +no_validation: False +loss_type: mse +target_transform: none +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 5 +project_name: socmapping-2DCNN +use_validation: True +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_c184 + ID: 3zqtwlg3 + Best Val R²: N/A + +Run 2: + Project: socmapping-2dcnn + Name: run_2_9ed4 + ID: kgccyllf + Best Val R²: N/A + +Run 3: + Project: socmapping-2dcnn + Name: run_3_ebf8 + ID: 8bnqqs4q + Best Val R²: N/A + +Run 4: + Project: socmapping-2dcnn + Name: run_4_330c + ID: xrl6bk46 + Best Val R²: N/A + +Run 5: + Project: socmapping-2dcnn + Name: run_5_21cb + ID: jgn5x40u + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 253.8041 +val_loss_avg: 102.6134 +val_r_squared: 0.2173 +val_rpiq: 0.6476 + +Average Min Distance Statistics (Validation Sets): +------------------------------ +avg_mean: 4.8890 +std_mean: 0.1459 +avg_median: 4.3088 +std_median: 0.1964 +avg_min: 0.3245 +std_min: 0.1283 +avg_max: 22.1431 +std_max: 4.5092 +avg_std: 2.9448 +std_std: 0.1947 + +Best Validation Metrics Aggregated Across Runs: +------------------------------ +val_r_squared: No finite values available. + +val_rmse: No finite values available. + +val_mae: No finite values available. + +val_rpiq: No finite values available. + diff --git a/Archive/2DCNN_output/training_summary_5ea43750.txt b/Archive/2DCNN_output/training_summary_5ea43750.txt new file mode 100644 index 0000000000000000000000000000000000000000..287e527754f71a99436ce2e47606ad8e544ed7e2 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_5ea43750.txt @@ -0,0 +1,37 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 200 +loss_alpha: 0.8 +no_validation: True +loss_type: mse +target_transform: none +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 1 +project_name: socmapping-2DCNN +use_validation: False +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_8bce + ID: w4t97nlg + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 172.7931 +val_loss_avg: 0.0000 + +Validation was disabled. No validation metrics available. + diff --git a/Archive/2DCNN_output/training_summary_691bac08.txt b/Archive/2DCNN_output/training_summary_691bac08.txt new file mode 100644 index 0000000000000000000000000000000000000000..29ff50726b616693ee8a5930afa458cd3fb3c495 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_691bac08.txt @@ -0,0 +1,85 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 16 +loss_alpha: 0.8 +no_validation: False +loss_type: composite_l2 +target_transform: log +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 5 +project_name: socmapping-2DCNN +no_save: False +use_validation: True +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_6902 + ID: 9gqhiye5 + Best Val R²: N/A + +Run 2: + Project: socmapping-2dcnn + Name: run_2_c76f + ID: ae3l8oph + Best Val R²: N/A + +Run 3: + Project: socmapping-2dcnn + Name: run_3_4ad7 + ID: xbjnyraw + Best Val R²: N/A + +Run 4: + Project: socmapping-2dcnn + Name: run_4_743c + ID: bseboa3j + Best Val R²: N/A + +Run 5: + Project: socmapping-2dcnn + Name: run_5_123a + ID: ykueipbt + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 1.3868 +val_loss_avg: 0.8525 +val_r_squared: 0.0723 +val_rpiq: 0.5652 + +Average Min Distance Statistics (Validation Sets): +------------------------------ +avg_mean: 4.7899 +std_mean: 0.0000 +avg_median: 4.2255 +std_median: 0.0000 +avg_min: 0.3763 +std_min: 0.0000 +avg_max: 18.8528 +std_max: 0.0000 +avg_std: 2.8893 +std_std: 0.0000 + +Best Validation Metrics Aggregated Across Runs: +------------------------------ +val_r_squared: No finite values available. + +val_rmse: No finite values available. + +val_mae: No finite values available. + +val_rpiq: No finite values available. + diff --git a/Archive/2DCNN_output/training_summary_7b4f5fa7.txt b/Archive/2DCNN_output/training_summary_7b4f5fa7.txt new file mode 100644 index 0000000000000000000000000000000000000000..3bd726a19a827494e456392101ff123242b48e2d --- /dev/null +++ b/Archive/2DCNN_output/training_summary_7b4f5fa7.txt @@ -0,0 +1,84 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 200 +loss_alpha: 0.8 +no_validation: False +loss_type: mse +target_transform: log +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 5 +project_name: socmapping-2DCNN +use_validation: True +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_63a0 + ID: 927misnr + Best Val R²: N/A + +Run 2: + Project: socmapping-2dcnn + Name: run_2_2114 + ID: nmyijryg + Best Val R²: N/A + +Run 3: + Project: socmapping-2dcnn + Name: run_3_f72d + ID: u5opzvrb + Best Val R²: N/A + +Run 4: + Project: socmapping-2dcnn + Name: run_4_dffb + ID: jobwdojz + Best Val R²: N/A + +Run 5: + Project: socmapping-2dcnn + Name: run_5_1cec + ID: 3rnt658r + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 0.4936 +val_loss_avg: 0.1699 +val_r_squared: 0.1363 +val_rpiq: 0.7611 + +Average Min Distance Statistics (Validation Sets): +------------------------------ +avg_mean: 4.7899 +std_mean: 0.0000 +avg_median: 4.2255 +std_median: 0.0000 +avg_min: 0.3763 +std_min: 0.0000 +avg_max: 18.8528 +std_max: 0.0000 +avg_std: 2.8893 +std_std: 0.0000 + +Best Validation Metrics Aggregated Across Runs: +------------------------------ +val_r_squared: No finite values available. + +val_rmse: No finite values available. + +val_mae: No finite values available. + +val_rpiq: No finite values available. + diff --git a/Archive/2DCNN_output/training_summary_81d6eceb.txt b/Archive/2DCNN_output/training_summary_81d6eceb.txt new file mode 100644 index 0000000000000000000000000000000000000000..74bfc4c516e14f0eb4b96fd29ec7589810946243 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_81d6eceb.txt @@ -0,0 +1,38 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 40 +loss_alpha: 0.8 +no_validation: True +loss_type: mse +target_transform: none +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 1 +project_name: socmapping-2DCNN +no_save: False +use_validation: False +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_c251 + ID: rnrlmhqw + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 271.9030 +val_loss_avg: 0.0000 + +Validation was disabled. No validation metrics available. + diff --git a/Archive/2DCNN_output/training_summary_8c7b94af.txt b/Archive/2DCNN_output/training_summary_8c7b94af.txt new file mode 100644 index 0000000000000000000000000000000000000000..74a0afc9eb06e83557ba55e6854e0252f7c09cc8 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_8c7b94af.txt @@ -0,0 +1,84 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 200 +loss_alpha: 0.8 +no_validation: False +loss_type: mse +target_transform: none +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 5 +project_name: socmapping-2DCNN +use_validation: True +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_5ed7 + ID: 08h947ph + Best Val R²: N/A + +Run 2: + Project: socmapping-2dcnn + Name: run_2_9601 + ID: oa9xwfrc + Best Val R²: N/A + +Run 3: + Project: socmapping-2dcnn + Name: run_3_f18c + ID: d5zeiegi + Best Val R²: N/A + +Run 4: + Project: socmapping-2dcnn + Name: run_4_7c0a + ID: id2ugjir + Best Val R²: N/A + +Run 5: + Project: socmapping-2dcnn + Name: run_5_fa61 + ID: v581lwcr + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 267.6717 +val_loss_avg: 119.2943 +val_r_squared: 0.1698 +val_rpiq: 0.5958 + +Average Min Distance Statistics (Validation Sets): +------------------------------ +avg_mean: 4.7899 +std_mean: 0.0000 +avg_median: 4.2255 +std_median: 0.0000 +avg_min: 0.3763 +std_min: 0.0000 +avg_max: 18.8528 +std_max: 0.0000 +avg_std: 2.8893 +std_std: 0.0000 + +Best Validation Metrics Aggregated Across Runs: +------------------------------ +val_r_squared: No finite values available. + +val_rmse: No finite values available. + +val_mae: No finite values available. + +val_rpiq: No finite values available. + diff --git a/Archive/2DCNN_output/training_summary_b31ff4fd.txt b/Archive/2DCNN_output/training_summary_b31ff4fd.txt new file mode 100644 index 0000000000000000000000000000000000000000..193149ed825f047ac0426c16556060b218856623 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_b31ff4fd.txt @@ -0,0 +1,84 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 200 +loss_alpha: 0.8 +no_validation: False +loss_type: composite_l2 +target_transform: none +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 5 +project_name: socmapping-2DCNN +use_validation: True +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_dd95 + ID: ckwpnxft + Best Val R²: N/A + +Run 2: + Project: socmapping-2dcnn + Name: run_2_b34e + ID: 01og5cjg + Best Val R²: N/A + +Run 3: + Project: socmapping-2dcnn + Name: run_3_c668 + ID: kmjnr7i7 + Best Val R²: N/A + +Run 4: + Project: socmapping-2dcnn + Name: run_4_c83f + ID: 6cjoa4v3 + Best Val R²: N/A + +Run 5: + Project: socmapping-2dcnn + Name: run_5_e39b + ID: ejp1wp03 + Best Val R²: N/A + +Average Metrics (Across Final Epochs of Runs): +------------------------------ +train_loss_avg: 199.7187 +val_loss_avg: 112.7489 +val_r_squared: 0.2037 +val_rpiq: 0.5712 + +Average Min Distance Statistics (Validation Sets): +------------------------------ +avg_mean: 4.7565 +std_mean: 0.0385 +avg_median: 4.1726 +std_median: 0.1528 +avg_min: 0.2477 +std_min: 0.1312 +avg_max: 17.7861 +std_max: 1.9220 +avg_std: 2.7755 +std_std: 0.1908 + +Best Validation Metrics Aggregated Across Runs: +------------------------------ +val_r_squared: No finite values available. + +val_rmse: No finite values available. + +val_mae: No finite values available. + +val_rpiq: No finite values available. + diff --git a/Archive/2DCNN_output/training_summary_bc473187.txt b/Archive/2DCNN_output/training_summary_bc473187.txt new file mode 100644 index 0000000000000000000000000000000000000000..18bf3cf711cf817aa398b11444fb7ede50a674e1 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_bc473187.txt @@ -0,0 +1,85 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 120 +loss_alpha: 0.8 +no_validation: False +loss_type: composite_l2 +target_transform: none +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 5 +project_name: socmapping-2DCNN +no_save: False +use_validation: True +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_a538 + ID: dgmzfgpe + Best Val R²: N/A + +Run 2: + Project: socmapping-2dcnn + Name: run_2_7286 + ID: s94ok51v + Best Val R²: N/A + +Run 3: + Project: socmapping-2dcnn + Name: run_3_ea28 + ID: yac1lhzk + Best Val R²: N/A + +Run 4: + Project: socmapping-2dcnn + Name: run_4_5c34 + ID: yu7q846v + Best Val R²: N/A + +Run 5: + Project: socmapping-2dcnn + Name: run_5_7249 + ID: inb82757 + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 21937.9095 +val_loss_avg: 276.4576 +val_r_squared: 0.0233 +val_rpiq: 0.4546 + +Average Min Distance Statistics (Validation Sets): +------------------------------ +avg_mean: 4.7899 +std_mean: 0.0000 +avg_median: 4.2255 +std_median: 0.0000 +avg_min: 0.3763 +std_min: 0.0000 +avg_max: 18.8528 +std_max: 0.0000 +avg_std: 2.8893 +std_std: 0.0000 + +Best Validation Metrics Aggregated Across Runs: +------------------------------ +val_r_squared: No finite values available. + +val_rmse: No finite values available. + +val_mae: No finite values available. + +val_rpiq: No finite values available. + diff --git a/Archive/2DCNN_output/training_summary_c602cfa5.txt b/Archive/2DCNN_output/training_summary_c602cfa5.txt new file mode 100644 index 0000000000000000000000000000000000000000..ee8f5c954a64b929fdece500dc298b3da118d830 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_c602cfa5.txt @@ -0,0 +1,38 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 70 +loss_alpha: 0.8 +no_validation: True +loss_type: mse +target_transform: log +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 1 +project_name: socmapping-2DCNN +no_save: False +use_validation: False +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_9f48 + ID: s6ss3j7c + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 0.4296 +val_loss_avg: 0.0000 + +Validation was disabled. No validation metrics available. + diff --git a/Archive/2DCNN_output/training_summary_c8501cfc.txt b/Archive/2DCNN_output/training_summary_c8501cfc.txt new file mode 100644 index 0000000000000000000000000000000000000000..1b7ca2a4dd76139c506d916427d8d23b9ad4938d --- /dev/null +++ b/Archive/2DCNN_output/training_summary_c8501cfc.txt @@ -0,0 +1,82 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 120 +loss_alpha: 0.8 +no_validation: False +loss_type: composite_l2 +target_transform: none +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 5 +project_name: socmapping-2DCNN +no_save: False +use_validation: True +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_eaac + ID: slfv6asc + Best Val R²: N/A + +Run 2: + Project: socmapping-2dcnn + Name: run_2_ae9c + ID: y4525ur2 + Best Val R²: N/A + +Run 3: + Project: socmapping-2dcnn + Name: run_3_f6af + ID: ofwdqc3j + Best Val R²: N/A + +Run 4: + Project: socmapping-2dcnn + Name: run_4_cd4e + ID: tyeoo99p + Best Val R²: N/A + +Run 5: + Project: socmapping-2dcnn + Name: run_5_0d11 + ID: rrfx0djs + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +No average metrics available. + +Average Min Distance Statistics (Validation Sets): +------------------------------ +avg_mean: 4.7899 +std_mean: 0.0000 +avg_median: 4.2255 +std_median: 0.0000 +avg_min: 0.3763 +std_min: 0.0000 +avg_max: 18.8528 +std_max: 0.0000 +avg_std: 2.8893 +std_std: 0.0000 + +Best Validation Metrics Aggregated Across Runs: +------------------------------ +val_r_squared: No finite values available. + +val_rmse: No finite values available. + +val_mae: No finite values available. + +val_rpiq: No finite values available. + diff --git a/Archive/2DCNN_output/training_summary_c883e728.txt b/Archive/2DCNN_output/training_summary_c883e728.txt new file mode 100644 index 0000000000000000000000000000000000000000..2acfe2e293ff24d767fdde74d0841ce0b811c5c4 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_c883e728.txt @@ -0,0 +1,38 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0004 +epochs: 250 +loss_alpha: 0.8 +no_validation: True +loss_type: mse +target_transform: normalize +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 1 +project_name: socmapping-2DCNN +no_save: False +use_validation: False +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_5f26 + ID: 1061ouif + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 19.4631 +val_loss_avg: 0.0000 + +Validation was disabled. No validation metrics available. + diff --git a/Archive/2DCNN_output/training_summary_dfb6951c.txt b/Archive/2DCNN_output/training_summary_dfb6951c.txt new file mode 100644 index 0000000000000000000000000000000000000000..2bd43ded098fd3436c425ec63d358529c8dcb329 --- /dev/null +++ b/Archive/2DCNN_output/training_summary_dfb6951c.txt @@ -0,0 +1,84 @@ +Training Summary Report +================================================== + +Configuration Arguments: +------------------------------ +lr: 0.0002 +epochs: 200 +loss_alpha: 0.8 +no_validation: False +loss_type: mse +target_transform: none +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +no_gpu: False +distance_threshold: 1.4 +target_fraction: 0.75 +num_runs: 5 +project_name: socmapping-2DCNN +use_validation: True +use_gpu: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-2dcnn + Name: run_1_8a1b + ID: sb21amrs + Best Val R²: N/A + +Run 2: + Project: socmapping-2dcnn + Name: run_2_1612 + ID: pgnpyip0 + Best Val R²: N/A + +Run 3: + Project: socmapping-2dcnn + Name: run_3_e65a + ID: 7x9j6q7u + Best Val R²: N/A + +Run 4: + Project: socmapping-2dcnn + Name: run_4_5fd2 + ID: x6m7z5p0 + Best Val R²: N/A + +Run 5: + Project: socmapping-2dcnn + Name: run_5_a966 + ID: yd38tr5z + Best Val R²: N/A + +Average Metrics (Across Best Epochs of Runs): +------------------------------ +train_loss_avg: 223.6123 +val_loss_avg: 106.3171 +val_r_squared: 0.2126 +val_rpiq: 0.6159 + +Average Min Distance Statistics (Validation Sets): +------------------------------ +avg_mean: 4.8212 +std_mean: 0.1600 +avg_median: 4.1732 +std_median: 0.1137 +avg_min: 0.3277 +std_min: 0.1081 +avg_max: 18.5712 +std_max: 0.3839 +avg_std: 2.8228 +std_std: 0.2506 + +Best Validation Metrics Aggregated Across Runs: +------------------------------ +val_r_squared: No finite values available. + +val_rmse: No finite values available. + +val_mae: No finite values available. + +val_rpiq: No finite values available. + diff --git a/Archive/3DCNNoutput_archive/final_training_df.parquet b/Archive/3DCNNoutput_archive/final_training_df.parquet new file mode 100644 index 0000000000000000000000000000000000000000..d7472b8bc2cecc2fb7d072b9e6510cde10fb728e --- /dev/null +++ b/Archive/3DCNNoutput_archive/final_training_df.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8d2093309069a5d5fe229b3735e75b430a18102062d854338e44a82f5663af4a +size 260771 diff --git a/Archive/3DCNNoutput_archive/final_validation_df.parquet b/Archive/3DCNNoutput_archive/final_validation_df.parquet new file mode 100644 index 0000000000000000000000000000000000000000..f32324c56d788f23f5053a65c98ec1ce94001f93 --- /dev/null +++ b/Archive/3DCNNoutput_archive/final_validation_df.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2821ffc858f4c43200d68c3400f6d4a0f1ae05a02526badc0d4c8e6e2849d920 +size 39348 diff --git a/Archive/3DCNNoutput_archive/model_statisticsMultiYear.txt b/Archive/3DCNNoutput_archive/model_statisticsMultiYear.txt new file mode 100644 index 0000000000000000000000000000000000000000..3901432c4f9c782b00cf35f84f3b2738e31e72c8 --- /dev/null +++ b/Archive/3DCNNoutput_archive/model_statisticsMultiYear.txt @@ -0,0 +1,6 @@ +Final Statistics: +Correlation: 0.6450 +R²: 0.4161 +MSE: 459.3834 +RMSE: 21.4332 +MAE: 13.2734 diff --git a/Archive/3DCNNoutput_archive/split_stats.log b/Archive/3DCNNoutput_archive/split_stats.log new file mode 100644 index 0000000000000000000000000000000000000000..144413da30b67f6692ac371385d80ec514d9f5a4 --- /dev/null +++ b/Archive/3DCNNoutput_archive/split_stats.log @@ -0,0 +1,7708 @@ +2025-04-17 23:35:37,437 - INFO - Validation and Training Set Split Statistics +2025-04-17 23:35:37,437 - INFO - Timestamp: 2025-04-17 23:35:37 +2025-04-17 23:35:37,437 - INFO - Validation set size: 1331 (8.06%) +2025-04-17 23:35:37,437 - INFO - Training set size: 15183 (91.94%) +2025-04-17 23:35:37,437 - INFO - Minimum Distance Statistics (km): +2025-04-17 23:35:37,437 - INFO - Mean: 67.5872 +2025-04-17 23:35:37,437 - INFO - Median: 4.4525 +2025-04-17 23:35:37,437 - INFO - Min: 0.5501 +2025-04-17 23:35:37,437 - INFO - Max: 770.4951 +2025-04-17 23:35:37,437 - INFO - Std: 110.9976 +2025-04-17 23:35:37,438 - INFO - OC Distribution Statistics: +2025-04-17 23:35:37,438 - INFO - Validation Mean: 16.7134 +2025-04-17 23:35:37,438 - INFO - Validation Std: 7.5433 +2025-04-17 23:35:37,438 - INFO - Training Mean: 23.0364 +2025-04-17 23:35:37,438 - INFO - Training Std: 20.3104 +2025-04-17 23:35:37,438 - INFO - Kolmogorov-Smirnov Test: +2025-04-17 23:35:37,438 - INFO - KS Statistic: 0.2138 +2025-04-17 23:35:37,438 - INFO - P-value: 0.0000 +2025-04-17 23:35:37,438 - INFO - Distribution Fit: Inverse Gamma +2025-04-17 23:35:37,438 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-17 23:35:37,451 - INFO - Validation and Training Set Split Statistics +2025-04-17 23:35:37,451 - INFO - Timestamp: 2025-04-17 23:35:37 +2025-04-17 23:35:37,451 - INFO - Validation set size: 1323 (8.01%) +2025-04-17 23:35:37,451 - INFO - Training set size: 15191 (91.99%) +2025-04-17 23:35:37,451 - INFO - Minimum Distance Statistics (km): +2025-04-17 23:35:37,451 - INFO - Mean: 61.5245 +2025-04-17 23:35:37,451 - INFO - Median: 4.0444 +2025-04-17 23:35:37,451 - INFO - Min: 0.8608 +2025-04-17 23:35:37,451 - INFO - Max: 655.9249 +2025-04-17 23:35:37,451 - INFO - Std: 100.1963 +2025-04-17 23:35:37,451 - INFO - OC Distribution Statistics: +2025-04-17 23:35:37,451 - INFO - Validation Mean: 16.6113 +2025-04-17 23:35:37,451 - INFO - Validation Std: 8.3854 +2025-04-17 23:35:37,451 - INFO - Training Mean: 23.0420 +2025-04-17 23:35:37,451 - INFO - Training Std: 20.2748 +2025-04-17 23:35:37,451 - INFO - Kolmogorov-Smirnov Test: +2025-04-17 23:35:37,451 - INFO - KS Statistic: 0.2294 +2025-04-17 23:35:37,451 - INFO - P-value: 0.0000 +2025-04-17 23:35:37,451 - INFO - Distribution Fit: Inverse Gamma +2025-04-17 23:35:37,451 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-17 23:35:37,542 - INFO - Validation and Training Set Split Statistics +2025-04-17 23:35:37,542 - INFO - Timestamp: 2025-04-17 23:35:37 +2025-04-17 23:35:37,542 - INFO - Validation set size: 1403 (8.50%) +2025-04-17 23:35:37,542 - INFO - Training set size: 15111 (91.50%) +2025-04-17 23:35:37,542 - INFO - Minimum Distance Statistics (km): +2025-04-17 23:35:37,542 - INFO - Mean: 69.2417 +2025-04-17 23:35:37,542 - INFO - Median: 4.4771 +2025-04-17 23:35:37,542 - INFO - Min: 0.8728 +2025-04-17 23:35:37,542 - INFO - Max: 917.1429 +2025-04-17 23:35:37,542 - INFO - Std: 116.9276 +2025-04-17 23:35:37,542 - INFO - OC Distribution Statistics: +2025-04-17 23:35:37,542 - INFO - Validation Mean: 16.5406 +2025-04-17 23:35:37,542 - INFO - Validation Std: 7.4755 +2025-04-17 23:35:37,542 - INFO - Training Mean: 23.0826 +2025-04-17 23:35:37,542 - INFO - Training Std: 20.3446 +2025-04-17 23:35:37,542 - INFO - Kolmogorov-Smirnov Test: +2025-04-17 23:35:37,542 - INFO - KS Statistic: 0.2105 +2025-04-17 23:35:37,542 - INFO - P-value: 0.0000 +2025-04-17 23:35:37,542 - INFO - Distribution Fit: Inverse Gamma +2025-04-17 23:35:37,542 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-17 23:35:38,874 - INFO - Validation and Training Set Split Statistics +2025-04-17 23:35:38,874 - INFO - Timestamp: 2025-04-17 23:35:38 +2025-04-17 23:35:38,874 - INFO - Validation set size: 1399 (8.47%) +2025-04-17 23:35:38,874 - INFO - Training set size: 15115 (91.53%) +2025-04-17 23:35:38,874 - INFO - Minimum Distance Statistics (km): +2025-04-17 23:35:38,874 - INFO - Mean: 62.3076 +2025-04-17 23:35:38,874 - INFO - Median: 4.2475 +2025-04-17 23:35:38,874 - INFO - Min: 1.2156 +2025-04-17 23:35:38,874 - INFO - Max: 772.4342 +2025-04-17 23:35:38,874 - INFO - Std: 104.3936 +2025-04-17 23:35:38,874 - INFO - OC Distribution Statistics: +2025-04-17 23:35:38,874 - INFO - Validation Mean: 16.3481 +2025-04-17 23:35:38,874 - INFO - Validation Std: 7.7691 +2025-04-17 23:35:38,874 - INFO - Training Mean: 23.0987 +2025-04-17 23:35:38,874 - INFO - Training Std: 20.3266 +2025-04-17 23:35:38,874 - INFO - Kolmogorov-Smirnov Test: +2025-04-17 23:35:38,874 - INFO - KS Statistic: 0.2290 +2025-04-17 23:35:38,875 - INFO - P-value: 0.0000 +2025-04-17 23:35:38,875 - INFO - Distribution Fit: Inverse Gamma +2025-04-17 23:35:38,875 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-17 23:39:17,861 - INFO - Validation and Training Set Split Statistics +2025-04-17 23:39:17,861 - INFO - Timestamp: 2025-04-17 23:39:17 +2025-04-17 23:39:17,861 - INFO - Validation set size: 1653 (10.01%) +2025-04-17 23:39:17,862 - INFO - Training set size: 14861 (89.99%) +2025-04-17 23:39:17,862 - INFO - Minimum Distance Statistics (km): +2025-04-17 23:39:17,862 - INFO - Mean: 65.5188 +2025-04-17 23:39:17,862 - INFO - Median: 4.0444 +2025-04-17 23:39:17,862 - INFO - Min: 1.2329 +2025-04-17 23:39:17,862 - INFO - Max: 670.0468 +2025-04-17 23:39:17,862 - INFO - Std: 111.6473 +2025-04-17 23:39:17,862 - INFO - OC Distribution Statistics: +2025-04-17 23:39:17,862 - INFO - Validation Mean: 16.1213 +2025-04-17 23:39:17,862 - INFO - Validation Std: 7.5079 +2025-04-17 23:39:17,862 - INFO - Training Mean: 23.2393 +2025-04-17 23:39:17,862 - INFO - Training Std: 20.4572 +2025-04-17 23:39:17,862 - INFO - Kolmogorov-Smirnov Test: +2025-04-17 23:39:17,862 - INFO - KS Statistic: 0.2312 +2025-04-17 23:39:17,862 - INFO - P-value: 0.0000 +2025-04-17 23:39:17,862 - INFO - Distribution Fit: Inverse Gamma +2025-04-17 23:39:17,862 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-17 23:39:18,059 - INFO - Validation and Training Set Split Statistics +2025-04-17 23:39:18,059 - INFO - Timestamp: 2025-04-17 23:39:18 +2025-04-17 23:39:18,059 - INFO - Validation set size: 1610 (9.75%) +2025-04-17 23:39:18,059 - INFO - Training set size: 14904 (90.25%) +2025-04-17 23:39:18,059 - INFO - Minimum Distance Statistics (km): +2025-04-17 23:39:18,059 - INFO - Mean: 64.4754 +2025-04-17 23:39:18,059 - INFO - Median: 4.4051 +2025-04-17 23:39:18,059 - INFO - Min: 1.1288 +2025-04-17 23:39:18,059 - INFO - Max: 705.4078 +2025-04-17 23:39:18,059 - INFO - Std: 109.0241 +2025-04-17 23:39:18,059 - INFO - OC Distribution Statistics: +2025-04-17 23:39:18,059 - INFO - Validation Mean: 16.7787 +2025-04-17 23:39:18,059 - INFO - Validation Std: 7.5959 +2025-04-17 23:39:18,059 - INFO - Training Mean: 23.1477 +2025-04-17 23:39:18,059 - INFO - Training Std: 20.4551 +2025-04-17 23:39:18,059 - INFO - Kolmogorov-Smirnov Test: +2025-04-17 23:39:18,059 - INFO - KS Statistic: 0.1962 +2025-04-17 23:39:18,059 - INFO - P-value: 0.0000 +2025-04-17 23:39:18,059 - INFO - Distribution Fit: Inverse Gamma +2025-04-17 23:39:18,059 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 14:43:13,826 - INFO - Validation and Training Set Split Statistics +2025-04-18 14:43:13,827 - INFO - Timestamp: 2025-04-18 14:43:13 +2025-04-18 14:43:13,827 - INFO - Validation set size: 1353 (8.19%) +2025-04-18 14:43:13,827 - INFO - Training set size: 15161 (91.81%) +2025-04-18 14:43:13,827 - INFO - Minimum Distance Statistics (km): +2025-04-18 14:43:13,827 - INFO - Mean: 73.7387 +2025-04-18 14:43:13,827 - INFO - Median: 4.5729 +2025-04-18 14:43:13,827 - INFO - Min: 1.2156 +2025-04-18 14:43:13,827 - INFO - Max: 875.2369 +2025-04-18 14:43:13,827 - INFO - Std: 128.6116 +2025-04-18 14:43:13,827 - INFO - OC Distribution Statistics: +2025-04-18 14:43:13,827 - INFO - Validation Mean: 16.4650 +2025-04-18 14:43:13,827 - INFO - Validation Std: 7.5645 +2025-04-18 14:43:13,827 - INFO - Training Mean: 23.0678 +2025-04-18 14:43:13,827 - INFO - Training Std: 20.3139 +2025-04-18 14:43:13,827 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 14:43:13,827 - INFO - KS Statistic: 0.2261 +2025-04-18 14:43:13,827 - INFO - P-value: 0.0000 +2025-04-18 14:43:13,827 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 14:43:13,827 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 14:43:13,944 - INFO - Validation and Training Set Split Statistics +2025-04-18 14:43:13,944 - INFO - Timestamp: 2025-04-18 14:43:13 +2025-04-18 14:43:13,944 - INFO - Validation set size: 1439 (8.71%) +2025-04-18 14:43:13,944 - INFO - Training set size: 15075 (91.29%) +2025-04-18 14:43:13,944 - INFO - Minimum Distance Statistics (km): +2025-04-18 14:43:13,945 - INFO - Mean: 70.8929 +2025-04-18 14:43:13,945 - INFO - Median: 4.7793 +2025-04-18 14:43:13,945 - INFO - Min: 1.2065 +2025-04-18 14:43:13,945 - INFO - Max: 743.7101 +2025-04-18 14:43:13,945 - INFO - Std: 116.0711 +2025-04-18 14:43:13,945 - INFO - OC Distribution Statistics: +2025-04-18 14:43:13,945 - INFO - Validation Mean: 16.4603 +2025-04-18 14:43:13,945 - INFO - Validation Std: 7.4367 +2025-04-18 14:43:13,945 - INFO - Training Mean: 23.1059 +2025-04-18 14:43:13,945 - INFO - Training Std: 20.3620 +2025-04-18 14:43:13,945 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 14:43:13,945 - INFO - KS Statistic: 0.2061 +2025-04-18 14:43:13,945 - INFO - P-value: 0.0000 +2025-04-18 14:43:13,945 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 14:43:13,945 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 14:43:14,110 - INFO - Validation and Training Set Split Statistics +2025-04-18 14:43:14,111 - INFO - Timestamp: 2025-04-18 14:43:14 +2025-04-18 14:43:14,111 - INFO - Validation set size: 1683 (10.19%) +2025-04-18 14:43:14,111 - INFO - Training set size: 14831 (89.81%) +2025-04-18 14:43:14,111 - INFO - Minimum Distance Statistics (km): +2025-04-18 14:43:14,111 - INFO - Mean: 67.3265 +2025-04-18 14:43:14,111 - INFO - Median: 4.4045 +2025-04-18 14:43:14,111 - INFO - Min: 0.5501 +2025-04-18 14:43:14,111 - INFO - Max: 824.9624 +2025-04-18 14:43:14,111 - INFO - Std: 122.2018 +2025-04-18 14:43:14,111 - INFO - OC Distribution Statistics: +2025-04-18 14:43:14,111 - INFO - Validation Mean: 16.1725 +2025-04-18 14:43:14,111 - INFO - Validation Std: 6.9967 +2025-04-18 14:43:14,111 - INFO - Training Mean: 23.2479 +2025-04-18 14:43:14,111 - INFO - Training Std: 20.4952 +2025-04-18 14:43:14,111 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 14:43:14,111 - INFO - KS Statistic: 0.2364 +2025-04-18 14:43:14,111 - INFO - P-value: 0.0000 +2025-04-18 14:43:14,111 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 14:43:14,111 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 14:43:15,270 - INFO - Validation and Training Set Split Statistics +2025-04-18 14:43:15,271 - INFO - Timestamp: 2025-04-18 14:43:15 +2025-04-18 14:43:15,271 - INFO - Validation set size: 1356 (8.21%) +2025-04-18 14:43:15,271 - INFO - Training set size: 15158 (91.79%) +2025-04-18 14:43:15,271 - INFO - Minimum Distance Statistics (km): +2025-04-18 14:43:15,271 - INFO - Mean: 62.3766 +2025-04-18 14:43:15,271 - INFO - Median: 5.7139 +2025-04-18 14:43:15,271 - INFO - Min: 1.2102 +2025-04-18 14:43:15,271 - INFO - Max: 655.9249 +2025-04-18 14:43:15,271 - INFO - Std: 102.2543 +2025-04-18 14:43:15,271 - INFO - OC Distribution Statistics: +2025-04-18 14:43:15,271 - INFO - Validation Mean: 16.8138 +2025-04-18 14:43:15,271 - INFO - Validation Std: 7.9054 +2025-04-18 14:43:15,271 - INFO - Training Mean: 23.0379 +2025-04-18 14:43:15,271 - INFO - Training Std: 20.3137 +2025-04-18 14:43:15,271 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 14:43:15,271 - INFO - KS Statistic: 0.2101 +2025-04-18 14:43:15,271 - INFO - P-value: 0.0000 +2025-04-18 14:43:15,271 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 14:43:15,271 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:37:10,925 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:37:10,925 - INFO - Timestamp: 2025-04-18 15:37:10 +2025-04-18 15:37:10,925 - INFO - Validation set size: 1396 (8.45%) +2025-04-18 15:37:10,925 - INFO - Training set size: 15118 (91.55%) +2025-04-18 15:37:10,925 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:37:10,925 - INFO - Mean: 67.5818 +2025-04-18 15:37:10,925 - INFO - Median: 4.3243 +2025-04-18 15:37:10,925 - INFO - Min: 1.2773 +2025-04-18 15:37:10,925 - INFO - Max: 702.5656 +2025-04-18 15:37:10,925 - INFO - Std: 115.8291 +2025-04-18 15:37:10,925 - INFO - OC Distribution Statistics: +2025-04-18 15:37:10,925 - INFO - Validation Mean: 16.5965 +2025-04-18 15:37:10,925 - INFO - Validation Std: 8.0192 +2025-04-18 15:37:10,926 - INFO - Training Mean: 23.0744 +2025-04-18 15:37:10,926 - INFO - Training Std: 20.3236 +2025-04-18 15:37:10,926 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:37:10,926 - INFO - KS Statistic: 0.2022 +2025-04-18 15:37:10,926 - INFO - P-value: 0.0000 +2025-04-18 15:37:10,926 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:37:10,926 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:37:10,964 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:37:10,964 - INFO - Timestamp: 2025-04-18 15:37:10 +2025-04-18 15:37:10,964 - INFO - Validation set size: 1425 (8.63%) +2025-04-18 15:37:10,964 - INFO - Training set size: 15089 (91.37%) +2025-04-18 15:37:10,964 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:37:10,964 - INFO - Mean: 64.6633 +2025-04-18 15:37:10,964 - INFO - Median: 4.9468 +2025-04-18 15:37:10,964 - INFO - Min: 0.7667 +2025-04-18 15:37:10,964 - INFO - Max: 875.2369 +2025-04-18 15:37:10,964 - INFO - Std: 110.2327 +2025-04-18 15:37:10,964 - INFO - OC Distribution Statistics: +2025-04-18 15:37:10,964 - INFO - Validation Mean: 16.2971 +2025-04-18 15:37:10,964 - INFO - Validation Std: 7.5710 +2025-04-18 15:37:10,964 - INFO - Training Mean: 23.1151 +2025-04-18 15:37:10,964 - INFO - Training Std: 20.3450 +2025-04-18 15:37:10,964 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:37:10,964 - INFO - KS Statistic: 0.2132 +2025-04-18 15:37:10,964 - INFO - P-value: 0.0000 +2025-04-18 15:37:10,964 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:37:10,964 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:37:11,254 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:37:11,254 - INFO - Timestamp: 2025-04-18 15:37:11 +2025-04-18 15:37:11,254 - INFO - Validation set size: 1629 (9.86%) +2025-04-18 15:37:11,254 - INFO - Training set size: 14885 (90.14%) +2025-04-18 15:37:11,254 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:37:11,254 - INFO - Mean: 71.3935 +2025-04-18 15:37:11,254 - INFO - Median: 4.6795 +2025-04-18 15:37:11,254 - INFO - Min: 1.2757 +2025-04-18 15:37:11,254 - INFO - Max: 706.9584 +2025-04-18 15:37:11,254 - INFO - Std: 118.9783 +2025-04-18 15:37:11,254 - INFO - OC Distribution Statistics: +2025-04-18 15:37:11,254 - INFO - Validation Mean: 16.6172 +2025-04-18 15:37:11,254 - INFO - Validation Std: 8.9516 +2025-04-18 15:37:11,254 - INFO - Training Mean: 23.1735 +2025-04-18 15:37:11,254 - INFO - Training Std: 20.3994 +2025-04-18 15:37:11,254 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:37:11,254 - INFO - KS Statistic: 0.1918 +2025-04-18 15:37:11,254 - INFO - P-value: 0.0000 +2025-04-18 15:37:11,254 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:37:11,254 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:37:12,169 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:37:12,169 - INFO - Timestamp: 2025-04-18 15:37:12 +2025-04-18 15:37:12,169 - INFO - Validation set size: 1355 (8.21%) +2025-04-18 15:37:12,170 - INFO - Training set size: 15159 (91.79%) +2025-04-18 15:37:12,170 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:37:12,170 - INFO - Mean: 73.8248 +2025-04-18 15:37:12,170 - INFO - Median: 5.5952 +2025-04-18 15:37:12,170 - INFO - Min: 1.2065 +2025-04-18 15:37:12,170 - INFO - Max: 702.5656 +2025-04-18 15:37:12,170 - INFO - Std: 115.7588 +2025-04-18 15:37:12,170 - INFO - OC Distribution Statistics: +2025-04-18 15:37:12,170 - INFO - Validation Mean: 16.7790 +2025-04-18 15:37:12,170 - INFO - Validation Std: 8.8250 +2025-04-18 15:37:12,170 - INFO - Training Mean: 23.0406 +2025-04-18 15:37:12,170 - INFO - Training Std: 20.2784 +2025-04-18 15:37:12,170 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:37:12,170 - INFO - KS Statistic: 0.2158 +2025-04-18 15:37:12,170 - INFO - P-value: 0.0000 +2025-04-18 15:37:12,170 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:37:12,170 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:41:03,216 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:41:03,216 - INFO - Timestamp: 2025-04-18 15:41:03 +2025-04-18 15:41:03,216 - INFO - Validation set size: 1418 (8.59%) +2025-04-18 15:41:03,216 - INFO - Training set size: 15096 (91.41%) +2025-04-18 15:41:03,216 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:41:03,216 - INFO - Mean: 66.8974 +2025-04-18 15:41:03,216 - INFO - Median: 4.5729 +2025-04-18 15:41:03,216 - INFO - Min: 1.2329 +2025-04-18 15:41:03,216 - INFO - Max: 629.8500 +2025-04-18 15:41:03,216 - INFO - Std: 106.8594 +2025-04-18 15:41:03,216 - INFO - OC Distribution Statistics: +2025-04-18 15:41:03,216 - INFO - Validation Mean: 16.2824 +2025-04-18 15:41:03,216 - INFO - Validation Std: 7.9920 +2025-04-18 15:41:03,217 - INFO - Training Mean: 23.1133 +2025-04-18 15:41:03,217 - INFO - Training Std: 20.3259 +2025-04-18 15:41:03,217 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:41:03,217 - INFO - KS Statistic: 0.2218 +2025-04-18 15:41:03,217 - INFO - P-value: 0.0000 +2025-04-18 15:41:03,217 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:41:03,217 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:41:03,501 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:41:03,501 - INFO - Timestamp: 2025-04-18 15:41:03 +2025-04-18 15:41:03,501 - INFO - Validation set size: 1498 (9.07%) +2025-04-18 15:41:03,501 - INFO - Training set size: 15016 (90.93%) +2025-04-18 15:41:03,501 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:41:03,501 - INFO - Mean: 68.8992 +2025-04-18 15:41:03,501 - INFO - Median: 4.7667 +2025-04-18 15:41:03,501 - INFO - Min: 0.2243 +2025-04-18 15:41:03,501 - INFO - Max: 862.7749 +2025-04-18 15:41:03,501 - INFO - Std: 122.0867 +2025-04-18 15:41:03,501 - INFO - OC Distribution Statistics: +2025-04-18 15:41:03,501 - INFO - Validation Mean: 16.6490 +2025-04-18 15:41:03,501 - INFO - Validation Std: 7.8373 +2025-04-18 15:41:03,501 - INFO - Training Mean: 23.1132 +2025-04-18 15:41:03,502 - INFO - Training Std: 20.3835 +2025-04-18 15:41:03,502 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:41:03,502 - INFO - KS Statistic: 0.2013 +2025-04-18 15:41:03,502 - INFO - P-value: 0.0000 +2025-04-18 15:41:03,502 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:41:03,502 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:41:03,518 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:41:03,518 - INFO - Timestamp: 2025-04-18 15:41:03 +2025-04-18 15:41:03,518 - INFO - Validation set size: 1711 (10.36%) +2025-04-18 15:41:03,518 - INFO - Training set size: 14803 (89.64%) +2025-04-18 15:41:03,519 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:41:03,519 - INFO - Mean: 58.4095 +2025-04-18 15:41:03,519 - INFO - Median: 4.1551 +2025-04-18 15:41:03,519 - INFO - Min: 1.0475 +2025-04-18 15:41:03,519 - INFO - Max: 611.1372 +2025-04-18 15:41:03,519 - INFO - Std: 99.6764 +2025-04-18 15:41:03,519 - INFO - OC Distribution Statistics: +2025-04-18 15:41:03,519 - INFO - Validation Mean: 16.4824 +2025-04-18 15:41:03,519 - INFO - Validation Std: 7.7671 +2025-04-18 15:41:03,519 - INFO - Training Mean: 23.2254 +2025-04-18 15:41:03,519 - INFO - Training Std: 20.4900 +2025-04-18 15:41:03,519 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:41:03,519 - INFO - KS Statistic: 0.2247 +2025-04-18 15:41:03,519 - INFO - P-value: 0.0000 +2025-04-18 15:41:03,519 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:41:03,519 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:41:06,892 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:41:06,892 - INFO - Timestamp: 2025-04-18 15:41:06 +2025-04-18 15:41:06,892 - INFO - Validation set size: 1508 (9.13%) +2025-04-18 15:41:06,892 - INFO - Training set size: 15006 (90.87%) +2025-04-18 15:41:06,892 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:41:06,892 - INFO - Mean: 61.4474 +2025-04-18 15:41:06,892 - INFO - Median: 4.5290 +2025-04-18 15:41:06,892 - INFO - Min: 0.3437 +2025-04-18 15:41:06,892 - INFO - Max: 706.9584 +2025-04-18 15:41:06,892 - INFO - Std: 105.7066 +2025-04-18 15:41:06,892 - INFO - OC Distribution Statistics: +2025-04-18 15:41:06,892 - INFO - Validation Mean: 16.1624 +2025-04-18 15:41:06,893 - INFO - Validation Std: 8.2148 +2025-04-18 15:41:06,893 - INFO - Training Mean: 23.1664 +2025-04-18 15:41:06,893 - INFO - Training Std: 20.3574 +2025-04-18 15:41:06,893 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:41:06,893 - INFO - KS Statistic: 0.2494 +2025-04-18 15:41:06,893 - INFO - P-value: 0.0000 +2025-04-18 15:41:06,893 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:41:06,893 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:42:34,209 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:42:34,209 - INFO - Timestamp: 2025-04-18 15:42:34 +2025-04-18 15:42:34,209 - INFO - Validation set size: 1404 (8.50%) +2025-04-18 15:42:34,209 - INFO - Training set size: 15110 (91.50%) +2025-04-18 15:42:34,209 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:42:34,209 - INFO - Mean: 74.0609 +2025-04-18 15:42:34,209 - INFO - Median: 5.3115 +2025-04-18 15:42:34,209 - INFO - Min: 1.2065 +2025-04-18 15:42:34,209 - INFO - Max: 702.5656 +2025-04-18 15:42:34,209 - INFO - Std: 113.3278 +2025-04-18 15:42:34,209 - INFO - OC Distribution Statistics: +2025-04-18 15:42:34,209 - INFO - Validation Mean: 16.4076 +2025-04-18 15:42:34,209 - INFO - Validation Std: 7.8487 +2025-04-18 15:42:34,209 - INFO - Training Mean: 23.0954 +2025-04-18 15:42:34,209 - INFO - Training Std: 20.3281 +2025-04-18 15:42:34,209 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:42:34,209 - INFO - KS Statistic: 0.2262 +2025-04-18 15:42:34,209 - INFO - P-value: 0.0000 +2025-04-18 15:42:34,209 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:42:34,209 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:42:34,272 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:42:34,272 - INFO - Timestamp: 2025-04-18 15:42:34 +2025-04-18 15:42:34,272 - INFO - Validation set size: 1356 (8.21%) +2025-04-18 15:42:34,272 - INFO - Training set size: 15158 (91.79%) +2025-04-18 15:42:34,272 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:42:34,272 - INFO - Mean: 68.0963 +2025-04-18 15:42:34,272 - INFO - Median: 4.5760 +2025-04-18 15:42:34,272 - INFO - Min: 1.1349 +2025-04-18 15:42:34,272 - INFO - Max: 706.9584 +2025-04-18 15:42:34,272 - INFO - Std: 118.3841 +2025-04-18 15:42:34,272 - INFO - OC Distribution Statistics: +2025-04-18 15:42:34,272 - INFO - Validation Mean: 16.5531 +2025-04-18 15:42:34,272 - INFO - Validation Std: 7.5728 +2025-04-18 15:42:34,272 - INFO - Training Mean: 23.0612 +2025-04-18 15:42:34,272 - INFO - Training Std: 20.3177 +2025-04-18 15:42:34,272 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:42:34,272 - INFO - KS Statistic: 0.1974 +2025-04-18 15:42:34,272 - INFO - P-value: 0.0000 +2025-04-18 15:42:34,272 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:42:34,272 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:42:34,513 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:42:34,514 - INFO - Timestamp: 2025-04-18 15:42:34 +2025-04-18 15:42:34,514 - INFO - Validation set size: 1634 (9.89%) +2025-04-18 15:42:34,514 - INFO - Training set size: 14880 (90.11%) +2025-04-18 15:42:34,514 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:42:34,514 - INFO - Mean: 61.9524 +2025-04-18 15:42:34,514 - INFO - Median: 4.2460 +2025-04-18 15:42:34,514 - INFO - Min: 1.2738 +2025-04-18 15:42:34,514 - INFO - Max: 706.9584 +2025-04-18 15:42:34,514 - INFO - Std: 108.9078 +2025-04-18 15:42:34,514 - INFO - OC Distribution Statistics: +2025-04-18 15:42:34,514 - INFO - Validation Mean: 16.3320 +2025-04-18 15:42:34,514 - INFO - Validation Std: 7.0835 +2025-04-18 15:42:34,514 - INFO - Training Mean: 23.2070 +2025-04-18 15:42:34,514 - INFO - Training Std: 20.4720 +2025-04-18 15:42:34,514 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:42:34,514 - INFO - KS Statistic: 0.2057 +2025-04-18 15:42:34,514 - INFO - P-value: 0.0000 +2025-04-18 15:42:34,514 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:42:34,514 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:42:38,035 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:42:38,035 - INFO - Timestamp: 2025-04-18 15:42:38 +2025-04-18 15:42:38,035 - INFO - Validation set size: 1352 (8.19%) +2025-04-18 15:42:38,035 - INFO - Training set size: 15162 (91.81%) +2025-04-18 15:42:38,035 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:42:38,035 - INFO - Mean: 67.7188 +2025-04-18 15:42:38,035 - INFO - Median: 5.0148 +2025-04-18 15:42:38,035 - INFO - Min: 1.2156 +2025-04-18 15:42:38,035 - INFO - Max: 707.4892 +2025-04-18 15:42:38,035 - INFO - Std: 110.5155 +2025-04-18 15:42:38,035 - INFO - OC Distribution Statistics: +2025-04-18 15:42:38,035 - INFO - Validation Mean: 17.0459 +2025-04-18 15:42:38,035 - INFO - Validation Std: 8.4513 +2025-04-18 15:42:38,035 - INFO - Training Mean: 23.0155 +2025-04-18 15:42:38,035 - INFO - Training Std: 20.2983 +2025-04-18 15:42:38,035 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:42:38,035 - INFO - KS Statistic: 0.1921 +2025-04-18 15:42:38,035 - INFO - P-value: 0.0000 +2025-04-18 15:42:38,035 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:42:38,035 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:47:17,738 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:47:17,738 - INFO - Timestamp: 2025-04-18 15:47:17 +2025-04-18 15:47:17,738 - INFO - Validation set size: 1348 (8.16%) +2025-04-18 15:47:17,738 - INFO - Training set size: 15166 (91.84%) +2025-04-18 15:47:17,738 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:47:17,738 - INFO - Mean: 64.9008 +2025-04-18 15:47:17,738 - INFO - Median: 4.6041 +2025-04-18 15:47:17,738 - INFO - Min: 1.2065 +2025-04-18 15:47:17,738 - INFO - Max: 705.4078 +2025-04-18 15:47:17,738 - INFO - Std: 102.8534 +2025-04-18 15:47:17,738 - INFO - OC Distribution Statistics: +2025-04-18 15:47:17,738 - INFO - Validation Mean: 16.6614 +2025-04-18 15:47:17,738 - INFO - Validation Std: 7.8311 +2025-04-18 15:47:17,738 - INFO - Training Mean: 23.0481 +2025-04-18 15:47:17,738 - INFO - Training Std: 20.3080 +2025-04-18 15:47:17,738 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:47:17,738 - INFO - KS Statistic: 0.1893 +2025-04-18 15:47:17,738 - INFO - P-value: 0.0000 +2025-04-18 15:47:17,738 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:47:17,738 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:47:17,752 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:47:17,752 - INFO - Timestamp: 2025-04-18 15:47:17 +2025-04-18 15:47:17,753 - INFO - Validation set size: 1362 (8.25%) +2025-04-18 15:47:17,753 - INFO - Training set size: 15152 (91.75%) +2025-04-18 15:47:17,753 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:47:17,753 - INFO - Mean: 75.4716 +2025-04-18 15:47:17,753 - INFO - Median: 5.2639 +2025-04-18 15:47:17,753 - INFO - Min: 1.2156 +2025-04-18 15:47:17,753 - INFO - Max: 835.9450 +2025-04-18 15:47:17,753 - INFO - Std: 123.2674 +2025-04-18 15:47:17,753 - INFO - OC Distribution Statistics: +2025-04-18 15:47:17,753 - INFO - Validation Mean: 16.5711 +2025-04-18 15:47:17,753 - INFO - Validation Std: 8.5312 +2025-04-18 15:47:17,753 - INFO - Training Mean: 23.0621 +2025-04-18 15:47:17,753 - INFO - Training Std: 20.2871 +2025-04-18 15:47:17,753 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:47:17,753 - INFO - KS Statistic: 0.2266 +2025-04-18 15:47:17,753 - INFO - P-value: 0.0000 +2025-04-18 15:47:17,753 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:47:17,753 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:47:17,939 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:47:17,940 - INFO - Timestamp: 2025-04-18 15:47:17 +2025-04-18 15:47:17,940 - INFO - Validation set size: 1348 (8.16%) +2025-04-18 15:47:17,940 - INFO - Training set size: 15166 (91.84%) +2025-04-18 15:47:17,940 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:47:17,940 - INFO - Mean: 71.9479 +2025-04-18 15:47:17,940 - INFO - Median: 4.2015 +2025-04-18 15:47:17,940 - INFO - Min: 1.0321 +2025-04-18 15:47:17,940 - INFO - Max: 1106.1626 +2025-04-18 15:47:17,940 - INFO - Std: 122.0630 +2025-04-18 15:47:17,940 - INFO - OC Distribution Statistics: +2025-04-18 15:47:17,940 - INFO - Validation Mean: 16.4793 +2025-04-18 15:47:17,940 - INFO - Validation Std: 7.7282 +2025-04-18 15:47:17,940 - INFO - Training Mean: 23.0643 +2025-04-18 15:47:17,940 - INFO - Training Std: 20.3063 +2025-04-18 15:47:17,940 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:47:17,940 - INFO - KS Statistic: 0.2163 +2025-04-18 15:47:17,940 - INFO - P-value: 0.0000 +2025-04-18 15:47:17,940 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:47:17,940 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:47:22,237 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:47:22,237 - INFO - Timestamp: 2025-04-18 15:47:22 +2025-04-18 15:47:22,237 - INFO - Validation set size: 1404 (8.50%) +2025-04-18 15:47:22,237 - INFO - Training set size: 15110 (91.50%) +2025-04-18 15:47:22,237 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:47:22,237 - INFO - Mean: 65.3570 +2025-04-18 15:47:22,237 - INFO - Median: 4.0360 +2025-04-18 15:47:22,237 - INFO - Min: 1.1288 +2025-04-18 15:47:22,237 - INFO - Max: 601.2913 +2025-04-18 15:47:22,238 - INFO - Std: 103.1632 +2025-04-18 15:47:22,238 - INFO - OC Distribution Statistics: +2025-04-18 15:47:22,238 - INFO - Validation Mean: 16.8443 +2025-04-18 15:47:22,238 - INFO - Validation Std: 8.4645 +2025-04-18 15:47:22,238 - INFO - Training Mean: 23.0548 +2025-04-18 15:47:22,238 - INFO - Training Std: 20.3180 +2025-04-18 15:47:22,238 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:47:22,238 - INFO - KS Statistic: 0.1884 +2025-04-18 15:47:22,238 - INFO - P-value: 0.0000 +2025-04-18 15:47:22,238 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:47:22,238 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:54:12,333 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:54:12,333 - INFO - Timestamp: 2025-04-18 15:54:12 +2025-04-18 15:54:12,333 - INFO - Validation set size: 1646 (9.97%) +2025-04-18 15:54:12,333 - INFO - Training set size: 14868 (90.03%) +2025-04-18 15:54:12,333 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:54:12,333 - INFO - Mean: 65.4694 +2025-04-18 15:54:12,333 - INFO - Median: 3.9616 +2025-04-18 15:54:12,333 - INFO - Min: 0.8124 +2025-04-18 15:54:12,333 - INFO - Max: 866.4875 +2025-04-18 15:54:12,333 - INFO - Std: 117.8881 +2025-04-18 15:54:12,333 - INFO - OC Distribution Statistics: +2025-04-18 15:54:12,333 - INFO - Validation Mean: 16.8088 +2025-04-18 15:54:12,333 - INFO - Validation Std: 8.2846 +2025-04-18 15:54:12,334 - INFO - Training Mean: 23.1598 +2025-04-18 15:54:12,334 - INFO - Training Std: 20.4455 +2025-04-18 15:54:12,334 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:54:12,334 - INFO - KS Statistic: 0.2071 +2025-04-18 15:54:12,334 - INFO - P-value: 0.0000 +2025-04-18 15:54:12,334 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:54:12,334 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:54:12,517 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:54:12,518 - INFO - Timestamp: 2025-04-18 15:54:12 +2025-04-18 15:54:12,518 - INFO - Validation set size: 1360 (8.24%) +2025-04-18 15:54:12,518 - INFO - Training set size: 15154 (91.76%) +2025-04-18 15:54:12,518 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:54:12,518 - INFO - Mean: 63.2189 +2025-04-18 15:54:12,518 - INFO - Median: 4.2475 +2025-04-18 15:54:12,518 - INFO - Min: 1.1342 +2025-04-18 15:54:12,518 - INFO - Max: 716.7388 +2025-04-18 15:54:12,518 - INFO - Std: 111.9960 +2025-04-18 15:54:12,518 - INFO - OC Distribution Statistics: +2025-04-18 15:54:12,518 - INFO - Validation Mean: 16.5682 +2025-04-18 15:54:12,518 - INFO - Validation Std: 8.2913 +2025-04-18 15:54:12,518 - INFO - Training Mean: 23.0615 +2025-04-18 15:54:12,518 - INFO - Training Std: 20.2950 +2025-04-18 15:54:12,518 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:54:12,518 - INFO - KS Statistic: 0.2015 +2025-04-18 15:54:12,518 - INFO - P-value: 0.0000 +2025-04-18 15:54:12,518 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:54:12,518 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:54:12,649 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:54:12,649 - INFO - Timestamp: 2025-04-18 15:54:12 +2025-04-18 15:54:12,649 - INFO - Validation set size: 1650 (9.99%) +2025-04-18 15:54:12,649 - INFO - Training set size: 14864 (90.01%) +2025-04-18 15:54:12,649 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:54:12,649 - INFO - Mean: 62.8710 +2025-04-18 15:54:12,649 - INFO - Median: 3.8455 +2025-04-18 15:54:12,649 - INFO - Min: 1.2497 +2025-04-18 15:54:12,649 - INFO - Max: 888.3558 +2025-04-18 15:54:12,649 - INFO - Std: 113.5432 +2025-04-18 15:54:12,649 - INFO - OC Distribution Statistics: +2025-04-18 15:54:12,649 - INFO - Validation Mean: 15.9435 +2025-04-18 15:54:12,649 - INFO - Validation Std: 7.0775 +2025-04-18 15:54:12,649 - INFO - Training Mean: 23.2576 +2025-04-18 15:54:12,649 - INFO - Training Std: 20.4658 +2025-04-18 15:54:12,650 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:54:12,650 - INFO - KS Statistic: 0.2490 +2025-04-18 15:54:12,650 - INFO - P-value: 0.0000 +2025-04-18 15:54:12,650 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:54:12,650 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:54:16,107 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:54:16,108 - INFO - Timestamp: 2025-04-18 15:54:16 +2025-04-18 15:54:16,108 - INFO - Validation set size: 1368 (8.28%) +2025-04-18 15:54:16,108 - INFO - Training set size: 15146 (91.72%) +2025-04-18 15:54:16,108 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:54:16,108 - INFO - Mean: 73.7441 +2025-04-18 15:54:16,108 - INFO - Median: 4.5043 +2025-04-18 15:54:16,108 - INFO - Min: 0.8608 +2025-04-18 15:54:16,108 - INFO - Max: 751.3159 +2025-04-18 15:54:16,108 - INFO - Std: 124.3706 +2025-04-18 15:54:16,108 - INFO - OC Distribution Statistics: +2025-04-18 15:54:16,108 - INFO - Validation Mean: 17.2158 +2025-04-18 15:54:16,108 - INFO - Validation Std: 9.1486 +2025-04-18 15:54:16,108 - INFO - Training Mean: 23.0065 +2025-04-18 15:54:16,108 - INFO - Training Std: 20.2834 +2025-04-18 15:54:16,108 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:54:16,108 - INFO - KS Statistic: 0.1867 +2025-04-18 15:54:16,108 - INFO - P-value: 0.0000 +2025-04-18 15:54:16,108 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:54:16,108 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:59:43,194 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:59:43,194 - INFO - Timestamp: 2025-04-18 15:59:43 +2025-04-18 15:59:43,194 - INFO - Validation set size: 1383 (8.37%) +2025-04-18 15:59:43,194 - INFO - Training set size: 15131 (91.63%) +2025-04-18 15:59:43,194 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:59:43,194 - INFO - Mean: 63.5356 +2025-04-18 15:59:43,194 - INFO - Median: 4.4468 +2025-04-18 15:59:43,194 - INFO - Min: 0.7371 +2025-04-18 15:59:43,194 - INFO - Max: 835.9450 +2025-04-18 15:59:43,194 - INFO - Std: 114.4143 +2025-04-18 15:59:43,194 - INFO - OC Distribution Statistics: +2025-04-18 15:59:43,194 - INFO - Validation Mean: 16.2801 +2025-04-18 15:59:43,194 - INFO - Validation Std: 7.4921 +2025-04-18 15:59:43,194 - INFO - Training Mean: 23.0977 +2025-04-18 15:59:43,194 - INFO - Training Std: 20.3260 +2025-04-18 15:59:43,194 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:59:43,194 - INFO - KS Statistic: 0.2233 +2025-04-18 15:59:43,194 - INFO - P-value: 0.0000 +2025-04-18 15:59:43,194 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:59:43,194 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:59:43,221 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:59:43,221 - INFO - Timestamp: 2025-04-18 15:59:43 +2025-04-18 15:59:43,221 - INFO - Validation set size: 1343 (8.13%) +2025-04-18 15:59:43,221 - INFO - Training set size: 15171 (91.87%) +2025-04-18 15:59:43,221 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:59:43,221 - INFO - Mean: 69.7134 +2025-04-18 15:59:43,221 - INFO - Median: 4.6353 +2025-04-18 15:59:43,221 - INFO - Min: 1.2065 +2025-04-18 15:59:43,221 - INFO - Max: 706.9584 +2025-04-18 15:59:43,221 - INFO - Std: 112.5664 +2025-04-18 15:59:43,221 - INFO - OC Distribution Statistics: +2025-04-18 15:59:43,221 - INFO - Validation Mean: 16.4026 +2025-04-18 15:59:43,221 - INFO - Validation Std: 7.0497 +2025-04-18 15:59:43,221 - INFO - Training Mean: 23.0689 +2025-04-18 15:59:43,221 - INFO - Training Std: 20.3235 +2025-04-18 15:59:43,222 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:59:43,222 - INFO - KS Statistic: 0.1945 +2025-04-18 15:59:43,222 - INFO - P-value: 0.0000 +2025-04-18 15:59:43,222 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:59:43,222 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:59:43,239 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:59:43,239 - INFO - Timestamp: 2025-04-18 15:59:43 +2025-04-18 15:59:43,239 - INFO - Validation set size: 1338 (8.10%) +2025-04-18 15:59:43,239 - INFO - Training set size: 15176 (91.90%) +2025-04-18 15:59:43,239 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:59:43,239 - INFO - Mean: 63.3024 +2025-04-18 15:59:43,239 - INFO - Median: 4.3323 +2025-04-18 15:59:43,239 - INFO - Min: 0.7102 +2025-04-18 15:59:43,239 - INFO - Max: 866.4875 +2025-04-18 15:59:43,239 - INFO - Std: 112.3327 +2025-04-18 15:59:43,239 - INFO - OC Distribution Statistics: +2025-04-18 15:59:43,240 - INFO - Validation Mean: 16.4520 +2025-04-18 15:59:43,240 - INFO - Validation Std: 8.0577 +2025-04-18 15:59:43,240 - INFO - Training Mean: 23.0624 +2025-04-18 15:59:43,240 - INFO - Training Std: 20.2892 +2025-04-18 15:59:43,240 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:59:43,240 - INFO - KS Statistic: 0.2204 +2025-04-18 15:59:43,240 - INFO - P-value: 0.0000 +2025-04-18 15:59:43,240 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:59:43,240 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 15:59:47,204 - INFO - Validation and Training Set Split Statistics +2025-04-18 15:59:47,204 - INFO - Timestamp: 2025-04-18 15:59:47 +2025-04-18 15:59:47,204 - INFO - Validation set size: 1677 (10.16%) +2025-04-18 15:59:47,204 - INFO - Training set size: 14837 (89.84%) +2025-04-18 15:59:47,204 - INFO - Minimum Distance Statistics (km): +2025-04-18 15:59:47,204 - INFO - Mean: 64.2315 +2025-04-18 15:59:47,204 - INFO - Median: 4.0399 +2025-04-18 15:59:47,204 - INFO - Min: 1.2065 +2025-04-18 15:59:47,204 - INFO - Max: 1106.1626 +2025-04-18 15:59:47,204 - INFO - Std: 130.7781 +2025-04-18 15:59:47,204 - INFO - OC Distribution Statistics: +2025-04-18 15:59:47,204 - INFO - Validation Mean: 16.6691 +2025-04-18 15:59:47,204 - INFO - Validation Std: 8.3968 +2025-04-18 15:59:47,204 - INFO - Training Mean: 23.1889 +2025-04-18 15:59:47,204 - INFO - Training Std: 20.4512 +2025-04-18 15:59:47,204 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 15:59:47,204 - INFO - KS Statistic: 0.2181 +2025-04-18 15:59:47,204 - INFO - P-value: 0.0000 +2025-04-18 15:59:47,204 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 15:59:47,204 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:18:34,560 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:18:34,561 - INFO - Timestamp: 2025-04-18 16:18:34 +2025-04-18 16:18:34,561 - INFO - Validation set size: 1409 (8.53%) +2025-04-18 16:18:34,561 - INFO - Training set size: 15105 (91.47%) +2025-04-18 16:18:34,561 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:18:34,561 - INFO - Mean: 62.1104 +2025-04-18 16:18:34,561 - INFO - Median: 5.0097 +2025-04-18 16:18:34,561 - INFO - Min: 0.9587 +2025-04-18 16:18:34,561 - INFO - Max: 705.4078 +2025-04-18 16:18:34,561 - INFO - Std: 105.4262 +2025-04-18 16:18:34,561 - INFO - OC Distribution Statistics: +2025-04-18 16:18:34,561 - INFO - Validation Mean: 16.5514 +2025-04-18 16:18:34,561 - INFO - Validation Std: 7.8868 +2025-04-18 16:18:34,561 - INFO - Training Mean: 23.0842 +2025-04-18 16:18:34,561 - INFO - Training Std: 20.3335 +2025-04-18 16:18:34,561 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:18:34,561 - INFO - KS Statistic: 0.2059 +2025-04-18 16:18:34,561 - INFO - P-value: 0.0000 +2025-04-18 16:18:34,561 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:18:34,561 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:18:34,883 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:18:34,883 - INFO - Timestamp: 2025-04-18 16:18:34 +2025-04-18 16:18:34,883 - INFO - Validation set size: 1546 (9.36%) +2025-04-18 16:18:34,883 - INFO - Training set size: 14968 (90.64%) +2025-04-18 16:18:34,883 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:18:34,883 - INFO - Mean: 60.9657 +2025-04-18 16:18:34,883 - INFO - Median: 4.2475 +2025-04-18 16:18:34,883 - INFO - Min: 0.3437 +2025-04-18 16:18:34,883 - INFO - Max: 917.1429 +2025-04-18 16:18:34,883 - INFO - Std: 107.2030 +2025-04-18 16:18:34,883 - INFO - OC Distribution Statistics: +2025-04-18 16:18:34,883 - INFO - Validation Mean: 16.4842 +2025-04-18 16:18:34,883 - INFO - Validation Std: 7.8586 +2025-04-18 16:18:34,883 - INFO - Training Mean: 23.1509 +2025-04-18 16:18:34,883 - INFO - Training Std: 20.4017 +2025-04-18 16:18:34,883 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:18:34,883 - INFO - KS Statistic: 0.2146 +2025-04-18 16:18:34,883 - INFO - P-value: 0.0000 +2025-04-18 16:18:34,884 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:18:34,884 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:18:34,887 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:18:34,887 - INFO - Timestamp: 2025-04-18 16:18:34 +2025-04-18 16:18:34,887 - INFO - Validation set size: 1556 (9.42%) +2025-04-18 16:18:34,887 - INFO - Training set size: 14958 (90.58%) +2025-04-18 16:18:34,887 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:18:34,887 - INFO - Mean: 58.6497 +2025-04-18 16:18:34,887 - INFO - Median: 4.2696 +2025-04-18 16:18:34,887 - INFO - Min: 0.7391 +2025-04-18 16:18:34,887 - INFO - Max: 705.4078 +2025-04-18 16:18:34,887 - INFO - Std: 99.4915 +2025-04-18 16:18:34,887 - INFO - OC Distribution Statistics: +2025-04-18 16:18:34,887 - INFO - Validation Mean: 16.7342 +2025-04-18 16:18:34,887 - INFO - Validation Std: 8.1778 +2025-04-18 16:18:34,887 - INFO - Training Mean: 23.1294 +2025-04-18 16:18:34,887 - INFO - Training Std: 20.4020 +2025-04-18 16:18:34,888 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:18:34,888 - INFO - KS Statistic: 0.2089 +2025-04-18 16:18:34,888 - INFO - P-value: 0.0000 +2025-04-18 16:18:34,888 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:18:34,888 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:18:36,052 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:18:36,052 - INFO - Timestamp: 2025-04-18 16:18:36 +2025-04-18 16:18:36,052 - INFO - Validation set size: 1363 (8.25%) +2025-04-18 16:18:36,052 - INFO - Training set size: 15151 (91.75%) +2025-04-18 16:18:36,052 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:18:36,052 - INFO - Mean: 61.8174 +2025-04-18 16:18:36,052 - INFO - Median: 4.2711 +2025-04-18 16:18:36,052 - INFO - Min: 1.2156 +2025-04-18 16:18:36,052 - INFO - Max: 917.2720 +2025-04-18 16:18:36,052 - INFO - Std: 109.2471 +2025-04-18 16:18:36,052 - INFO - OC Distribution Statistics: +2025-04-18 16:18:36,052 - INFO - Validation Mean: 16.4842 +2025-04-18 16:18:36,052 - INFO - Validation Std: 7.8769 +2025-04-18 16:18:36,052 - INFO - Training Mean: 23.0704 +2025-04-18 16:18:36,052 - INFO - Training Std: 20.3089 +2025-04-18 16:18:36,052 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:18:36,052 - INFO - KS Statistic: 0.2220 +2025-04-18 16:18:36,052 - INFO - P-value: 0.0000 +2025-04-18 16:18:36,052 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:18:36,052 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:23:17,265 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:23:17,266 - INFO - Timestamp: 2025-04-18 16:23:17 +2025-04-18 16:23:17,266 - INFO - Validation set size: 1432 (8.67%) +2025-04-18 16:23:17,266 - INFO - Training set size: 15082 (91.33%) +2025-04-18 16:23:17,266 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:23:17,266 - INFO - Mean: 67.7408 +2025-04-18 16:23:17,266 - INFO - Median: 4.5236 +2025-04-18 16:23:17,266 - INFO - Min: 1.2497 +2025-04-18 16:23:17,266 - INFO - Max: 743.7101 +2025-04-18 16:23:17,266 - INFO - Std: 110.9264 +2025-04-18 16:23:17,266 - INFO - OC Distribution Statistics: +2025-04-18 16:23:17,266 - INFO - Validation Mean: 16.3081 +2025-04-18 16:23:17,266 - INFO - Validation Std: 7.7456 +2025-04-18 16:23:17,266 - INFO - Training Mean: 23.1172 +2025-04-18 16:23:17,266 - INFO - Training Std: 20.3427 +2025-04-18 16:23:17,266 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:23:17,266 - INFO - KS Statistic: 0.2250 +2025-04-18 16:23:17,266 - INFO - P-value: 0.0000 +2025-04-18 16:23:17,266 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:23:17,266 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:23:17,279 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:23:17,279 - INFO - Timestamp: 2025-04-18 16:23:17 +2025-04-18 16:23:17,279 - INFO - Validation set size: 1419 (8.59%) +2025-04-18 16:23:17,279 - INFO - Training set size: 15095 (91.41%) +2025-04-18 16:23:17,279 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:23:17,279 - INFO - Mean: 60.0343 +2025-04-18 16:23:17,279 - INFO - Median: 4.0771 +2025-04-18 16:23:17,279 - INFO - Min: 1.0979 +2025-04-18 16:23:17,279 - INFO - Max: 888.3558 +2025-04-18 16:23:17,279 - INFO - Std: 105.9987 +2025-04-18 16:23:17,279 - INFO - OC Distribution Statistics: +2025-04-18 16:23:17,279 - INFO - Validation Mean: 16.6318 +2025-04-18 16:23:17,279 - INFO - Validation Std: 8.1667 +2025-04-18 16:23:17,279 - INFO - Training Mean: 23.0809 +2025-04-18 16:23:17,279 - INFO - Training Std: 20.3306 +2025-04-18 16:23:17,279 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:23:17,279 - INFO - KS Statistic: 0.2272 +2025-04-18 16:23:17,279 - INFO - P-value: 0.0000 +2025-04-18 16:23:17,279 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:23:17,279 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:23:17,539 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:23:17,539 - INFO - Timestamp: 2025-04-18 16:23:17 +2025-04-18 16:23:17,539 - INFO - Validation set size: 1477 (8.94%) +2025-04-18 16:23:17,539 - INFO - Training set size: 15037 (91.06%) +2025-04-18 16:23:17,539 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:23:17,539 - INFO - Mean: 67.4486 +2025-04-18 16:23:17,539 - INFO - Median: 4.2696 +2025-04-18 16:23:17,539 - INFO - Min: 1.0321 +2025-04-18 16:23:17,539 - INFO - Max: 1464.8564 +2025-04-18 16:23:17,539 - INFO - Std: 129.1775 +2025-04-18 16:23:17,539 - INFO - OC Distribution Statistics: +2025-04-18 16:23:17,539 - INFO - Validation Mean: 16.6305 +2025-04-18 16:23:17,539 - INFO - Validation Std: 7.7316 +2025-04-18 16:23:17,539 - INFO - Training Mean: 23.1059 +2025-04-18 16:23:17,539 - INFO - Training Std: 20.3761 +2025-04-18 16:23:17,539 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:23:17,539 - INFO - KS Statistic: 0.2070 +2025-04-18 16:23:17,540 - INFO - P-value: 0.0000 +2025-04-18 16:23:17,540 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:23:17,540 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:23:21,273 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:23:21,273 - INFO - Timestamp: 2025-04-18 16:23:21 +2025-04-18 16:23:21,273 - INFO - Validation set size: 1606 (9.73%) +2025-04-18 16:23:21,273 - INFO - Training set size: 14908 (90.27%) +2025-04-18 16:23:21,273 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:23:21,273 - INFO - Mean: 69.1817 +2025-04-18 16:23:21,273 - INFO - Median: 4.0944 +2025-04-18 16:23:21,273 - INFO - Min: 0.8728 +2025-04-18 16:23:21,273 - INFO - Max: 1106.1626 +2025-04-18 16:23:21,273 - INFO - Std: 130.5800 +2025-04-18 16:23:21,273 - INFO - OC Distribution Statistics: +2025-04-18 16:23:21,273 - INFO - Validation Mean: 16.7636 +2025-04-18 16:23:21,273 - INFO - Validation Std: 7.7651 +2025-04-18 16:23:21,273 - INFO - Training Mean: 23.1476 +2025-04-18 16:23:21,273 - INFO - Training Std: 20.4457 +2025-04-18 16:23:21,273 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:23:21,273 - INFO - KS Statistic: 0.2107 +2025-04-18 16:23:21,273 - INFO - P-value: 0.0000 +2025-04-18 16:23:21,273 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:23:21,273 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:30:26,869 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:30:26,869 - INFO - Timestamp: 2025-04-18 16:30:26 +2025-04-18 16:30:26,869 - INFO - Validation set size: 1324 (8.02%) +2025-04-18 16:30:26,869 - INFO - Training set size: 15190 (91.98%) +2025-04-18 16:30:26,870 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:30:26,870 - INFO - Mean: 76.5445 +2025-04-18 16:30:26,870 - INFO - Median: 4.6944 +2025-04-18 16:30:26,870 - INFO - Min: 0.5501 +2025-04-18 16:30:26,870 - INFO - Max: 862.7749 +2025-04-18 16:30:26,870 - INFO - Std: 125.1766 +2025-04-18 16:30:26,870 - INFO - OC Distribution Statistics: +2025-04-18 16:30:26,870 - INFO - Validation Mean: 16.6185 +2025-04-18 16:30:26,870 - INFO - Validation Std: 8.0381 +2025-04-18 16:30:26,870 - INFO - Training Mean: 23.0418 +2025-04-18 16:30:26,870 - INFO - Training Std: 20.2877 +2025-04-18 16:30:26,870 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:30:26,870 - INFO - KS Statistic: 0.2074 +2025-04-18 16:30:26,870 - INFO - P-value: 0.0000 +2025-04-18 16:30:26,870 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:30:26,870 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:30:26,885 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:30:26,885 - INFO - Timestamp: 2025-04-18 16:30:26 +2025-04-18 16:30:26,885 - INFO - Validation set size: 1337 (8.10%) +2025-04-18 16:30:26,885 - INFO - Training set size: 15177 (91.90%) +2025-04-18 16:30:26,885 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:30:26,885 - INFO - Mean: 67.8548 +2025-04-18 16:30:26,885 - INFO - Median: 4.1551 +2025-04-18 16:30:26,885 - INFO - Min: 0.6067 +2025-04-18 16:30:26,886 - INFO - Max: 888.3558 +2025-04-18 16:30:26,886 - INFO - Std: 115.7669 +2025-04-18 16:30:26,886 - INFO - OC Distribution Statistics: +2025-04-18 16:30:26,886 - INFO - Validation Mean: 16.5071 +2025-04-18 16:30:26,886 - INFO - Validation Std: 7.9354 +2025-04-18 16:30:26,886 - INFO - Training Mean: 23.0571 +2025-04-18 16:30:26,886 - INFO - Training Std: 20.2946 +2025-04-18 16:30:26,886 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:30:26,886 - INFO - KS Statistic: 0.2091 +2025-04-18 16:30:26,886 - INFO - P-value: 0.0000 +2025-04-18 16:30:26,886 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:30:26,886 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:30:27,022 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:30:27,022 - INFO - Timestamp: 2025-04-18 16:30:27 +2025-04-18 16:30:27,022 - INFO - Validation set size: 1402 (8.49%) +2025-04-18 16:30:27,022 - INFO - Training set size: 15112 (91.51%) +2025-04-18 16:30:27,022 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:30:27,022 - INFO - Mean: 76.7981 +2025-04-18 16:30:27,022 - INFO - Median: 5.5185 +2025-04-18 16:30:27,022 - INFO - Min: 0.8608 +2025-04-18 16:30:27,022 - INFO - Max: 705.4078 +2025-04-18 16:30:27,022 - INFO - Std: 124.8871 +2025-04-18 16:30:27,022 - INFO - OC Distribution Statistics: +2025-04-18 16:30:27,022 - INFO - Validation Mean: 17.0174 +2025-04-18 16:30:27,022 - INFO - Validation Std: 8.9841 +2025-04-18 16:30:27,022 - INFO - Training Mean: 23.0379 +2025-04-18 16:30:27,022 - INFO - Training Std: 20.3011 +2025-04-18 16:30:27,022 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:30:27,022 - INFO - KS Statistic: 0.1765 +2025-04-18 16:30:27,022 - INFO - P-value: 0.0000 +2025-04-18 16:30:27,022 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:30:27,022 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:30:31,370 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:30:31,370 - INFO - Timestamp: 2025-04-18 16:30:31 +2025-04-18 16:30:31,370 - INFO - Validation set size: 1371 (8.30%) +2025-04-18 16:30:31,370 - INFO - Training set size: 15143 (91.70%) +2025-04-18 16:30:31,370 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:30:31,370 - INFO - Mean: 77.4261 +2025-04-18 16:30:31,370 - INFO - Median: 5.2369 +2025-04-18 16:30:31,370 - INFO - Min: 1.0890 +2025-04-18 16:30:31,370 - INFO - Max: 917.1429 +2025-04-18 16:30:31,370 - INFO - Std: 130.9726 +2025-04-18 16:30:31,370 - INFO - OC Distribution Statistics: +2025-04-18 16:30:31,370 - INFO - Validation Mean: 16.7643 +2025-04-18 16:30:31,370 - INFO - Validation Std: 8.3889 +2025-04-18 16:30:31,370 - INFO - Training Mean: 23.0485 +2025-04-18 16:30:31,370 - INFO - Training Std: 20.3023 +2025-04-18 16:30:31,370 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:30:31,370 - INFO - KS Statistic: 0.2192 +2025-04-18 16:30:31,370 - INFO - P-value: 0.0000 +2025-04-18 16:30:31,370 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:30:31,370 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:35:35,853 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:35:35,853 - INFO - Timestamp: 2025-04-18 16:35:35 +2025-04-18 16:35:35,853 - INFO - Validation set size: 1363 (8.25%) +2025-04-18 16:35:35,853 - INFO - Training set size: 15151 (91.75%) +2025-04-18 16:35:35,853 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:35:35,853 - INFO - Mean: 67.7765 +2025-04-18 16:35:35,853 - INFO - Median: 5.4365 +2025-04-18 16:35:35,853 - INFO - Min: 1.2596 +2025-04-18 16:35:35,853 - INFO - Max: 743.7101 +2025-04-18 16:35:35,853 - INFO - Std: 113.7314 +2025-04-18 16:35:35,853 - INFO - OC Distribution Statistics: +2025-04-18 16:35:35,853 - INFO - Validation Mean: 16.8363 +2025-04-18 16:35:35,853 - INFO - Validation Std: 9.2580 +2025-04-18 16:35:35,853 - INFO - Training Mean: 23.0387 +2025-04-18 16:35:35,853 - INFO - Training Std: 20.2664 +2025-04-18 16:35:35,853 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:35:35,853 - INFO - KS Statistic: 0.2024 +2025-04-18 16:35:35,853 - INFO - P-value: 0.0000 +2025-04-18 16:35:35,853 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:35:35,853 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:35:35,902 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:35:35,902 - INFO - Timestamp: 2025-04-18 16:35:35 +2025-04-18 16:35:35,902 - INFO - Validation set size: 1387 (8.40%) +2025-04-18 16:35:35,902 - INFO - Training set size: 15127 (91.60%) +2025-04-18 16:35:35,902 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:35:35,902 - INFO - Mean: 68.0799 +2025-04-18 16:35:35,902 - INFO - Median: 4.4468 +2025-04-18 16:35:35,902 - INFO - Min: 0.6067 +2025-04-18 16:35:35,902 - INFO - Max: 870.7197 +2025-04-18 16:35:35,902 - INFO - Std: 113.7007 +2025-04-18 16:35:35,902 - INFO - OC Distribution Statistics: +2025-04-18 16:35:35,902 - INFO - Validation Mean: 16.1664 +2025-04-18 16:35:35,902 - INFO - Validation Std: 7.8261 +2025-04-18 16:35:35,902 - INFO - Training Mean: 23.1100 +2025-04-18 16:35:35,902 - INFO - Training Std: 20.3130 +2025-04-18 16:35:35,902 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:35:35,902 - INFO - KS Statistic: 0.2280 +2025-04-18 16:35:35,902 - INFO - P-value: 0.0000 +2025-04-18 16:35:35,902 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:35:35,902 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:35:36,207 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:35:36,207 - INFO - Timestamp: 2025-04-18 16:35:36 +2025-04-18 16:35:36,207 - INFO - Validation set size: 1333 (8.07%) +2025-04-18 16:35:36,207 - INFO - Training set size: 15181 (91.93%) +2025-04-18 16:35:36,207 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:35:36,207 - INFO - Mean: 67.8144 +2025-04-18 16:35:36,207 - INFO - Median: 4.2447 +2025-04-18 16:35:36,207 - INFO - Min: 1.2156 +2025-04-18 16:35:36,207 - INFO - Max: 969.1721 +2025-04-18 16:35:36,207 - INFO - Std: 114.1289 +2025-04-18 16:35:36,207 - INFO - OC Distribution Statistics: +2025-04-18 16:35:36,207 - INFO - Validation Mean: 17.0631 +2025-04-18 16:35:36,207 - INFO - Validation Std: 9.5272 +2025-04-18 16:35:36,207 - INFO - Training Mean: 23.0065 +2025-04-18 16:35:36,207 - INFO - Training Std: 20.2474 +2025-04-18 16:35:36,208 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:35:36,208 - INFO - KS Statistic: 0.2196 +2025-04-18 16:35:36,208 - INFO - P-value: 0.0000 +2025-04-18 16:35:36,208 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:35:36,208 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:35:39,968 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:35:39,968 - INFO - Timestamp: 2025-04-18 16:35:39 +2025-04-18 16:35:39,968 - INFO - Validation set size: 1362 (8.25%) +2025-04-18 16:35:39,968 - INFO - Training set size: 15152 (91.75%) +2025-04-18 16:35:39,968 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:35:39,968 - INFO - Mean: 70.4194 +2025-04-18 16:35:39,969 - INFO - Median: 4.2187 +2025-04-18 16:35:39,969 - INFO - Min: 0.2243 +2025-04-18 16:35:39,969 - INFO - Max: 743.7101 +2025-04-18 16:35:39,969 - INFO - Std: 121.6578 +2025-04-18 16:35:39,969 - INFO - OC Distribution Statistics: +2025-04-18 16:35:39,969 - INFO - Validation Mean: 16.6015 +2025-04-18 16:35:39,969 - INFO - Validation Std: 8.0727 +2025-04-18 16:35:39,969 - INFO - Training Mean: 23.0594 +2025-04-18 16:35:39,969 - INFO - Training Std: 20.3049 +2025-04-18 16:35:39,969 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:35:39,969 - INFO - KS Statistic: 0.2114 +2025-04-18 16:35:39,969 - INFO - P-value: 0.0000 +2025-04-18 16:35:39,969 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:35:39,969 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:41:15,254 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:41:15,255 - INFO - Timestamp: 2025-04-18 16:41:15 +2025-04-18 16:41:15,255 - INFO - Validation set size: 1338 (8.10%) +2025-04-18 16:41:15,255 - INFO - Training set size: 15176 (91.90%) +2025-04-18 16:41:15,255 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:41:15,255 - INFO - Mean: 65.9343 +2025-04-18 16:41:15,255 - INFO - Median: 4.5729 +2025-04-18 16:41:15,255 - INFO - Min: 1.2757 +2025-04-18 16:41:15,255 - INFO - Max: 529.4376 +2025-04-18 16:41:15,255 - INFO - Std: 107.5098 +2025-04-18 16:41:15,255 - INFO - OC Distribution Statistics: +2025-04-18 16:41:15,255 - INFO - Validation Mean: 16.6233 +2025-04-18 16:41:15,255 - INFO - Validation Std: 8.3888 +2025-04-18 16:41:15,255 - INFO - Training Mean: 23.0473 +2025-04-18 16:41:15,255 - INFO - Training Std: 20.2823 +2025-04-18 16:41:15,255 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:41:15,255 - INFO - KS Statistic: 0.2071 +2025-04-18 16:41:15,255 - INFO - P-value: 0.0000 +2025-04-18 16:41:15,255 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:41:15,255 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:41:15,336 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:41:15,337 - INFO - Timestamp: 2025-04-18 16:41:15 +2025-04-18 16:41:15,337 - INFO - Validation set size: 1411 (8.54%) +2025-04-18 16:41:15,337 - INFO - Training set size: 15103 (91.46%) +2025-04-18 16:41:15,337 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:41:15,337 - INFO - Mean: 60.7245 +2025-04-18 16:41:15,337 - INFO - Median: 4.3143 +2025-04-18 16:41:15,337 - INFO - Min: 1.0711 +2025-04-18 16:41:15,337 - INFO - Max: 866.4875 +2025-04-18 16:41:15,337 - INFO - Std: 110.1491 +2025-04-18 16:41:15,337 - INFO - OC Distribution Statistics: +2025-04-18 16:41:15,337 - INFO - Validation Mean: 16.5184 +2025-04-18 16:41:15,337 - INFO - Validation Std: 8.1514 +2025-04-18 16:41:15,337 - INFO - Training Mean: 23.0881 +2025-04-18 16:41:15,337 - INFO - Training Std: 20.3238 +2025-04-18 16:41:15,337 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:41:15,337 - INFO - KS Statistic: 0.2221 +2025-04-18 16:41:15,337 - INFO - P-value: 0.0000 +2025-04-18 16:41:15,337 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:41:15,337 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:41:15,349 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:41:15,349 - INFO - Timestamp: 2025-04-18 16:41:15 +2025-04-18 16:41:15,349 - INFO - Validation set size: 1595 (9.66%) +2025-04-18 16:41:15,349 - INFO - Training set size: 14919 (90.34%) +2025-04-18 16:41:15,349 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:41:15,349 - INFO - Mean: 65.2962 +2025-04-18 16:41:15,349 - INFO - Median: 4.1444 +2025-04-18 16:41:15,349 - INFO - Min: 1.1496 +2025-04-18 16:41:15,349 - INFO - Max: 706.9584 +2025-04-18 16:41:15,349 - INFO - Std: 109.7132 +2025-04-18 16:41:15,349 - INFO - OC Distribution Statistics: +2025-04-18 16:41:15,349 - INFO - Validation Mean: 16.2822 +2025-04-18 16:41:15,349 - INFO - Validation Std: 7.8055 +2025-04-18 16:41:15,349 - INFO - Training Mean: 23.1944 +2025-04-18 16:41:15,349 - INFO - Training Std: 20.4216 +2025-04-18 16:41:15,349 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:41:15,349 - INFO - KS Statistic: 0.2439 +2025-04-18 16:41:15,349 - INFO - P-value: 0.0000 +2025-04-18 16:41:15,349 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:41:15,349 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:41:19,242 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:41:19,242 - INFO - Timestamp: 2025-04-18 16:41:19 +2025-04-18 16:41:19,242 - INFO - Validation set size: 1348 (8.16%) +2025-04-18 16:41:19,242 - INFO - Training set size: 15166 (91.84%) +2025-04-18 16:41:19,242 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:41:19,242 - INFO - Mean: 70.0595 +2025-04-18 16:41:19,242 - INFO - Median: 4.6795 +2025-04-18 16:41:19,243 - INFO - Min: 1.2156 +2025-04-18 16:41:19,243 - INFO - Max: 743.7101 +2025-04-18 16:41:19,243 - INFO - Std: 115.5479 +2025-04-18 16:41:19,243 - INFO - OC Distribution Statistics: +2025-04-18 16:41:19,243 - INFO - Validation Mean: 16.5829 +2025-04-18 16:41:19,243 - INFO - Validation Std: 7.4480 +2025-04-18 16:41:19,243 - INFO - Training Mean: 23.0551 +2025-04-18 16:41:19,243 - INFO - Training Std: 20.3186 +2025-04-18 16:41:19,243 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:41:19,243 - INFO - KS Statistic: 0.2032 +2025-04-18 16:41:19,243 - INFO - P-value: 0.0000 +2025-04-18 16:41:19,243 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:41:19,243 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:43:17,162 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:43:17,162 - INFO - Timestamp: 2025-04-18 16:43:17 +2025-04-18 16:43:17,162 - INFO - Validation set size: 1435 (8.69%) +2025-04-18 16:43:17,162 - INFO - Training set size: 15079 (91.31%) +2025-04-18 16:43:17,162 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:43:17,162 - INFO - Mean: 58.7545 +2025-04-18 16:43:17,162 - INFO - Median: 4.4045 +2025-04-18 16:43:17,162 - INFO - Min: 0.9397 +2025-04-18 16:43:17,162 - INFO - Max: 743.7101 +2025-04-18 16:43:17,162 - INFO - Std: 104.5512 +2025-04-18 16:43:17,162 - INFO - OC Distribution Statistics: +2025-04-18 16:43:17,162 - INFO - Validation Mean: 16.3338 +2025-04-18 16:43:17,162 - INFO - Validation Std: 7.4882 +2025-04-18 16:43:17,162 - INFO - Training Mean: 23.1161 +2025-04-18 16:43:17,162 - INFO - Training Std: 20.3542 +2025-04-18 16:43:17,162 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:43:17,162 - INFO - KS Statistic: 0.2289 +2025-04-18 16:43:17,162 - INFO - P-value: 0.0000 +2025-04-18 16:43:17,162 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:43:17,162 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:43:17,426 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:43:17,427 - INFO - Timestamp: 2025-04-18 16:43:17 +2025-04-18 16:43:17,427 - INFO - Validation set size: 1533 (9.28%) +2025-04-18 16:43:17,427 - INFO - Training set size: 14981 (90.72%) +2025-04-18 16:43:17,427 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:43:17,427 - INFO - Mean: 55.6033 +2025-04-18 16:43:17,427 - INFO - Median: 3.9233 +2025-04-18 16:43:17,427 - INFO - Min: 0.7391 +2025-04-18 16:43:17,427 - INFO - Max: 501.6700 +2025-04-18 16:43:17,427 - INFO - Std: 95.6023 +2025-04-18 16:43:17,427 - INFO - OC Distribution Statistics: +2025-04-18 16:43:17,427 - INFO - Validation Mean: 16.5364 +2025-04-18 16:43:17,427 - INFO - Validation Std: 8.4055 +2025-04-18 16:43:17,427 - INFO - Training Mean: 23.1398 +2025-04-18 16:43:17,427 - INFO - Training Std: 20.3745 +2025-04-18 16:43:17,427 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:43:17,427 - INFO - KS Statistic: 0.2304 +2025-04-18 16:43:17,427 - INFO - P-value: 0.0000 +2025-04-18 16:43:17,427 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:43:17,427 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:43:17,519 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:43:17,519 - INFO - Timestamp: 2025-04-18 16:43:17 +2025-04-18 16:43:17,519 - INFO - Validation set size: 1412 (8.55%) +2025-04-18 16:43:17,519 - INFO - Training set size: 15102 (91.45%) +2025-04-18 16:43:17,519 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:43:17,519 - INFO - Mean: 71.2799 +2025-04-18 16:43:17,519 - INFO - Median: 4.4333 +2025-04-18 16:43:17,519 - INFO - Min: 1.1596 +2025-04-18 16:43:17,519 - INFO - Max: 862.7749 +2025-04-18 16:43:17,520 - INFO - Std: 128.6497 +2025-04-18 16:43:17,520 - INFO - OC Distribution Statistics: +2025-04-18 16:43:17,520 - INFO - Validation Mean: 16.2987 +2025-04-18 16:43:17,520 - INFO - Validation Std: 7.1997 +2025-04-18 16:43:17,520 - INFO - Training Mean: 23.1091 +2025-04-18 16:43:17,520 - INFO - Training Std: 20.3511 +2025-04-18 16:43:17,520 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:43:17,520 - INFO - KS Statistic: 0.2220 +2025-04-18 16:43:17,520 - INFO - P-value: 0.0000 +2025-04-18 16:43:17,520 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:43:17,520 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:43:21,356 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:43:21,356 - INFO - Timestamp: 2025-04-18 16:43:21 +2025-04-18 16:43:21,356 - INFO - Validation set size: 1357 (8.22%) +2025-04-18 16:43:21,356 - INFO - Training set size: 15157 (91.78%) +2025-04-18 16:43:21,356 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:43:21,356 - INFO - Mean: 57.6279 +2025-04-18 16:43:21,356 - INFO - Median: 4.2015 +2025-04-18 16:43:21,356 - INFO - Min: 0.8514 +2025-04-18 16:43:21,356 - INFO - Max: 681.9863 +2025-04-18 16:43:21,356 - INFO - Std: 95.6303 +2025-04-18 16:43:21,356 - INFO - OC Distribution Statistics: +2025-04-18 16:43:21,356 - INFO - Validation Mean: 16.9016 +2025-04-18 16:43:21,356 - INFO - Validation Std: 8.6653 +2025-04-18 16:43:21,356 - INFO - Training Mean: 23.0304 +2025-04-18 16:43:21,356 - INFO - Training Std: 20.2888 +2025-04-18 16:43:21,356 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:43:21,356 - INFO - KS Statistic: 0.2231 +2025-04-18 16:43:21,356 - INFO - P-value: 0.0000 +2025-04-18 16:43:21,357 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:43:21,357 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:47:18,705 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:47:18,706 - INFO - Timestamp: 2025-04-18 16:47:18 +2025-04-18 16:47:18,706 - INFO - Validation set size: 1445 (8.75%) +2025-04-18 16:47:18,706 - INFO - Training set size: 15069 (91.25%) +2025-04-18 16:47:18,706 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:47:18,706 - INFO - Mean: 64.6688 +2025-04-18 16:47:18,706 - INFO - Median: 4.2711 +2025-04-18 16:47:18,706 - INFO - Min: 1.2329 +2025-04-18 16:47:18,706 - INFO - Max: 706.9584 +2025-04-18 16:47:18,706 - INFO - Std: 105.3630 +2025-04-18 16:47:18,706 - INFO - OC Distribution Statistics: +2025-04-18 16:47:18,706 - INFO - Validation Mean: 16.3744 +2025-04-18 16:47:18,706 - INFO - Validation Std: 7.1685 +2025-04-18 16:47:18,706 - INFO - Training Mean: 23.1167 +2025-04-18 16:47:18,706 - INFO - Training Std: 20.3716 +2025-04-18 16:47:18,706 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:47:18,706 - INFO - KS Statistic: 0.2091 +2025-04-18 16:47:18,706 - INFO - P-value: 0.0000 +2025-04-18 16:47:18,706 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:47:18,706 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 16:47:18,893 - INFO - Validation and Training Set Split Statistics +2025-04-18 16:47:18,893 - INFO - Timestamp: 2025-04-18 16:47:18 +2025-04-18 16:47:18,893 - INFO - Validation set size: 1422 (8.61%) +2025-04-18 16:47:18,893 - INFO - Training set size: 15092 (91.39%) +2025-04-18 16:47:18,893 - INFO - Minimum Distance Statistics (km): +2025-04-18 16:47:18,893 - INFO - Mean: 72.6572 +2025-04-18 16:47:18,893 - INFO - Median: 4.6795 +2025-04-18 16:47:18,893 - INFO - Min: 1.2065 +2025-04-18 16:47:18,893 - INFO - Max: 706.9584 +2025-04-18 16:47:18,893 - INFO - Std: 118.6527 +2025-04-18 16:47:18,893 - INFO - OC Distribution Statistics: +2025-04-18 16:47:18,893 - INFO - Validation Mean: 16.9587 +2025-04-18 16:47:18,893 - INFO - Validation Std: 8.5616 +2025-04-18 16:47:18,893 - INFO - Training Mean: 23.0514 +2025-04-18 16:47:18,893 - INFO - Training Std: 20.3263 +2025-04-18 16:47:18,893 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 16:47:18,893 - INFO - KS Statistic: 0.1782 +2025-04-18 16:47:18,893 - INFO - P-value: 0.0000 +2025-04-18 16:47:18,893 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 16:47:18,893 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:46:13,671 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:46:13,672 - INFO - Timestamp: 2025-04-18 21:46:13 +2025-04-18 21:46:13,672 - INFO - Validation set size: 1359 (8.23%) +2025-04-18 21:46:13,672 - INFO - Training set size: 15155 (91.77%) +2025-04-18 21:46:13,672 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:46:13,672 - INFO - Mean: 72.2031 +2025-04-18 21:46:13,672 - INFO - Median: 4.5713 +2025-04-18 21:46:13,672 - INFO - Min: 1.2065 +2025-04-18 21:46:13,672 - INFO - Max: 743.7101 +2025-04-18 21:46:13,672 - INFO - Std: 119.4972 +2025-04-18 21:46:13,672 - INFO - OC Distribution Statistics: +2025-04-18 21:46:13,672 - INFO - Validation Mean: 16.2392 +2025-04-18 21:46:13,672 - INFO - Validation Std: 7.4989 +2025-04-18 21:46:13,672 - INFO - Training Mean: 23.0906 +2025-04-18 21:46:13,672 - INFO - Training Std: 20.3124 +2025-04-18 21:46:13,672 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:46:13,672 - INFO - KS Statistic: 0.2349 +2025-04-18 21:46:13,672 - INFO - P-value: 0.0000 +2025-04-18 21:46:13,672 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:46:13,672 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:46:13,673 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:46:13,673 - INFO - Timestamp: 2025-04-18 21:46:13 +2025-04-18 21:46:13,673 - INFO - Validation set size: 1431 (8.67%) +2025-04-18 21:46:13,673 - INFO - Training set size: 15083 (91.33%) +2025-04-18 21:46:13,673 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:46:13,673 - INFO - Mean: 65.1133 +2025-04-18 21:46:13,673 - INFO - Median: 4.5217 +2025-04-18 21:46:13,673 - INFO - Min: 1.2065 +2025-04-18 21:46:13,673 - INFO - Max: 743.7101 +2025-04-18 21:46:13,673 - INFO - Std: 112.2801 +2025-04-18 21:46:13,673 - INFO - OC Distribution Statistics: +2025-04-18 21:46:13,673 - INFO - Validation Mean: 16.8932 +2025-04-18 21:46:13,673 - INFO - Validation Std: 7.9051 +2025-04-18 21:46:13,673 - INFO - Training Mean: 23.0613 +2025-04-18 21:46:13,674 - INFO - Training Std: 20.3541 +2025-04-18 21:46:13,674 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:46:13,674 - INFO - KS Statistic: 0.1908 +2025-04-18 21:46:13,674 - INFO - P-value: 0.0000 +2025-04-18 21:46:13,674 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:46:13,674 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:46:13,914 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:46:13,914 - INFO - Timestamp: 2025-04-18 21:46:13 +2025-04-18 21:46:13,914 - INFO - Validation set size: 1376 (8.33%) +2025-04-18 21:46:13,914 - INFO - Training set size: 15138 (91.67%) +2025-04-18 21:46:13,914 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:46:13,914 - INFO - Mean: 65.6833 +2025-04-18 21:46:13,914 - INFO - Median: 5.0148 +2025-04-18 21:46:13,914 - INFO - Min: 1.2156 +2025-04-18 21:46:13,914 - INFO - Max: 706.9584 +2025-04-18 21:46:13,914 - INFO - Std: 105.6248 +2025-04-18 21:46:13,914 - INFO - OC Distribution Statistics: +2025-04-18 21:46:13,914 - INFO - Validation Mean: 16.6594 +2025-04-18 21:46:13,914 - INFO - Validation Std: 7.3521 +2025-04-18 21:46:13,914 - INFO - Training Mean: 23.0601 +2025-04-18 21:46:13,914 - INFO - Training Std: 20.3383 +2025-04-18 21:46:13,914 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:46:13,914 - INFO - KS Statistic: 0.1940 +2025-04-18 21:46:13,914 - INFO - P-value: 0.0000 +2025-04-18 21:46:13,914 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:46:13,914 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:46:20,389 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:46:20,390 - INFO - Timestamp: 2025-04-18 21:46:20 +2025-04-18 21:46:20,390 - INFO - Validation set size: 1363 (8.25%) +2025-04-18 21:46:20,390 - INFO - Training set size: 15151 (91.75%) +2025-04-18 21:46:20,390 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:46:20,390 - INFO - Mean: 70.0349 +2025-04-18 21:46:20,390 - INFO - Median: 4.4057 +2025-04-18 21:46:20,390 - INFO - Min: 1.2287 +2025-04-18 21:46:20,390 - INFO - Max: 622.3819 +2025-04-18 21:46:20,390 - INFO - Std: 117.2186 +2025-04-18 21:46:20,390 - INFO - OC Distribution Statistics: +2025-04-18 21:46:20,390 - INFO - Validation Mean: 16.5214 +2025-04-18 21:46:20,390 - INFO - Validation Std: 7.6168 +2025-04-18 21:46:20,390 - INFO - Training Mean: 23.0670 +2025-04-18 21:46:20,390 - INFO - Training Std: 20.3189 +2025-04-18 21:46:20,390 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:46:20,390 - INFO - KS Statistic: 0.2264 +2025-04-18 21:46:20,390 - INFO - P-value: 0.0000 +2025-04-18 21:46:20,390 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:46:20,390 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:51:24,969 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:51:24,969 - INFO - Timestamp: 2025-04-18 21:51:24 +2025-04-18 21:51:24,969 - INFO - Validation set size: 1358 (8.22%) +2025-04-18 21:51:24,969 - INFO - Training set size: 15156 (91.78%) +2025-04-18 21:51:24,969 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:51:24,969 - INFO - Mean: 72.1490 +2025-04-18 21:51:24,969 - INFO - Median: 5.1405 +2025-04-18 21:51:24,969 - INFO - Min: 1.2065 +2025-04-18 21:51:24,969 - INFO - Max: 743.7101 +2025-04-18 21:51:24,969 - INFO - Std: 118.7768 +2025-04-18 21:51:24,969 - INFO - OC Distribution Statistics: +2025-04-18 21:51:24,969 - INFO - Validation Mean: 16.3640 +2025-04-18 21:51:24,969 - INFO - Validation Std: 7.3311 +2025-04-18 21:51:24,969 - INFO - Training Mean: 23.0790 +2025-04-18 21:51:24,969 - INFO - Training Std: 20.3212 +2025-04-18 21:51:24,969 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:51:24,969 - INFO - KS Statistic: 0.2238 +2025-04-18 21:51:24,969 - INFO - P-value: 0.0000 +2025-04-18 21:51:24,969 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:51:24,969 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:51:25,054 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:51:25,054 - INFO - Timestamp: 2025-04-18 21:51:25 +2025-04-18 21:51:25,054 - INFO - Validation set size: 1324 (8.02%) +2025-04-18 21:51:25,054 - INFO - Training set size: 15190 (91.98%) +2025-04-18 21:51:25,054 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:51:25,054 - INFO - Mean: 65.6977 +2025-04-18 21:51:25,054 - INFO - Median: 4.4085 +2025-04-18 21:51:25,054 - INFO - Min: 1.1220 +2025-04-18 21:51:25,054 - INFO - Max: 706.9584 +2025-04-18 21:51:25,054 - INFO - Std: 113.8539 +2025-04-18 21:51:25,054 - INFO - OC Distribution Statistics: +2025-04-18 21:51:25,054 - INFO - Validation Mean: 16.0636 +2025-04-18 21:51:25,054 - INFO - Validation Std: 7.5397 +2025-04-18 21:51:25,054 - INFO - Training Mean: 23.0901 +2025-04-18 21:51:25,054 - INFO - Training Std: 20.2883 +2025-04-18 21:51:25,054 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:51:25,054 - INFO - KS Statistic: 0.2291 +2025-04-18 21:51:25,054 - INFO - P-value: 0.0000 +2025-04-18 21:51:25,054 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:51:25,054 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:51:25,083 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:51:25,084 - INFO - Timestamp: 2025-04-18 21:51:25 +2025-04-18 21:51:25,084 - INFO - Validation set size: 1445 (8.75%) +2025-04-18 21:51:25,084 - INFO - Training set size: 15069 (91.25%) +2025-04-18 21:51:25,084 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:51:25,084 - INFO - Mean: 67.5359 +2025-04-18 21:51:25,084 - INFO - Median: 4.3277 +2025-04-18 21:51:25,084 - INFO - Min: 1.2065 +2025-04-18 21:51:25,084 - INFO - Max: 706.9584 +2025-04-18 21:51:25,084 - INFO - Std: 112.9348 +2025-04-18 21:51:25,084 - INFO - OC Distribution Statistics: +2025-04-18 21:51:25,084 - INFO - Validation Mean: 16.6475 +2025-04-18 21:51:25,084 - INFO - Validation Std: 8.3278 +2025-04-18 21:51:25,084 - INFO - Training Mean: 23.0906 +2025-04-18 21:51:25,084 - INFO - Training Std: 20.3378 +2025-04-18 21:51:25,084 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:51:25,084 - INFO - KS Statistic: 0.2205 +2025-04-18 21:51:25,084 - INFO - P-value: 0.0000 +2025-04-18 21:51:25,084 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:51:25,084 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:51:26,250 - INFO - Validation and Training Set Split Statistics +2025-04-18 21:51:26,250 - INFO - Timestamp: 2025-04-18 21:51:26 +2025-04-18 21:51:26,250 - INFO - Validation set size: 1381 (8.36%) +2025-04-18 21:51:26,250 - INFO - Training set size: 15133 (91.64%) +2025-04-18 21:51:26,250 - INFO - Minimum Distance Statistics (km): +2025-04-18 21:51:26,250 - INFO - Mean: 73.2448 +2025-04-18 21:51:26,250 - INFO - Median: 5.2688 +2025-04-18 21:51:26,250 - INFO - Min: 1.0311 +2025-04-18 21:51:26,250 - INFO - Max: 707.4892 +2025-04-18 21:51:26,250 - INFO - Std: 118.6790 +2025-04-18 21:51:26,251 - INFO - OC Distribution Statistics: +2025-04-18 21:51:26,251 - INFO - Validation Mean: 16.8179 +2025-04-18 21:51:26,251 - INFO - Validation Std: 8.6727 +2025-04-18 21:51:26,251 - INFO - Training Mean: 23.0478 +2025-04-18 21:51:26,251 - INFO - Training Std: 20.2979 +2025-04-18 21:51:26,251 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 21:51:26,251 - INFO - KS Statistic: 0.2114 +2025-04-18 21:51:26,251 - INFO - P-value: 0.0000 +2025-04-18 21:51:26,251 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 21:51:26,251 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 21:53:55,840 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 21:53:55,841 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 21:53:55,840 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 21:53:55,846 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-18 22:52:33,891 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:52:33,891 - INFO - Timestamp: 2025-04-18 22:52:33 +2025-04-18 22:52:33,891 - INFO - Validation set size: 1469 (8.90%) +2025-04-18 22:52:33,891 - INFO - Training set size: 15045 (91.10%) +2025-04-18 22:52:33,891 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:52:33,891 - INFO - Mean: 72.6864 +2025-04-18 22:52:33,891 - INFO - Median: 4.4085 +2025-04-18 22:52:33,891 - INFO - Min: 1.2065 +2025-04-18 22:52:33,891 - INFO - Max: 1063.3892 +2025-04-18 22:52:33,891 - INFO - Std: 128.2298 +2025-04-18 22:52:33,891 - INFO - OC Distribution Statistics: +2025-04-18 22:52:33,891 - INFO - Validation Mean: 16.3627 +2025-04-18 22:52:33,891 - INFO - Validation Std: 7.0916 +2025-04-18 22:52:33,892 - INFO - Training Mean: 23.1286 +2025-04-18 22:52:33,892 - INFO - Training Std: 20.3863 +2025-04-18 22:52:33,892 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:52:33,892 - INFO - KS Statistic: 0.2087 +2025-04-18 22:52:33,892 - INFO - P-value: 0.0000 +2025-04-18 22:52:33,892 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:52:33,892 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:52:33,921 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:52:33,921 - INFO - Timestamp: 2025-04-18 22:52:33 +2025-04-18 22:52:33,921 - INFO - Validation set size: 1478 (8.95%) +2025-04-18 22:52:33,921 - INFO - Training set size: 15036 (91.05%) +2025-04-18 22:52:33,921 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:52:33,921 - INFO - Mean: 67.4983 +2025-04-18 22:52:33,921 - INFO - Median: 4.3862 +2025-04-18 22:52:33,921 - INFO - Min: 1.3002 +2025-04-18 22:52:33,921 - INFO - Max: 875.2369 +2025-04-18 22:52:33,921 - INFO - Std: 120.0135 +2025-04-18 22:52:33,921 - INFO - OC Distribution Statistics: +2025-04-18 22:52:33,921 - INFO - Validation Mean: 17.0273 +2025-04-18 22:52:33,921 - INFO - Validation Std: 8.7560 +2025-04-18 22:52:33,921 - INFO - Training Mean: 23.0674 +2025-04-18 22:52:33,921 - INFO - Training Std: 20.3479 +2025-04-18 22:52:33,921 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:52:33,921 - INFO - KS Statistic: 0.1943 +2025-04-18 22:52:33,921 - INFO - P-value: 0.0000 +2025-04-18 22:52:33,921 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:52:33,921 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:52:34,129 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:52:34,129 - INFO - Timestamp: 2025-04-18 22:52:34 +2025-04-18 22:52:34,129 - INFO - Validation set size: 1567 (9.49%) +2025-04-18 22:52:34,129 - INFO - Training set size: 14947 (90.51%) +2025-04-18 22:52:34,129 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:52:34,129 - INFO - Mean: 68.2357 +2025-04-18 22:52:34,129 - INFO - Median: 4.6741 +2025-04-18 22:52:34,129 - INFO - Min: 0.7391 +2025-04-18 22:52:34,129 - INFO - Max: 862.7749 +2025-04-18 22:52:34,130 - INFO - Std: 115.8636 +2025-04-18 22:52:34,130 - INFO - OC Distribution Statistics: +2025-04-18 22:52:34,130 - INFO - Validation Mean: 16.4925 +2025-04-18 22:52:34,130 - INFO - Validation Std: 7.4899 +2025-04-18 22:52:34,130 - INFO - Training Mean: 23.1594 +2025-04-18 22:52:34,130 - INFO - Training Std: 20.4271 +2025-04-18 22:52:34,130 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:52:34,130 - INFO - KS Statistic: 0.2122 +2025-04-18 22:52:34,130 - INFO - P-value: 0.0000 +2025-04-18 22:52:34,130 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:52:34,130 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:52:37,443 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:52:37,443 - INFO - Timestamp: 2025-04-18 22:52:37 +2025-04-18 22:52:37,443 - INFO - Validation set size: 1376 (8.33%) +2025-04-18 22:52:37,443 - INFO - Training set size: 15138 (91.67%) +2025-04-18 22:52:37,443 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:52:37,443 - INFO - Mean: 67.5689 +2025-04-18 22:52:37,444 - INFO - Median: 4.5882 +2025-04-18 22:52:37,444 - INFO - Min: 1.1958 +2025-04-18 22:52:37,444 - INFO - Max: 743.7101 +2025-04-18 22:52:37,444 - INFO - Std: 114.6129 +2025-04-18 22:52:37,444 - INFO - OC Distribution Statistics: +2025-04-18 22:52:37,444 - INFO - Validation Mean: 16.5187 +2025-04-18 22:52:37,444 - INFO - Validation Std: 8.3047 +2025-04-18 22:52:37,444 - INFO - Training Mean: 23.0729 +2025-04-18 22:52:37,444 - INFO - Training Std: 20.3009 +2025-04-18 22:52:37,444 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:52:37,444 - INFO - KS Statistic: 0.2222 +2025-04-18 22:52:37,444 - INFO - P-value: 0.0000 +2025-04-18 22:52:37,444 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:52:37,444 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:57:48,321 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:57:48,321 - INFO - Timestamp: 2025-04-18 22:57:48 +2025-04-18 22:57:48,321 - INFO - Validation set size: 1472 (8.91%) +2025-04-18 22:57:48,321 - INFO - Training set size: 15042 (91.09%) +2025-04-18 22:57:48,321 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:57:48,321 - INFO - Mean: 61.8399 +2025-04-18 22:57:48,321 - INFO - Median: 4.1731 +2025-04-18 22:57:48,321 - INFO - Min: 0.8608 +2025-04-18 22:57:48,321 - INFO - Max: 875.2369 +2025-04-18 22:57:48,321 - INFO - Std: 110.6310 +2025-04-18 22:57:48,321 - INFO - OC Distribution Statistics: +2025-04-18 22:57:48,321 - INFO - Validation Mean: 16.1700 +2025-04-18 22:57:48,321 - INFO - Validation Std: 7.6636 +2025-04-18 22:57:48,321 - INFO - Training Mean: 23.1489 +2025-04-18 22:57:48,321 - INFO - Training Std: 20.3612 +2025-04-18 22:57:48,321 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:57:48,321 - INFO - KS Statistic: 0.2405 +2025-04-18 22:57:48,321 - INFO - P-value: 0.0000 +2025-04-18 22:57:48,321 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:57:48,321 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:57:48,323 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:57:48,323 - INFO - Timestamp: 2025-04-18 22:57:48 +2025-04-18 22:57:48,323 - INFO - Validation set size: 1486 (9.00%) +2025-04-18 22:57:48,323 - INFO - Training set size: 15028 (91.00%) +2025-04-18 22:57:48,323 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:57:48,323 - INFO - Mean: 58.5528 +2025-04-18 22:57:48,323 - INFO - Median: 4.0444 +2025-04-18 22:57:48,323 - INFO - Min: 1.1596 +2025-04-18 22:57:48,323 - INFO - Max: 743.7101 +2025-04-18 22:57:48,323 - INFO - Std: 106.9426 +2025-04-18 22:57:48,323 - INFO - OC Distribution Statistics: +2025-04-18 22:57:48,323 - INFO - Validation Mean: 16.0394 +2025-04-18 22:57:48,323 - INFO - Validation Std: 8.0406 +2025-04-18 22:57:48,323 - INFO - Training Mean: 23.1683 +2025-04-18 22:57:48,323 - INFO - Training Std: 20.3494 +2025-04-18 22:57:48,323 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:57:48,323 - INFO - KS Statistic: 0.2374 +2025-04-18 22:57:48,323 - INFO - P-value: 0.0000 +2025-04-18 22:57:48,323 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:57:48,323 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:57:48,324 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:57:48,324 - INFO - Timestamp: 2025-04-18 22:57:48 +2025-04-18 22:57:48,324 - INFO - Validation set size: 1396 (8.45%) +2025-04-18 22:57:48,324 - INFO - Training set size: 15118 (91.55%) +2025-04-18 22:57:48,324 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:57:48,324 - INFO - Mean: 66.8166 +2025-04-18 22:57:48,324 - INFO - Median: 5.1691 +2025-04-18 22:57:48,324 - INFO - Min: 1.2065 +2025-04-18 22:57:48,324 - INFO - Max: 525.8753 +2025-04-18 22:57:48,324 - INFO - Std: 106.3483 +2025-04-18 22:57:48,324 - INFO - OC Distribution Statistics: +2025-04-18 22:57:48,324 - INFO - Validation Mean: 16.5948 +2025-04-18 22:57:48,325 - INFO - Validation Std: 7.3955 +2025-04-18 22:57:48,325 - INFO - Training Mean: 23.0745 +2025-04-18 22:57:48,325 - INFO - Training Std: 20.3453 +2025-04-18 22:57:48,325 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:57:48,325 - INFO - KS Statistic: 0.2242 +2025-04-18 22:57:48,325 - INFO - P-value: 0.0000 +2025-04-18 22:57:48,325 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:57:48,325 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:57:52,049 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:57:52,050 - INFO - Timestamp: 2025-04-18 22:57:52 +2025-04-18 22:57:52,050 - INFO - Validation set size: 1328 (8.04%) +2025-04-18 22:57:52,050 - INFO - Training set size: 15186 (91.96%) +2025-04-18 22:57:52,050 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:57:52,050 - INFO - Mean: 73.3313 +2025-04-18 22:57:52,050 - INFO - Median: 5.1368 +2025-04-18 22:57:52,050 - INFO - Min: 0.5501 +2025-04-18 22:57:52,050 - INFO - Max: 862.7749 +2025-04-18 22:57:52,050 - INFO - Std: 121.5579 +2025-04-18 22:57:52,050 - INFO - OC Distribution Statistics: +2025-04-18 22:57:52,050 - INFO - Validation Mean: 16.4145 +2025-04-18 22:57:52,050 - INFO - Validation Std: 7.3719 +2025-04-18 22:57:52,050 - INFO - Training Mean: 23.0613 +2025-04-18 22:57:52,050 - INFO - Training Std: 20.3060 +2025-04-18 22:57:52,050 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:57:52,050 - INFO - KS Statistic: 0.2084 +2025-04-18 22:57:52,050 - INFO - P-value: 0.0000 +2025-04-18 22:57:52,050 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:57:52,050 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:59:00,830 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:59:00,830 - INFO - Timestamp: 2025-04-18 22:59:00 +2025-04-18 22:59:00,830 - INFO - Validation set size: 1395 (8.45%) +2025-04-18 22:59:00,830 - INFO - Training set size: 15119 (91.55%) +2025-04-18 22:59:00,830 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:59:00,830 - INFO - Mean: 67.6879 +2025-04-18 22:59:00,830 - INFO - Median: 4.3243 +2025-04-18 22:59:00,830 - INFO - Min: 0.8124 +2025-04-18 22:59:00,830 - INFO - Max: 706.9584 +2025-04-18 22:59:00,831 - INFO - Std: 106.2698 +2025-04-18 22:59:00,831 - INFO - OC Distribution Statistics: +2025-04-18 22:59:00,831 - INFO - Validation Mean: 16.5028 +2025-04-18 22:59:00,831 - INFO - Validation Std: 7.5727 +2025-04-18 22:59:00,831 - INFO - Training Mean: 23.0826 +2025-04-18 22:59:00,831 - INFO - Training Std: 20.3361 +2025-04-18 22:59:00,831 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:59:00,831 - INFO - KS Statistic: 0.2045 +2025-04-18 22:59:00,831 - INFO - P-value: 0.0000 +2025-04-18 22:59:00,831 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:59:00,831 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:59:01,063 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:59:01,063 - INFO - Timestamp: 2025-04-18 22:59:01 +2025-04-18 22:59:01,063 - INFO - Validation set size: 1403 (8.50%) +2025-04-18 22:59:01,063 - INFO - Training set size: 15111 (91.50%) +2025-04-18 22:59:01,063 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:59:01,063 - INFO - Mean: 75.2748 +2025-04-18 22:59:01,063 - INFO - Median: 4.9833 +2025-04-18 22:59:01,063 - INFO - Min: 1.0890 +2025-04-18 22:59:01,063 - INFO - Max: 870.7197 +2025-04-18 22:59:01,063 - INFO - Std: 128.2944 +2025-04-18 22:59:01,063 - INFO - OC Distribution Statistics: +2025-04-18 22:59:01,063 - INFO - Validation Mean: 16.5007 +2025-04-18 22:59:01,063 - INFO - Validation Std: 8.0295 +2025-04-18 22:59:01,063 - INFO - Training Mean: 23.0863 +2025-04-18 22:59:01,063 - INFO - Training Std: 20.3238 +2025-04-18 22:59:01,063 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:59:01,063 - INFO - KS Statistic: 0.2177 +2025-04-18 22:59:01,063 - INFO - P-value: 0.0000 +2025-04-18 22:59:01,063 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:59:01,063 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:59:01,066 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:59:01,066 - INFO - Timestamp: 2025-04-18 22:59:01 +2025-04-18 22:59:01,066 - INFO - Validation set size: 1381 (8.36%) +2025-04-18 22:59:01,066 - INFO - Training set size: 15133 (91.64%) +2025-04-18 22:59:01,066 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:59:01,066 - INFO - Mean: 65.8322 +2025-04-18 22:59:01,066 - INFO - Median: 4.3609 +2025-04-18 22:59:01,066 - INFO - Min: 1.2773 +2025-04-18 22:59:01,066 - INFO - Max: 862.7749 +2025-04-18 22:59:01,066 - INFO - Std: 114.0796 +2025-04-18 22:59:01,066 - INFO - OC Distribution Statistics: +2025-04-18 22:59:01,066 - INFO - Validation Mean: 16.5686 +2025-04-18 22:59:01,066 - INFO - Validation Std: 8.2669 +2025-04-18 22:59:01,066 - INFO - Training Mean: 23.0705 +2025-04-18 22:59:01,066 - INFO - Training Std: 20.3062 +2025-04-18 22:59:01,066 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:59:01,066 - INFO - KS Statistic: 0.2195 +2025-04-18 22:59:01,066 - INFO - P-value: 0.0000 +2025-04-18 22:59:01,066 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:59:01,066 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 22:59:05,068 - INFO - Validation and Training Set Split Statistics +2025-04-18 22:59:05,068 - INFO - Timestamp: 2025-04-18 22:59:05 +2025-04-18 22:59:05,068 - INFO - Validation set size: 1436 (8.70%) +2025-04-18 22:59:05,068 - INFO - Training set size: 15078 (91.30%) +2025-04-18 22:59:05,068 - INFO - Minimum Distance Statistics (km): +2025-04-18 22:59:05,068 - INFO - Mean: 70.1643 +2025-04-18 22:59:05,069 - INFO - Median: 5.1441 +2025-04-18 22:59:05,069 - INFO - Min: 0.2243 +2025-04-18 22:59:05,069 - INFO - Max: 706.1646 +2025-04-18 22:59:05,069 - INFO - Std: 112.2612 +2025-04-18 22:59:05,069 - INFO - OC Distribution Statistics: +2025-04-18 22:59:05,069 - INFO - Validation Mean: 16.6640 +2025-04-18 22:59:05,069 - INFO - Validation Std: 8.8085 +2025-04-18 22:59:05,069 - INFO - Training Mean: 23.0851 +2025-04-18 22:59:05,069 - INFO - Training Std: 20.3145 +2025-04-18 22:59:05,069 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 22:59:05,069 - INFO - KS Statistic: 0.2119 +2025-04-18 22:59:05,069 - INFO - P-value: 0.0000 +2025-04-18 22:59:05,069 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 22:59:05,069 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 23:05:05,793 - INFO - Validation and Training Set Split Statistics +2025-04-18 23:05:05,793 - INFO - Timestamp: 2025-04-18 23:05:05 +2025-04-18 23:05:05,793 - INFO - Validation set size: 1353 (8.19%) +2025-04-18 23:05:05,794 - INFO - Training set size: 15161 (91.81%) +2025-04-18 23:05:05,794 - INFO - Minimum Distance Statistics (km): +2025-04-18 23:05:05,794 - INFO - Mean: 63.5997 +2025-04-18 23:05:05,794 - INFO - Median: 4.4525 +2025-04-18 23:05:05,794 - INFO - Min: 0.8728 +2025-04-18 23:05:05,794 - INFO - Max: 888.3558 +2025-04-18 23:05:05,794 - INFO - Std: 116.2908 +2025-04-18 23:05:05,794 - INFO - OC Distribution Statistics: +2025-04-18 23:05:05,794 - INFO - Validation Mean: 16.7552 +2025-04-18 23:05:05,794 - INFO - Validation Std: 7.6558 +2025-04-18 23:05:05,794 - INFO - Training Mean: 23.0419 +2025-04-18 23:05:05,794 - INFO - Training Std: 20.3191 +2025-04-18 23:05:05,794 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 23:05:05,794 - INFO - KS Statistic: 0.1811 +2025-04-18 23:05:05,794 - INFO - P-value: 0.0000 +2025-04-18 23:05:05,794 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 23:05:05,794 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 23:05:06,073 - INFO - Validation and Training Set Split Statistics +2025-04-18 23:05:06,074 - INFO - Timestamp: 2025-04-18 23:05:06 +2025-04-18 23:05:06,074 - INFO - Validation set size: 1409 (8.53%) +2025-04-18 23:05:06,074 - INFO - Training set size: 15105 (91.47%) +2025-04-18 23:05:06,074 - INFO - Minimum Distance Statistics (km): +2025-04-18 23:05:06,074 - INFO - Mean: 68.6945 +2025-04-18 23:05:06,074 - INFO - Median: 4.3243 +2025-04-18 23:05:06,074 - INFO - Min: 0.5501 +2025-04-18 23:05:06,074 - INFO - Max: 706.9584 +2025-04-18 23:05:06,074 - INFO - Std: 115.0098 +2025-04-18 23:05:06,074 - INFO - OC Distribution Statistics: +2025-04-18 23:05:06,074 - INFO - Validation Mean: 16.0329 +2025-04-18 23:05:06,074 - INFO - Validation Std: 7.0994 +2025-04-18 23:05:06,074 - INFO - Training Mean: 23.1325 +2025-04-18 23:05:06,074 - INFO - Training Std: 20.3444 +2025-04-18 23:05:06,074 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 23:05:06,074 - INFO - KS Statistic: 0.2270 +2025-04-18 23:05:06,074 - INFO - P-value: 0.0000 +2025-04-18 23:05:06,074 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 23:05:06,074 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 23:05:06,181 - INFO - Validation and Training Set Split Statistics +2025-04-18 23:05:06,181 - INFO - Timestamp: 2025-04-18 23:05:06 +2025-04-18 23:05:06,181 - INFO - Validation set size: 1579 (9.56%) +2025-04-18 23:05:06,181 - INFO - Training set size: 14935 (90.44%) +2025-04-18 23:05:06,181 - INFO - Minimum Distance Statistics (km): +2025-04-18 23:05:06,181 - INFO - Mean: 64.6964 +2025-04-18 23:05:06,181 - INFO - Median: 4.4155 +2025-04-18 23:05:06,181 - INFO - Min: 0.6067 +2025-04-18 23:05:06,181 - INFO - Max: 716.7388 +2025-04-18 23:05:06,181 - INFO - Std: 112.3917 +2025-04-18 23:05:06,181 - INFO - OC Distribution Statistics: +2025-04-18 23:05:06,181 - INFO - Validation Mean: 16.4083 +2025-04-18 23:05:06,181 - INFO - Validation Std: 7.8808 +2025-04-18 23:05:06,181 - INFO - Training Mean: 23.1737 +2025-04-18 23:05:06,181 - INFO - Training Std: 20.4149 +2025-04-18 23:05:06,181 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 23:05:06,181 - INFO - KS Statistic: 0.2425 +2025-04-18 23:05:06,181 - INFO - P-value: 0.0000 +2025-04-18 23:05:06,181 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 23:05:06,181 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 23:05:10,141 - INFO - Validation and Training Set Split Statistics +2025-04-18 23:05:10,141 - INFO - Timestamp: 2025-04-18 23:05:10 +2025-04-18 23:05:10,141 - INFO - Validation set size: 1407 (8.52%) +2025-04-18 23:05:10,141 - INFO - Training set size: 15107 (91.48%) +2025-04-18 23:05:10,141 - INFO - Minimum Distance Statistics (km): +2025-04-18 23:05:10,141 - INFO - Mean: 68.6777 +2025-04-18 23:05:10,141 - INFO - Median: 4.5236 +2025-04-18 23:05:10,141 - INFO - Min: 1.2156 +2025-04-18 23:05:10,141 - INFO - Max: 917.1429 +2025-04-18 23:05:10,141 - INFO - Std: 114.7726 +2025-04-18 23:05:10,141 - INFO - OC Distribution Statistics: +2025-04-18 23:05:10,141 - INFO - Validation Mean: 16.5787 +2025-04-18 23:05:10,141 - INFO - Validation Std: 8.1643 +2025-04-18 23:05:10,141 - INFO - Training Mean: 23.0808 +2025-04-18 23:05:10,141 - INFO - Training Std: 20.3232 +2025-04-18 23:05:10,141 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 23:05:10,141 - INFO - KS Statistic: 0.2531 +2025-04-18 23:05:10,141 - INFO - P-value: 0.0000 +2025-04-18 23:05:10,141 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 23:05:10,141 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 23:23:23,736 - INFO - Validation and Training Set Split Statistics +2025-04-18 23:23:23,736 - INFO - Timestamp: 2025-04-18 23:23:23 +2025-04-18 23:23:23,736 - INFO - Validation set size: 1500 (9.08%) +2025-04-18 23:23:23,736 - INFO - Training set size: 15014 (90.92%) +2025-04-18 23:23:23,736 - INFO - Minimum Distance Statistics (km): +2025-04-18 23:23:23,736 - INFO - Mean: 62.0901 +2025-04-18 23:23:23,736 - INFO - Median: 4.1847 +2025-04-18 23:23:23,736 - INFO - Min: 1.0321 +2025-04-18 23:23:23,736 - INFO - Max: 743.7101 +2025-04-18 23:23:23,736 - INFO - Std: 103.1029 +2025-04-18 23:23:23,736 - INFO - OC Distribution Statistics: +2025-04-18 23:23:23,736 - INFO - Validation Mean: 16.6552 +2025-04-18 23:23:23,736 - INFO - Validation Std: 8.8692 +2025-04-18 23:23:23,736 - INFO - Training Mean: 23.1134 +2025-04-18 23:23:23,736 - INFO - Training Std: 20.3424 +2025-04-18 23:23:23,736 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 23:23:23,736 - INFO - KS Statistic: 0.2210 +2025-04-18 23:23:23,736 - INFO - P-value: 0.0000 +2025-04-18 23:23:23,736 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 23:23:23,736 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 23:23:23,762 - INFO - Validation and Training Set Split Statistics +2025-04-18 23:23:23,762 - INFO - Timestamp: 2025-04-18 23:23:23 +2025-04-18 23:23:23,762 - INFO - Validation set size: 1487 (9.00%) +2025-04-18 23:23:23,762 - INFO - Training set size: 15027 (91.00%) +2025-04-18 23:23:23,762 - INFO - Minimum Distance Statistics (km): +2025-04-18 23:23:23,762 - INFO - Mean: 64.3895 +2025-04-18 23:23:23,762 - INFO - Median: 4.3323 +2025-04-18 23:23:23,762 - INFO - Min: 1.2156 +2025-04-18 23:23:23,762 - INFO - Max: 680.6553 +2025-04-18 23:23:23,762 - INFO - Std: 106.3947 +2025-04-18 23:23:23,762 - INFO - OC Distribution Statistics: +2025-04-18 23:23:23,762 - INFO - Validation Mean: 16.7661 +2025-04-18 23:23:23,762 - INFO - Validation Std: 8.7042 +2025-04-18 23:23:23,762 - INFO - Training Mean: 23.0968 +2025-04-18 23:23:23,762 - INFO - Training Std: 20.3466 +2025-04-18 23:23:23,762 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 23:23:23,762 - INFO - KS Statistic: 0.2225 +2025-04-18 23:23:23,762 - INFO - P-value: 0.0000 +2025-04-18 23:23:23,762 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 23:23:23,762 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 23:23:23,852 - INFO - Validation and Training Set Split Statistics +2025-04-18 23:23:23,853 - INFO - Timestamp: 2025-04-18 23:23:23 +2025-04-18 23:23:23,853 - INFO - Validation set size: 1568 (9.49%) +2025-04-18 23:23:23,853 - INFO - Training set size: 14946 (90.51%) +2025-04-18 23:23:23,853 - INFO - Minimum Distance Statistics (km): +2025-04-18 23:23:23,853 - INFO - Mean: 63.1710 +2025-04-18 23:23:23,853 - INFO - Median: 4.0138 +2025-04-18 23:23:23,853 - INFO - Min: 0.7391 +2025-04-18 23:23:23,853 - INFO - Max: 706.9584 +2025-04-18 23:23:23,853 - INFO - Std: 113.0555 +2025-04-18 23:23:23,853 - INFO - OC Distribution Statistics: +2025-04-18 23:23:23,853 - INFO - Validation Mean: 15.9241 +2025-04-18 23:23:23,853 - INFO - Validation Std: 7.2055 +2025-04-18 23:23:23,853 - INFO - Training Mean: 23.2195 +2025-04-18 23:23:23,853 - INFO - Training Std: 20.4180 +2025-04-18 23:23:23,853 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 23:23:23,853 - INFO - KS Statistic: 0.2525 +2025-04-18 23:23:23,853 - INFO - P-value: 0.0000 +2025-04-18 23:23:23,853 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 23:23:23,853 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 23:23:27,691 - INFO - Validation and Training Set Split Statistics +2025-04-18 23:23:27,691 - INFO - Timestamp: 2025-04-18 23:23:27 +2025-04-18 23:23:27,691 - INFO - Validation set size: 1363 (8.25%) +2025-04-18 23:23:27,691 - INFO - Training set size: 15151 (91.75%) +2025-04-18 23:23:27,691 - INFO - Minimum Distance Statistics (km): +2025-04-18 23:23:27,691 - INFO - Mean: 59.0700 +2025-04-18 23:23:27,691 - INFO - Median: 4.0201 +2025-04-18 23:23:27,691 - INFO - Min: 0.2243 +2025-04-18 23:23:27,691 - INFO - Max: 706.9584 +2025-04-18 23:23:27,691 - INFO - Std: 105.6457 +2025-04-18 23:23:27,691 - INFO - OC Distribution Statistics: +2025-04-18 23:23:27,691 - INFO - Validation Mean: 16.4237 +2025-04-18 23:23:27,691 - INFO - Validation Std: 7.7896 +2025-04-18 23:23:27,691 - INFO - Training Mean: 23.0758 +2025-04-18 23:23:27,691 - INFO - Training Std: 20.3101 +2025-04-18 23:23:27,691 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 23:23:27,691 - INFO - KS Statistic: 0.2304 +2025-04-18 23:23:27,691 - INFO - P-value: 0.0000 +2025-04-18 23:23:27,691 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 23:23:27,692 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-18 23:50:38,782 - INFO - Validation and Training Set Split Statistics +2025-04-18 23:50:38,783 - INFO - Timestamp: 2025-04-18 23:50:38 +2025-04-18 23:50:38,783 - INFO - Validation set size: 1617 (9.79%) +2025-04-18 23:50:38,783 - INFO - Training set size: 14897 (90.21%) +2025-04-18 23:50:38,783 - INFO - Minimum Distance Statistics (km): +2025-04-18 23:50:38,783 - INFO - Mean: 64.3459 +2025-04-18 23:50:38,783 - INFO - Median: 4.2103 +2025-04-18 23:50:38,783 - INFO - Min: 1.0311 +2025-04-18 23:50:38,783 - INFO - Max: 862.7749 +2025-04-18 23:50:38,783 - INFO - Std: 117.2962 +2025-04-18 23:50:38,783 - INFO - OC Distribution Statistics: +2025-04-18 23:50:38,783 - INFO - Validation Mean: 16.5036 +2025-04-18 23:50:38,783 - INFO - Validation Std: 7.3940 +2025-04-18 23:50:38,783 - INFO - Training Mean: 23.1806 +2025-04-18 23:50:38,783 - INFO - Training Std: 20.4573 +2025-04-18 23:50:38,783 - INFO - Kolmogorov-Smirnov Test: +2025-04-18 23:50:38,783 - INFO - KS Statistic: 0.1975 +2025-04-18 23:50:38,783 - INFO - P-value: 0.0000 +2025-04-18 23:50:38,783 - INFO - Distribution Fit: Inverse Gamma +2025-04-18 23:50:38,783 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 00:30:53,208 - INFO - Validation and Training Set Split Statistics +2025-04-19 00:30:53,208 - INFO - Timestamp: 2025-04-19 00:30:53 +2025-04-19 00:30:53,208 - INFO - Validation set size: 1368 (8.28%) +2025-04-19 00:30:53,208 - INFO - Training set size: 15146 (91.72%) +2025-04-19 00:30:53,208 - INFO - Minimum Distance Statistics (km): +2025-04-19 00:30:53,208 - INFO - Mean: 68.6850 +2025-04-19 00:30:53,208 - INFO - Median: 5.1441 +2025-04-19 00:30:53,208 - INFO - Min: 0.7102 +2025-04-19 00:30:53,208 - INFO - Max: 706.9584 +2025-04-19 00:30:53,208 - INFO - Std: 112.6535 +2025-04-19 00:30:53,208 - INFO - OC Distribution Statistics: +2025-04-19 00:30:53,208 - INFO - Validation Mean: 16.6175 +2025-04-19 00:30:53,208 - INFO - Validation Std: 7.4984 +2025-04-19 00:30:53,208 - INFO - Training Mean: 23.0605 +2025-04-19 00:30:53,208 - INFO - Training Std: 20.3282 +2025-04-19 00:30:53,208 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 00:30:53,208 - INFO - KS Statistic: 0.1999 +2025-04-19 00:30:53,208 - INFO - P-value: 0.0000 +2025-04-19 00:30:53,208 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 00:30:53,208 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 00:30:53,230 - INFO - Validation and Training Set Split Statistics +2025-04-19 00:30:53,230 - INFO - Timestamp: 2025-04-19 00:30:53 +2025-04-19 00:30:53,230 - INFO - Validation set size: 1410 (8.54%) +2025-04-19 00:30:53,230 - INFO - Training set size: 15104 (91.46%) +2025-04-19 00:30:53,230 - INFO - Minimum Distance Statistics (km): +2025-04-19 00:30:53,230 - INFO - Mean: 72.8422 +2025-04-19 00:30:53,230 - INFO - Median: 4.5882 +2025-04-19 00:30:53,230 - INFO - Min: 1.1220 +2025-04-19 00:30:53,230 - INFO - Max: 706.9584 +2025-04-19 00:30:53,230 - INFO - Std: 118.6004 +2025-04-19 00:30:53,231 - INFO - OC Distribution Statistics: +2025-04-19 00:30:53,231 - INFO - Validation Mean: 17.0206 +2025-04-19 00:30:53,231 - INFO - Validation Std: 7.6371 +2025-04-19 00:30:53,231 - INFO - Training Mean: 23.0408 +2025-04-19 00:30:53,231 - INFO - Training Std: 20.3565 +2025-04-19 00:30:53,231 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 00:30:53,231 - INFO - KS Statistic: 0.1831 +2025-04-19 00:30:53,231 - INFO - P-value: 0.0000 +2025-04-19 00:30:53,231 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 00:30:53,231 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 00:30:53,324 - INFO - Validation and Training Set Split Statistics +2025-04-19 00:30:53,324 - INFO - Timestamp: 2025-04-19 00:30:53 +2025-04-19 00:30:53,324 - INFO - Validation set size: 1426 (8.64%) +2025-04-19 00:30:53,324 - INFO - Training set size: 15088 (91.36%) +2025-04-19 00:30:53,324 - INFO - Minimum Distance Statistics (km): +2025-04-19 00:30:53,324 - INFO - Mean: 69.8631 +2025-04-19 00:30:53,324 - INFO - Median: 4.6080 +2025-04-19 00:30:53,324 - INFO - Min: 1.2065 +2025-04-19 00:30:53,324 - INFO - Max: 875.2369 +2025-04-19 00:30:53,324 - INFO - Std: 121.3386 +2025-04-19 00:30:53,324 - INFO - OC Distribution Statistics: +2025-04-19 00:30:53,324 - INFO - Validation Mean: 16.2853 +2025-04-19 00:30:53,324 - INFO - Validation Std: 6.9475 +2025-04-19 00:30:53,324 - INFO - Training Mean: 23.1167 +2025-04-19 00:30:53,324 - INFO - Training Std: 20.3662 +2025-04-19 00:30:53,324 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 00:30:53,324 - INFO - KS Statistic: 0.2313 +2025-04-19 00:30:53,324 - INFO - P-value: 0.0000 +2025-04-19 00:30:53,324 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 00:30:53,324 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 00:30:54,391 - INFO - Validation and Training Set Split Statistics +2025-04-19 00:30:54,391 - INFO - Timestamp: 2025-04-19 00:30:54 +2025-04-19 00:30:54,391 - INFO - Validation set size: 1458 (8.83%) +2025-04-19 00:30:54,391 - INFO - Training set size: 15056 (91.17%) +2025-04-19 00:30:54,391 - INFO - Minimum Distance Statistics (km): +2025-04-19 00:30:54,391 - INFO - Mean: 70.8684 +2025-04-19 00:30:54,391 - INFO - Median: 4.8643 +2025-04-19 00:30:54,391 - INFO - Min: 1.0596 +2025-04-19 00:30:54,391 - INFO - Max: 862.7749 +2025-04-19 00:30:54,391 - INFO - Std: 114.7370 +2025-04-19 00:30:54,391 - INFO - OC Distribution Statistics: +2025-04-19 00:30:54,391 - INFO - Validation Mean: 16.6034 +2025-04-19 00:30:54,391 - INFO - Validation Std: 7.6732 +2025-04-19 00:30:54,392 - INFO - Training Mean: 23.1004 +2025-04-19 00:30:54,392 - INFO - Training Std: 20.3677 +2025-04-19 00:30:54,392 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 00:30:54,392 - INFO - KS Statistic: 0.2122 +2025-04-19 00:30:54,392 - INFO - P-value: 0.0000 +2025-04-19 00:30:54,392 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 00:30:54,392 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 00:33:24,131 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 00:33:24,131 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 00:33:24,131 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 00:33:24,150 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 00:43:06,561 - INFO - Validation and Training Set Split Statistics +2025-04-19 00:43:06,561 - INFO - Timestamp: 2025-04-19 00:43:06 +2025-04-19 00:43:06,561 - INFO - Validation set size: 1421 (8.60%) +2025-04-19 00:43:06,561 - INFO - Training set size: 15093 (91.40%) +2025-04-19 00:43:06,561 - INFO - Minimum Distance Statistics (km): +2025-04-19 00:43:06,561 - INFO - Mean: 62.1360 +2025-04-19 00:43:06,561 - INFO - Median: 4.4333 +2025-04-19 00:43:06,561 - INFO - Min: 0.7371 +2025-04-19 00:43:06,561 - INFO - Max: 706.9584 +2025-04-19 00:43:06,561 - INFO - Std: 101.5789 +2025-04-19 00:43:06,561 - INFO - OC Distribution Statistics: +2025-04-19 00:43:06,561 - INFO - Validation Mean: 16.6893 +2025-04-19 00:43:06,561 - INFO - Validation Std: 7.8682 +2025-04-19 00:43:06,561 - INFO - Training Mean: 23.0764 +2025-04-19 00:43:06,561 - INFO - Training Std: 20.3444 +2025-04-19 00:43:06,561 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 00:43:06,561 - INFO - KS Statistic: 0.2185 +2025-04-19 00:43:06,561 - INFO - P-value: 0.0000 +2025-04-19 00:43:06,561 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 00:43:06,561 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 00:43:06,845 - INFO - Validation and Training Set Split Statistics +2025-04-19 00:43:06,845 - INFO - Timestamp: 2025-04-19 00:43:06 +2025-04-19 00:43:06,845 - INFO - Validation set size: 1599 (9.68%) +2025-04-19 00:43:06,845 - INFO - Training set size: 14915 (90.32%) +2025-04-19 00:43:06,845 - INFO - Minimum Distance Statistics (km): +2025-04-19 00:43:06,846 - INFO - Mean: 67.4171 +2025-04-19 00:43:06,846 - INFO - Median: 4.6101 +2025-04-19 00:43:06,846 - INFO - Min: 1.0311 +2025-04-19 00:43:06,846 - INFO - Max: 743.7101 +2025-04-19 00:43:06,846 - INFO - Std: 119.5413 +2025-04-19 00:43:06,846 - INFO - OC Distribution Statistics: +2025-04-19 00:43:06,846 - INFO - Validation Mean: 16.4015 +2025-04-19 00:43:06,846 - INFO - Validation Std: 7.5159 +2025-04-19 00:43:06,846 - INFO - Training Mean: 23.1835 +2025-04-19 00:43:06,846 - INFO - Training Std: 20.4395 +2025-04-19 00:43:06,846 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 00:43:06,846 - INFO - KS Statistic: 0.2142 +2025-04-19 00:43:06,846 - INFO - P-value: 0.0000 +2025-04-19 00:43:06,846 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 00:43:06,846 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 00:43:06,865 - INFO - Validation and Training Set Split Statistics +2025-04-19 00:43:06,865 - INFO - Timestamp: 2025-04-19 00:43:06 +2025-04-19 00:43:06,865 - INFO - Validation set size: 1601 (9.69%) +2025-04-19 00:43:06,865 - INFO - Training set size: 14913 (90.31%) +2025-04-19 00:43:06,865 - INFO - Minimum Distance Statistics (km): +2025-04-19 00:43:06,865 - INFO - Mean: 60.2624 +2025-04-19 00:43:06,865 - INFO - Median: 3.7661 +2025-04-19 00:43:06,865 - INFO - Min: 1.1220 +2025-04-19 00:43:06,865 - INFO - Max: 875.2369 +2025-04-19 00:43:06,865 - INFO - Std: 117.6367 +2025-04-19 00:43:06,865 - INFO - OC Distribution Statistics: +2025-04-19 00:43:06,865 - INFO - Validation Mean: 16.6044 +2025-04-19 00:43:06,865 - INFO - Validation Std: 7.7650 +2025-04-19 00:43:06,865 - INFO - Training Mean: 23.1626 +2025-04-19 00:43:06,865 - INFO - Training Std: 20.4377 +2025-04-19 00:43:06,865 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 00:43:06,865 - INFO - KS Statistic: 0.2023 +2025-04-19 00:43:06,865 - INFO - P-value: 0.0000 +2025-04-19 00:43:06,865 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 00:43:06,865 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 00:48:16,656 - INFO - Validation and Training Set Split Statistics +2025-04-19 00:48:16,656 - INFO - Timestamp: 2025-04-19 00:48:16 +2025-04-19 00:48:16,656 - INFO - Validation set size: 1330 (8.05%) +2025-04-19 00:48:16,656 - INFO - Training set size: 15184 (91.95%) +2025-04-19 00:48:16,656 - INFO - Minimum Distance Statistics (km): +2025-04-19 00:48:16,656 - INFO - Mean: 70.7668 +2025-04-19 00:48:16,656 - INFO - Median: 4.5760 +2025-04-19 00:48:16,656 - INFO - Min: 1.2065 +2025-04-19 00:48:16,656 - INFO - Max: 601.2913 +2025-04-19 00:48:16,656 - INFO - Std: 108.7697 +2025-04-19 00:48:16,656 - INFO - OC Distribution Statistics: +2025-04-19 00:48:16,656 - INFO - Validation Mean: 17.0482 +2025-04-19 00:48:16,656 - INFO - Validation Std: 8.9905 +2025-04-19 00:48:16,656 - INFO - Training Mean: 23.0067 +2025-04-19 00:48:16,656 - INFO - Training Std: 20.2671 +2025-04-19 00:48:16,656 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 00:48:16,656 - INFO - KS Statistic: 0.1914 +2025-04-19 00:48:16,656 - INFO - P-value: 0.0000 +2025-04-19 00:48:16,656 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 00:48:16,656 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 00:48:16,687 - INFO - Validation and Training Set Split Statistics +2025-04-19 00:48:16,687 - INFO - Timestamp: 2025-04-19 00:48:16 +2025-04-19 00:48:16,687 - INFO - Validation set size: 1525 (9.23%) +2025-04-19 00:48:16,688 - INFO - Training set size: 14989 (90.77%) +2025-04-19 00:48:16,688 - INFO - Minimum Distance Statistics (km): +2025-04-19 00:48:16,688 - INFO - Mean: 61.7840 +2025-04-19 00:48:16,688 - INFO - Median: 4.4468 +2025-04-19 00:48:16,688 - INFO - Min: 1.1958 +2025-04-19 00:48:16,688 - INFO - Max: 875.2369 +2025-04-19 00:48:16,688 - INFO - Std: 108.4928 +2025-04-19 00:48:16,688 - INFO - OC Distribution Statistics: +2025-04-19 00:48:16,688 - INFO - Validation Mean: 16.7385 +2025-04-19 00:48:16,688 - INFO - Validation Std: 7.6891 +2025-04-19 00:48:16,688 - INFO - Training Mean: 23.1157 +2025-04-19 00:48:16,688 - INFO - Training Std: 20.4058 +2025-04-19 00:48:16,688 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 00:48:16,688 - INFO - KS Statistic: 0.1939 +2025-04-19 00:48:16,688 - INFO - P-value: 0.0000 +2025-04-19 00:48:16,688 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 00:48:16,688 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 00:48:16,998 - INFO - Validation and Training Set Split Statistics +2025-04-19 00:48:16,998 - INFO - Timestamp: 2025-04-19 00:48:16 +2025-04-19 00:48:16,998 - INFO - Validation set size: 1707 (10.34%) +2025-04-19 00:48:16,998 - INFO - Training set size: 14807 (89.66%) +2025-04-19 00:48:16,998 - INFO - Minimum Distance Statistics (km): +2025-04-19 00:48:16,999 - INFO - Mean: 63.1075 +2025-04-19 00:48:16,999 - INFO - Median: 4.2800 +2025-04-19 00:48:16,999 - INFO - Min: 1.0321 +2025-04-19 00:48:16,999 - INFO - Max: 655.9249 +2025-04-19 00:48:16,999 - INFO - Std: 109.1817 +2025-04-19 00:48:16,999 - INFO - OC Distribution Statistics: +2025-04-19 00:48:16,999 - INFO - Validation Mean: 16.0878 +2025-04-19 00:48:16,999 - INFO - Validation Std: 7.4525 +2025-04-19 00:48:16,999 - INFO - Training Mean: 23.2691 +2025-04-19 00:48:16,999 - INFO - Training Std: 20.4859 +2025-04-19 00:48:16,999 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 00:48:16,999 - INFO - KS Statistic: 0.2395 +2025-04-19 00:48:16,999 - INFO - P-value: 0.0000 +2025-04-19 00:48:16,999 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 00:48:16,999 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 00:48:17,750 - INFO - Validation and Training Set Split Statistics +2025-04-19 00:48:17,750 - INFO - Timestamp: 2025-04-19 00:48:17 +2025-04-19 00:48:17,750 - INFO - Validation set size: 1364 (8.26%) +2025-04-19 00:48:17,750 - INFO - Training set size: 15150 (91.74%) +2025-04-19 00:48:17,750 - INFO - Minimum Distance Statistics (km): +2025-04-19 00:48:17,750 - INFO - Mean: 70.0767 +2025-04-19 00:48:17,750 - INFO - Median: 5.1703 +2025-04-19 00:48:17,750 - INFO - Min: 1.1496 +2025-04-19 00:48:17,750 - INFO - Max: 706.9584 +2025-04-19 00:48:17,750 - INFO - Std: 111.5537 +2025-04-19 00:48:17,750 - INFO - OC Distribution Statistics: +2025-04-19 00:48:17,750 - INFO - Validation Mean: 16.5429 +2025-04-19 00:48:17,750 - INFO - Validation Std: 7.4028 +2025-04-19 00:48:17,750 - INFO - Training Mean: 23.0655 +2025-04-19 00:48:17,750 - INFO - Training Std: 20.3271 +2025-04-19 00:48:17,750 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 00:48:17,750 - INFO - KS Statistic: 0.1949 +2025-04-19 00:48:17,750 - INFO - P-value: 0.0000 +2025-04-19 00:48:17,750 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 00:48:17,750 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 00:50:46,097 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 00:50:46,107 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 00:50:46,114 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 00:50:46,114 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 01:01:24,536 - INFO - Validation and Training Set Split Statistics +2025-04-19 01:01:24,537 - INFO - Timestamp: 2025-04-19 01:01:24 +2025-04-19 01:01:24,537 - INFO - Validation set size: 1403 (8.50%) +2025-04-19 01:01:24,537 - INFO - Training set size: 15111 (91.50%) +2025-04-19 01:01:24,537 - INFO - Minimum Distance Statistics (km): +2025-04-19 01:01:24,537 - INFO - Mean: 65.7611 +2025-04-19 01:01:24,537 - INFO - Median: 3.9951 +2025-04-19 01:01:24,537 - INFO - Min: 1.0890 +2025-04-19 01:01:24,537 - INFO - Max: 706.9584 +2025-04-19 01:01:24,537 - INFO - Std: 109.4086 +2025-04-19 01:01:24,537 - INFO - OC Distribution Statistics: +2025-04-19 01:01:24,537 - INFO - Validation Mean: 16.6129 +2025-04-19 01:01:24,537 - INFO - Validation Std: 7.6088 +2025-04-19 01:01:24,537 - INFO - Training Mean: 23.0759 +2025-04-19 01:01:24,537 - INFO - Training Std: 20.3422 +2025-04-19 01:01:24,537 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 01:01:24,537 - INFO - KS Statistic: 0.2151 +2025-04-19 01:01:24,537 - INFO - P-value: 0.0000 +2025-04-19 01:01:24,537 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 01:01:24,537 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 01:01:24,542 - INFO - Validation and Training Set Split Statistics +2025-04-19 01:01:24,543 - INFO - Timestamp: 2025-04-19 01:01:24 +2025-04-19 01:01:24,543 - INFO - Validation set size: 1353 (8.19%) +2025-04-19 01:01:24,543 - INFO - Training set size: 15161 (91.81%) +2025-04-19 01:01:24,543 - INFO - Minimum Distance Statistics (km): +2025-04-19 01:01:24,543 - INFO - Mean: 72.0956 +2025-04-19 01:01:24,543 - INFO - Median: 5.2688 +2025-04-19 01:01:24,543 - INFO - Min: 1.2287 +2025-04-19 01:01:24,543 - INFO - Max: 743.7101 +2025-04-19 01:01:24,543 - INFO - Std: 121.8632 +2025-04-19 01:01:24,543 - INFO - OC Distribution Statistics: +2025-04-19 01:01:24,543 - INFO - Validation Mean: 16.5618 +2025-04-19 01:01:24,543 - INFO - Validation Std: 7.6833 +2025-04-19 01:01:24,543 - INFO - Training Mean: 23.0591 +2025-04-19 01:01:24,543 - INFO - Training Std: 20.3127 +2025-04-19 01:01:24,543 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 01:01:24,543 - INFO - KS Statistic: 0.2074 +2025-04-19 01:01:24,543 - INFO - P-value: 0.0000 +2025-04-19 01:01:24,543 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 01:01:24,543 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 01:01:24,552 - INFO - Validation and Training Set Split Statistics +2025-04-19 01:01:24,552 - INFO - Timestamp: 2025-04-19 01:01:24 +2025-04-19 01:01:24,552 - INFO - Validation set size: 1378 (8.34%) +2025-04-19 01:01:24,552 - INFO - Training set size: 15136 (91.66%) +2025-04-19 01:01:24,552 - INFO - Minimum Distance Statistics (km): +2025-04-19 01:01:24,552 - INFO - Mean: 65.7917 +2025-04-19 01:01:24,552 - INFO - Median: 4.0591 +2025-04-19 01:01:24,552 - INFO - Min: 0.6067 +2025-04-19 01:01:24,552 - INFO - Max: 681.9863 +2025-04-19 01:01:24,552 - INFO - Std: 110.1298 +2025-04-19 01:01:24,552 - INFO - OC Distribution Statistics: +2025-04-19 01:01:24,552 - INFO - Validation Mean: 15.9601 +2025-04-19 01:01:24,552 - INFO - Validation Std: 6.9743 +2025-04-19 01:01:24,552 - INFO - Training Mean: 23.1246 +2025-04-19 01:01:24,552 - INFO - Training Std: 20.3302 +2025-04-19 01:01:24,552 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 01:01:24,552 - INFO - KS Statistic: 0.2382 +2025-04-19 01:01:24,552 - INFO - P-value: 0.0000 +2025-04-19 01:01:24,552 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 01:01:24,552 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 01:01:28,083 - INFO - Validation and Training Set Split Statistics +2025-04-19 01:01:28,084 - INFO - Timestamp: 2025-04-19 01:01:28 +2025-04-19 01:01:28,084 - INFO - Validation set size: 1405 (8.51%) +2025-04-19 01:01:28,084 - INFO - Training set size: 15109 (91.49%) +2025-04-19 01:01:28,084 - INFO - Minimum Distance Statistics (km): +2025-04-19 01:01:28,084 - INFO - Mean: 73.3764 +2025-04-19 01:01:28,084 - INFO - Median: 4.5729 +2025-04-19 01:01:28,084 - INFO - Min: 1.2904 +2025-04-19 01:01:28,084 - INFO - Max: 622.3819 +2025-04-19 01:01:28,084 - INFO - Std: 120.3370 +2025-04-19 01:01:28,084 - INFO - OC Distribution Statistics: +2025-04-19 01:01:28,084 - INFO - Validation Mean: 16.3805 +2025-04-19 01:01:28,084 - INFO - Validation Std: 7.6395 +2025-04-19 01:01:28,084 - INFO - Training Mean: 23.0983 +2025-04-19 01:01:28,084 - INFO - Training Std: 20.3351 +2025-04-19 01:01:28,084 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 01:01:28,084 - INFO - KS Statistic: 0.1998 +2025-04-19 01:01:28,084 - INFO - P-value: 0.0000 +2025-04-19 01:01:28,084 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 01:01:28,084 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 01:03:53,791 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 01:03:53,792 - INFO - Reducer buckets 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- INFO - Training Mean: 23.0967 +2025-04-19 02:08:49,815 - INFO - Training Std: 20.3401 +2025-04-19 02:08:49,815 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 02:08:49,815 - INFO - KS Statistic: 0.2227 +2025-04-19 02:08:49,815 - INFO - P-value: 0.0000 +2025-04-19 02:08:49,815 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 02:08:49,815 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 02:08:49,952 - INFO - Validation and Training Set Split Statistics +2025-04-19 02:08:49,953 - INFO - Timestamp: 2025-04-19 02:08:49 +2025-04-19 02:08:49,953 - INFO - Validation set size: 1583 (9.59%) +2025-04-19 02:08:49,953 - INFO - Training set size: 14931 (90.41%) +2025-04-19 02:08:49,953 - INFO - Minimum Distance Statistics (km): +2025-04-19 02:08:49,953 - INFO - Mean: 69.1451 +2025-04-19 02:08:49,953 - INFO - Median: 3.9951 +2025-04-19 02:08:49,953 - INFO - Min: 0.5501 +2025-04-19 02:08:49,953 - INFO - Max: 824.9624 +2025-04-19 02:08:49,953 - INFO - Std: 125.9472 +2025-04-19 02:08:49,953 - INFO - OC Distribution Statistics: +2025-04-19 02:08:49,953 - INFO - Validation Mean: 16.5993 +2025-04-19 02:08:49,953 - INFO - Validation Std: 7.7638 +2025-04-19 02:08:49,953 - INFO - Training Mean: 23.1552 +2025-04-19 02:08:49,953 - INFO - Training Std: 20.4283 +2025-04-19 02:08:49,953 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 02:08:49,953 - INFO - KS Statistic: 0.2069 +2025-04-19 02:08:49,953 - INFO - P-value: 0.0000 +2025-04-19 02:08:49,953 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 02:08:49,953 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 02:08:50,236 - INFO - Validation and Training Set Split Statistics +2025-04-19 02:08:50,236 - INFO - Timestamp: 2025-04-19 02:08:50 +2025-04-19 02:08:50,236 - INFO - Validation set size: 1371 (8.30%) +2025-04-19 02:08:50,236 - INFO - Training set size: 15143 (91.70%) +2025-04-19 02:08:50,236 - INFO - Minimum Distance Statistics (km): +2025-04-19 02:08:50,236 - INFO - Mean: 57.7320 +2025-04-19 02:08:50,236 - INFO - Median: 4.7210 +2025-04-19 02:08:50,236 - INFO - Min: 1.2926 +2025-04-19 02:08:50,236 - INFO - Max: 706.9584 +2025-04-19 02:08:50,236 - INFO - Std: 101.1532 +2025-04-19 02:08:50,236 - INFO - OC Distribution Statistics: +2025-04-19 02:08:50,236 - INFO - Validation Mean: 16.2991 +2025-04-19 02:08:50,236 - INFO - Validation Std: 7.7271 +2025-04-19 02:08:50,236 - INFO - Training Mean: 23.0906 +2025-04-19 02:08:50,236 - INFO - Training Std: 20.3126 +2025-04-19 02:08:50,236 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 02:08:50,236 - INFO - KS Statistic: 0.2243 +2025-04-19 02:08:50,236 - INFO - P-value: 0.0000 +2025-04-19 02:08:50,236 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 02:08:50,236 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 02:08:53,581 - INFO - Validation and Training Set Split Statistics +2025-04-19 02:08:53,582 - INFO - Timestamp: 2025-04-19 02:08:53 +2025-04-19 02:08:53,582 - INFO - Validation set size: 1396 (8.45%) +2025-04-19 02:08:53,582 - INFO - Training set size: 15118 (91.55%) +2025-04-19 02:08:53,582 - INFO - Minimum Distance Statistics (km): +2025-04-19 02:08:53,582 - INFO - Mean: 67.3047 +2025-04-19 02:08:53,582 - INFO - Median: 4.2696 +2025-04-19 02:08:53,582 - INFO - Min: 1.2757 +2025-04-19 02:08:53,582 - INFO - Max: 835.9450 +2025-04-19 02:08:53,582 - INFO - Std: 111.1873 +2025-04-19 02:08:53,582 - INFO - OC Distribution Statistics: +2025-04-19 02:08:53,582 - INFO - Validation Mean: 16.5809 +2025-04-19 02:08:53,582 - INFO - Validation Std: 7.7615 +2025-04-19 02:08:53,582 - INFO - Training Mean: 23.0758 +2025-04-19 02:08:53,582 - INFO - Training Std: 20.3323 +2025-04-19 02:08:53,582 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 02:08:53,582 - INFO - KS Statistic: 0.1996 +2025-04-19 02:08:53,582 - INFO - P-value: 0.0000 +2025-04-19 02:08:53,582 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 02:08:53,582 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 02:11:21,350 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 02:11:21,357 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 02:11:21,358 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 02:11:21,358 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 02:32:28,827 - INFO - Validation and Training Set Split Statistics +2025-04-19 02:32:28,827 - INFO - Timestamp: 2025-04-19 02:32:28 +2025-04-19 02:32:28,827 - INFO - Validation set size: 1664 (10.08%) +2025-04-19 02:32:28,827 - INFO - Training set size: 14850 (89.92%) +2025-04-19 02:32:28,827 - INFO - Minimum Distance Statistics (km): +2025-04-19 02:32:28,827 - INFO - Mean: 62.0989 +2025-04-19 02:32:28,827 - INFO - Median: 4.3243 +2025-04-19 02:32:28,827 - INFO - Min: 0.9954 +2025-04-19 02:32:28,827 - INFO - Max: 716.7632 +2025-04-19 02:32:28,827 - INFO - Std: 107.6052 +2025-04-19 02:32:28,827 - INFO - OC Distribution Statistics: +2025-04-19 02:32:28,827 - INFO - Validation Mean: 16.2536 +2025-04-19 02:32:28,827 - INFO - Validation Std: 8.0658 +2025-04-19 02:32:28,827 - INFO - Training Mean: 23.2297 +2025-04-19 02:32:28,828 - INFO - Training Std: 20.4442 +2025-04-19 02:32:28,828 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 02:32:28,828 - INFO - KS Statistic: 0.2486 +2025-04-19 02:32:28,828 - INFO - P-value: 0.0000 +2025-04-19 02:32:28,828 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 02:32:28,828 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 02:32:28,911 - INFO - Validation and Training Set Split Statistics +2025-04-19 02:32:28,911 - INFO - Timestamp: 2025-04-19 02:32:28 +2025-04-19 02:32:28,911 - INFO - Validation set size: 1368 (8.28%) +2025-04-19 02:32:28,911 - INFO - Training set size: 15146 (91.72%) +2025-04-19 02:32:28,911 - INFO - Minimum Distance Statistics (km): +2025-04-19 02:32:28,911 - INFO - Mean: 72.6677 +2025-04-19 02:32:28,911 - INFO - Median: 4.9622 +2025-04-19 02:32:28,911 - INFO - Min: 0.7391 +2025-04-19 02:32:28,911 - INFO - Max: 706.9584 +2025-04-19 02:32:28,911 - INFO - Std: 111.2327 +2025-04-19 02:32:28,911 - INFO - OC Distribution Statistics: +2025-04-19 02:32:28,911 - INFO - Validation Mean: 16.7840 +2025-04-19 02:32:28,911 - INFO - Validation Std: 8.2193 +2025-04-19 02:32:28,911 - INFO - Training Mean: 23.0455 +2025-04-19 02:32:28,912 - INFO - Training Std: 20.3077 +2025-04-19 02:32:28,912 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 02:32:28,912 - INFO - KS Statistic: 0.2039 +2025-04-19 02:32:28,912 - INFO - P-value: 0.0000 +2025-04-19 02:32:28,912 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 02:32:28,912 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 02:32:28,958 - INFO - Validation and Training Set Split Statistics +2025-04-19 02:32:28,959 - INFO - Timestamp: 2025-04-19 02:32:28 +2025-04-19 02:32:28,959 - INFO - Validation set size: 1404 (8.50%) +2025-04-19 02:32:28,959 - INFO - Training set size: 15110 (91.50%) +2025-04-19 02:32:28,959 - INFO - Minimum Distance Statistics (km): +2025-04-19 02:32:28,959 - INFO - Mean: 73.9171 +2025-04-19 02:32:28,959 - INFO - Median: 4.8578 +2025-04-19 02:32:28,959 - INFO - Min: 1.2497 +2025-04-19 02:32:28,959 - INFO - Max: 888.3558 +2025-04-19 02:32:28,959 - INFO - Std: 121.2886 +2025-04-19 02:32:28,959 - INFO - OC Distribution Statistics: +2025-04-19 02:32:28,959 - INFO - Validation Mean: 17.0336 +2025-04-19 02:32:28,959 - INFO - Validation Std: 9.0738 +2025-04-19 02:32:28,959 - INFO - Training Mean: 23.0372 +2025-04-19 02:32:28,959 - INFO - Training Std: 20.2988 +2025-04-19 02:32:28,959 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 02:32:28,959 - INFO - KS Statistic: 0.1838 +2025-04-19 02:32:28,959 - INFO - P-value: 0.0000 +2025-04-19 02:32:28,959 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 02:32:28,959 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 02:32:32,711 - INFO - Validation and Training Set Split Statistics +2025-04-19 02:32:32,711 - INFO - Timestamp: 2025-04-19 02:32:32 +2025-04-19 02:32:32,711 - INFO - Validation set size: 1661 (10.06%) +2025-04-19 02:32:32,711 - INFO - Training set size: 14853 (89.94%) +2025-04-19 02:32:32,711 - INFO - Minimum Distance Statistics (km): +2025-04-19 02:32:32,711 - INFO - Mean: 67.3016 +2025-04-19 02:32:32,711 - INFO - Median: 3.8354 +2025-04-19 02:32:32,711 - INFO - Min: 0.4760 +2025-04-19 02:32:32,711 - INFO - Max: 706.9584 +2025-04-19 02:32:32,711 - INFO - Std: 117.4934 +2025-04-19 02:32:32,711 - INFO - OC Distribution Statistics: +2025-04-19 02:32:32,711 - INFO - Validation Mean: 16.5438 +2025-04-19 02:32:32,711 - INFO - Validation Std: 7.8718 +2025-04-19 02:32:32,711 - INFO - Training Mean: 23.1959 +2025-04-19 02:32:32,711 - INFO - Training Std: 20.4620 +2025-04-19 02:32:32,711 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 02:32:32,711 - INFO - KS Statistic: 0.2183 +2025-04-19 02:32:32,711 - INFO - P-value: 0.0000 +2025-04-19 02:32:32,711 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 02:32:32,711 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 02:34:59,898 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 02:34:59,907 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 02:34:59,908 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 02:34:59,916 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 03:40:45,539 - INFO - Validation and Training Set Split Statistics +2025-04-19 03:40:45,539 - INFO - Timestamp: 2025-04-19 03:40:45 +2025-04-19 03:40:45,539 - INFO - Validation set size: 1371 (8.30%) +2025-04-19 03:40:45,539 - INFO - Training set size: 15143 (91.70%) +2025-04-19 03:40:45,540 - INFO - Minimum Distance Statistics (km): +2025-04-19 03:40:45,540 - INFO - Mean: 74.1781 +2025-04-19 03:40:45,540 - INFO - Median: 5.7863 +2025-04-19 03:40:45,540 - INFO - Min: 1.1496 +2025-04-19 03:40:45,540 - INFO - Max: 706.9584 +2025-04-19 03:40:45,540 - INFO - Std: 118.1695 +2025-04-19 03:40:45,540 - INFO - OC Distribution Statistics: +2025-04-19 03:40:45,540 - INFO - Validation Mean: 16.4035 +2025-04-19 03:40:45,540 - INFO - Validation Std: 7.3167 +2025-04-19 03:40:45,540 - INFO - Training Mean: 23.0812 +2025-04-19 03:40:45,540 - INFO - Training Std: 20.3294 +2025-04-19 03:40:45,540 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 03:40:45,540 - INFO - KS Statistic: 0.1947 +2025-04-19 03:40:45,540 - INFO - P-value: 0.0000 +2025-04-19 03:40:45,540 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 03:40:45,540 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 03:40:45,565 - INFO - Validation and Training Set Split Statistics +2025-04-19 03:40:45,565 - INFO - Timestamp: 2025-04-19 03:40:45 +2025-04-19 03:40:45,565 - INFO - Validation set size: 1386 (8.39%) +2025-04-19 03:40:45,565 - INFO - Training set size: 15128 (91.61%) +2025-04-19 03:40:45,565 - INFO - Minimum Distance Statistics (km): +2025-04-19 03:40:45,565 - INFO - Mean: 56.7034 +2025-04-19 03:40:45,565 - INFO - Median: 3.5973 +2025-04-19 03:40:45,565 - INFO - Min: 0.8728 +2025-04-19 03:40:45,565 - INFO - Max: 826.2195 +2025-04-19 03:40:45,565 - INFO - Std: 105.3051 +2025-04-19 03:40:45,565 - INFO - OC Distribution Statistics: +2025-04-19 03:40:45,565 - INFO - Validation Mean: 16.5169 +2025-04-19 03:40:45,565 - INFO - Validation Std: 8.4805 +2025-04-19 03:40:45,565 - INFO - Training Mean: 23.0774 +2025-04-19 03:40:45,565 - INFO - Training Std: 20.2991 +2025-04-19 03:40:45,565 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 03:40:45,565 - INFO - KS Statistic: 0.2444 +2025-04-19 03:40:45,565 - INFO - P-value: 0.0000 +2025-04-19 03:40:45,565 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 03:40:45,565 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 03:40:45,704 - INFO - Validation and Training Set Split Statistics +2025-04-19 03:40:45,704 - INFO - Timestamp: 2025-04-19 03:40:45 +2025-04-19 03:40:45,704 - INFO - Validation set size: 1515 (9.17%) +2025-04-19 03:40:45,704 - INFO - Training set size: 14999 (90.83%) +2025-04-19 03:40:45,704 - INFO - Minimum Distance Statistics (km): +2025-04-19 03:40:45,704 - INFO - Mean: 61.5834 +2025-04-19 03:40:45,704 - INFO - Median: 4.0524 +2025-04-19 03:40:45,704 - INFO - Min: 0.4760 +2025-04-19 03:40:45,704 - INFO - Max: 568.4258 +2025-04-19 03:40:45,704 - INFO - Std: 104.1685 +2025-04-19 03:40:45,704 - INFO - OC Distribution Statistics: +2025-04-19 03:40:45,704 - INFO - Validation Mean: 16.3388 +2025-04-19 03:40:45,704 - INFO - Validation Std: 7.8975 +2025-04-19 03:40:45,704 - INFO - Training Mean: 23.1518 +2025-04-19 03:40:45,704 - INFO - Training Std: 20.3796 +2025-04-19 03:40:45,704 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 03:40:45,704 - INFO - KS Statistic: 0.2346 +2025-04-19 03:40:45,704 - INFO - P-value: 0.0000 +2025-04-19 03:40:45,704 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 03:40:45,704 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 03:40:49,452 - INFO - Validation and Training Set Split Statistics +2025-04-19 03:40:49,452 - INFO - Timestamp: 2025-04-19 03:40:49 +2025-04-19 03:40:49,452 - INFO - Validation set size: 1409 (8.53%) +2025-04-19 03:40:49,452 - INFO - Training set size: 15105 (91.47%) +2025-04-19 03:40:49,452 - INFO - Minimum Distance Statistics (km): +2025-04-19 03:40:49,452 - INFO - Mean: 65.4942 +2025-04-19 03:40:49,452 - INFO - Median: 3.9657 +2025-04-19 03:40:49,453 - INFO - Min: 1.1523 +2025-04-19 03:40:49,453 - INFO - Max: 706.9584 +2025-04-19 03:40:49,453 - INFO - Std: 109.4205 +2025-04-19 03:40:49,453 - INFO - OC Distribution Statistics: +2025-04-19 03:40:49,453 - INFO - Validation Mean: 16.1519 +2025-04-19 03:40:49,453 - INFO - Validation Std: 7.1467 +2025-04-19 03:40:49,453 - INFO - Training Mean: 23.1214 +2025-04-19 03:40:49,453 - INFO - Training Std: 20.3467 +2025-04-19 03:40:49,453 - INFO - Kolmogorov-Smirnov Test: +2025-04-19 03:40:49,453 - INFO - KS Statistic: 0.2217 +2025-04-19 03:40:49,453 - INFO - P-value: 0.0000 +2025-04-19 03:40:49,453 - INFO - Distribution Fit: Inverse Gamma +2025-04-19 03:40:49,453 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-19 03:43:18,880 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 03:43:18,887 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 03:43:18,887 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-19 03:43:18,887 - INFO - Reducer buckets have been rebuilt in this iteration. +2025-04-20 12:59:39,526 - INFO - Validation and Training Set Split Statistics +2025-04-20 12:59:39,527 - INFO - Timestamp: 2025-04-20 12:59:39 +2025-04-20 12:59:39,527 - INFO - Validation set size: 1361 (8.24%) +2025-04-20 12:59:39,527 - INFO - Training set size: 15153 (91.76%) +2025-04-20 12:59:39,527 - INFO - Minimum Distance Statistics (km): +2025-04-20 12:59:39,527 - INFO - Validation and Training Set Split Statistics +2025-04-20 12:59:39,527 - INFO - Mean: 70.4933 +2025-04-20 12:59:39,527 - INFO - Median: 4.3243 +2025-04-20 12:59:39,527 - INFO - Timestamp: 2025-04-20 12:59:39 +2025-04-20 12:59:39,527 - INFO - Min: 0.3437 +2025-04-20 12:59:39,527 - INFO - Max: 1106.1626 +2025-04-20 12:59:39,527 - INFO - Validation set size: 1409 (8.53%) +2025-04-20 12:59:39,527 - INFO - Std: 128.7481 +2025-04-20 12:59:39,527 - INFO - Training set size: 15105 (91.47%) +2025-04-20 12:59:39,527 - INFO - OC Distribution Statistics: +2025-04-20 12:59:39,527 - INFO - Minimum Distance Statistics (km): +2025-04-20 12:59:39,527 - INFO - Validation Mean: 16.6045 +2025-04-20 12:59:39,527 - INFO - Mean: 71.0653 +2025-04-20 12:59:39,527 - INFO - Validation Std: 8.0158 +2025-04-20 12:59:39,527 - INFO - Median: 4.2460 +2025-04-20 12:59:39,527 - INFO - Training Mean: 23.0587 +2025-04-20 12:59:39,527 - INFO - Training Std: 20.3065 +2025-04-20 12:59:39,527 - INFO - Min: 0.8728 +2025-04-20 12:59:39,527 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 12:59:39,527 - INFO - Max: 917.2720 +2025-04-20 12:59:39,527 - INFO - KS Statistic: 0.2291 +2025-04-20 12:59:39,527 - INFO - Std: 130.5927 +2025-04-20 12:59:39,527 - INFO - P-value: 0.0000 +2025-04-20 12:59:39,527 - INFO - OC Distribution Statistics: +2025-04-20 12:59:39,527 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 12:59:39,527 - INFO - Validation Mean: 16.1248 +2025-04-20 12:59:39,527 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 12:59:39,527 - INFO - Validation Std: 7.2694 +2025-04-20 12:59:39,527 - INFO - Training Mean: 23.1240 +2025-04-20 12:59:39,527 - INFO - Training Std: 20.3418 +2025-04-20 12:59:39,527 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 12:59:39,527 - INFO - KS Statistic: 0.2275 +2025-04-20 12:59:39,527 - INFO - P-value: 0.0000 +2025-04-20 12:59:39,527 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 12:59:39,527 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 12:59:40,303 - INFO - Validation and Training Set Split Statistics +2025-04-20 12:59:40,303 - INFO - Timestamp: 2025-04-20 12:59:40 +2025-04-20 12:59:40,303 - INFO - Validation set size: 1637 (9.91%) +2025-04-20 12:59:40,303 - INFO - Training set size: 14877 (90.09%) +2025-04-20 12:59:40,303 - INFO - Minimum Distance Statistics (km): +2025-04-20 12:59:40,303 - INFO - Mean: 70.8796 +2025-04-20 12:59:40,303 - INFO - Median: 4.4155 +2025-04-20 12:59:40,303 - INFO - Min: 1.0318 +2025-04-20 12:59:40,303 - INFO - Max: 875.2369 +2025-04-20 12:59:40,303 - INFO - Std: 119.2910 +2025-04-20 12:59:40,303 - INFO - OC Distribution Statistics: +2025-04-20 12:59:40,303 - INFO - Validation Mean: 16.6676 +2025-04-20 12:59:40,303 - INFO - Validation Std: 8.4167 +2025-04-20 12:59:40,303 - INFO - Training Mean: 23.1715 +2025-04-20 12:59:40,303 - INFO - Training Std: 20.4301 +2025-04-20 12:59:40,303 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 12:59:40,303 - INFO - KS Statistic: 0.2189 +2025-04-20 12:59:40,303 - INFO - P-value: 0.0000 +2025-04-20 12:59:40,303 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 12:59:40,303 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 12:59:41,035 - INFO - Validation and Training Set Split Statistics +2025-04-20 12:59:41,036 - INFO - Timestamp: 2025-04-20 12:59:41 +2025-04-20 12:59:41,036 - INFO - Validation set size: 1597 (9.67%) +2025-04-20 12:59:41,036 - INFO - Training set size: 14917 (90.33%) +2025-04-20 12:59:41,036 - INFO - Minimum Distance Statistics (km): +2025-04-20 12:59:41,036 - INFO - Mean: 75.7618 +2025-04-20 12:59:41,036 - INFO - Median: 5.2369 +2025-04-20 12:59:41,036 - INFO - Min: 1.2065 +2025-04-20 12:59:41,036 - INFO - Max: 743.7101 +2025-04-20 12:59:41,036 - INFO - Std: 125.6164 +2025-04-20 12:59:41,036 - INFO - OC Distribution Statistics: +2025-04-20 12:59:41,036 - INFO - Validation Mean: 16.7217 +2025-04-20 12:59:41,036 - INFO - Validation Std: 8.1106 +2025-04-20 12:59:41,036 - INFO - Training Mean: 23.1483 +2025-04-20 12:59:41,036 - INFO - Training Std: 20.4252 +2025-04-20 12:59:41,036 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 12:59:41,036 - INFO - KS Statistic: 0.2138 +2025-04-20 12:59:41,036 - INFO - P-value: 0.0000 +2025-04-20 12:59:41,036 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 12:59:41,036 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:00:06,644 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:00:06,644 - INFO - Timestamp: 2025-04-20 13:00:06 +2025-04-20 13:00:06,644 - INFO - Validation set size: 1494 (9.05%) +2025-04-20 13:00:06,644 - INFO - Training set size: 15020 (90.95%) +2025-04-20 13:00:06,644 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:00:06,644 - INFO - Mean: 61.2186 +2025-04-20 13:00:06,644 - INFO - Median: 4.2919 +2025-04-20 13:00:06,644 - INFO - Min: 1.2065 +2025-04-20 13:00:06,644 - INFO - Max: 706.9584 +2025-04-20 13:00:06,644 - INFO - Std: 100.5008 +2025-04-20 13:00:06,644 - INFO - OC Distribution Statistics: +2025-04-20 13:00:06,644 - INFO - Validation Mean: 15.9771 +2025-04-20 13:00:06,644 - INFO - Validation Std: 7.1498 +2025-04-20 13:00:06,644 - INFO - Training Mean: 23.1783 +2025-04-20 13:00:06,644 - INFO - Training Std: 20.3842 +2025-04-20 13:00:06,644 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:00:06,644 - INFO - KS Statistic: 0.2519 +2025-04-20 13:00:06,644 - INFO - P-value: 0.0000 +2025-04-20 13:00:06,644 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:00:06,644 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:00:07,794 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:00:07,794 - INFO - Timestamp: 2025-04-20 13:00:07 +2025-04-20 13:00:07,794 - INFO - Validation set size: 1433 (8.68%) +2025-04-20 13:00:07,794 - INFO - Training set size: 15081 (91.32%) +2025-04-20 13:00:07,794 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:00:07,794 - INFO - Mean: 62.2263 +2025-04-20 13:00:07,794 - INFO - Median: 4.4155 +2025-04-20 13:00:07,794 - INFO - Min: 1.2497 +2025-04-20 13:00:07,794 - INFO - Max: 706.9584 +2025-04-20 13:00:07,794 - INFO - Std: 103.6927 +2025-04-20 13:00:07,794 - INFO - OC Distribution Statistics: +2025-04-20 13:00:07,794 - INFO - Validation Mean: 16.2379 +2025-04-20 13:00:07,794 - INFO - Validation Std: 7.0556 +2025-04-20 13:00:07,794 - INFO - Training Mean: 23.1244 +2025-04-20 13:00:07,794 - INFO - Training Std: 20.3648 +2025-04-20 13:00:07,794 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:00:07,794 - INFO - KS Statistic: 0.2212 +2025-04-20 13:00:07,794 - INFO - P-value: 0.0000 +2025-04-20 13:00:07,794 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:00:07,794 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:00:07,806 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:00:07,806 - INFO - Timestamp: 2025-04-20 13:00:07 +2025-04-20 13:00:07,806 - INFO - Validation set size: 1362 (8.25%) +2025-04-20 13:00:07,806 - INFO - Training set size: 15152 (91.75%) +2025-04-20 13:00:07,806 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:00:07,806 - INFO - Mean: 79.3460 +2025-04-20 13:00:07,806 - INFO - Median: 5.2663 +2025-04-20 13:00:07,806 - INFO - Min: 1.2065 +2025-04-20 13:00:07,806 - INFO - Max: 743.7101 +2025-04-20 13:00:07,806 - INFO - Std: 125.7807 +2025-04-20 13:00:07,806 - INFO - OC Distribution Statistics: +2025-04-20 13:00:07,806 - INFO - Validation Mean: 16.3285 +2025-04-20 13:00:07,806 - INFO - Validation Std: 7.5336 +2025-04-20 13:00:07,806 - INFO - Training Mean: 23.0839 +2025-04-20 13:00:07,806 - INFO - Training Std: 20.3155 +2025-04-20 13:00:07,806 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:00:07,806 - INFO - KS Statistic: 0.2212 +2025-04-20 13:00:07,806 - INFO - P-value: 0.0000 +2025-04-20 13:00:07,806 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:00:07,806 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:00:07,918 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:00:07,918 - INFO - Timestamp: 2025-04-20 13:00:07 +2025-04-20 13:00:07,918 - INFO - Validation set size: 1497 (9.07%) +2025-04-20 13:00:07,918 - INFO - Training set size: 15017 (90.93%) +2025-04-20 13:00:07,918 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:00:07,919 - INFO - Mean: 60.1251 +2025-04-20 13:00:07,919 - INFO - Median: 4.2696 +2025-04-20 13:00:07,919 - INFO - Min: 1.2247 +2025-04-20 13:00:07,919 - INFO - Max: 680.6553 +2025-04-20 13:00:07,919 - INFO - Std: 102.6170 +2025-04-20 13:00:07,919 - INFO - OC Distribution Statistics: +2025-04-20 13:00:07,919 - INFO - Validation Mean: 16.0386 +2025-04-20 13:00:07,919 - INFO - Validation Std: 7.0467 +2025-04-20 13:00:07,919 - INFO - Training Mean: 23.1736 +2025-04-20 13:00:07,919 - INFO - Training Std: 20.3914 +2025-04-20 13:00:07,919 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:00:07,919 - INFO - KS Statistic: 0.2318 +2025-04-20 13:00:07,919 - INFO - P-value: 0.0000 +2025-04-20 13:00:07,919 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:00:07,919 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:00:30,334 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:00:30,334 - INFO - Timestamp: 2025-04-20 13:00:30 +2025-04-20 13:00:30,334 - INFO - Validation set size: 1371 (8.30%) +2025-04-20 13:00:30,334 - INFO - Training set size: 15143 (91.70%) +2025-04-20 13:00:30,334 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:00:30,335 - INFO - Mean: 66.1405 +2025-04-20 13:00:30,335 - INFO - Median: 4.8123 +2025-04-20 13:00:30,335 - INFO - Min: 1.0596 +2025-04-20 13:00:30,335 - INFO - Max: 551.7024 +2025-04-20 13:00:30,335 - INFO - Std: 107.0493 +2025-04-20 13:00:30,335 - INFO - OC Distribution Statistics: +2025-04-20 13:00:30,335 - INFO - Validation Mean: 16.7313 +2025-04-20 13:00:30,335 - INFO - Validation Std: 7.5879 +2025-04-20 13:00:30,335 - INFO - Training Mean: 23.0515 +2025-04-20 13:00:30,335 - INFO - Training Std: 20.3299 +2025-04-20 13:00:30,335 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:00:30,335 - INFO - KS Statistic: 0.1839 +2025-04-20 13:00:30,335 - INFO - P-value: 0.0000 +2025-04-20 13:00:30,335 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:00:30,335 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:00:31,732 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:00:31,732 - INFO - Timestamp: 2025-04-20 13:00:31 +2025-04-20 13:00:31,732 - INFO - Validation set size: 1380 (8.36%) +2025-04-20 13:00:31,732 - INFO - Training set size: 15134 (91.64%) +2025-04-20 13:00:31,732 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:00:31,732 - INFO - Mean: 64.4603 +2025-04-20 13:00:31,732 - INFO - Median: 4.5882 +2025-04-20 13:00:31,732 - INFO - Min: 0.6067 +2025-04-20 13:00:31,732 - INFO - Max: 917.2720 +2025-04-20 13:00:31,732 - INFO - Std: 119.4669 +2025-04-20 13:00:31,732 - INFO - OC Distribution Statistics: +2025-04-20 13:00:31,732 - INFO - Validation Mean: 16.4652 +2025-04-20 13:00:31,732 - INFO - Validation Std: 7.9562 +2025-04-20 13:00:31,732 - INFO - Training Mean: 23.0795 +2025-04-20 13:00:31,732 - INFO - Training Std: 20.3140 +2025-04-20 13:00:31,732 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:00:31,732 - INFO - KS Statistic: 0.2198 +2025-04-20 13:00:31,732 - INFO - P-value: 0.0000 +2025-04-20 13:00:31,732 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:00:31,732 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:00:31,822 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:00:31,823 - INFO - Timestamp: 2025-04-20 13:00:31 +2025-04-20 13:00:31,823 - INFO - Validation set size: 1429 (8.65%) +2025-04-20 13:00:31,823 - INFO - Training set size: 15085 (91.35%) +2025-04-20 13:00:31,823 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:00:31,823 - INFO - Mean: 64.0166 +2025-04-20 13:00:31,823 - INFO - Median: 4.1787 +2025-04-20 13:00:31,823 - INFO - Min: 1.2156 +2025-04-20 13:00:31,823 - INFO - Max: 835.9450 +2025-04-20 13:00:31,823 - INFO - Std: 109.3489 +2025-04-20 13:00:31,823 - INFO - OC Distribution Statistics: +2025-04-20 13:00:31,823 - INFO - Validation Mean: 16.1406 +2025-04-20 13:00:31,823 - INFO - Validation Std: 7.2524 +2025-04-20 13:00:31,823 - INFO - Training Mean: 23.1317 +2025-04-20 13:00:31,823 - INFO - Training Std: 20.3530 +2025-04-20 13:00:31,823 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:00:31,823 - INFO - KS Statistic: 0.2477 +2025-04-20 13:00:31,823 - INFO - P-value: 0.0000 +2025-04-20 13:00:31,823 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:00:31,823 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:00:31,939 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:00:31,939 - INFO - Timestamp: 2025-04-20 13:00:31 +2025-04-20 13:00:31,939 - INFO - Validation set size: 1577 (9.55%) +2025-04-20 13:00:31,939 - INFO - Training set size: 14937 (90.45%) +2025-04-20 13:00:31,939 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:00:31,939 - INFO - Mean: 62.8784 +2025-04-20 13:00:31,939 - INFO - Median: 4.2447 +2025-04-20 13:00:31,939 - INFO - Min: 0.7391 +2025-04-20 13:00:31,939 - INFO - Max: 590.2565 +2025-04-20 13:00:31,939 - INFO - Std: 103.1137 +2025-04-20 13:00:31,939 - INFO - OC Distribution Statistics: +2025-04-20 13:00:31,939 - INFO - Validation Mean: 16.2525 +2025-04-20 13:00:31,939 - INFO - Validation Std: 6.7545 +2025-04-20 13:00:31,939 - INFO - Training Mean: 23.1892 +2025-04-20 13:00:31,939 - INFO - Training Std: 20.4509 +2025-04-20 13:00:31,939 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:00:31,939 - INFO - KS Statistic: 0.2414 +2025-04-20 13:00:31,939 - INFO - P-value: 0.0000 +2025-04-20 13:00:31,939 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:00:31,939 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:58:27,120 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:58:27,120 - INFO - Timestamp: 2025-04-20 13:58:27 +2025-04-20 13:58:27,120 - INFO - Validation set size: 1393 (8.44%) +2025-04-20 13:58:27,120 - INFO - Training set size: 15121 (91.56%) +2025-04-20 13:58:27,120 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:58:27,120 - INFO - Mean: 67.9994 +2025-04-20 13:58:27,120 - INFO - Median: 5.1703 +2025-04-20 13:58:27,120 - INFO - Min: 1.2156 +2025-04-20 13:58:27,120 - INFO - Max: 655.9249 +2025-04-20 13:58:27,120 - INFO - Std: 110.9849 +2025-04-20 13:58:27,120 - INFO - OC Distribution Statistics: +2025-04-20 13:58:27,120 - INFO - Validation Mean: 16.7336 +2025-04-20 13:58:27,120 - INFO - Validation Std: 8.8003 +2025-04-20 13:58:27,120 - INFO - Training Mean: 23.0605 +2025-04-20 13:58:27,120 - INFO - Training Std: 20.2963 +2025-04-20 13:58:27,120 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:58:27,120 - INFO - KS Statistic: 0.2116 +2025-04-20 13:58:27,120 - INFO - P-value: 0.0000 +2025-04-20 13:58:27,120 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:58:27,120 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:58:27,154 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:58:27,154 - INFO - Timestamp: 2025-04-20 13:58:27 +2025-04-20 13:58:27,154 - INFO - Validation set size: 1478 (8.95%) +2025-04-20 13:58:27,154 - INFO - Training set size: 15036 (91.05%) +2025-04-20 13:58:27,154 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:58:27,154 - INFO - Mean: 74.3278 +2025-04-20 13:58:27,154 - INFO - Median: 4.4525 +2025-04-20 13:58:27,154 - INFO - Min: 0.5501 +2025-04-20 13:58:27,154 - INFO - Max: 1106.1626 +2025-04-20 13:58:27,154 - INFO - Std: 130.1787 +2025-04-20 13:58:27,154 - INFO - OC Distribution Statistics: +2025-04-20 13:58:27,154 - INFO - Validation Mean: 16.3251 +2025-04-20 13:58:27,154 - INFO - Validation Std: 7.1359 +2025-04-20 13:58:27,154 - INFO - Training Mean: 23.1364 +2025-04-20 13:58:27,154 - INFO - Training Std: 20.3882 +2025-04-20 13:58:27,154 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:58:27,154 - INFO - KS Statistic: 0.2228 +2025-04-20 13:58:27,154 - INFO - P-value: 0.0000 +2025-04-20 13:58:27,154 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:58:27,154 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:58:27,919 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:58:27,919 - INFO - Timestamp: 2025-04-20 13:58:27 +2025-04-20 13:58:27,919 - INFO - Validation set size: 1334 (8.08%) +2025-04-20 13:58:27,919 - INFO - Training set size: 15180 (91.92%) +2025-04-20 13:58:27,919 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:58:27,919 - INFO - Mean: 65.3219 +2025-04-20 13:58:27,919 - INFO - Median: 4.3243 +2025-04-20 13:58:27,919 - INFO - Min: 0.6067 +2025-04-20 13:58:27,919 - INFO - Max: 917.1429 +2025-04-20 13:58:27,919 - INFO - Std: 107.0328 +2025-04-20 13:58:27,919 - INFO - OC Distribution Statistics: +2025-04-20 13:58:27,919 - INFO - Validation Mean: 16.4605 +2025-04-20 13:58:27,919 - INFO - Validation Std: 8.0053 +2025-04-20 13:58:27,919 - INFO - Training Mean: 23.0599 +2025-04-20 13:58:27,919 - INFO - Training Std: 20.2893 +2025-04-20 13:58:27,919 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:58:27,919 - INFO - KS Statistic: 0.2045 +2025-04-20 13:58:27,919 - INFO - P-value: 0.0000 +2025-04-20 13:58:27,919 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:58:27,919 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 13:58:31,823 - INFO - Validation and Training Set Split Statistics +2025-04-20 13:58:31,823 - INFO - Timestamp: 2025-04-20 13:58:31 +2025-04-20 13:58:31,823 - INFO - Validation set size: 1406 (8.51%) +2025-04-20 13:58:31,823 - INFO - Training set size: 15108 (91.49%) +2025-04-20 13:58:31,823 - INFO - Minimum Distance Statistics (km): +2025-04-20 13:58:31,823 - INFO - Mean: 64.0289 +2025-04-20 13:58:31,823 - INFO - Median: 3.9233 +2025-04-20 13:58:31,823 - INFO - Min: 0.8608 +2025-04-20 13:58:31,823 - INFO - Max: 702.5656 +2025-04-20 13:58:31,823 - INFO - Std: 111.8872 +2025-04-20 13:58:31,823 - INFO - OC Distribution Statistics: +2025-04-20 13:58:31,823 - INFO - Validation Mean: 16.6927 +2025-04-20 13:58:31,823 - INFO - Validation Std: 8.3106 +2025-04-20 13:58:31,823 - INFO - Training Mean: 23.0697 +2025-04-20 13:58:31,823 - INFO - Training Std: 20.3205 +2025-04-20 13:58:31,823 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 13:58:31,823 - INFO - KS Statistic: 0.2046 +2025-04-20 13:58:31,823 - INFO - P-value: 0.0000 +2025-04-20 13:58:31,823 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 13:58:31,823 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:02:29,769 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:02:29,770 - INFO - Timestamp: 2025-04-20 14:02:29 +2025-04-20 14:02:29,770 - INFO - Validation set size: 1488 (9.01%) +2025-04-20 14:02:29,770 - INFO - Training set size: 15026 (90.99%) +2025-04-20 14:02:29,770 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:02:29,770 - INFO - Mean: 67.0661 +2025-04-20 14:02:29,770 - INFO - Median: 4.6494 +2025-04-20 14:02:29,770 - INFO - Min: 1.2156 +2025-04-20 14:02:29,770 - INFO - Max: 743.7101 +2025-04-20 14:02:29,770 - INFO - Std: 107.0556 +2025-04-20 14:02:29,770 - INFO - OC Distribution Statistics: +2025-04-20 14:02:29,770 - INFO - Validation Mean: 16.1619 +2025-04-20 14:02:29,770 - INFO - Validation Std: 6.6708 +2025-04-20 14:02:29,770 - INFO - Training Mean: 23.1571 +2025-04-20 14:02:29,770 - INFO - Training Std: 20.4035 +2025-04-20 14:02:29,770 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:02:29,770 - INFO - KS Statistic: 0.2338 +2025-04-20 14:02:29,770 - INFO - P-value: 0.0000 +2025-04-20 14:02:29,770 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:02:29,770 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:02:30,099 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:02:30,099 - INFO - Timestamp: 2025-04-20 14:02:30 +2025-04-20 14:02:30,099 - INFO - Validation set size: 1533 (9.28%) +2025-04-20 14:02:30,099 - INFO - Training set size: 14981 (90.72%) +2025-04-20 14:02:30,099 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:02:30,099 - INFO - Mean: 61.5062 +2025-04-20 14:02:30,099 - INFO - Median: 3.9951 +2025-04-20 14:02:30,099 - INFO - Min: 0.8258 +2025-04-20 14:02:30,099 - INFO - Max: 622.3819 +2025-04-20 14:02:30,099 - INFO - Std: 107.2689 +2025-04-20 14:02:30,099 - INFO - OC Distribution Statistics: +2025-04-20 14:02:30,099 - INFO - Validation Mean: 16.0512 +2025-04-20 14:02:30,099 - INFO - Validation Std: 7.8337 +2025-04-20 14:02:30,099 - INFO - Training Mean: 23.1894 +2025-04-20 14:02:30,099 - INFO - Training Std: 20.3811 +2025-04-20 14:02:30,099 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:02:30,099 - INFO - KS Statistic: 0.2584 +2025-04-20 14:02:30,099 - INFO - P-value: 0.0000 +2025-04-20 14:02:30,099 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:02:30,099 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:02:30,569 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:02:30,569 - INFO - Timestamp: 2025-04-20 14:02:30 +2025-04-20 14:02:30,569 - INFO - Validation set size: 1560 (9.45%) +2025-04-20 14:02:30,569 - INFO - Training set size: 14954 (90.55%) +2025-04-20 14:02:30,569 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:02:30,569 - INFO - Mean: 60.9071 +2025-04-20 14:02:30,569 - INFO - Median: 4.2653 +2025-04-20 14:02:30,569 - INFO - Min: 0.9587 +2025-04-20 14:02:30,569 - INFO - Max: 705.4078 +2025-04-20 14:02:30,569 - INFO - Std: 103.9952 +2025-04-20 14:02:30,569 - INFO - OC Distribution Statistics: +2025-04-20 14:02:30,569 - INFO - Validation Mean: 16.5290 +2025-04-20 14:02:30,569 - INFO - Validation Std: 7.4402 +2025-04-20 14:02:30,569 - INFO - Training Mean: 23.1525 +2025-04-20 14:02:30,569 - INFO - Training Std: 20.4266 +2025-04-20 14:02:30,569 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:02:30,569 - INFO - KS Statistic: 0.2175 +2025-04-20 14:02:30,569 - INFO - P-value: 0.0000 +2025-04-20 14:02:30,569 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:02:30,569 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:02:33,569 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:02:33,569 - INFO - Timestamp: 2025-04-20 14:02:33 +2025-04-20 14:02:33,569 - INFO - Validation set size: 1414 (8.56%) +2025-04-20 14:02:33,569 - INFO - Training set size: 15100 (91.44%) +2025-04-20 14:02:33,569 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:02:33,569 - INFO - Mean: 68.9811 +2025-04-20 14:02:33,569 - INFO - Median: 4.7210 +2025-04-20 14:02:33,569 - INFO - Min: 0.8728 +2025-04-20 14:02:33,569 - INFO - Max: 655.9249 +2025-04-20 14:02:33,569 - INFO - Std: 111.2659 +2025-04-20 14:02:33,569 - INFO - OC Distribution Statistics: +2025-04-20 14:02:33,569 - INFO - Validation Mean: 16.4251 +2025-04-20 14:02:33,569 - INFO - Validation Std: 7.5999 +2025-04-20 14:02:33,569 - INFO - Training Mean: 23.0982 +2025-04-20 14:02:33,569 - INFO - Training Std: 20.3424 +2025-04-20 14:02:33,569 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:02:33,569 - INFO - KS Statistic: 0.2339 +2025-04-20 14:02:33,569 - INFO - P-value: 0.0000 +2025-04-20 14:02:33,569 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:02:33,569 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:05:20,402 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:05:20,402 - INFO - Timestamp: 2025-04-20 14:05:20 +2025-04-20 14:05:20,402 - INFO - Validation set size: 1371 (8.30%) +2025-04-20 14:05:20,402 - INFO - Training set size: 15143 (91.70%) +2025-04-20 14:05:20,402 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:05:20,402 - INFO - Mean: 69.9853 +2025-04-20 14:05:20,402 - INFO - Median: 4.6181 +2025-04-20 14:05:20,402 - INFO - Min: 0.9397 +2025-04-20 14:05:20,402 - INFO - Max: 706.9584 +2025-04-20 14:05:20,402 - INFO - Std: 116.3089 +2025-04-20 14:05:20,402 - INFO - OC Distribution Statistics: +2025-04-20 14:05:20,402 - INFO - Validation Mean: 16.7467 +2025-04-20 14:05:20,402 - INFO - Validation Std: 7.9219 +2025-04-20 14:05:20,402 - INFO - Training Mean: 23.0501 +2025-04-20 14:05:20,402 - INFO - Training Std: 20.3188 +2025-04-20 14:05:20,402 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:05:20,402 - INFO - KS Statistic: 0.2053 +2025-04-20 14:05:20,402 - INFO - P-value: 0.0000 +2025-04-20 14:05:20,403 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:05:20,403 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:05:20,419 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:05:20,419 - INFO - Timestamp: 2025-04-20 14:05:20 +2025-04-20 14:05:20,419 - INFO - Validation set size: 1379 (8.35%) +2025-04-20 14:05:20,419 - INFO - Training set size: 15135 (91.65%) +2025-04-20 14:05:20,419 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:05:20,420 - INFO - Mean: 63.7637 +2025-04-20 14:05:20,420 - INFO - Median: 4.1294 +2025-04-20 14:05:20,420 - INFO - Min: 0.8608 +2025-04-20 14:05:20,420 - INFO - Max: 707.4892 +2025-04-20 14:05:20,420 - INFO - Std: 107.4402 +2025-04-20 14:05:20,420 - INFO - OC Distribution Statistics: +2025-04-20 14:05:20,420 - INFO - Validation Mean: 16.3268 +2025-04-20 14:05:20,420 - INFO - Validation Std: 7.4411 +2025-04-20 14:05:20,420 - INFO - Training Mean: 23.0917 +2025-04-20 14:05:20,420 - INFO - Training Std: 20.3271 +2025-04-20 14:05:20,420 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:05:20,420 - INFO - KS Statistic: 0.2212 +2025-04-20 14:05:20,420 - INFO - P-value: 0.0000 +2025-04-20 14:05:20,420 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:05:20,420 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:05:21,332 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:05:21,332 - INFO - Timestamp: 2025-04-20 14:05:21 +2025-04-20 14:05:21,333 - INFO - Validation set size: 1395 (8.45%) +2025-04-20 14:05:21,333 - INFO - Training set size: 15119 (91.55%) +2025-04-20 14:05:21,333 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:05:21,333 - INFO - Mean: 68.4991 +2025-04-20 14:05:21,333 - INFO - Median: 4.8053 +2025-04-20 14:05:21,333 - INFO - Min: 1.2156 +2025-04-20 14:05:21,333 - INFO - Max: 888.3558 +2025-04-20 14:05:21,333 - INFO - Std: 116.1517 +2025-04-20 14:05:21,333 - INFO - OC Distribution Statistics: +2025-04-20 14:05:21,333 - INFO - Validation Mean: 16.9271 +2025-04-20 14:05:21,333 - INFO - Validation Std: 8.1975 +2025-04-20 14:05:21,333 - INFO - Training Mean: 23.0435 +2025-04-20 14:05:21,333 - INFO - Training Std: 20.3260 +2025-04-20 14:05:21,333 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:05:21,333 - INFO - KS Statistic: 0.1933 +2025-04-20 14:05:21,333 - INFO - P-value: 0.0000 +2025-04-20 14:05:21,333 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:05:21,333 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:05:25,727 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:05:25,727 - INFO - Timestamp: 2025-04-20 14:05:25 +2025-04-20 14:05:25,727 - INFO - Validation set size: 1531 (9.27%) +2025-04-20 14:05:25,727 - INFO - Training set size: 14983 (90.73%) +2025-04-20 14:05:25,727 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:05:25,727 - INFO - Mean: 71.5218 +2025-04-20 14:05:25,727 - INFO - Median: 4.6106 +2025-04-20 14:05:25,727 - INFO - Min: 0.8258 +2025-04-20 14:05:25,727 - INFO - Max: 870.7197 +2025-04-20 14:05:25,727 - INFO - Std: 117.3509 +2025-04-20 14:05:25,727 - INFO - OC Distribution Statistics: +2025-04-20 14:05:25,727 - INFO - Validation Mean: 16.4618 +2025-04-20 14:05:25,727 - INFO - Validation Std: 7.4774 +2025-04-20 14:05:25,727 - INFO - Training Mean: 23.1465 +2025-04-20 14:05:25,727 - INFO - Training Std: 20.4079 +2025-04-20 14:05:25,727 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:05:25,727 - INFO - KS Statistic: 0.2150 +2025-04-20 14:05:25,727 - INFO - P-value: 0.0000 +2025-04-20 14:05:25,727 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:05:25,727 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:09:19,353 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:09:19,354 - INFO - Timestamp: 2025-04-20 14:09:19 +2025-04-20 14:09:19,354 - INFO - Validation set size: 1448 (8.77%) +2025-04-20 14:09:19,354 - INFO - Training set size: 15066 (91.23%) +2025-04-20 14:09:19,354 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:09:19,354 - INFO - Mean: 67.9115 +2025-04-20 14:09:19,354 - INFO - Median: 4.2475 +2025-04-20 14:09:19,354 - INFO - Min: 1.2301 +2025-04-20 14:09:19,354 - INFO - Max: 743.7101 +2025-04-20 14:09:19,354 - INFO - Std: 113.4789 +2025-04-20 14:09:19,354 - INFO - OC Distribution Statistics: +2025-04-20 14:09:19,354 - INFO - Validation Mean: 16.1621 +2025-04-20 14:09:19,354 - INFO - Validation Std: 6.7844 +2025-04-20 14:09:19,354 - INFO - Training Mean: 23.1385 +2025-04-20 14:09:19,354 - INFO - Training Std: 20.3789 +2025-04-20 14:09:19,354 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:09:19,354 - INFO - KS Statistic: 0.2388 +2025-04-20 14:09:19,354 - INFO - P-value: 0.0000 +2025-04-20 14:09:19,354 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:09:19,354 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:09:19,538 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:09:19,538 - INFO - Timestamp: 2025-04-20 14:09:19 +2025-04-20 14:09:19,538 - INFO - Validation set size: 1671 (10.12%) +2025-04-20 14:09:19,538 - INFO - Training set size: 14843 (89.88%) +2025-04-20 14:09:19,538 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:09:19,538 - INFO - Mean: 65.0763 +2025-04-20 14:09:19,538 - INFO - Median: 4.2800 +2025-04-20 14:09:19,538 - INFO - Min: 0.8258 +2025-04-20 14:09:19,538 - INFO - Max: 870.7197 +2025-04-20 14:09:19,538 - INFO - Std: 117.1477 +2025-04-20 14:09:19,538 - INFO - OC Distribution Statistics: +2025-04-20 14:09:19,538 - INFO - Validation Mean: 16.4790 +2025-04-20 14:09:19,538 - INFO - Validation Std: 7.6566 +2025-04-20 14:09:19,538 - INFO - Training Mean: 23.2076 +2025-04-20 14:09:19,538 - INFO - Training Std: 20.4739 +2025-04-20 14:09:19,538 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:09:19,538 - INFO - KS Statistic: 0.2211 +2025-04-20 14:09:19,538 - INFO - P-value: 0.0000 +2025-04-20 14:09:19,538 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:09:19,538 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:09:19,595 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:09:19,595 - INFO - Timestamp: 2025-04-20 14:09:19 +2025-04-20 14:09:19,595 - INFO - Validation set size: 1383 (8.37%) +2025-04-20 14:09:19,595 - INFO - Training set size: 15131 (91.63%) +2025-04-20 14:09:19,595 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:09:19,595 - INFO - Mean: 67.2436 +2025-04-20 14:09:19,595 - INFO - Median: 4.4085 +2025-04-20 14:09:19,595 - INFO - Min: 0.7391 +2025-04-20 14:09:19,595 - INFO - Max: 706.9584 +2025-04-20 14:09:19,595 - INFO - Std: 113.0797 +2025-04-20 14:09:19,595 - INFO - OC Distribution Statistics: +2025-04-20 14:09:19,595 - INFO - Validation Mean: 16.6576 +2025-04-20 14:09:19,595 - INFO - Validation Std: 8.0838 +2025-04-20 14:09:19,595 - INFO - Training Mean: 23.0632 +2025-04-20 14:09:19,595 - INFO - Training Std: 20.3165 +2025-04-20 14:09:19,595 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:09:19,595 - INFO - KS Statistic: 0.1977 +2025-04-20 14:09:19,595 - INFO - P-value: 0.0000 +2025-04-20 14:09:19,595 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:09:19,595 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 14:09:23,212 - INFO - Validation and Training Set Split Statistics +2025-04-20 14:09:23,213 - INFO - Timestamp: 2025-04-20 14:09:23 +2025-04-20 14:09:23,213 - INFO - Validation set size: 1434 (8.68%) +2025-04-20 14:09:23,213 - INFO - Training set size: 15080 (91.32%) +2025-04-20 14:09:23,213 - INFO - Minimum Distance Statistics (km): +2025-04-20 14:09:23,213 - INFO - Mean: 67.8536 +2025-04-20 14:09:23,213 - INFO - Median: 4.2711 +2025-04-20 14:09:23,213 - INFO - Min: 1.0979 +2025-04-20 14:09:23,213 - INFO - Max: 888.3558 +2025-04-20 14:09:23,213 - INFO - Std: 114.9512 +2025-04-20 14:09:23,213 - INFO - OC Distribution Statistics: +2025-04-20 14:09:23,213 - INFO - Validation Mean: 16.7172 +2025-04-20 14:09:23,213 - INFO - Validation Std: 8.4092 +2025-04-20 14:09:23,213 - INFO - Training Mean: 23.0792 +2025-04-20 14:09:23,213 - INFO - Training Std: 20.3312 +2025-04-20 14:09:23,213 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 14:09:23,213 - INFO - KS Statistic: 0.2106 +2025-04-20 14:09:23,213 - INFO - P-value: 0.0000 +2025-04-20 14:09:23,213 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 14:09:23,213 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:02:59,294 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:02:59,295 - INFO - Timestamp: 2025-04-20 15:02:59 +2025-04-20 15:02:59,295 - INFO - Validation set size: 1352 (8.19%) +2025-04-20 15:02:59,295 - INFO - Training set size: 15162 (91.81%) +2025-04-20 15:02:59,295 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:02:59,295 - INFO - Mean: 68.2104 +2025-04-20 15:02:59,295 - INFO - Median: 5.1405 +2025-04-20 15:02:59,295 - INFO - Min: 1.2773 +2025-04-20 15:02:59,295 - INFO - Max: 743.7101 +2025-04-20 15:02:59,295 - INFO - Std: 111.7095 +2025-04-20 15:02:59,295 - INFO - OC Distribution Statistics: +2025-04-20 15:02:59,295 - INFO - Validation Mean: 16.4617 +2025-04-20 15:02:59,295 - INFO - Validation Std: 8.0757 +2025-04-20 15:02:59,295 - INFO - Training Mean: 23.0676 +2025-04-20 15:02:59,295 - INFO - Training Std: 20.2958 +2025-04-20 15:02:59,295 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:02:59,295 - INFO - KS Statistic: 0.2334 +2025-04-20 15:02:59,295 - INFO - P-value: 0.0000 +2025-04-20 15:02:59,295 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:02:59,295 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:02:59,675 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:02:59,675 - INFO - Timestamp: 2025-04-20 15:02:59 +2025-04-20 15:02:59,676 - INFO - Validation set size: 1369 (8.29%) +2025-04-20 15:02:59,676 - INFO - Training set size: 15145 (91.71%) +2025-04-20 15:02:59,676 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:02:59,676 - INFO - Mean: 63.5202 +2025-04-20 15:02:59,676 - INFO - Median: 4.4057 +2025-04-20 15:02:59,676 - INFO - Min: 1.2287 +2025-04-20 15:02:59,676 - INFO - Max: 716.7388 +2025-04-20 15:02:59,676 - INFO - Std: 110.8446 +2025-04-20 15:02:59,676 - INFO - OC Distribution Statistics: +2025-04-20 15:02:59,676 - INFO - Validation Mean: 16.5899 +2025-04-20 15:02:59,676 - INFO - Validation Std: 8.2423 +2025-04-20 15:02:59,676 - INFO - Training Mean: 23.0634 +2025-04-20 15:02:59,676 - INFO - Training Std: 20.3018 +2025-04-20 15:02:59,676 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:02:59,676 - INFO - KS Statistic: 0.2061 +2025-04-20 15:02:59,676 - INFO - P-value: 0.0000 +2025-04-20 15:02:59,676 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:02:59,676 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:03:00,017 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:03:00,018 - INFO - Timestamp: 2025-04-20 15:03:00 +2025-04-20 15:03:00,018 - INFO - Validation set size: 1587 (9.61%) +2025-04-20 15:03:00,018 - INFO - Training set size: 14927 (90.39%) +2025-04-20 15:03:00,018 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:03:00,018 - INFO - Mean: 64.6811 +2025-04-20 15:03:00,018 - INFO - Median: 4.7993 +2025-04-20 15:03:00,018 - INFO - Min: 1.2413 +2025-04-20 15:03:00,018 - INFO - Max: 835.9450 +2025-04-20 15:03:00,018 - INFO - Std: 111.0838 +2025-04-20 15:03:00,018 - INFO - OC Distribution Statistics: +2025-04-20 15:03:00,018 - INFO - Validation Mean: 16.4548 +2025-04-20 15:03:00,018 - INFO - Validation Std: 8.3555 +2025-04-20 15:03:00,018 - INFO - Training Mean: 23.1723 +2025-04-20 15:03:00,018 - INFO - Training Std: 20.4005 +2025-04-20 15:03:00,018 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:03:00,018 - INFO - KS Statistic: 0.2266 +2025-04-20 15:03:00,018 - INFO - P-value: 0.0000 +2025-04-20 15:03:00,018 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:03:00,018 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:03:03,800 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:03:03,800 - INFO - Timestamp: 2025-04-20 15:03:03 +2025-04-20 15:03:03,800 - INFO - Validation set size: 1383 (8.37%) +2025-04-20 15:03:03,800 - INFO - Training set size: 15131 (91.63%) +2025-04-20 15:03:03,800 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:03:03,800 - INFO - Mean: 64.7644 +2025-04-20 15:03:03,800 - INFO - Median: 4.4778 +2025-04-20 15:03:03,800 - INFO - Min: 1.0311 +2025-04-20 15:03:03,801 - INFO - Max: 743.7101 +2025-04-20 15:03:03,801 - INFO - Std: 108.0083 +2025-04-20 15:03:03,801 - INFO - OC Distribution Statistics: +2025-04-20 15:03:03,801 - INFO - Validation Mean: 16.1358 +2025-04-20 15:03:03,801 - INFO - Validation Std: 7.5574 +2025-04-20 15:03:03,801 - INFO - Training Mean: 23.1109 +2025-04-20 15:03:03,801 - INFO - Training Std: 20.3193 +2025-04-20 15:03:03,801 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:03:03,801 - INFO - KS Statistic: 0.2481 +2025-04-20 15:03:03,801 - INFO - P-value: 0.0000 +2025-04-20 15:03:03,801 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:03:03,801 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:08:38,022 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:08:38,022 - INFO - Timestamp: 2025-04-20 15:08:38 +2025-04-20 15:08:38,022 - INFO - Validation set size: 1379 (8.35%) +2025-04-20 15:08:38,022 - INFO - Training set size: 15135 (91.65%) +2025-04-20 15:08:38,022 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:08:38,022 - INFO - Mean: 70.9060 +2025-04-20 15:08:38,022 - INFO - Median: 5.7535 +2025-04-20 15:08:38,022 - INFO - Min: 0.8258 +2025-04-20 15:08:38,022 - INFO - Max: 706.9584 +2025-04-20 15:08:38,022 - INFO - Std: 112.4606 +2025-04-20 15:08:38,022 - INFO - OC Distribution Statistics: +2025-04-20 15:08:38,022 - INFO - Validation Mean: 16.4564 +2025-04-20 15:08:38,022 - INFO - Validation Std: 7.9433 +2025-04-20 15:08:38,022 - INFO - Training Mean: 23.0799 +2025-04-20 15:08:38,022 - INFO - Training Std: 20.3137 +2025-04-20 15:08:38,022 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:08:38,022 - INFO - KS Statistic: 0.2319 +2025-04-20 15:08:38,022 - INFO - P-value: 0.0000 +2025-04-20 15:08:38,022 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:08:38,022 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:08:38,928 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:08:38,928 - INFO - Timestamp: 2025-04-20 15:08:38 +2025-04-20 15:08:38,928 - INFO - Validation set size: 1342 (8.13%) +2025-04-20 15:08:38,928 - INFO - Training set size: 15172 (91.87%) +2025-04-20 15:08:38,928 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:08:38,928 - INFO - Mean: 69.6228 +2025-04-20 15:08:38,928 - INFO - Median: 4.4057 +2025-04-20 15:08:38,928 - INFO - Min: 1.2065 +2025-04-20 15:08:38,928 - INFO - Max: 705.4078 +2025-04-20 15:08:38,928 - INFO - Std: 115.8252 +2025-04-20 15:08:38,928 - INFO - OC Distribution Statistics: +2025-04-20 15:08:38,928 - INFO - Validation Mean: 16.4497 +2025-04-20 15:08:38,928 - INFO - Validation Std: 7.7656 +2025-04-20 15:08:38,929 - INFO - Training Mean: 23.0643 +2025-04-20 15:08:38,929 - INFO - Training Std: 20.3012 +2025-04-20 15:08:38,929 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:08:38,929 - INFO - KS Statistic: 0.2271 +2025-04-20 15:08:38,929 - INFO - P-value: 0.0000 +2025-04-20 15:08:38,929 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:08:38,929 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:08:39,695 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:08:39,695 - INFO - Timestamp: 2025-04-20 15:08:39 +2025-04-20 15:08:39,695 - INFO - Validation set size: 1333 (8.07%) +2025-04-20 15:08:39,695 - INFO - Training set size: 15181 (91.93%) +2025-04-20 15:08:39,696 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:08:39,696 - INFO - Mean: 77.4758 +2025-04-20 15:08:39,696 - INFO - Median: 6.0803 +2025-04-20 15:08:39,696 - INFO - Min: 1.2065 +2025-04-20 15:08:39,696 - INFO - Max: 706.9584 +2025-04-20 15:08:39,696 - INFO - Std: 123.0128 +2025-04-20 15:08:39,696 - INFO - OC Distribution Statistics: +2025-04-20 15:08:39,696 - INFO - Validation Mean: 16.7927 +2025-04-20 15:08:39,696 - INFO - Validation Std: 7.1712 +2025-04-20 15:08:39,696 - INFO - Training Mean: 23.0303 +2025-04-20 15:08:39,696 - INFO - Training Std: 20.3254 +2025-04-20 15:08:39,696 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:08:39,696 - INFO - KS Statistic: 0.1845 +2025-04-20 15:08:39,696 - INFO - P-value: 0.0000 +2025-04-20 15:08:39,696 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:08:39,696 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:08:42,668 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:08:42,668 - INFO - Timestamp: 2025-04-20 15:08:42 +2025-04-20 15:08:42,668 - INFO - Validation set size: 1408 (8.53%) +2025-04-20 15:08:42,668 - INFO - Training set size: 15106 (91.47%) +2025-04-20 15:08:42,668 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:08:42,668 - INFO - Mean: 67.2386 +2025-04-20 15:08:42,668 - INFO - Median: 4.2196 +2025-04-20 15:08:42,668 - INFO - Min: 1.0596 +2025-04-20 15:08:42,668 - INFO - Max: 716.7388 +2025-04-20 15:08:42,668 - INFO - Std: 116.0495 +2025-04-20 15:08:42,668 - INFO - OC Distribution Statistics: +2025-04-20 15:08:42,668 - INFO - Validation Mean: 16.4341 +2025-04-20 15:08:42,669 - INFO - Validation Std: 7.7745 +2025-04-20 15:08:42,669 - INFO - Training Mean: 23.0947 +2025-04-20 15:08:42,669 - INFO - Training Std: 20.3335 +2025-04-20 15:08:42,669 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:08:42,669 - INFO - KS Statistic: 0.2095 +2025-04-20 15:08:42,669 - INFO - P-value: 0.0000 +2025-04-20 15:08:42,669 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:08:42,669 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:10:34,092 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:10:34,092 - INFO - Timestamp: 2025-04-20 15:10:34 +2025-04-20 15:10:34,092 - INFO - Validation set size: 1363 (8.25%) +2025-04-20 15:10:34,092 - INFO - Training set size: 15151 (91.75%) +2025-04-20 15:10:34,092 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:10:34,092 - INFO - Mean: 64.5556 +2025-04-20 15:10:34,092 - INFO - Median: 4.2015 +2025-04-20 15:10:34,092 - INFO - Min: 0.8608 +2025-04-20 15:10:34,092 - INFO - Max: 1106.1626 +2025-04-20 15:10:34,093 - INFO - Std: 117.6835 +2025-04-20 15:10:34,093 - INFO - OC Distribution Statistics: +2025-04-20 15:10:34,093 - INFO - Validation Mean: 16.5142 +2025-04-20 15:10:34,093 - INFO - Validation Std: 8.3492 +2025-04-20 15:10:34,093 - INFO - Training Mean: 23.0677 +2025-04-20 15:10:34,093 - INFO - Training Std: 20.2928 +2025-04-20 15:10:34,093 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:10:34,093 - INFO - KS Statistic: 0.2277 +2025-04-20 15:10:34,093 - INFO - P-value: 0.0000 +2025-04-20 15:10:34,093 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:10:34,093 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:10:35,143 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:10:35,144 - INFO - Timestamp: 2025-04-20 15:10:35 +2025-04-20 15:10:35,144 - INFO - Validation set size: 1572 (9.52%) +2025-04-20 15:10:35,144 - INFO - Training set size: 14942 (90.48%) +2025-04-20 15:10:35,144 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:10:35,144 - INFO - Mean: 61.3632 +2025-04-20 15:10:35,144 - INFO - Median: 4.2447 +2025-04-20 15:10:35,144 - INFO - Min: 0.7667 +2025-04-20 15:10:35,144 - INFO - Max: 563.5259 +2025-04-20 15:10:35,144 - INFO - Std: 98.4921 +2025-04-20 15:10:35,144 - INFO - OC Distribution Statistics: +2025-04-20 15:10:35,144 - INFO - Validation Mean: 16.1008 +2025-04-20 15:10:35,144 - INFO - Validation Std: 7.6627 +2025-04-20 15:10:35,144 - INFO - Training Mean: 23.2028 +2025-04-20 15:10:35,144 - INFO - Training Std: 20.4091 +2025-04-20 15:10:35,144 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:10:35,144 - INFO - KS Statistic: 0.2552 +2025-04-20 15:10:35,144 - INFO - P-value: 0.0000 +2025-04-20 15:10:35,144 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:10:35,144 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:10:35,315 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:10:35,315 - INFO - Timestamp: 2025-04-20 15:10:35 +2025-04-20 15:10:35,315 - INFO - Validation set size: 1619 (9.80%) +2025-04-20 15:10:35,315 - INFO - Training set size: 14895 (90.20%) +2025-04-20 15:10:35,315 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:10:35,315 - INFO - Mean: 65.9883 +2025-04-20 15:10:35,315 - INFO - Median: 3.9657 +2025-04-20 15:10:35,315 - INFO - Min: 1.2497 +2025-04-20 15:10:35,315 - INFO - Max: 705.4078 +2025-04-20 15:10:35,315 - INFO - Std: 119.1119 +2025-04-20 15:10:35,315 - INFO - OC Distribution Statistics: +2025-04-20 15:10:35,315 - INFO - Validation Mean: 16.5805 +2025-04-20 15:10:35,315 - INFO - Validation Std: 7.2052 +2025-04-20 15:10:35,315 - INFO - Training Mean: 23.1731 +2025-04-20 15:10:35,315 - INFO - Training Std: 20.4684 +2025-04-20 15:10:35,315 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:10:35,315 - INFO - KS Statistic: 0.2137 +2025-04-20 15:10:35,315 - INFO - P-value: 0.0000 +2025-04-20 15:10:35,315 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:10:35,316 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:10:38,282 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:10:38,282 - INFO - Timestamp: 2025-04-20 15:10:38 +2025-04-20 15:10:38,282 - INFO - Validation set size: 1376 (8.33%) +2025-04-20 15:10:38,282 - INFO - Training set size: 15138 (91.67%) +2025-04-20 15:10:38,282 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:10:38,282 - INFO - Mean: 68.1717 +2025-04-20 15:10:38,282 - INFO - Median: 5.1368 +2025-04-20 15:10:38,282 - INFO - Min: 1.2102 +2025-04-20 15:10:38,282 - INFO - Max: 702.5656 +2025-04-20 15:10:38,282 - INFO - Std: 112.5019 +2025-04-20 15:10:38,282 - INFO - OC Distribution Statistics: +2025-04-20 15:10:38,282 - INFO - Validation Mean: 16.7330 +2025-04-20 15:10:38,282 - INFO - Validation Std: 8.4504 +2025-04-20 15:10:38,282 - INFO - Training Mean: 23.0534 +2025-04-20 15:10:38,282 - INFO - Training Std: 20.3016 +2025-04-20 15:10:38,282 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:10:38,282 - INFO - KS Statistic: 0.2109 +2025-04-20 15:10:38,282 - INFO - P-value: 0.0000 +2025-04-20 15:10:38,283 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:10:38,283 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:14:27,672 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:14:27,673 - INFO - Timestamp: 2025-04-20 15:14:27 +2025-04-20 15:14:27,673 - INFO - Validation set size: 1625 (9.84%) +2025-04-20 15:14:27,673 - INFO - Training set size: 14889 (90.16%) +2025-04-20 15:14:27,673 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:14:27,673 - INFO - Mean: 67.8430 +2025-04-20 15:14:27,673 - INFO - Median: 4.9161 +2025-04-20 15:14:27,673 - INFO - Min: 0.6477 +2025-04-20 15:14:27,673 - INFO - Max: 705.4078 +2025-04-20 15:14:27,673 - INFO - Std: 108.8665 +2025-04-20 15:14:27,673 - INFO - OC Distribution Statistics: +2025-04-20 15:14:27,673 - INFO - Validation Mean: 16.7918 +2025-04-20 15:14:27,673 - INFO - Validation Std: 8.0980 +2025-04-20 15:14:27,673 - INFO - Training Mean: 23.1527 +2025-04-20 15:14:27,673 - INFO - Training Std: 20.4424 +2025-04-20 15:14:27,673 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:14:27,673 - INFO - KS Statistic: 0.2134 +2025-04-20 15:14:27,673 - INFO - P-value: 0.0000 +2025-04-20 15:14:27,673 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:14:27,673 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:14:27,892 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:14:27,892 - INFO - Timestamp: 2025-04-20 15:14:27 +2025-04-20 15:14:27,892 - INFO - Validation set size: 1664 (10.08%) +2025-04-20 15:14:27,892 - INFO - Training set size: 14850 (89.92%) +2025-04-20 15:14:27,892 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:14:27,892 - INFO - Mean: 62.5485 +2025-04-20 15:14:27,892 - INFO - Median: 4.2593 +2025-04-20 15:14:27,892 - INFO - Min: 1.2067 +2025-04-20 15:14:27,892 - INFO - Max: 706.9584 +2025-04-20 15:14:27,892 - INFO - Std: 109.8121 +2025-04-20 15:14:27,892 - INFO - OC Distribution Statistics: +2025-04-20 15:14:27,892 - INFO - Validation Mean: 16.1521 +2025-04-20 15:14:27,892 - INFO - Validation Std: 6.8272 +2025-04-20 15:14:27,892 - INFO - Training Mean: 23.2411 +2025-04-20 15:14:27,892 - INFO - Training Std: 20.4908 +2025-04-20 15:14:27,892 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:14:27,892 - INFO - KS Statistic: 0.2360 +2025-04-20 15:14:27,892 - INFO - P-value: 0.0000 +2025-04-20 15:14:27,892 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:14:27,892 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:14:28,057 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:14:28,057 - INFO - Timestamp: 2025-04-20 15:14:28 +2025-04-20 15:14:28,057 - INFO - Validation set size: 1339 (8.11%) +2025-04-20 15:14:28,057 - INFO - Training set size: 15175 (91.89%) +2025-04-20 15:14:28,057 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:14:28,057 - INFO - Mean: 82.6078 +2025-04-20 15:14:28,057 - INFO - Median: 5.9821 +2025-04-20 15:14:28,057 - INFO - Min: 1.0979 +2025-04-20 15:14:28,057 - INFO - Max: 1480.6281 +2025-04-20 15:14:28,057 - INFO - Std: 145.7023 +2025-04-20 15:14:28,057 - INFO - OC Distribution Statistics: +2025-04-20 15:14:28,057 - INFO - Validation Mean: 16.9438 +2025-04-20 15:14:28,057 - INFO - Validation Std: 8.0047 +2025-04-20 15:14:28,057 - INFO - Training Mean: 23.0194 +2025-04-20 15:14:28,057 - INFO - Training Std: 20.3051 +2025-04-20 15:14:28,057 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:14:28,057 - INFO - KS Statistic: 0.1686 +2025-04-20 15:14:28,057 - INFO - P-value: 0.0000 +2025-04-20 15:14:28,057 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:14:28,057 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:14:32,242 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:14:32,242 - INFO - Timestamp: 2025-04-20 15:14:32 +2025-04-20 15:14:32,242 - INFO - Validation set size: 1856 (11.24%) +2025-04-20 15:14:32,242 - INFO - Training set size: 14658 (88.76%) +2025-04-20 15:14:32,242 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:14:32,242 - INFO - Mean: 54.8262 +2025-04-20 15:14:32,242 - INFO - Median: 3.9792 +2025-04-20 15:14:32,242 - INFO - Min: 0.8394 +2025-04-20 15:14:32,242 - INFO - Max: 875.2369 +2025-04-20 15:14:32,242 - INFO - Std: 110.1353 +2025-04-20 15:14:32,242 - INFO - OC Distribution Statistics: +2025-04-20 15:14:32,242 - INFO - Validation Mean: 16.0965 +2025-04-20 15:14:32,242 - INFO - Validation Std: 7.2441 +2025-04-20 15:14:32,242 - INFO - Training Mean: 23.3410 +2025-04-20 15:14:32,242 - INFO - Training Std: 20.5730 +2025-04-20 15:14:32,242 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:14:32,242 - INFO - KS Statistic: 0.2384 +2025-04-20 15:14:32,242 - INFO - P-value: 0.0000 +2025-04-20 15:14:32,243 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:14:32,243 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:17:11,010 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:17:11,011 - INFO - Timestamp: 2025-04-20 15:17:10 +2025-04-20 15:17:11,011 - INFO - Validation set size: 1364 (8.26%) +2025-04-20 15:17:11,011 - INFO - Training set size: 15150 (91.74%) +2025-04-20 15:17:11,011 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:17:11,011 - INFO - Mean: 70.6676 +2025-04-20 15:17:11,011 - INFO - Median: 4.5343 +2025-04-20 15:17:11,011 - INFO - Min: 1.2156 +2025-04-20 15:17:11,011 - INFO - Max: 1480.6281 +2025-04-20 15:17:11,011 - INFO - Std: 137.4066 +2025-04-20 15:17:11,011 - INFO - OC Distribution Statistics: +2025-04-20 15:17:11,011 - INFO - Validation Mean: 16.3683 +2025-04-20 15:17:11,011 - INFO - Validation Std: 8.1366 +2025-04-20 15:17:11,012 - INFO - Training Mean: 23.0813 +2025-04-20 15:17:11,012 - INFO - Training Std: 20.2967 +2025-04-20 15:17:11,012 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:17:11,012 - INFO - KS Statistic: 0.2270 +2025-04-20 15:17:11,012 - INFO - P-value: 0.0000 +2025-04-20 15:17:11,012 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:17:11,012 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:17:11,369 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:17:11,369 - INFO - Timestamp: 2025-04-20 15:17:11 +2025-04-20 15:17:11,369 - INFO - Validation set size: 1358 (8.22%) +2025-04-20 15:17:11,369 - INFO - Training set size: 15156 (91.78%) +2025-04-20 15:17:11,369 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:17:11,369 - INFO - Mean: 68.3080 +2025-04-20 15:17:11,369 - INFO - Median: 4.6741 +2025-04-20 15:17:11,369 - INFO - Min: 0.8728 +2025-04-20 15:17:11,369 - INFO - Max: 835.9450 +2025-04-20 15:17:11,369 - INFO - Std: 118.6963 +2025-04-20 15:17:11,369 - INFO - OC Distribution Statistics: +2025-04-20 15:17:11,369 - INFO - Validation Mean: 16.5321 +2025-04-20 15:17:11,369 - INFO - Validation Std: 8.0463 +2025-04-20 15:17:11,369 - INFO - Training Mean: 23.0639 +2025-04-20 15:17:11,369 - INFO - Training Std: 20.3018 +2025-04-20 15:17:11,369 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:17:11,369 - INFO - KS Statistic: 0.2066 +2025-04-20 15:17:11,370 - INFO - P-value: 0.0000 +2025-04-20 15:17:11,370 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:17:11,370 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:17:12,099 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:17:12,099 - INFO - Timestamp: 2025-04-20 15:17:12 +2025-04-20 15:17:12,099 - INFO - Validation set size: 1380 (8.36%) +2025-04-20 15:17:12,099 - INFO - Training set size: 15134 (91.64%) +2025-04-20 15:17:12,099 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:17:12,099 - INFO - Mean: 62.0366 +2025-04-20 15:17:12,099 - INFO - Median: 4.8124 +2025-04-20 15:17:12,099 - INFO - Min: 1.2156 +2025-04-20 15:17:12,099 - INFO - Max: 681.9863 +2025-04-20 15:17:12,099 - INFO - Std: 104.4567 +2025-04-20 15:17:12,099 - INFO - OC Distribution Statistics: +2025-04-20 15:17:12,100 - INFO - Validation Mean: 16.1414 +2025-04-20 15:17:12,100 - INFO - Validation Std: 7.2868 +2025-04-20 15:17:12,100 - INFO - Training Mean: 23.1090 +2025-04-20 15:17:12,100 - INFO - Training Std: 20.3270 +2025-04-20 15:17:12,100 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:17:12,100 - INFO - KS Statistic: 0.2103 +2025-04-20 15:17:12,100 - INFO - P-value: 0.0000 +2025-04-20 15:17:12,100 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:17:12,100 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:17:15,479 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:17:15,479 - INFO - Timestamp: 2025-04-20 15:17:15 +2025-04-20 15:17:15,479 - INFO - Validation set size: 1338 (8.10%) +2025-04-20 15:17:15,479 - INFO - Training set size: 15176 (91.90%) +2025-04-20 15:17:15,479 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:17:15,479 - INFO - Mean: 66.4260 +2025-04-20 15:17:15,479 - INFO - Median: 5.5898 +2025-04-20 15:17:15,479 - INFO - Min: 1.2497 +2025-04-20 15:17:15,479 - INFO - Max: 681.9863 +2025-04-20 15:17:15,479 - INFO - Std: 107.5922 +2025-04-20 15:17:15,479 - INFO - OC Distribution Statistics: +2025-04-20 15:17:15,479 - INFO - Validation Mean: 16.8485 +2025-04-20 15:17:15,479 - INFO - Validation Std: 8.4474 +2025-04-20 15:17:15,479 - INFO - Training Mean: 23.0274 +2025-04-20 15:17:15,479 - INFO - Training Std: 20.2863 +2025-04-20 15:17:15,479 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:17:15,479 - INFO - KS Statistic: 0.1981 +2025-04-20 15:17:15,479 - INFO - P-value: 0.0000 +2025-04-20 15:17:15,479 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:17:15,480 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:25:19,729 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:25:19,729 - INFO - Timestamp: 2025-04-20 15:25:19 +2025-04-20 15:25:19,729 - INFO - Validation set size: 1411 (8.54%) +2025-04-20 15:25:19,729 - INFO - Training set size: 15103 (91.46%) +2025-04-20 15:25:19,729 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:25:19,729 - INFO - Mean: 72.6452 +2025-04-20 15:25:19,729 - INFO - Median: 4.6795 +2025-04-20 15:25:19,729 - INFO - Min: 1.1496 +2025-04-20 15:25:19,729 - INFO - Max: 743.7101 +2025-04-20 15:25:19,730 - INFO - Std: 125.0418 +2025-04-20 15:25:19,730 - INFO - OC Distribution Statistics: +2025-04-20 15:25:19,730 - INFO - Validation Mean: 16.5343 +2025-04-20 15:25:19,730 - INFO - Validation Std: 7.9114 +2025-04-20 15:25:19,730 - INFO - Training Mean: 23.0866 +2025-04-20 15:25:19,730 - INFO - Training Std: 20.3331 +2025-04-20 15:25:19,730 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:25:19,730 - INFO - KS Statistic: 0.2074 +2025-04-20 15:25:19,730 - INFO - P-value: 0.0000 +2025-04-20 15:25:19,730 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:25:19,730 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:25:19,776 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:25:19,776 - INFO - Timestamp: 2025-04-20 15:25:19 +2025-04-20 15:25:19,776 - INFO - Validation set size: 1394 (8.44%) +2025-04-20 15:25:19,776 - INFO - Training set size: 15120 (91.56%) +2025-04-20 15:25:19,776 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:25:19,776 - INFO - Mean: 62.8016 +2025-04-20 15:25:19,776 - INFO - Median: 4.0944 +2025-04-20 15:25:19,776 - INFO - Min: 1.2156 +2025-04-20 15:25:19,776 - INFO - Max: 888.3558 +2025-04-20 15:25:19,776 - INFO - Std: 106.2542 +2025-04-20 15:25:19,776 - INFO - OC Distribution Statistics: +2025-04-20 15:25:19,776 - INFO - Validation Mean: 16.4443 +2025-04-20 15:25:19,776 - INFO - Validation Std: 7.4671 +2025-04-20 15:25:19,776 - INFO - Training Mean: 23.0876 +2025-04-20 15:25:19,776 - INFO - Training Std: 20.3374 +2025-04-20 15:25:19,776 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:25:19,776 - INFO - KS Statistic: 0.2156 +2025-04-20 15:25:19,776 - INFO - P-value: 0.0000 +2025-04-20 15:25:19,776 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:25:19,776 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:25:20,569 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:25:20,569 - INFO - Timestamp: 2025-04-20 15:25:20 +2025-04-20 15:25:20,569 - INFO - Validation set size: 1358 (8.22%) +2025-04-20 15:25:20,569 - INFO - Training set size: 15156 (91.78%) +2025-04-20 15:25:20,569 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:25:20,569 - INFO - Mean: 66.3409 +2025-04-20 15:25:20,569 - INFO - Median: 4.6602 +2025-04-20 15:25:20,569 - INFO - Min: 0.6775 +2025-04-20 15:25:20,569 - INFO - Max: 706.9584 +2025-04-20 15:25:20,569 - INFO - Std: 105.6282 +2025-04-20 15:25:20,569 - INFO - OC Distribution Statistics: +2025-04-20 15:25:20,569 - INFO - Validation Mean: 16.8733 +2025-04-20 15:25:20,569 - INFO - Validation Std: 8.5556 +2025-04-20 15:25:20,569 - INFO - Training Mean: 23.0333 +2025-04-20 15:25:20,569 - INFO - Training Std: 20.2927 +2025-04-20 15:25:20,569 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:25:20,569 - INFO - KS Statistic: 0.1990 +2025-04-20 15:25:20,569 - INFO - P-value: 0.0000 +2025-04-20 15:25:20,569 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:25:20,569 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 15:25:24,253 - INFO - Validation and Training Set Split Statistics +2025-04-20 15:25:24,253 - INFO - Timestamp: 2025-04-20 15:25:24 +2025-04-20 15:25:24,253 - INFO - Validation set size: 1590 (9.63%) +2025-04-20 15:25:24,253 - INFO - Training set size: 14924 (90.37%) +2025-04-20 15:25:24,253 - INFO - Minimum Distance Statistics (km): +2025-04-20 15:25:24,253 - INFO - Mean: 60.9084 +2025-04-20 15:25:24,253 - INFO - Median: 3.8455 +2025-04-20 15:25:24,253 - INFO - Min: 0.8258 +2025-04-20 15:25:24,253 - INFO - Max: 862.7749 +2025-04-20 15:25:24,253 - INFO - Std: 107.3496 +2025-04-20 15:25:24,253 - INFO - OC Distribution Statistics: +2025-04-20 15:25:24,253 - INFO - Validation Mean: 16.2281 +2025-04-20 15:25:24,253 - INFO - Validation Std: 6.9250 +2025-04-20 15:25:24,253 - INFO - Training Mean: 23.1978 +2025-04-20 15:25:24,253 - INFO - Training Std: 20.4509 +2025-04-20 15:25:24,253 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 15:25:24,253 - INFO - KS Statistic: 0.2337 +2025-04-20 15:25:24,253 - INFO - P-value: 0.0000 +2025-04-20 15:25:24,253 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 15:25:24,253 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 16:18:31,550 - INFO - Validation and Training Set Split Statistics +2025-04-20 16:18:31,551 - INFO - Timestamp: 2025-04-20 16:18:31 +2025-04-20 16:18:31,551 - INFO - Validation set size: 1653 (10.01%) +2025-04-20 16:18:31,551 - INFO - Training set size: 14861 (89.99%) +2025-04-20 16:18:31,551 - INFO - Minimum Distance Statistics (km): +2025-04-20 16:18:31,551 - INFO - Mean: 66.2088 +2025-04-20 16:18:31,551 - INFO - Median: 4.5420 +2025-04-20 16:18:31,551 - INFO - Min: 0.2243 +2025-04-20 16:18:31,551 - INFO - Max: 706.9584 +2025-04-20 16:18:31,551 - INFO - Std: 110.6180 +2025-04-20 16:18:31,551 - INFO - OC Distribution Statistics: +2025-04-20 16:18:31,551 - INFO - Validation Mean: 16.4068 +2025-04-20 16:18:31,551 - INFO - Validation Std: 7.8609 +2025-04-20 16:18:31,551 - INFO - Training Mean: 23.2075 +2025-04-20 16:18:31,551 - INFO - Training Std: 20.4533 +2025-04-20 16:18:31,551 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 16:18:31,551 - INFO - KS Statistic: 0.2337 +2025-04-20 16:18:31,551 - INFO - P-value: 0.0000 +2025-04-20 16:18:31,551 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 16:18:31,551 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 16:18:32,277 - INFO - Validation and Training Set Split Statistics +2025-04-20 16:18:32,278 - INFO - Timestamp: 2025-04-20 16:18:32 +2025-04-20 16:18:32,278 - INFO - Validation set size: 1358 (8.22%) +2025-04-20 16:18:32,278 - INFO - Training set size: 15156 (91.78%) +2025-04-20 16:18:32,278 - INFO - Minimum Distance Statistics (km): +2025-04-20 16:18:32,278 - INFO - Mean: 69.1992 +2025-04-20 16:18:32,278 - INFO - Median: 4.4854 +2025-04-20 16:18:32,279 - INFO - Min: 1.0311 +2025-04-20 16:18:32,279 - INFO - Max: 862.7749 +2025-04-20 16:18:32,279 - INFO - Std: 116.9158 +2025-04-20 16:18:32,279 - INFO - OC Distribution Statistics: +2025-04-20 16:18:32,279 - INFO - Validation Mean: 16.6543 +2025-04-20 16:18:32,279 - INFO - Validation Std: 7.5458 +2025-04-20 16:18:32,279 - INFO - Training Mean: 23.0530 +2025-04-20 16:18:32,279 - INFO - Training Std: 20.3225 +2025-04-20 16:18:32,279 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 16:18:32,279 - INFO - KS Statistic: 0.2020 +2025-04-20 16:18:32,279 - INFO - P-value: 0.0000 +2025-04-20 16:18:32,279 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 16:18:32,279 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 16:18:32,797 - INFO - Validation and Training Set Split Statistics +2025-04-20 16:18:32,797 - INFO - Timestamp: 2025-04-20 16:18:32 +2025-04-20 16:18:32,797 - INFO - Validation set size: 1384 (8.38%) +2025-04-20 16:18:32,797 - INFO - Training set size: 15130 (91.62%) +2025-04-20 16:18:32,797 - INFO - Minimum Distance Statistics (km): +2025-04-20 16:18:32,797 - INFO - Mean: 68.7949 +2025-04-20 16:18:32,797 - INFO - Median: 4.3277 +2025-04-20 16:18:32,797 - INFO - Min: 1.2773 +2025-04-20 16:18:32,797 - INFO - Max: 917.1429 +2025-04-20 16:18:32,797 - INFO - Std: 120.1352 +2025-04-20 16:18:32,797 - INFO - OC Distribution Statistics: +2025-04-20 16:18:32,798 - INFO - Validation Mean: 16.9958 +2025-04-20 16:18:32,798 - INFO - Validation Std: 8.6895 +2025-04-20 16:18:32,798 - INFO - Training Mean: 23.0327 +2025-04-20 16:18:32,798 - INFO - Training Std: 20.3036 +2025-04-20 16:18:32,798 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 16:18:32,798 - INFO - KS Statistic: 0.2098 +2025-04-20 16:18:32,798 - INFO - P-value: 0.0000 +2025-04-20 16:18:32,798 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 16:18:32,798 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 16:18:35,035 - INFO - Validation and Training Set Split Statistics +2025-04-20 16:18:35,036 - INFO - Timestamp: 2025-04-20 16:18:35 +2025-04-20 16:18:35,036 - INFO - Validation set size: 1410 (8.54%) +2025-04-20 16:18:35,036 - INFO - Training set size: 15104 (91.46%) +2025-04-20 16:18:35,036 - INFO - Minimum Distance Statistics (km): +2025-04-20 16:18:35,036 - INFO - Mean: 71.8210 +2025-04-20 16:18:35,036 - INFO - Median: 4.6795 +2025-04-20 16:18:35,036 - INFO - Min: 0.8728 +2025-04-20 16:18:35,036 - INFO - Max: 743.7101 +2025-04-20 16:18:35,036 - INFO - Std: 114.8572 +2025-04-20 16:18:35,036 - INFO - OC Distribution Statistics: +2025-04-20 16:18:35,036 - INFO - Validation Mean: 16.3434 +2025-04-20 16:18:35,036 - INFO - Validation Std: 7.6437 +2025-04-20 16:18:35,036 - INFO - Training Mean: 23.1040 +2025-04-20 16:18:35,036 - INFO - Training Std: 20.3364 +2025-04-20 16:18:35,036 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 16:18:35,036 - INFO - KS Statistic: 0.2346 +2025-04-20 16:18:35,036 - INFO - P-value: 0.0000 +2025-04-20 16:18:35,036 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 16:18:35,036 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 17:27:26,542 - INFO - Validation and Training Set Split Statistics +2025-04-20 17:27:26,543 - INFO - Timestamp: 2025-04-20 17:27:26 +2025-04-20 17:27:26,543 - INFO - Validation set size: 1391 (8.42%) +2025-04-20 17:27:26,543 - INFO - Training set size: 15123 (91.58%) +2025-04-20 17:27:26,543 - INFO - Minimum Distance Statistics (km): +2025-04-20 17:27:26,543 - INFO - Mean: 66.6190 +2025-04-20 17:27:26,543 - INFO - Median: 4.3243 +2025-04-20 17:27:26,543 - INFO - Min: 1.2005 +2025-04-20 17:27:26,543 - INFO - Max: 862.7749 +2025-04-20 17:27:26,543 - INFO - Std: 113.9528 +2025-04-20 17:27:26,543 - INFO - OC Distribution Statistics: +2025-04-20 17:27:26,543 - INFO - Validation Mean: 16.3690 +2025-04-20 17:27:26,543 - INFO - Validation Std: 7.2565 +2025-04-20 17:27:26,543 - INFO - Training Mean: 23.0932 +2025-04-20 17:27:26,543 - INFO - Training Std: 20.3406 +2025-04-20 17:27:26,543 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 17:27:26,543 - INFO - KS Statistic: 0.2140 +2025-04-20 17:27:26,543 - INFO - P-value: 0.0000 +2025-04-20 17:27:26,543 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 17:27:26,543 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 17:27:27,423 - INFO - Validation and Training Set Split Statistics +2025-04-20 17:27:27,423 - INFO - Timestamp: 2025-04-20 17:27:27 +2025-04-20 17:27:27,423 - INFO - Validation set size: 1531 (9.27%) +2025-04-20 17:27:27,423 - INFO - Training set size: 14983 (90.73%) +2025-04-20 17:27:27,423 - INFO - Minimum Distance Statistics (km): +2025-04-20 17:27:27,423 - INFO - Mean: 62.9018 +2025-04-20 17:27:27,423 - INFO - Median: 4.7210 +2025-04-20 17:27:27,423 - INFO - Min: 1.0311 +2025-04-20 17:27:27,423 - INFO - Max: 716.7388 +2025-04-20 17:27:27,423 - INFO - Std: 108.1457 +2025-04-20 17:27:27,423 - INFO - OC Distribution Statistics: +2025-04-20 17:27:27,423 - INFO - Validation Mean: 16.5198 +2025-04-20 17:27:27,423 - INFO - Validation Std: 7.5240 +2025-04-20 17:27:27,423 - INFO - Training Mean: 23.1406 +2025-04-20 17:27:27,423 - INFO - Training Std: 20.4081 +2025-04-20 17:27:27,423 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 17:27:27,423 - INFO - KS Statistic: 0.2147 +2025-04-20 17:27:27,423 - INFO - P-value: 0.0000 +2025-04-20 17:27:27,423 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 17:27:27,423 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 17:27:27,472 - INFO - Validation and Training Set Split Statistics +2025-04-20 17:27:27,472 - INFO - Timestamp: 2025-04-20 17:27:27 +2025-04-20 17:27:27,472 - INFO - Validation set size: 1362 (8.25%) +2025-04-20 17:27:27,472 - INFO - Training set size: 15152 (91.75%) +2025-04-20 17:27:27,472 - INFO - Minimum Distance Statistics (km): +2025-04-20 17:27:27,472 - INFO - Mean: 65.5910 +2025-04-20 17:27:27,472 - INFO - Median: 4.9833 +2025-04-20 17:27:27,472 - INFO - Min: 1.2287 +2025-04-20 17:27:27,472 - INFO - Max: 716.7388 +2025-04-20 17:27:27,472 - INFO - Std: 110.4820 +2025-04-20 17:27:27,472 - INFO - OC Distribution Statistics: +2025-04-20 17:27:27,472 - INFO - Validation Mean: 16.3999 +2025-04-20 17:27:27,472 - INFO - Validation Std: 7.9768 +2025-04-20 17:27:27,472 - INFO - Training Mean: 23.0775 +2025-04-20 17:27:27,472 - INFO - Training Std: 20.3024 +2025-04-20 17:27:27,472 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 17:27:27,472 - INFO - KS Statistic: 0.2208 +2025-04-20 17:27:27,472 - INFO - P-value: 0.0000 +2025-04-20 17:27:27,472 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 17:27:27,472 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-20 17:27:30,618 - INFO - Validation and Training Set Split Statistics +2025-04-20 17:27:30,618 - INFO - Timestamp: 2025-04-20 17:27:30 +2025-04-20 17:27:30,618 - INFO - Validation set size: 1665 (10.08%) +2025-04-20 17:27:30,618 - INFO - Training set size: 14849 (89.92%) +2025-04-20 17:27:30,618 - INFO - Minimum Distance Statistics (km): +2025-04-20 17:27:30,618 - INFO - Mean: 60.0588 +2025-04-20 17:27:30,619 - INFO - Median: 3.7922 +2025-04-20 17:27:30,619 - INFO - Min: 0.9102 +2025-04-20 17:27:30,619 - INFO - Max: 716.7388 +2025-04-20 17:27:30,619 - INFO - Std: 105.7132 +2025-04-20 17:27:30,619 - INFO - OC Distribution Statistics: +2025-04-20 17:27:30,619 - INFO - Validation Mean: 16.4766 +2025-04-20 17:27:30,619 - INFO - Validation Std: 7.4806 +2025-04-20 17:27:30,619 - INFO - Training Mean: 23.2052 +2025-04-20 17:27:30,619 - INFO - Training Std: 20.4780 +2025-04-20 17:27:30,619 - INFO - Kolmogorov-Smirnov Test: +2025-04-20 17:27:30,619 - INFO - KS Statistic: 0.2237 +2025-04-20 17:27:30,619 - INFO - P-value: 0.0000 +2025-04-20 17:27:30,619 - INFO - Distribution Fit: Inverse Gamma +2025-04-20 17:27:30,619 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 14:56:15,859 - INFO - Validation and Training Set Split Statistics +2025-04-21 14:56:15,859 - INFO - Timestamp: 2025-04-21 14:56:15 +2025-04-21 14:56:15,859 - INFO - Validation set size: 1369 (8.29%) +2025-04-21 14:56:15,859 - INFO - Training set size: 15145 (91.71%) +2025-04-21 14:56:15,859 - INFO - Minimum Distance Statistics (km): +2025-04-21 14:56:15,859 - INFO - Mean: 73.4666 +2025-04-21 14:56:15,859 - INFO - Median: 4.6101 +2025-04-21 14:56:15,859 - INFO - Min: 1.1342 +2025-04-21 14:56:15,859 - INFO - Max: 772.2996 +2025-04-21 14:56:15,859 - INFO - Std: 121.2988 +2025-04-21 14:56:15,859 - INFO - OC Distribution Statistics: +2025-04-21 14:56:15,859 - INFO - Validation Mean: 16.4408 +2025-04-21 14:56:15,859 - INFO - Validation Std: 8.1744 +2025-04-21 14:56:15,859 - INFO - Training Mean: 23.0769 +2025-04-21 14:56:15,859 - INFO - Training Std: 20.3000 +2025-04-21 14:56:15,859 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 14:56:15,859 - INFO - KS Statistic: 0.1965 +2025-04-21 14:56:15,859 - INFO - P-value: 0.0000 +2025-04-21 14:56:15,859 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 14:56:15,859 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 14:56:15,861 - INFO - Validation and Training Set Split Statistics +2025-04-21 14:56:15,861 - INFO - Timestamp: 2025-04-21 14:56:15 +2025-04-21 14:56:15,861 - INFO - Validation set size: 1375 (8.33%) +2025-04-21 14:56:15,861 - INFO - Training set size: 15139 (91.67%) +2025-04-21 14:56:15,861 - INFO - Minimum Distance Statistics (km): +2025-04-21 14:56:15,861 - INFO - Mean: 71.9927 +2025-04-21 14:56:15,861 - INFO - Median: 4.0201 +2025-04-21 14:56:15,861 - INFO - Min: 1.2596 +2025-04-21 14:56:15,861 - INFO - Max: 1106.1626 +2025-04-21 14:56:15,861 - INFO - Std: 127.2940 +2025-04-21 14:56:15,861 - INFO - OC Distribution Statistics: +2025-04-21 14:56:15,861 - INFO - Validation Mean: 16.7272 +2025-04-21 14:56:15,861 - INFO - Validation Std: 7.5739 +2025-04-21 14:56:15,861 - INFO - Training Mean: 23.0535 +2025-04-21 14:56:15,861 - INFO - Training Std: 20.3323 +2025-04-21 14:56:15,861 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 14:56:15,861 - INFO - KS Statistic: 0.2026 +2025-04-21 14:56:15,861 - INFO - P-value: 0.0000 +2025-04-21 14:56:15,861 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 14:56:15,861 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 14:56:15,979 - INFO - Validation and Training Set Split Statistics +2025-04-21 14:56:15,979 - INFO - Timestamp: 2025-04-21 14:56:15 +2025-04-21 14:56:15,979 - INFO - Validation set size: 1535 (9.30%) +2025-04-21 14:56:15,979 - INFO - Training set size: 14979 (90.70%) +2025-04-21 14:56:15,979 - INFO - Minimum Distance Statistics (km): +2025-04-21 14:56:15,979 - INFO - Mean: 71.4832 +2025-04-21 14:56:15,979 - INFO - Median: 4.8130 +2025-04-21 14:56:15,979 - INFO - Min: 0.2243 +2025-04-21 14:56:15,979 - INFO - Max: 835.9450 +2025-04-21 14:56:15,979 - INFO - Std: 117.2958 +2025-04-21 14:56:15,979 - INFO - OC Distribution Statistics: +2025-04-21 14:56:15,979 - INFO - Validation Mean: 16.5112 +2025-04-21 14:56:15,979 - INFO - Validation Std: 8.4260 +2025-04-21 14:56:15,979 - INFO - Training Mean: 23.1432 +2025-04-21 14:56:15,979 - INFO - Training Std: 20.3738 +2025-04-21 14:56:15,979 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 14:56:15,979 - INFO - KS Statistic: 0.2165 +2025-04-21 14:56:15,979 - INFO - P-value: 0.0000 +2025-04-21 14:56:15,979 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 14:56:15,979 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 14:56:17,362 - INFO - Validation and Training Set Split Statistics +2025-04-21 14:56:17,362 - INFO - Timestamp: 2025-04-21 14:56:17 +2025-04-21 14:56:17,362 - INFO - Validation set size: 1458 (8.83%) +2025-04-21 14:56:17,362 - INFO - Training set size: 15056 (91.17%) +2025-04-21 14:56:17,362 - INFO - Minimum Distance Statistics (km): +2025-04-21 14:56:17,362 - INFO - Mean: 65.6186 +2025-04-21 14:56:17,362 - INFO - Median: 4.0201 +2025-04-21 14:56:17,362 - INFO - Min: 1.2287 +2025-04-21 14:56:17,362 - INFO - Max: 917.1429 +2025-04-21 14:56:17,362 - INFO - Std: 115.6990 +2025-04-21 14:56:17,362 - INFO - OC Distribution Statistics: +2025-04-21 14:56:17,362 - INFO - Validation Mean: 16.6447 +2025-04-21 14:56:17,362 - INFO - Validation Std: 7.9486 +2025-04-21 14:56:17,362 - INFO - Training Mean: 23.0964 +2025-04-21 14:56:17,362 - INFO - Training Std: 20.3588 +2025-04-21 14:56:17,362 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 14:56:17,362 - INFO - KS Statistic: 0.2229 +2025-04-21 14:56:17,362 - INFO - P-value: 0.0000 +2025-04-21 14:56:17,362 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 14:56:17,362 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 15:26:48,108 - INFO - Validation and Training Set Split Statistics +2025-04-21 15:26:48,109 - INFO - Timestamp: 2025-04-21 15:26:48 +2025-04-21 15:26:48,109 - INFO - Validation set size: 1404 (8.50%) +2025-04-21 15:26:48,109 - INFO - Training set size: 15110 (91.50%) +2025-04-21 15:26:48,109 - INFO - Minimum Distance Statistics (km): +2025-04-21 15:26:48,109 - INFO - Mean: 71.0193 +2025-04-21 15:26:48,109 - INFO - Median: 4.5217 +2025-04-21 15:26:48,109 - INFO - Min: 1.2065 +2025-04-21 15:26:48,109 - INFO - Max: 706.9584 +2025-04-21 15:26:48,109 - INFO - Std: 120.2714 +2025-04-21 15:26:48,109 - INFO - OC Distribution Statistics: +2025-04-21 15:26:48,109 - INFO - Validation Mean: 16.0831 +2025-04-21 15:26:48,109 - INFO - Validation Std: 7.3261 +2025-04-21 15:26:48,109 - INFO - Training Mean: 23.1255 +2025-04-21 15:26:48,109 - INFO - Training Std: 20.3360 +2025-04-21 15:26:48,109 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 15:26:48,109 - INFO - KS Statistic: 0.2352 +2025-04-21 15:26:48,109 - INFO - P-value: 0.0000 +2025-04-21 15:26:48,109 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 15:26:48,109 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 15:26:48,111 - INFO - Validation and Training Set Split Statistics +2025-04-21 15:26:48,111 - INFO - Timestamp: 2025-04-21 15:26:48 +2025-04-21 15:26:48,111 - INFO - Validation set size: 1405 (8.51%) +2025-04-21 15:26:48,111 - INFO - Training set size: 15109 (91.49%) +2025-04-21 15:26:48,111 - INFO - Minimum Distance Statistics (km): +2025-04-21 15:26:48,111 - INFO - Mean: 63.2711 +2025-04-21 15:26:48,111 - INFO - Median: 4.3036 +2025-04-21 15:26:48,111 - INFO - Min: 1.2067 +2025-04-21 15:26:48,111 - INFO - Max: 655.9249 +2025-04-21 15:26:48,111 - INFO - Std: 105.0988 +2025-04-21 15:26:48,111 - INFO - OC Distribution Statistics: +2025-04-21 15:26:48,111 - INFO - Validation Mean: 16.1619 +2025-04-21 15:26:48,111 - INFO - Validation Std: 7.9101 +2025-04-21 15:26:48,111 - INFO - Training Mean: 23.1187 +2025-04-21 15:26:48,111 - INFO - Training Std: 20.3187 +2025-04-21 15:26:48,111 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 15:26:48,111 - INFO - KS Statistic: 0.2373 +2025-04-21 15:26:48,111 - INFO - P-value: 0.0000 +2025-04-21 15:26:48,111 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 15:26:48,111 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 15:26:48,113 - INFO - Validation and Training Set Split Statistics +2025-04-21 15:26:48,114 - INFO - Timestamp: 2025-04-21 15:26:48 +2025-04-21 15:26:48,114 - INFO - Validation set size: 1401 (8.48%) +2025-04-21 15:26:48,114 - INFO - Training set size: 15113 (91.52%) +2025-04-21 15:26:48,114 - INFO - Minimum Distance Statistics (km): +2025-04-21 15:26:48,114 - INFO - Mean: 62.6372 +2025-04-21 15:26:48,114 - INFO - Median: 4.0771 +2025-04-21 15:26:48,114 - INFO - Min: 0.5501 +2025-04-21 15:26:48,114 - INFO - Max: 706.9584 +2025-04-21 15:26:48,114 - INFO - Std: 105.8102 +2025-04-21 15:26:48,114 - INFO - OC Distribution Statistics: +2025-04-21 15:26:48,114 - INFO - Validation Mean: 16.2340 +2025-04-21 15:26:48,114 - INFO - Validation Std: 8.3444 +2025-04-21 15:26:48,114 - INFO - Training Mean: 23.1101 +2025-04-21 15:26:48,114 - INFO - Training Std: 20.3029 +2025-04-21 15:26:48,114 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 15:26:48,114 - INFO - KS Statistic: 0.2527 +2025-04-21 15:26:48,114 - INFO - P-value: 0.0000 +2025-04-21 15:26:48,114 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 15:26:48,114 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 15:26:51,481 - INFO - Validation and Training Set Split Statistics +2025-04-21 15:26:51,481 - INFO - Timestamp: 2025-04-21 15:26:51 +2025-04-21 15:26:51,481 - INFO - Validation set size: 1362 (8.25%) +2025-04-21 15:26:51,481 - INFO - Training set size: 15152 (91.75%) +2025-04-21 15:26:51,481 - INFO - Minimum Distance Statistics (km): +2025-04-21 15:26:51,481 - INFO - Mean: 75.1082 +2025-04-21 15:26:51,481 - INFO - Median: 6.3570 +2025-04-21 15:26:51,481 - INFO - Min: 0.3437 +2025-04-21 15:26:51,481 - INFO - Max: 706.9761 +2025-04-21 15:26:51,481 - INFO - Std: 120.5841 +2025-04-21 15:26:51,481 - INFO - OC Distribution Statistics: +2025-04-21 15:26:51,481 - INFO - Validation Mean: 16.6422 +2025-04-21 15:26:51,481 - INFO - Validation Std: 8.5086 +2025-04-21 15:26:51,481 - INFO - Training Mean: 23.0557 +2025-04-21 15:26:51,481 - INFO - Training Std: 20.2900 +2025-04-21 15:26:51,481 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 15:26:51,481 - INFO - KS Statistic: 0.2194 +2025-04-21 15:26:51,481 - INFO - P-value: 0.0000 +2025-04-21 15:26:51,481 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 15:26:51,481 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 15:51:09,795 - INFO - Validation and Training Set Split Statistics +2025-04-21 15:51:09,796 - INFO - Timestamp: 2025-04-21 15:51:09 +2025-04-21 15:51:09,796 - INFO - Validation set size: 1326 (8.03%) +2025-04-21 15:51:09,796 - INFO - Training set size: 15188 (91.97%) +2025-04-21 15:51:09,796 - INFO - Minimum Distance Statistics (km): +2025-04-21 15:51:09,796 - INFO - Mean: 64.3547 +2025-04-21 15:51:09,796 - INFO - Median: 4.8053 +2025-04-21 15:51:09,796 - INFO - Min: 1.2757 +2025-04-21 15:51:09,796 - INFO - Max: 680.6553 +2025-04-21 15:51:09,796 - INFO - Std: 105.3953 +2025-04-21 15:51:09,796 - INFO - OC Distribution Statistics: +2025-04-21 15:51:09,796 - INFO - Validation Mean: 16.8606 +2025-04-21 15:51:09,796 - INFO - Validation Std: 8.2459 +2025-04-21 15:51:09,796 - INFO - Training Mean: 23.0215 +2025-04-21 15:51:09,796 - INFO - Training Std: 20.2880 +2025-04-21 15:51:09,796 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 15:51:09,796 - INFO - KS Statistic: 0.1858 +2025-04-21 15:51:09,796 - INFO - P-value: 0.0000 +2025-04-21 15:51:09,796 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 15:51:09,796 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 15:51:09,849 - INFO - Validation and Training Set Split Statistics +2025-04-21 15:51:09,849 - INFO - Timestamp: 2025-04-21 15:51:09 +2025-04-21 15:51:09,849 - INFO - Validation set size: 1369 (8.29%) +2025-04-21 15:51:09,849 - INFO - Training set size: 15145 (91.71%) +2025-04-21 15:51:09,849 - INFO - Minimum Distance Statistics (km): +2025-04-21 15:51:09,849 - INFO - Mean: 62.7827 +2025-04-21 15:51:09,849 - INFO - Median: 4.5882 +2025-04-21 15:51:09,849 - INFO - Min: 1.2156 +2025-04-21 15:51:09,849 - INFO - Max: 681.9863 +2025-04-21 15:51:09,849 - INFO - Std: 102.2608 +2025-04-21 15:51:09,849 - INFO - OC Distribution Statistics: +2025-04-21 15:51:09,849 - INFO - Validation Mean: 16.8355 +2025-04-21 15:51:09,849 - INFO - Validation Std: 8.2606 +2025-04-21 15:51:09,849 - INFO - Training Mean: 23.0412 +2025-04-21 15:51:09,849 - INFO - Training Std: 20.3081 +2025-04-21 15:51:09,849 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 15:51:09,849 - INFO - KS Statistic: 0.1957 +2025-04-21 15:51:09,849 - INFO - P-value: 0.0000 +2025-04-21 15:51:09,849 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 15:51:09,849 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 15:51:09,917 - INFO - Validation and Training Set Split Statistics +2025-04-21 15:51:09,917 - INFO - Timestamp: 2025-04-21 15:51:09 +2025-04-21 15:51:09,917 - INFO - Validation set size: 1355 (8.21%) +2025-04-21 15:51:09,917 - INFO - Training set size: 15159 (91.79%) +2025-04-21 15:51:09,917 - INFO - Minimum Distance Statistics (km): +2025-04-21 15:51:09,917 - INFO - Mean: 71.4321 +2025-04-21 15:51:09,917 - INFO - Median: 4.6049 +2025-04-21 15:51:09,917 - INFO - Min: 1.2156 +2025-04-21 15:51:09,917 - INFO - Max: 1106.1626 +2025-04-21 15:51:09,917 - INFO - Std: 136.9321 +2025-04-21 15:51:09,917 - INFO - OC Distribution Statistics: +2025-04-21 15:51:09,917 - INFO - Validation Mean: 16.8502 +2025-04-21 15:51:09,917 - INFO - Validation Std: 7.8458 +2025-04-21 15:51:09,917 - INFO - Training Mean: 23.0342 +2025-04-21 15:51:09,917 - INFO - Training Std: 20.3163 +2025-04-21 15:51:09,917 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 15:51:09,917 - INFO - KS Statistic: 0.1850 +2025-04-21 15:51:09,917 - INFO - P-value: 0.0000 +2025-04-21 15:51:09,917 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 15:51:09,917 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 16:17:58,293 - INFO - Validation and Training Set Split Statistics +2025-04-21 16:17:58,293 - INFO - Timestamp: 2025-04-21 16:17:58 +2025-04-21 16:17:58,293 - INFO - Validation set size: 1470 (8.90%) +2025-04-21 16:17:58,293 - INFO - Training set size: 15044 (91.10%) +2025-04-21 16:17:58,293 - INFO - Minimum Distance Statistics (km): +2025-04-21 16:17:58,293 - INFO - Mean: 65.9729 +2025-04-21 16:17:58,293 - INFO - Median: 4.4251 +2025-04-21 16:17:58,293 - INFO - Min: 0.9995 +2025-04-21 16:17:58,293 - INFO - Max: 706.9584 +2025-04-21 16:17:58,293 - INFO - Std: 111.4266 +2025-04-21 16:17:58,293 - INFO - OC Distribution Statistics: +2025-04-21 16:17:58,293 - INFO - Validation Mean: 16.4731 +2025-04-21 16:17:58,293 - INFO - Validation Std: 6.9991 +2025-04-21 16:17:58,293 - INFO - Training Mean: 23.1183 +2025-04-21 16:17:58,293 - INFO - Training Std: 20.3935 +2025-04-21 16:17:58,293 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 16:17:58,293 - INFO - KS Statistic: 0.2193 +2025-04-21 16:17:58,293 - INFO - P-value: 0.0000 +2025-04-21 16:17:58,293 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 16:17:58,293 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 16:17:58,329 - INFO - Validation and Training Set Split Statistics +2025-04-21 16:17:58,329 - INFO - Timestamp: 2025-04-21 16:17:58 +2025-04-21 16:17:58,329 - INFO - Validation set size: 1388 (8.40%) +2025-04-21 16:17:58,329 - INFO - Training set size: 15126 (91.60%) +2025-04-21 16:17:58,329 - INFO - Minimum Distance Statistics (km): +2025-04-21 16:17:58,329 - INFO - Mean: 68.4292 +2025-04-21 16:17:58,329 - INFO - Median: 4.2475 +2025-04-21 16:17:58,329 - INFO - Min: 0.8258 +2025-04-21 16:17:58,329 - INFO - Max: 601.2913 +2025-04-21 16:17:58,329 - INFO - Std: 108.1979 +2025-04-21 16:17:58,329 - INFO - OC Distribution Statistics: +2025-04-21 16:17:58,329 - INFO - Validation Mean: 16.2843 +2025-04-21 16:17:58,329 - INFO - Validation Std: 7.1434 +2025-04-21 16:17:58,329 - INFO - Training Mean: 23.0996 +2025-04-21 16:17:58,329 - INFO - Training Std: 20.3402 +2025-04-21 16:17:58,329 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 16:17:58,329 - INFO - KS Statistic: 0.2083 +2025-04-21 16:17:58,329 - INFO - P-value: 0.0000 +2025-04-21 16:17:58,329 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 16:17:58,329 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 16:17:59,717 - INFO - Validation and Training Set Split Statistics +2025-04-21 16:17:59,717 - INFO - Timestamp: 2025-04-21 16:17:59 +2025-04-21 16:17:59,717 - INFO - Validation set size: 1428 (8.65%) +2025-04-21 16:17:59,717 - INFO - Training set size: 15086 (91.35%) +2025-04-21 16:17:59,717 - INFO - Minimum Distance Statistics (km): +2025-04-21 16:17:59,718 - INFO - Mean: 68.3867 +2025-04-21 16:17:59,718 - INFO - Median: 5.3462 +2025-04-21 16:17:59,718 - INFO - Min: 1.2065 +2025-04-21 16:17:59,718 - INFO - Max: 827.5907 +2025-04-21 16:17:59,718 - INFO - Std: 119.2579 +2025-04-21 16:17:59,718 - INFO - OC Distribution Statistics: +2025-04-21 16:17:59,718 - INFO - Validation Mean: 16.3399 +2025-04-21 16:17:59,718 - INFO - Validation Std: 7.0816 +2025-04-21 16:17:59,718 - INFO - Training Mean: 23.1124 +2025-04-21 16:17:59,718 - INFO - Training Std: 20.3646 +2025-04-21 16:17:59,718 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 16:17:59,718 - INFO - KS Statistic: 0.1988 +2025-04-21 16:17:59,718 - INFO - P-value: 0.0000 +2025-04-21 16:17:59,718 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 16:17:59,718 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 16:18:02,659 - INFO - Validation and Training Set Split Statistics +2025-04-21 16:18:02,659 - INFO - Timestamp: 2025-04-21 16:18:02 +2025-04-21 16:18:02,659 - INFO - Validation set size: 1338 (8.10%) +2025-04-21 16:18:02,659 - INFO - Training set size: 15176 (91.90%) +2025-04-21 16:18:02,659 - INFO - Minimum Distance Statistics (km): +2025-04-21 16:18:02,659 - INFO - Mean: 70.0719 +2025-04-21 16:18:02,659 - INFO - Median: 4.8509 +2025-04-21 16:18:02,659 - INFO - Min: 1.2773 +2025-04-21 16:18:02,659 - INFO - Max: 917.1429 +2025-04-21 16:18:02,659 - INFO - Std: 111.7993 +2025-04-21 16:18:02,659 - INFO - OC Distribution Statistics: +2025-04-21 16:18:02,659 - INFO - Validation Mean: 16.4468 +2025-04-21 16:18:02,659 - INFO - Validation Std: 7.4898 +2025-04-21 16:18:02,659 - INFO - Training Mean: 23.0628 +2025-04-21 16:18:02,659 - INFO - Training Std: 20.3083 +2025-04-21 16:18:02,659 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 16:18:02,659 - INFO - KS Statistic: 0.2059 +2025-04-21 16:18:02,659 - INFO - P-value: 0.0000 +2025-04-21 16:18:02,659 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 16:18:02,659 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 16:45:27,581 - INFO - Validation and Training Set Split Statistics +2025-04-21 16:45:27,582 - INFO - Timestamp: 2025-04-21 16:45:27 +2025-04-21 16:45:27,582 - INFO - Validation set size: 1336 (8.09%) +2025-04-21 16:45:27,582 - INFO - Training set size: 15178 (91.91%) +2025-04-21 16:45:27,582 - INFO - Minimum Distance Statistics (km): +2025-04-21 16:45:27,582 - INFO - Mean: 60.3583 +2025-04-21 16:45:27,582 - INFO - Median: 4.7721 +2025-04-21 16:45:27,582 - INFO - Min: 1.2156 +2025-04-21 16:45:27,582 - INFO - Max: 563.5259 +2025-04-21 16:45:27,582 - INFO - Std: 96.9448 +2025-04-21 16:45:27,582 - INFO - OC Distribution Statistics: +2025-04-21 16:45:27,582 - INFO - Validation Mean: 16.7457 +2025-04-21 16:45:27,582 - INFO - Validation Std: 7.4793 +2025-04-21 16:45:27,582 - INFO - Training Mean: 23.0356 +2025-04-21 16:45:27,582 - INFO - Training Std: 20.3160 +2025-04-21 16:45:27,582 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 16:45:27,582 - INFO - KS Statistic: 0.2122 +2025-04-21 16:45:27,582 - INFO - P-value: 0.0000 +2025-04-21 16:45:27,582 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 16:45:27,582 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 16:45:27,589 - INFO - Validation and Training Set Split Statistics +2025-04-21 16:45:27,589 - INFO - Timestamp: 2025-04-21 16:45:27 +2025-04-21 16:45:27,589 - INFO - Validation set size: 1436 (8.70%) +2025-04-21 16:45:27,589 - INFO - Training set size: 15078 (91.30%) +2025-04-21 16:45:27,589 - INFO - Minimum Distance Statistics (km): +2025-04-21 16:45:27,589 - INFO - Mean: 69.9284 +2025-04-21 16:45:27,589 - INFO - Median: 4.0579 +2025-04-21 16:45:27,589 - INFO - Min: 0.2243 +2025-04-21 16:45:27,589 - INFO - Max: 1106.1626 +2025-04-21 16:45:27,589 - INFO - Std: 137.8966 +2025-04-21 16:45:27,589 - INFO - OC Distribution Statistics: +2025-04-21 16:45:27,589 - INFO - Validation Mean: 16.0976 +2025-04-21 16:45:27,589 - INFO - Validation Std: 6.8442 +2025-04-21 16:45:27,589 - INFO - Training Mean: 23.1391 +2025-04-21 16:45:27,589 - INFO - Training Std: 20.3686 +2025-04-21 16:45:27,589 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 16:45:27,589 - INFO - KS Statistic: 0.2168 +2025-04-21 16:45:27,589 - INFO - P-value: 0.0000 +2025-04-21 16:45:27,590 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 16:45:27,590 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 16:45:27,758 - INFO - Validation and Training Set Split Statistics +2025-04-21 16:45:27,758 - INFO - Timestamp: 2025-04-21 16:45:27 +2025-04-21 16:45:27,758 - INFO - Validation set size: 1656 (10.03%) +2025-04-21 16:45:27,758 - INFO - Training set size: 14858 (89.97%) +2025-04-21 16:45:27,758 - INFO - Minimum Distance Statistics (km): +2025-04-21 16:45:27,758 - INFO - Mean: 61.3671 +2025-04-21 16:45:27,758 - INFO - Median: 4.2962 +2025-04-21 16:45:27,758 - INFO - Min: 1.2329 +2025-04-21 16:45:27,758 - INFO - Max: 702.5656 +2025-04-21 16:45:27,758 - INFO - Std: 101.6861 +2025-04-21 16:45:27,758 - INFO - OC Distribution Statistics: +2025-04-21 16:45:27,758 - INFO - Validation Mean: 16.4318 +2025-04-21 16:45:27,758 - INFO - Validation Std: 7.6147 +2025-04-21 16:45:27,758 - INFO - Training Mean: 23.2061 +2025-04-21 16:45:27,758 - INFO - Training Std: 20.4661 +2025-04-21 16:45:27,758 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 16:45:27,758 - INFO - KS Statistic: 0.2313 +2025-04-21 16:45:27,758 - INFO - P-value: 0.0000 +2025-04-21 16:45:27,759 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 16:45:27,759 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 16:45:31,087 - INFO - Validation and Training Set Split Statistics +2025-04-21 16:45:31,088 - INFO - Timestamp: 2025-04-21 16:45:31 +2025-04-21 16:45:31,088 - INFO - Validation set size: 1354 (8.20%) +2025-04-21 16:45:31,088 - INFO - Training set size: 15160 (91.80%) +2025-04-21 16:45:31,088 - INFO - Minimum Distance Statistics (km): +2025-04-21 16:45:31,088 - INFO - Mean: 72.5368 +2025-04-21 16:45:31,088 - INFO - Median: 5.4941 +2025-04-21 16:45:31,088 - INFO - Min: 1.2329 +2025-04-21 16:45:31,088 - INFO - Max: 556.5609 +2025-04-21 16:45:31,088 - INFO - Std: 106.8424 +2025-04-21 16:45:31,088 - INFO - OC Distribution Statistics: +2025-04-21 16:45:31,088 - INFO - Validation Mean: 16.5798 +2025-04-21 16:45:31,088 - INFO - Validation Std: 8.6741 +2025-04-21 16:45:31,088 - INFO - Training Mean: 23.0579 +2025-04-21 16:45:31,088 - INFO - Training Std: 20.2781 +2025-04-21 16:45:31,088 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 16:45:31,088 - INFO - KS Statistic: 0.2142 +2025-04-21 16:45:31,088 - INFO - P-value: 0.0000 +2025-04-21 16:45:31,088 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 16:45:31,088 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 16:54:32,791 - INFO - Validation and Training Set Split Statistics +2025-04-21 16:54:32,791 - INFO - Timestamp: 2025-04-21 16:54:32 +2025-04-21 16:54:32,791 - INFO - Validation set size: 1384 (8.38%) +2025-04-21 16:54:32,791 - INFO - Training set size: 15130 (91.62%) +2025-04-21 16:54:32,791 - INFO - Minimum Distance Statistics (km): +2025-04-21 16:54:32,791 - INFO - Mean: 59.7701 +2025-04-21 16:54:32,791 - INFO - Median: 3.9792 +2025-04-21 16:54:32,791 - INFO - Min: 1.1958 +2025-04-21 16:54:32,791 - INFO - Max: 706.9584 +2025-04-21 16:54:32,792 - INFO - Std: 109.6880 +2025-04-21 16:54:32,792 - INFO - OC Distribution Statistics: +2025-04-21 16:54:32,792 - INFO - Validation Mean: 16.4809 +2025-04-21 16:54:32,792 - INFO - Validation Std: 7.1318 +2025-04-21 16:54:32,792 - INFO - Training Mean: 23.0798 +2025-04-21 16:54:32,792 - INFO - Training Std: 20.3444 +2025-04-21 16:54:32,792 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 16:54:32,792 - INFO - KS Statistic: 0.2166 +2025-04-21 16:54:32,792 - INFO - P-value: 0.0000 +2025-04-21 16:54:32,792 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 16:54:32,792 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 16:54:32,793 - INFO - Validation and Training Set Split Statistics +2025-04-21 16:54:32,793 - INFO - Timestamp: 2025-04-21 16:54:32 +2025-04-21 16:54:32,793 - INFO - Validation set size: 1520 (9.20%) +2025-04-21 16:54:32,793 - INFO - Training set size: 14994 (90.80%) +2025-04-21 16:54:32,793 - INFO - Minimum Distance Statistics (km): +2025-04-21 16:54:32,793 - INFO - Mean: 63.1197 +2025-04-21 16:54:32,793 - INFO - Median: 4.0944 +2025-04-21 16:54:32,793 - INFO - Min: 1.2065 +2025-04-21 16:54:32,793 - INFO - Max: 862.7749 +2025-04-21 16:54:32,793 - INFO - Std: 112.8392 +2025-04-21 16:54:32,793 - INFO - OC Distribution Statistics: +2025-04-21 16:54:32,793 - INFO - Validation Mean: 16.0232 +2025-04-21 16:54:32,793 - INFO - Validation Std: 7.1482 +2025-04-21 16:54:32,793 - INFO - Training Mean: 23.1861 +2025-04-21 16:54:32,793 - INFO - Training Std: 20.3991 +2025-04-21 16:54:32,793 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 16:54:32,793 - INFO - KS Statistic: 0.2394 +2025-04-21 16:54:32,793 - INFO - P-value: 0.0000 +2025-04-21 16:54:32,793 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 16:54:32,793 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 16:54:32,820 - INFO - Validation and Training Set Split Statistics +2025-04-21 16:54:32,820 - INFO - Timestamp: 2025-04-21 16:54:32 +2025-04-21 16:54:32,820 - INFO - Validation set size: 1407 (8.52%) +2025-04-21 16:54:32,820 - INFO - Training set size: 15107 (91.48%) +2025-04-21 16:54:32,820 - INFO - Minimum Distance Statistics (km): +2025-04-21 16:54:32,820 - INFO - Mean: 74.4597 +2025-04-21 16:54:32,820 - INFO - Median: 5.1441 +2025-04-21 16:54:32,820 - INFO - Min: 1.2065 +2025-04-21 16:54:32,820 - INFO - Max: 743.7101 +2025-04-21 16:54:32,820 - INFO - Std: 120.5324 +2025-04-21 16:54:32,820 - INFO - OC Distribution Statistics: +2025-04-21 16:54:32,820 - INFO - Validation Mean: 16.4651 +2025-04-21 16:54:32,820 - INFO - Validation Std: 7.9836 +2025-04-21 16:54:32,820 - INFO - Training Mean: 23.0913 +2025-04-21 16:54:32,820 - INFO - Training Std: 20.3264 +2025-04-21 16:54:32,820 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 16:54:32,820 - INFO - KS Statistic: 0.2167 +2025-04-21 16:54:32,820 - INFO - P-value: 0.0000 +2025-04-21 16:54:32,820 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 16:54:32,820 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 16:54:34,612 - INFO - Validation and Training Set Split Statistics +2025-04-21 16:54:34,612 - INFO - Timestamp: 2025-04-21 16:54:34 +2025-04-21 16:54:34,612 - INFO - Validation set size: 1641 (9.94%) +2025-04-21 16:54:34,612 - INFO - Training set size: 14873 (90.06%) +2025-04-21 16:54:34,612 - INFO - Minimum Distance Statistics (km): +2025-04-21 16:54:34,612 - INFO - Mean: 59.0396 +2025-04-21 16:54:34,612 - INFO - Median: 3.9792 +2025-04-21 16:54:34,612 - INFO - Min: 0.6067 +2025-04-21 16:54:34,612 - INFO - Max: 706.9584 +2025-04-21 16:54:34,612 - INFO - Std: 101.5598 +2025-04-21 16:54:34,612 - INFO - OC Distribution Statistics: +2025-04-21 16:54:34,612 - INFO - Validation Mean: 15.5996 +2025-04-21 16:54:34,612 - INFO - Validation Std: 6.6290 +2025-04-21 16:54:34,612 - INFO - Training Mean: 23.2911 +2025-04-21 16:54:34,612 - INFO - Training Std: 20.4638 +2025-04-21 16:54:34,612 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 16:54:34,612 - INFO - KS Statistic: 0.2448 +2025-04-21 16:54:34,612 - INFO - P-value: 0.0000 +2025-04-21 16:54:34,612 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 16:54:34,612 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 18:01:33,348 - INFO - Validation and Training Set Split Statistics +2025-04-21 18:01:33,349 - INFO - Timestamp: 2025-04-21 18:01:33 +2025-04-21 18:01:33,349 - INFO - Validation set size: 1449 (8.77%) +2025-04-21 18:01:33,349 - INFO - Training set size: 15065 (91.23%) +2025-04-21 18:01:33,349 - INFO - Minimum Distance Statistics (km): +2025-04-21 18:01:33,349 - INFO - Mean: 65.2575 +2025-04-21 18:01:33,349 - INFO - Median: 4.6106 +2025-04-21 18:01:33,349 - INFO - Min: 0.7667 +2025-04-21 18:01:33,349 - INFO - Max: 743.7101 +2025-04-21 18:01:33,349 - INFO - Std: 112.1482 +2025-04-21 18:01:33,349 - INFO - OC Distribution Statistics: +2025-04-21 18:01:33,349 - INFO - Validation Mean: 16.3961 +2025-04-21 18:01:33,349 - INFO - Validation Std: 8.3900 +2025-04-21 18:01:33,349 - INFO - Training Mean: 23.1165 +2025-04-21 18:01:33,349 - INFO - Training Std: 20.3294 +2025-04-21 18:01:33,349 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 18:01:33,349 - INFO - KS Statistic: 0.2290 +2025-04-21 18:01:33,349 - INFO - P-value: 0.0000 +2025-04-21 18:01:33,349 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 18:01:33,349 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 18:01:33,408 - INFO - Validation and Training Set Split Statistics +2025-04-21 18:01:33,409 - INFO - Timestamp: 2025-04-21 18:01:33 +2025-04-21 18:01:33,409 - INFO - Validation set size: 1449 (8.77%) +2025-04-21 18:01:33,409 - INFO - Training set size: 15065 (91.23%) +2025-04-21 18:01:33,409 - INFO - Minimum Distance Statistics (km): +2025-04-21 18:01:33,409 - INFO - Mean: 65.3417 +2025-04-21 18:01:33,409 - INFO - Median: 4.3243 +2025-04-21 18:01:33,409 - INFO - Min: 1.2287 +2025-04-21 18:01:33,409 - INFO - Max: 866.4875 +2025-04-21 18:01:33,409 - INFO - Std: 112.1114 +2025-04-21 18:01:33,409 - INFO - OC Distribution Statistics: +2025-04-21 18:01:33,409 - INFO - Validation Mean: 16.6292 +2025-04-21 18:01:33,409 - INFO - Validation Std: 8.2162 +2025-04-21 18:01:33,409 - INFO - Training Mean: 23.0940 +2025-04-21 18:01:33,409 - INFO - Training Std: 20.3435 +2025-04-21 18:01:33,409 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 18:01:33,409 - INFO - KS Statistic: 0.2094 +2025-04-21 18:01:33,409 - INFO - P-value: 0.0000 +2025-04-21 18:01:33,409 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 18:01:33,409 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 18:01:33,417 - INFO - Validation and Training Set Split Statistics +2025-04-21 18:01:33,417 - INFO - Timestamp: 2025-04-21 18:01:33 +2025-04-21 18:01:33,417 - INFO - Validation set size: 1357 (8.22%) +2025-04-21 18:01:33,417 - INFO - Training set size: 15157 (91.78%) +2025-04-21 18:01:33,417 - INFO - Minimum Distance Statistics (km): +2025-04-21 18:01:33,417 - INFO - Mean: 75.0861 +2025-04-21 18:01:33,418 - INFO - Median: 4.8130 +2025-04-21 18:01:33,418 - INFO - Min: 0.9954 +2025-04-21 18:01:33,418 - INFO - Max: 1106.1626 +2025-04-21 18:01:33,418 - INFO - Std: 134.5641 +2025-04-21 18:01:33,418 - INFO - OC Distribution Statistics: +2025-04-21 18:01:33,418 - INFO - Validation Mean: 16.7823 +2025-04-21 18:01:33,418 - INFO - Validation Std: 8.2169 +2025-04-21 18:01:33,418 - INFO - Training Mean: 23.0411 +2025-04-21 18:01:33,418 - INFO - Training Std: 20.3023 +2025-04-21 18:01:33,418 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 18:01:33,418 - INFO - KS Statistic: 0.2067 +2025-04-21 18:01:33,418 - INFO - P-value: 0.0000 +2025-04-21 18:01:33,418 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 18:01:33,418 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 18:01:37,456 - INFO - Validation and Training Set Split Statistics +2025-04-21 18:01:37,456 - INFO - Timestamp: 2025-04-21 18:01:37 +2025-04-21 18:01:37,456 - INFO - Validation set size: 1376 (8.33%) +2025-04-21 18:01:37,456 - INFO - Training set size: 15138 (91.67%) +2025-04-21 18:01:37,456 - INFO - Minimum Distance Statistics (km): +2025-04-21 18:01:37,456 - INFO - Mean: 70.6596 +2025-04-21 18:01:37,456 - INFO - Median: 5.1703 +2025-04-21 18:01:37,456 - INFO - Min: 1.1496 +2025-04-21 18:01:37,456 - INFO - Max: 824.9624 +2025-04-21 18:01:37,456 - INFO - Std: 125.9875 +2025-04-21 18:01:37,456 - INFO - OC Distribution Statistics: +2025-04-21 18:01:37,456 - INFO - Validation Mean: 16.5092 +2025-04-21 18:01:37,456 - INFO - Validation Std: 8.3329 +2025-04-21 18:01:37,456 - INFO - Training Mean: 23.0738 +2025-04-21 18:01:37,456 - INFO - Training Std: 20.2996 +2025-04-21 18:01:37,456 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 18:01:37,456 - INFO - KS Statistic: 0.2100 +2025-04-21 18:01:37,456 - INFO - P-value: 0.0000 +2025-04-21 18:01:37,456 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 18:01:37,456 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 18:14:22,215 - INFO - Validation and Training Set Split Statistics +2025-04-21 18:14:22,215 - INFO - Timestamp: 2025-04-21 18:14:22 +2025-04-21 18:14:22,215 - INFO - Validation set size: 1636 (9.91%) +2025-04-21 18:14:22,215 - INFO - Training set size: 14878 (90.09%) +2025-04-21 18:14:22,215 - INFO - Minimum Distance Statistics (km): +2025-04-21 18:14:22,215 - INFO - Mean: 60.3369 +2025-04-21 18:14:22,215 - INFO - Median: 4.1787 +2025-04-21 18:14:22,215 - INFO - Min: 0.8124 +2025-04-21 18:14:22,216 - INFO - Max: 835.9450 +2025-04-21 18:14:22,216 - INFO - Std: 107.9527 +2025-04-21 18:14:22,216 - INFO - OC Distribution Statistics: +2025-04-21 18:14:22,216 - INFO - Validation Mean: 16.3778 +2025-04-21 18:14:22,216 - INFO - Validation Std: 8.1209 +2025-04-21 18:14:22,216 - INFO - Training Mean: 23.2029 +2025-04-21 18:14:22,216 - INFO - Training Std: 20.4324 +2025-04-21 18:14:22,216 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 18:14:22,216 - INFO - KS Statistic: 0.2314 +2025-04-21 18:14:22,216 - INFO - P-value: 0.0000 +2025-04-21 18:14:22,216 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 18:14:22,216 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-21 18:14:22,635 - INFO - Validation and Training Set Split Statistics +2025-04-21 18:14:22,635 - INFO - Timestamp: 2025-04-21 18:14:22 +2025-04-21 18:14:22,635 - INFO - Validation set size: 1552 (9.40%) +2025-04-21 18:14:22,635 - INFO - Training set size: 14962 (90.60%) +2025-04-21 18:14:22,635 - INFO - Minimum Distance Statistics (km): +2025-04-21 18:14:22,635 - INFO - Mean: 63.9905 +2025-04-21 18:14:22,635 - INFO - Median: 3.6037 +2025-04-21 18:14:22,635 - INFO - Min: 1.1342 +2025-04-21 18:14:22,635 - INFO - Max: 772.4342 +2025-04-21 18:14:22,635 - INFO - Std: 114.2998 +2025-04-21 18:14:22,635 - INFO - OC Distribution Statistics: +2025-04-21 18:14:22,635 - INFO - Validation Mean: 15.7695 +2025-04-21 18:14:22,635 - INFO - Validation Std: 7.0144 +2025-04-21 18:14:22,635 - INFO - Training Mean: 23.2277 +2025-04-21 18:14:22,635 - INFO - Training Std: 20.4110 +2025-04-21 18:14:22,635 - INFO - Kolmogorov-Smirnov Test: +2025-04-21 18:14:22,635 - INFO - KS Statistic: 0.2344 +2025-04-21 18:14:22,636 - INFO - P-value: 0.0000 +2025-04-21 18:14:22,636 - INFO - Distribution Fit: Inverse Gamma +2025-04-21 18:14:22,636 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 14:31:55,010 - INFO - Validation and Training Set Split Statistics +2025-04-22 14:31:55,010 - INFO - Timestamp: 2025-04-22 14:31:55 +2025-04-22 14:31:55,010 - INFO - Validation set size: 1545 (9.36%) +2025-04-22 14:31:55,010 - INFO - Training set size: 14969 (90.64%) +2025-04-22 14:31:55,010 - INFO - Minimum Distance Statistics (km): +2025-04-22 14:31:55,010 - INFO - Mean: 49.8614 +2025-04-22 14:31:55,010 - INFO - Median: 3.6868 +2025-04-22 14:31:55,010 - INFO - Min: 0.9102 +2025-04-22 14:31:55,010 - INFO - Max: 862.7749 +2025-04-22 14:31:55,010 - INFO - Std: 90.9516 +2025-04-22 14:31:55,010 - INFO - OC Distribution Statistics: +2025-04-22 14:31:55,010 - INFO - Validation Mean: 16.0400 +2025-04-22 14:31:55,010 - INFO - Validation Std: 6.5972 +2025-04-22 14:31:55,010 - INFO - Training Mean: 23.1963 +2025-04-22 14:31:55,010 - INFO - Training Std: 20.4317 +2025-04-22 14:31:55,010 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 14:31:55,010 - INFO - KS Statistic: 0.2459 +2025-04-22 14:31:55,010 - INFO - P-value: 0.0000 +2025-04-22 14:31:55,010 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 14:31:55,010 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 14:31:55,343 - INFO - Validation and Training Set Split Statistics +2025-04-22 14:31:55,344 - INFO - Timestamp: 2025-04-22 14:31:55 +2025-04-22 14:31:55,344 - INFO - Validation set size: 1427 (8.64%) +2025-04-22 14:31:55,344 - INFO - Training set size: 15087 (91.36%) +2025-04-22 14:31:55,344 - INFO - Minimum Distance Statistics (km): +2025-04-22 14:31:55,344 - INFO - Mean: 63.0438 +2025-04-22 14:31:55,344 - INFO - Median: 4.2447 +2025-04-22 14:31:55,344 - INFO - Min: 0.8258 +2025-04-22 14:31:55,344 - INFO - Max: 702.5656 +2025-04-22 14:31:55,344 - INFO - Std: 111.6509 +2025-04-22 14:31:55,344 - INFO - OC Distribution Statistics: +2025-04-22 14:31:55,344 - INFO - Validation Mean: 16.2816 +2025-04-22 14:31:55,344 - INFO - Validation Std: 7.5922 +2025-04-22 14:31:55,344 - INFO - Training Mean: 23.1175 +2025-04-22 14:31:55,344 - INFO - Training Std: 20.3448 +2025-04-22 14:31:55,344 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 14:31:55,344 - INFO - KS Statistic: 0.2258 +2025-04-22 14:31:55,344 - INFO - P-value: 0.0000 +2025-04-22 14:31:55,344 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 14:31:55,344 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 14:31:55,370 - INFO - Validation and Training Set Split Statistics +2025-04-22 14:31:55,370 - INFO - Timestamp: 2025-04-22 14:31:55 +2025-04-22 14:31:55,370 - INFO - Validation set size: 1512 (9.16%) +2025-04-22 14:31:55,370 - INFO - Training set size: 15002 (90.84%) +2025-04-22 14:31:55,370 - INFO - Minimum Distance Statistics (km): +2025-04-22 14:31:55,370 - INFO - Mean: 61.1044 +2025-04-22 14:31:55,370 - INFO - Median: 4.2711 +2025-04-22 14:31:55,370 - INFO - Min: 0.7391 +2025-04-22 14:31:55,370 - INFO - Max: 875.2369 +2025-04-22 14:31:55,370 - INFO - Std: 107.8280 +2025-04-22 14:31:55,370 - INFO - OC Distribution Statistics: +2025-04-22 14:31:55,370 - INFO - Validation Mean: 16.5872 +2025-04-22 14:31:55,370 - INFO - Validation Std: 8.1143 +2025-04-22 14:31:55,370 - INFO - Training Mean: 23.1254 +2025-04-22 14:31:55,370 - INFO - Training Std: 20.3777 +2025-04-22 14:31:55,370 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 14:31:55,370 - INFO - KS Statistic: 0.2176 +2025-04-22 14:31:55,370 - INFO - P-value: 0.0000 +2025-04-22 14:31:55,370 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 14:31:55,370 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 14:31:56,898 - INFO - Validation and Training Set Split Statistics +2025-04-22 14:31:56,898 - INFO - Timestamp: 2025-04-22 14:31:56 +2025-04-22 14:31:56,898 - INFO - Validation set size: 1677 (10.16%) +2025-04-22 14:31:56,898 - INFO - Training set size: 14837 (89.84%) +2025-04-22 14:31:56,898 - INFO - Minimum Distance Statistics (km): +2025-04-22 14:31:56,898 - INFO - Mean: 60.8634 +2025-04-22 14:31:56,898 - INFO - Median: 3.7089 +2025-04-22 14:31:56,898 - INFO - Min: 0.9954 +2025-04-22 14:31:56,898 - INFO - Max: 917.1429 +2025-04-22 14:31:56,898 - INFO - Std: 119.0602 +2025-04-22 14:31:56,898 - INFO - OC Distribution Statistics: +2025-04-22 14:31:56,898 - INFO - Validation Mean: 16.0718 +2025-04-22 14:31:56,898 - INFO - Validation Std: 7.5152 +2025-04-22 14:31:56,898 - INFO - Training Mean: 23.2564 +2025-04-22 14:31:56,898 - INFO - Training Std: 20.4673 +2025-04-22 14:31:56,898 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 14:31:56,898 - INFO - KS Statistic: 0.2612 +2025-04-22 14:31:56,898 - INFO - P-value: 0.0000 +2025-04-22 14:31:56,898 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 14:31:56,898 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 14:31:57,236 - INFO - Validation and Training Set Split Statistics +2025-04-22 14:31:57,236 - INFO - Timestamp: 2025-04-22 14:31:57 +2025-04-22 14:31:57,236 - INFO - Validation set size: 1331 (8.06%) +2025-04-22 14:31:57,236 - INFO - Training set size: 15183 (91.94%) +2025-04-22 14:31:57,236 - INFO - Minimum Distance Statistics (km): +2025-04-22 14:31:57,236 - INFO - Mean: 68.1506 +2025-04-22 14:31:57,236 - INFO - Median: 4.2475 +2025-04-22 14:31:57,236 - INFO - Min: 1.2773 +2025-04-22 14:31:57,236 - INFO - Max: 705.4078 +2025-04-22 14:31:57,236 - INFO - Std: 111.8892 +2025-04-22 14:31:57,236 - INFO - OC Distribution Statistics: +2025-04-22 14:31:57,236 - INFO - Validation Mean: 16.4519 +2025-04-22 14:31:57,236 - INFO - Validation Std: 7.4950 +2025-04-22 14:31:57,236 - INFO - Training Mean: 23.0593 +2025-04-22 14:31:57,236 - INFO - Training Std: 20.3047 +2025-04-22 14:31:57,236 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 14:31:57,236 - INFO - KS Statistic: 0.2077 +2025-04-22 14:31:57,236 - INFO - P-value: 0.0000 +2025-04-22 14:31:57,236 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 14:31:57,236 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 14:31:57,776 - INFO - Validation and Training Set Split Statistics +2025-04-22 14:31:57,776 - INFO - Timestamp: 2025-04-22 14:31:57 +2025-04-22 14:31:57,777 - INFO - Validation set size: 1508 (9.13%) +2025-04-22 14:31:57,777 - INFO - Training set size: 15006 (90.87%) +2025-04-22 14:31:57,777 - INFO - Minimum Distance Statistics (km): +2025-04-22 14:31:57,777 - INFO - Mean: 67.4737 +2025-04-22 14:31:57,777 - INFO - Median: 4.3277 +2025-04-22 14:31:57,777 - INFO - Min: 1.0596 +2025-04-22 14:31:57,777 - INFO - Max: 888.3558 +2025-04-22 14:31:57,777 - INFO - Std: 116.8356 +2025-04-22 14:31:57,777 - INFO - OC Distribution Statistics: +2025-04-22 14:31:57,777 - INFO - Validation Mean: 16.9196 +2025-04-22 14:31:57,777 - INFO - Validation Std: 8.1791 +2025-04-22 14:31:57,777 - INFO - Training Mean: 23.0903 +2025-04-22 14:31:57,777 - INFO - Training Std: 20.3835 +2025-04-22 14:31:57,777 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 14:31:57,777 - INFO - KS Statistic: 0.1881 +2025-04-22 14:31:57,777 - INFO - P-value: 0.0000 +2025-04-22 14:31:57,777 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 14:31:57,777 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 14:31:57,868 - INFO - Validation and Training Set Split Statistics +2025-04-22 14:31:57,869 - INFO - Timestamp: 2025-04-22 14:31:57 +2025-04-22 14:31:57,869 - INFO - Validation set size: 1386 (8.39%) +2025-04-22 14:31:57,869 - INFO - Training set size: 15128 (91.61%) +2025-04-22 14:31:57,869 - INFO - Minimum Distance Statistics (km): +2025-04-22 14:31:57,869 - INFO - Mean: 59.8358 +2025-04-22 14:31:57,869 - INFO - Median: 4.1395 +2025-04-22 14:31:57,869 - INFO - Min: 1.2065 +2025-04-22 14:31:57,869 - INFO - Max: 536.0955 +2025-04-22 14:31:57,869 - INFO - Std: 104.9020 +2025-04-22 14:31:57,869 - INFO - OC Distribution Statistics: +2025-04-22 14:31:57,869 - INFO - Validation Mean: 16.5512 +2025-04-22 14:31:57,869 - INFO - Validation Std: 7.5283 +2025-04-22 14:31:57,869 - INFO - Training Mean: 23.0743 +2025-04-22 14:31:57,869 - INFO - Training Std: 20.3345 +2025-04-22 14:31:57,869 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 14:31:57,869 - INFO - KS Statistic: 0.1919 +2025-04-22 14:31:57,869 - INFO - P-value: 0.0000 +2025-04-22 14:31:57,869 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 14:31:57,869 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 14:32:00,450 - INFO - Validation and Training Set Split Statistics +2025-04-22 14:32:00,450 - INFO - Timestamp: 2025-04-22 14:32:00 +2025-04-22 14:32:00,450 - INFO - Validation set size: 1329 (8.05%) +2025-04-22 14:32:00,450 - INFO - Training set size: 15185 (91.95%) +2025-04-22 14:32:00,450 - INFO - Minimum Distance Statistics (km): +2025-04-22 14:32:00,450 - INFO - Mean: 61.1421 +2025-04-22 14:32:00,450 - INFO - Median: 4.2447 +2025-04-22 14:32:00,450 - INFO - Min: 1.1349 +2025-04-22 14:32:00,450 - INFO - Max: 706.9584 +2025-04-22 14:32:00,450 - INFO - Std: 100.3494 +2025-04-22 14:32:00,450 - INFO - OC Distribution Statistics: +2025-04-22 14:32:00,450 - INFO - Validation Mean: 16.6653 +2025-04-22 14:32:00,450 - INFO - Validation Std: 6.9958 +2025-04-22 14:32:00,450 - INFO - Training Mean: 23.0398 +2025-04-22 14:32:00,450 - INFO - Training Std: 20.3252 +2025-04-22 14:32:00,450 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 14:32:00,450 - INFO - KS Statistic: 0.1998 +2025-04-22 14:32:00,450 - INFO - P-value: 0.0000 +2025-04-22 14:32:00,450 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 14:32:00,450 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 15:01:38,003 - INFO - Validation and Training Set Split Statistics +2025-04-22 15:01:38,004 - INFO - Timestamp: 2025-04-22 15:01:38 +2025-04-22 15:01:38,004 - INFO - Validation set size: 1358 (8.22%) +2025-04-22 15:01:38,004 - INFO - Training set size: 15156 (91.78%) +2025-04-22 15:01:38,004 - INFO - Minimum Distance Statistics (km): +2025-04-22 15:01:38,004 - INFO - Mean: 63.7639 +2025-04-22 15:01:38,004 - INFO - Median: 5.1405 +2025-04-22 15:01:38,004 - INFO - Min: 0.7391 +2025-04-22 15:01:38,004 - INFO - Max: 707.4892 +2025-04-22 15:01:38,004 - INFO - Std: 102.3172 +2025-04-22 15:01:38,004 - INFO - OC Distribution Statistics: +2025-04-22 15:01:38,004 - INFO - Validation Mean: 15.9463 +2025-04-22 15:01:38,004 - INFO - Validation Std: 7.0332 +2025-04-22 15:01:38,004 - INFO - Training Mean: 23.1164 +2025-04-22 15:01:38,004 - INFO - Training Std: 20.3178 +2025-04-22 15:01:38,004 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 15:01:38,004 - INFO - KS Statistic: 0.2419 +2025-04-22 15:01:38,004 - INFO - P-value: 0.0000 +2025-04-22 15:01:38,004 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 15:01:38,004 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 15:01:38,294 - INFO - Validation and Training Set Split Statistics +2025-04-22 15:01:38,294 - INFO - Timestamp: 2025-04-22 15:01:38 +2025-04-22 15:01:38,294 - INFO - Validation set size: 1505 (9.11%) +2025-04-22 15:01:38,294 - INFO - Training set size: 15009 (90.89%) +2025-04-22 15:01:38,294 - INFO - Minimum Distance Statistics (km): +2025-04-22 15:01:38,294 - INFO - Mean: 59.4286 +2025-04-22 15:01:38,294 - INFO - Median: 4.2015 +2025-04-22 15:01:38,294 - INFO - Min: 1.0321 +2025-04-22 15:01:38,294 - INFO - Max: 706.9584 +2025-04-22 15:01:38,294 - INFO - Std: 112.5215 +2025-04-22 15:01:38,294 - INFO - OC Distribution Statistics: +2025-04-22 15:01:38,294 - INFO - Validation Mean: 15.9980 +2025-04-22 15:01:38,294 - INFO - Validation Std: 7.1754 +2025-04-22 15:01:38,294 - INFO - Training Mean: 23.1815 +2025-04-22 15:01:38,294 - INFO - Training Std: 20.3896 +2025-04-22 15:01:38,294 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 15:01:38,294 - INFO - KS Statistic: 0.2490 +2025-04-22 15:01:38,294 - INFO - P-value: 0.0000 +2025-04-22 15:01:38,294 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 15:01:38,294 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 15:01:39,020 - INFO - Validation and Training Set Split Statistics +2025-04-22 15:01:39,020 - INFO - Timestamp: 2025-04-22 15:01:39 +2025-04-22 15:01:39,020 - INFO - Validation set size: 1355 (8.21%) +2025-04-22 15:01:39,020 - INFO - Training set size: 15159 (91.79%) +2025-04-22 15:01:39,020 - INFO - Minimum Distance Statistics (km): +2025-04-22 15:01:39,020 - INFO - Mean: 69.9650 +2025-04-22 15:01:39,020 - INFO - Median: 4.5043 +2025-04-22 15:01:39,020 - INFO - Min: 1.1220 +2025-04-22 15:01:39,020 - INFO - Max: 601.2913 +2025-04-22 15:01:39,020 - INFO - Std: 110.9113 +2025-04-22 15:01:39,020 - INFO - OC Distribution Statistics: +2025-04-22 15:01:39,020 - INFO - Validation Mean: 16.7973 +2025-04-22 15:01:39,020 - INFO - Validation Std: 7.9599 +2025-04-22 15:01:39,020 - INFO - Training Mean: 23.0389 +2025-04-22 15:01:39,020 - INFO - Training Std: 20.3108 +2025-04-22 15:01:39,020 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 15:01:39,020 - INFO - KS Statistic: 0.1800 +2025-04-22 15:01:39,020 - INFO - P-value: 0.0000 +2025-04-22 15:01:39,020 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 15:01:39,020 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 15:01:39,283 - INFO - Validation and Training Set Split Statistics +2025-04-22 15:01:39,283 - INFO - Timestamp: 2025-04-22 15:01:39 +2025-04-22 15:01:39,283 - INFO - Validation set size: 1357 (8.22%) +2025-04-22 15:01:39,283 - INFO - Training set size: 15157 (91.78%) +2025-04-22 15:01:39,283 - INFO - Minimum Distance Statistics (km): +2025-04-22 15:01:39,283 - INFO - Mean: 67.4926 +2025-04-22 15:01:39,283 - INFO - Median: 5.2755 +2025-04-22 15:01:39,283 - INFO - Min: 0.6477 +2025-04-22 15:01:39,283 - INFO - Max: 702.5656 +2025-04-22 15:01:39,283 - INFO - Std: 107.9368 +2025-04-22 15:01:39,283 - INFO - OC Distribution Statistics: +2025-04-22 15:01:39,283 - INFO - Validation Mean: 16.7746 +2025-04-22 15:01:39,283 - INFO - Validation Std: 8.0003 +2025-04-22 15:01:39,283 - INFO - Training Mean: 23.0418 +2025-04-22 15:01:39,283 - INFO - Training Std: 20.3098 +2025-04-22 15:01:39,284 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 15:01:39,284 - INFO - KS Statistic: 0.1891 +2025-04-22 15:01:39,284 - INFO - P-value: 0.0000 +2025-04-22 15:01:39,284 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 15:01:39,284 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 15:01:41,102 - INFO - Validation and Training Set Split Statistics +2025-04-22 15:01:41,102 - INFO - Timestamp: 2025-04-22 15:01:41 +2025-04-22 15:01:41,102 - INFO - Validation set size: 1328 (8.04%) +2025-04-22 15:01:41,102 - INFO - Training set size: 15186 (91.96%) +2025-04-22 15:01:41,102 - INFO - Minimum Distance Statistics (km): +2025-04-22 15:01:41,102 - INFO - Mean: 73.7366 +2025-04-22 15:01:41,102 - INFO - Median: 4.8124 +2025-04-22 15:01:41,102 - INFO - Min: 0.8608 +2025-04-22 15:01:41,102 - INFO - Max: 862.7749 +2025-04-22 15:01:41,102 - INFO - Std: 122.6779 +2025-04-22 15:01:41,102 - INFO - OC Distribution Statistics: +2025-04-22 15:01:41,102 - INFO - Validation Mean: 16.6417 +2025-04-22 15:01:41,102 - INFO - Validation Std: 8.0601 +2025-04-22 15:01:41,102 - INFO - Training Mean: 23.0414 +2025-04-22 15:01:41,102 - INFO - Training Std: 20.2896 +2025-04-22 15:01:41,102 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 15:01:41,102 - INFO - KS Statistic: 0.2099 +2025-04-22 15:01:41,102 - INFO - P-value: 0.0000 +2025-04-22 15:01:41,102 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 15:01:41,102 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 15:01:42,297 - INFO - Validation and Training Set Split Statistics +2025-04-22 15:01:42,297 - INFO - Timestamp: 2025-04-22 15:01:42 +2025-04-22 15:01:42,297 - INFO - Validation set size: 1351 (8.18%) +2025-04-22 15:01:42,297 - INFO - Training set size: 15163 (91.82%) +2025-04-22 15:01:42,297 - INFO - Minimum Distance Statistics (km): +2025-04-22 15:01:42,297 - INFO - Mean: 66.1910 +2025-04-22 15:01:42,297 - INFO - Median: 4.4468 +2025-04-22 15:01:42,297 - INFO - Min: 0.5501 +2025-04-22 15:01:42,297 - INFO - Max: 655.9249 +2025-04-22 15:01:42,297 - INFO - Std: 109.8313 +2025-04-22 15:01:42,297 - INFO - OC Distribution Statistics: +2025-04-22 15:01:42,297 - INFO - Validation Mean: 16.1836 +2025-04-22 15:01:42,297 - INFO - Validation Std: 7.0843 +2025-04-22 15:01:42,297 - INFO - Training Mean: 23.0920 +2025-04-22 15:01:42,297 - INFO - Training Std: 20.3200 +2025-04-22 15:01:42,297 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 15:01:42,297 - INFO - KS Statistic: 0.2449 +2025-04-22 15:01:42,297 - INFO - P-value: 0.0000 +2025-04-22 15:01:42,297 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 15:01:42,297 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 15:01:43,561 - INFO - Validation and Training Set Split Statistics +2025-04-22 15:01:43,562 - INFO - Timestamp: 2025-04-22 15:01:43 +2025-04-22 15:01:43,562 - INFO - Validation set size: 1660 (10.05%) +2025-04-22 15:01:43,562 - INFO - Training set size: 14854 (89.95%) +2025-04-22 15:01:43,562 - INFO - Minimum Distance Statistics (km): +2025-04-22 15:01:43,562 - INFO - Mean: 64.5375 +2025-04-22 15:01:43,562 - INFO - Median: 5.0468 +2025-04-22 15:01:43,562 - INFO - Min: 0.5501 +2025-04-22 15:01:43,562 - INFO - Max: 888.3558 +2025-04-22 15:01:43,562 - INFO - Std: 111.3474 +2025-04-22 15:01:43,562 - INFO - OC Distribution Statistics: +2025-04-22 15:01:43,562 - INFO - Validation Mean: 16.2460 +2025-04-22 15:01:43,562 - INFO - Validation Std: 7.7372 +2025-04-22 15:01:43,562 - INFO - Training Mean: 23.2287 +2025-04-22 15:01:43,562 - INFO - Training Std: 20.4561 +2025-04-22 15:01:43,562 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 15:01:43,562 - INFO - KS Statistic: 0.2238 +2025-04-22 15:01:43,562 - INFO - P-value: 0.0000 +2025-04-22 15:01:43,562 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 15:01:43,562 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-04-22 15:01:45,834 - INFO - Validation and Training Set Split Statistics +2025-04-22 15:01:45,834 - INFO - Timestamp: 2025-04-22 15:01:45 +2025-04-22 15:01:45,834 - INFO - Validation set size: 1338 (8.10%) +2025-04-22 15:01:45,834 - INFO - Training set size: 15176 (91.90%) +2025-04-22 15:01:45,834 - INFO - Minimum Distance Statistics (km): +2025-04-22 15:01:45,834 - INFO - Mean: 73.7973 +2025-04-22 15:01:45,834 - INFO - Median: 4.6106 +2025-04-22 15:01:45,834 - INFO - Min: 0.8394 +2025-04-22 15:01:45,834 - INFO - Max: 743.7101 +2025-04-22 15:01:45,834 - INFO - Std: 126.2635 +2025-04-22 15:01:45,834 - INFO - OC Distribution Statistics: +2025-04-22 15:01:45,834 - INFO - Validation Mean: 16.3910 +2025-04-22 15:01:45,834 - INFO - Validation Std: 7.7907 +2025-04-22 15:01:45,834 - INFO - Training Mean: 23.0677 +2025-04-22 15:01:45,834 - INFO - Training Std: 20.2967 +2025-04-22 15:01:45,834 - INFO - Kolmogorov-Smirnov Test: +2025-04-22 15:01:45,834 - INFO - KS Statistic: 0.2202 +2025-04-22 15:01:45,834 - INFO - P-value: 0.0000 +2025-04-22 15:01:45,834 - INFO - Distribution Fit: Inverse Gamma +2025-04-22 15:01:45,834 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 19:34:41,932 - INFO - Validation and Training Set Split Statistics +2025-05-24 19:34:41,933 - INFO - Timestamp: 2025-05-24 19:34:41 +2025-05-24 19:34:41,933 - INFO - Validation set size: 1392 (8.43%) +2025-05-24 19:34:41,933 - INFO - Training set size: 15122 (91.57%) +2025-05-24 19:34:41,933 - INFO - Minimum Distance Statistics (km): +2025-05-24 19:34:41,933 - INFO - Mean: 64.8012 +2025-05-24 19:34:41,933 - INFO - Median: 4.6976 +2025-05-24 19:34:41,933 - INFO - Min: 0.9954 +2025-05-24 19:34:41,933 - INFO - Max: 706.9584 +2025-05-24 19:34:41,933 - INFO - Std: 104.5628 +2025-05-24 19:34:41,933 - INFO - OC Distribution Statistics: +2025-05-24 19:34:41,933 - INFO - Validation Mean: 16.3294 +2025-05-24 19:34:41,933 - INFO - Validation Std: 7.8296 +2025-05-24 19:34:41,933 - INFO - Training Mean: 23.0973 +2025-05-24 19:34:41,933 - INFO - Training Std: 20.3204 +2025-05-24 19:34:41,933 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 19:34:41,933 - INFO - KS Statistic: 0.2313 +2025-05-24 19:34:41,933 - INFO - P-value: 0.0000 +2025-05-24 19:34:41,933 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 19:34:41,933 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 19:34:41,934 - INFO - Validation and Training Set Split Statistics +2025-05-24 19:34:41,934 - INFO - Timestamp: 2025-05-24 19:34:41 +2025-05-24 19:34:41,934 - INFO - Validation set size: 1362 (8.25%) +2025-05-24 19:34:41,934 - INFO - Training set size: 15152 (91.75%) +2025-05-24 19:34:41,934 - INFO - Minimum Distance Statistics (km): +2025-05-24 19:34:41,934 - INFO - Mean: 73.5564 +2025-05-24 19:34:41,934 - INFO - Median: 4.3143 +2025-05-24 19:34:41,934 - INFO - Min: 1.0890 +2025-05-24 19:34:41,934 - INFO - Max: 835.9450 +2025-05-24 19:34:41,934 - INFO - Std: 118.3769 +2025-05-24 19:34:41,934 - INFO - OC Distribution Statistics: +2025-05-24 19:34:41,934 - INFO - Validation Mean: 16.2689 +2025-05-24 19:34:41,934 - INFO - Validation Std: 7.3203 +2025-05-24 19:34:41,935 - INFO - Training Mean: 23.0893 +2025-05-24 19:34:41,935 - INFO - Training Std: 20.3207 +2025-05-24 19:34:41,935 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 19:34:41,935 - INFO - KS Statistic: 0.2192 +2025-05-24 19:34:41,935 - INFO - P-value: 0.0000 +2025-05-24 19:34:41,935 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 19:34:41,935 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 19:34:43,374 - INFO - Validation and Training Set Split Statistics +2025-05-24 19:34:43,374 - INFO - Timestamp: 2025-05-24 19:34:43 +2025-05-24 19:34:43,374 - INFO - Validation set size: 1658 (10.04%) +2025-05-24 19:34:43,374 - INFO - Training set size: 14856 (89.96%) +2025-05-24 19:34:43,374 - INFO - Minimum Distance Statistics (km): +2025-05-24 19:34:43,374 - INFO - Mean: 64.9434 +2025-05-24 19:34:43,375 - INFO - Median: 4.1358 +2025-05-24 19:34:43,375 - INFO - Min: 0.4760 +2025-05-24 19:34:43,375 - INFO - Max: 866.4875 +2025-05-24 19:34:43,375 - INFO - Std: 120.6217 +2025-05-24 19:34:43,375 - INFO - OC Distribution Statistics: +2025-05-24 19:34:43,375 - INFO - Validation Mean: 16.3206 +2025-05-24 19:34:43,375 - INFO - Validation Std: 7.3466 +2025-05-24 19:34:43,375 - INFO - Training Mean: 23.2194 +2025-05-24 19:34:43,375 - INFO - Training Std: 20.4740 +2025-05-24 19:34:43,375 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 19:34:43,375 - INFO - KS Statistic: 0.2205 +2025-05-24 19:34:43,375 - INFO - P-value: 0.0000 +2025-05-24 19:34:43,375 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 19:34:43,375 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 19:34:43,810 - INFO - Validation and Training Set Split Statistics +2025-05-24 19:34:43,810 - INFO - Timestamp: 2025-05-24 19:34:43 +2025-05-24 19:34:43,810 - INFO - Validation set size: 1453 (8.80%) +2025-05-24 19:34:43,810 - INFO - Training set size: 15061 (91.20%) +2025-05-24 19:34:43,810 - INFO - Minimum Distance Statistics (km): +2025-05-24 19:34:43,810 - INFO - Mean: 67.4895 +2025-05-24 19:34:43,810 - INFO - Median: 4.4468 +2025-05-24 19:34:43,810 - INFO - Min: 1.2156 +2025-05-24 19:34:43,810 - INFO - Max: 1063.3892 +2025-05-24 19:34:43,810 - INFO - Std: 121.2375 +2025-05-24 19:34:43,810 - INFO - OC Distribution Statistics: +2025-05-24 19:34:43,810 - INFO - Validation Mean: 16.6332 +2025-05-24 19:34:43,810 - INFO - Validation Std: 8.0117 +2025-05-24 19:34:43,810 - INFO - Training Mean: 23.0954 +2025-05-24 19:34:43,810 - INFO - Training Std: 20.3535 +2025-05-24 19:34:43,811 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 19:34:43,811 - INFO - KS Statistic: 0.2221 +2025-05-24 19:34:43,811 - INFO - P-value: 0.0000 +2025-05-24 19:34:43,811 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 19:34:43,811 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 19:34:44,043 - INFO - Validation and Training Set Split Statistics +2025-05-24 19:34:44,043 - INFO - Timestamp: 2025-05-24 19:34:44 +2025-05-24 19:34:44,043 - INFO - Validation set size: 1325 (8.02%) +2025-05-24 19:34:44,043 - INFO - Training set size: 15189 (91.98%) +2025-05-24 19:34:44,043 - INFO - Minimum Distance Statistics (km): +2025-05-24 19:34:44,043 - INFO - Mean: 63.6381 +2025-05-24 19:34:44,043 - INFO - Median: 4.2447 +2025-05-24 19:34:44,043 - INFO - Min: 1.2065 +2025-05-24 19:34:44,043 - INFO - Max: 680.6553 +2025-05-24 19:34:44,043 - INFO - Std: 107.4637 +2025-05-24 19:34:44,043 - INFO - OC Distribution Statistics: +2025-05-24 19:34:44,043 - INFO - Validation Mean: 16.4296 +2025-05-24 19:34:44,044 - INFO - Validation Std: 7.8488 +2025-05-24 19:34:44,044 - INFO - Training Mean: 23.0587 +2025-05-24 19:34:44,044 - INFO - Training Std: 20.2894 +2025-05-24 19:34:44,044 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 19:34:44,044 - INFO - KS Statistic: 0.2166 +2025-05-24 19:34:44,044 - INFO - P-value: 0.0000 +2025-05-24 19:34:44,044 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 19:34:44,044 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 19:34:44,434 - INFO - Validation and Training Set Split Statistics +2025-05-24 19:34:44,434 - INFO - Timestamp: 2025-05-24 19:34:44 +2025-05-24 19:34:44,434 - INFO - Validation set size: 1389 (8.41%) +2025-05-24 19:34:44,434 - INFO - Training set size: 15125 (91.59%) +2025-05-24 19:34:44,434 - INFO - Minimum Distance Statistics (km): +2025-05-24 19:34:44,434 - INFO - Mean: 74.4401 +2025-05-24 19:34:44,434 - INFO - Median: 5.9124 +2025-05-24 19:34:44,434 - INFO - Min: 1.2156 +2025-05-24 19:34:44,434 - INFO - Max: 917.1429 +2025-05-24 19:34:44,434 - INFO - Std: 117.5884 +2025-05-24 19:34:44,434 - INFO - OC Distribution Statistics: +2025-05-24 19:34:44,434 - INFO - Validation Mean: 16.7697 +2025-05-24 19:34:44,434 - INFO - Validation Std: 8.6627 +2025-05-24 19:34:44,434 - INFO - Training Mean: 23.0555 +2025-05-24 19:34:44,434 - INFO - Training Std: 20.3008 +2025-05-24 19:34:44,434 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 19:34:44,434 - INFO - KS Statistic: 0.2203 +2025-05-24 19:34:44,435 - INFO - P-value: 0.0000 +2025-05-24 19:34:44,435 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 19:34:44,435 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 19:34:45,112 - INFO - Validation and Training Set Split Statistics +2025-05-24 19:34:45,112 - INFO - Timestamp: 2025-05-24 19:34:45 +2025-05-24 19:34:45,112 - INFO - Validation set size: 1427 (8.64%) +2025-05-24 19:34:45,112 - INFO - Training set size: 15087 (91.36%) +2025-05-24 19:34:45,112 - INFO - Minimum Distance Statistics (km): +2025-05-24 19:34:45,112 - INFO - Mean: 63.1973 +2025-05-24 19:34:45,112 - INFO - Median: 4.8521 +2025-05-24 19:34:45,112 - INFO - Min: 1.2497 +2025-05-24 19:34:45,112 - INFO - Max: 743.7101 +2025-05-24 19:34:45,112 - INFO - Std: 103.0107 +2025-05-24 19:34:45,112 - INFO - OC Distribution Statistics: +2025-05-24 19:34:45,112 - INFO - Validation Mean: 16.5063 +2025-05-24 19:34:45,112 - INFO - Validation Std: 8.0130 +2025-05-24 19:34:45,112 - INFO - Training Mean: 23.0962 +2025-05-24 19:34:45,112 - INFO - Training Std: 20.3366 +2025-05-24 19:34:45,112 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 19:34:45,112 - INFO - KS Statistic: 0.2491 +2025-05-24 19:34:45,112 - INFO - P-value: 0.0000 +2025-05-24 19:34:45,112 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 19:34:45,112 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 19:34:45,760 - INFO - Validation and Training Set Split Statistics +2025-05-24 19:34:45,760 - INFO - Timestamp: 2025-05-24 19:34:45 +2025-05-24 19:34:45,760 - INFO - Validation set size: 1380 (8.36%) +2025-05-24 19:34:45,760 - INFO - Training set size: 15134 (91.64%) +2025-05-24 19:34:45,760 - INFO - Minimum Distance Statistics (km): +2025-05-24 19:34:45,760 - INFO - Mean: 72.3635 +2025-05-24 19:34:45,760 - INFO - Median: 5.4365 +2025-05-24 19:34:45,760 - INFO - Min: 1.1523 +2025-05-24 19:34:45,760 - INFO - Max: 706.9584 +2025-05-24 19:34:45,760 - INFO - Std: 114.9049 +2025-05-24 19:34:45,760 - INFO - OC Distribution Statistics: +2025-05-24 19:34:45,760 - INFO - Validation Mean: 16.4417 +2025-05-24 19:34:45,760 - INFO - Validation Std: 7.4593 +2025-05-24 19:34:45,760 - INFO - Training Mean: 23.0817 +2025-05-24 19:34:45,760 - INFO - Training Std: 20.3305 +2025-05-24 19:34:45,760 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 19:34:45,760 - INFO - KS Statistic: 0.2164 +2025-05-24 19:34:45,760 - INFO - P-value: 0.0000 +2025-05-24 19:34:45,760 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 19:34:45,760 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 20:48:26,175 - INFO - Validation and Training Set Split Statistics +2025-05-24 20:48:26,176 - INFO - Timestamp: 2025-05-24 20:48:26 +2025-05-24 20:48:26,176 - INFO - Validation set size: 1448 (8.77%) +2025-05-24 20:48:26,176 - INFO - Training set size: 15066 (91.23%) +2025-05-24 20:48:26,176 - INFO - Minimum Distance Statistics (km): +2025-05-24 20:48:26,176 - INFO - Mean: 67.8156 +2025-05-24 20:48:26,176 - INFO - Median: 5.1405 +2025-05-24 20:48:26,176 - INFO - Min: 1.1288 +2025-05-24 20:48:26,176 - INFO - Max: 835.9450 +2025-05-24 20:48:26,176 - INFO - Std: 115.9982 +2025-05-24 20:48:26,176 - INFO - OC Distribution Statistics: +2025-05-24 20:48:26,176 - INFO - Validation Mean: 16.5957 +2025-05-24 20:48:26,176 - INFO - Validation Std: 9.0129 +2025-05-24 20:48:26,176 - INFO - Training Mean: 23.0968 +2025-05-24 20:48:26,176 - INFO - Training Std: 20.3096 +2025-05-24 20:48:26,176 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 20:48:26,177 - INFO - KS Statistic: 0.2324 +2025-05-24 20:48:26,177 - INFO - P-value: 0.0000 +2025-05-24 20:48:26,177 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 20:48:26,177 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 20:48:26,567 - INFO - Validation and Training Set Split Statistics +2025-05-24 20:48:26,567 - INFO - Timestamp: 2025-05-24 20:48:26 +2025-05-24 20:48:26,567 - INFO - Validation set size: 1372 (8.31%) +2025-05-24 20:48:26,567 - INFO - Training set size: 15142 (91.69%) +2025-05-24 20:48:26,567 - INFO - Minimum Distance Statistics (km): +2025-05-24 20:48:26,567 - INFO - Mean: 68.0613 +2025-05-24 20:48:26,567 - INFO - Median: 4.1444 +2025-05-24 20:48:26,567 - INFO - Min: 1.2067 +2025-05-24 20:48:26,567 - INFO - Max: 917.1429 +2025-05-24 20:48:26,567 - INFO - Std: 118.1758 +2025-05-24 20:48:26,567 - INFO - OC Distribution Statistics: +2025-05-24 20:48:26,567 - INFO - Validation Mean: 16.3606 +2025-05-24 20:48:26,567 - INFO - Validation Std: 7.4725 +2025-05-24 20:48:26,567 - INFO - Training Mean: 23.0855 +2025-05-24 20:48:26,567 - INFO - Training Std: 20.3235 +2025-05-24 20:48:26,567 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 20:48:26,567 - INFO - KS Statistic: 0.2151 +2025-05-24 20:48:26,567 - INFO - P-value: 0.0000 +2025-05-24 20:48:26,567 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 20:48:26,567 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 20:48:28,284 - INFO - Validation and Training Set Split Statistics +2025-05-24 20:48:28,285 - INFO - Timestamp: 2025-05-24 20:48:28 +2025-05-24 20:48:28,285 - INFO - Validation set size: 1451 (8.79%) +2025-05-24 20:48:28,285 - INFO - Training set size: 15063 (91.21%) +2025-05-24 20:48:28,285 - INFO - Minimum Distance Statistics (km): +2025-05-24 20:48:28,285 - INFO - Mean: 62.8475 +2025-05-24 20:48:28,285 - INFO - Median: 4.2475 +2025-05-24 20:48:28,285 - INFO - Min: 1.1829 +2025-05-24 20:48:28,285 - INFO - Max: 683.3575 +2025-05-24 20:48:28,285 - INFO - Std: 105.3658 +2025-05-24 20:48:28,285 - INFO - OC Distribution Statistics: +2025-05-24 20:48:28,285 - INFO - Validation Mean: 16.6855 +2025-05-24 20:48:28,285 - INFO - Validation Std: 8.2026 +2025-05-24 20:48:28,285 - INFO - Training Mean: 23.0895 +2025-05-24 20:48:28,285 - INFO - Training Std: 20.3468 +2025-05-24 20:48:28,285 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 20:48:28,285 - INFO - KS Statistic: 0.2264 +2025-05-24 20:48:28,285 - INFO - P-value: 0.0000 +2025-05-24 20:48:28,285 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 20:48:28,285 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 20:48:30,511 - INFO - Validation and Training Set Split Statistics +2025-05-24 20:48:30,512 - INFO - Timestamp: 2025-05-24 20:48:30 +2025-05-24 20:48:30,512 - INFO - Validation set size: 1333 (8.07%) +2025-05-24 20:48:30,512 - INFO - Training set size: 15181 (91.93%) +2025-05-24 20:48:30,512 - INFO - Minimum Distance Statistics (km): +2025-05-24 20:48:30,512 - INFO - Mean: 74.6124 +2025-05-24 20:48:30,512 - INFO - Median: 4.9833 +2025-05-24 20:48:30,512 - INFO - Min: 0.9455 +2025-05-24 20:48:30,512 - INFO - Max: 826.2195 +2025-05-24 20:48:30,512 - INFO - Std: 118.6889 +2025-05-24 20:48:30,512 - INFO - OC Distribution Statistics: +2025-05-24 20:48:30,512 - INFO - Validation Mean: 16.5601 +2025-05-24 20:48:30,512 - INFO - Validation Std: 7.3358 +2025-05-24 20:48:30,512 - INFO - Training Mean: 23.0507 +2025-05-24 20:48:30,512 - INFO - Training Std: 20.3139 +2025-05-24 20:48:30,512 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 20:48:30,512 - INFO - KS Statistic: 0.1987 +2025-05-24 20:48:30,512 - INFO - P-value: 0.0000 +2025-05-24 20:48:30,512 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 20:48:30,512 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 20:48:30,829 - INFO - Validation and Training Set Split Statistics +2025-05-24 20:48:30,829 - INFO - Timestamp: 2025-05-24 20:48:30 +2025-05-24 20:48:30,829 - INFO - Validation set size: 1409 (8.53%) +2025-05-24 20:48:30,829 - INFO - Training set size: 15105 (91.47%) +2025-05-24 20:48:30,829 - INFO - Minimum Distance Statistics (km): +2025-05-24 20:48:30,829 - INFO - Mean: 63.8808 +2025-05-24 20:48:30,829 - INFO - Median: 4.5882 +2025-05-24 20:48:30,829 - INFO - Min: 1.2329 +2025-05-24 20:48:30,829 - INFO - Max: 716.7388 +2025-05-24 20:48:30,829 - INFO - Std: 106.8634 +2025-05-24 20:48:30,829 - INFO - OC Distribution Statistics: +2025-05-24 20:48:30,829 - INFO - Validation Mean: 15.9208 +2025-05-24 20:48:30,829 - INFO - Validation Std: 7.2367 +2025-05-24 20:48:30,829 - INFO - Training Mean: 23.1430 +2025-05-24 20:48:30,829 - INFO - Training Std: 20.3362 +2025-05-24 20:48:30,829 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 20:48:30,829 - INFO - KS Statistic: 0.2434 +2025-05-24 20:48:30,829 - INFO - P-value: 0.0000 +2025-05-24 20:48:30,829 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 20:48:30,829 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 20:48:31,291 - INFO - Validation and Training Set Split Statistics +2025-05-24 20:48:31,291 - INFO - Timestamp: 2025-05-24 20:48:31 +2025-05-24 20:48:31,291 - INFO - Validation set size: 1420 (8.60%) +2025-05-24 20:48:31,291 - INFO - Training set size: 15094 (91.40%) +2025-05-24 20:48:31,291 - INFO - Minimum Distance Statistics (km): +2025-05-24 20:48:31,291 - INFO - Mean: 70.5755 +2025-05-24 20:48:31,291 - INFO - Median: 4.5217 +2025-05-24 20:48:31,291 - INFO - Min: 1.1342 +2025-05-24 20:48:31,291 - INFO - Max: 835.9450 +2025-05-24 20:48:31,291 - INFO - Std: 125.8964 +2025-05-24 20:48:31,291 - INFO - OC Distribution Statistics: +2025-05-24 20:48:31,291 - INFO - Validation Mean: 16.4681 +2025-05-24 20:48:31,291 - INFO - Validation Std: 7.2182 +2025-05-24 20:48:31,291 - INFO - Training Mean: 23.0968 +2025-05-24 20:48:31,291 - INFO - Training Std: 20.3599 +2025-05-24 20:48:31,291 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 20:48:31,291 - INFO - KS Statistic: 0.2077 +2025-05-24 20:48:31,291 - INFO - P-value: 0.0000 +2025-05-24 20:48:31,291 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 20:48:31,291 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 20:48:31,594 - INFO - Validation and Training Set Split Statistics +2025-05-24 20:48:31,594 - INFO - Timestamp: 2025-05-24 20:48:31 +2025-05-24 20:48:31,594 - INFO - Validation set size: 1391 (8.42%) +2025-05-24 20:48:31,594 - INFO - Training set size: 15123 (91.58%) +2025-05-24 20:48:31,594 - INFO - Minimum Distance Statistics (km): +2025-05-24 20:48:31,594 - INFO - Mean: 70.7970 +2025-05-24 20:48:31,594 - INFO - Median: 5.1441 +2025-05-24 20:48:31,594 - INFO - Min: 1.2156 +2025-05-24 20:48:31,594 - INFO - Max: 655.9249 +2025-05-24 20:48:31,594 - INFO - Std: 109.8173 +2025-05-24 20:48:31,594 - INFO - OC Distribution Statistics: +2025-05-24 20:48:31,594 - INFO - Validation Mean: 16.4660 +2025-05-24 20:48:31,594 - INFO - Validation Std: 7.2697 +2025-05-24 20:48:31,594 - INFO - Training Mean: 23.0842 +2025-05-24 20:48:31,594 - INFO - Training Std: 20.3431 +2025-05-24 20:48:31,594 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 20:48:31,594 - INFO - KS Statistic: 0.1910 +2025-05-24 20:48:31,594 - INFO - P-value: 0.0000 +2025-05-24 20:48:31,594 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 20:48:31,594 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 20:48:34,236 - INFO - Validation and Training Set Split Statistics +2025-05-24 20:48:34,237 - INFO - Timestamp: 2025-05-24 20:48:34 +2025-05-24 20:48:34,237 - INFO - Validation set size: 1355 (8.21%) +2025-05-24 20:48:34,237 - INFO - Training set size: 15159 (91.79%) +2025-05-24 20:48:34,237 - INFO - Minimum Distance Statistics (km): +2025-05-24 20:48:34,237 - INFO - Mean: 66.7192 +2025-05-24 20:48:34,237 - INFO - Median: 4.4085 +2025-05-24 20:48:34,237 - INFO - Min: 1.2156 +2025-05-24 20:48:34,237 - INFO - Max: 706.9584 +2025-05-24 20:48:34,237 - INFO - Std: 106.3971 +2025-05-24 20:48:34,237 - INFO - OC Distribution Statistics: +2025-05-24 20:48:34,237 - INFO - Validation Mean: 16.3728 +2025-05-24 20:48:34,237 - INFO - Validation Std: 7.2215 +2025-05-24 20:48:34,237 - INFO - Training Mean: 23.0769 +2025-05-24 20:48:34,237 - INFO - Training Std: 20.3234 +2025-05-24 20:48:34,237 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 20:48:34,237 - INFO - KS Statistic: 0.2073 +2025-05-24 20:48:34,237 - INFO - P-value: 0.0000 +2025-05-24 20:48:34,237 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 20:48:34,237 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 22:04:42,575 - INFO - Validation and Training Set Split Statistics +2025-05-24 22:04:42,576 - INFO - Timestamp: 2025-05-24 22:04:42 +2025-05-24 22:04:42,576 - INFO - Validation set size: 1328 (8.04%) +2025-05-24 22:04:42,576 - INFO - Training set size: 15186 (91.96%) +2025-05-24 22:04:42,576 - INFO - Minimum Distance Statistics (km): +2025-05-24 22:04:42,576 - INFO - Mean: 73.1312 +2025-05-24 22:04:42,576 - INFO - Median: 5.1441 +2025-05-24 22:04:42,576 - INFO - Min: 1.2773 +2025-05-24 22:04:42,576 - INFO - Max: 866.4875 +2025-05-24 22:04:42,576 - INFO - Std: 123.3549 +2025-05-24 22:04:42,576 - INFO - OC Distribution Statistics: +2025-05-24 22:04:42,576 - INFO - Validation Mean: 16.9430 +2025-05-24 22:04:42,576 - INFO - Validation Std: 8.1395 +2025-05-24 22:04:42,576 - INFO - Training Mean: 23.0151 +2025-05-24 22:04:42,576 - INFO - Training Std: 20.2949 +2025-05-24 22:04:42,576 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 22:04:42,576 - INFO - KS Statistic: 0.1948 +2025-05-24 22:04:42,576 - INFO - P-value: 0.0000 +2025-05-24 22:04:42,576 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 22:04:42,576 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 22:04:44,260 - INFO - Validation and Training Set Split Statistics +2025-05-24 22:04:44,260 - INFO - Timestamp: 2025-05-24 22:04:44 +2025-05-24 22:04:44,260 - INFO - Validation set size: 1356 (8.21%) +2025-05-24 22:04:44,260 - INFO - Training set size: 15158 (91.79%) +2025-05-24 22:04:44,260 - INFO - Minimum Distance Statistics (km): +2025-05-24 22:04:44,260 - INFO - Mean: 64.1825 +2025-05-24 22:04:44,260 - INFO - Median: 4.5882 +2025-05-24 22:04:44,260 - INFO - Min: 1.0979 +2025-05-24 22:04:44,260 - INFO - Max: 706.9584 +2025-05-24 22:04:44,260 - INFO - Std: 110.6635 +2025-05-24 22:04:44,260 - INFO - OC Distribution Statistics: +2025-05-24 22:04:44,260 - INFO - Validation Mean: 16.0921 +2025-05-24 22:04:44,260 - INFO - Validation Std: 8.0259 +2025-05-24 22:04:44,260 - INFO - Training Mean: 23.1024 +2025-05-24 22:04:44,260 - INFO - Training Std: 20.2884 +2025-05-24 22:04:44,260 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 22:04:44,260 - INFO - KS Statistic: 0.2518 +2025-05-24 22:04:44,260 - INFO - P-value: 0.0000 +2025-05-24 22:04:44,260 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 22:04:44,260 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 22:04:44,294 - INFO - Validation and Training Set Split Statistics +2025-05-24 22:04:44,294 - INFO - Timestamp: 2025-05-24 22:04:44 +2025-05-24 22:04:44,294 - INFO - Validation set size: 1643 (9.95%) +2025-05-24 22:04:44,294 - INFO - Training set size: 14871 (90.05%) +2025-05-24 22:04:44,294 - INFO - Minimum Distance Statistics (km): +2025-05-24 22:04:44,294 - INFO - Mean: 57.4092 +2025-05-24 22:04:44,294 - INFO - Median: 3.8305 +2025-05-24 22:04:44,294 - INFO - Min: 1.2065 +2025-05-24 22:04:44,294 - INFO - Max: 706.9584 +2025-05-24 22:04:44,294 - INFO - Std: 108.6604 +2025-05-24 22:04:44,294 - INFO - OC Distribution Statistics: +2025-05-24 22:04:44,294 - INFO - Validation Mean: 16.1968 +2025-05-24 22:04:44,294 - INFO - Validation Std: 7.5933 +2025-05-24 22:04:44,294 - INFO - Training Mean: 23.2261 +2025-05-24 22:04:44,294 - INFO - Training Std: 20.4515 +2025-05-24 22:04:44,294 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 22:04:44,294 - INFO - KS Statistic: 0.2392 +2025-05-24 22:04:44,294 - INFO - P-value: 0.0000 +2025-05-24 22:04:44,294 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 22:04:44,294 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 22:04:46,056 - INFO - Validation and Training Set Split Statistics +2025-05-24 22:04:46,056 - INFO - Timestamp: 2025-05-24 22:04:46 +2025-05-24 22:04:46,056 - INFO - Validation set size: 1383 (8.37%) +2025-05-24 22:04:46,056 - INFO - Training set size: 15131 (91.63%) +2025-05-24 22:04:46,056 - INFO - Minimum Distance Statistics (km): +2025-05-24 22:04:46,056 - INFO - Mean: 64.1933 +2025-05-24 22:04:46,056 - INFO - Median: 4.1444 +2025-05-24 22:04:46,056 - INFO - Min: 1.2773 +2025-05-24 22:04:46,056 - INFO - Max: 706.9584 +2025-05-24 22:04:46,056 - INFO - Std: 107.3383 +2025-05-24 22:04:46,056 - INFO - OC Distribution Statistics: +2025-05-24 22:04:46,056 - INFO - Validation Mean: 16.5202 +2025-05-24 22:04:46,056 - INFO - Validation Std: 7.8996 +2025-05-24 22:04:46,056 - INFO - Training Mean: 23.0758 +2025-05-24 22:04:46,056 - INFO - Training Std: 20.3191 +2025-05-24 22:04:46,056 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 22:04:46,056 - INFO - KS Statistic: 0.1976 +2025-05-24 22:04:46,056 - INFO - P-value: 0.0000 +2025-05-24 22:04:46,056 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 22:04:46,056 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 22:04:46,730 - INFO - Validation and Training Set Split Statistics +2025-05-24 22:04:46,730 - INFO - Timestamp: 2025-05-24 22:04:46 +2025-05-24 22:04:46,730 - INFO - Validation set size: 1399 (8.47%) +2025-05-24 22:04:46,730 - INFO - Training set size: 15115 (91.53%) +2025-05-24 22:04:46,730 - INFO - Minimum Distance Statistics (km): +2025-05-24 22:04:46,730 - INFO - Mean: 70.2643 +2025-05-24 22:04:46,730 - INFO - Median: 5.5898 +2025-05-24 22:04:46,730 - INFO - Min: 1.0979 +2025-05-24 22:04:46,730 - INFO - Max: 743.7101 +2025-05-24 22:04:46,730 - INFO - Std: 112.5236 +2025-05-24 22:04:46,730 - INFO - OC Distribution Statistics: +2025-05-24 22:04:46,730 - INFO - Validation Mean: 16.5258 +2025-05-24 22:04:46,730 - INFO - Validation Std: 8.2783 +2025-05-24 22:04:46,730 - INFO - Training Mean: 23.0822 +2025-05-24 22:04:46,730 - INFO - Training Std: 20.3134 +2025-05-24 22:04:46,730 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 22:04:46,730 - INFO - KS Statistic: 0.2257 +2025-05-24 22:04:46,730 - INFO - P-value: 0.0000 +2025-05-24 22:04:46,730 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 22:04:46,730 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 22:04:47,328 - INFO - Validation and Training Set Split Statistics +2025-05-24 22:04:47,328 - INFO - Timestamp: 2025-05-24 22:04:47 +2025-05-24 22:04:47,328 - INFO - Validation set size: 1384 (8.38%) +2025-05-24 22:04:47,328 - INFO - Training set size: 15130 (91.62%) +2025-05-24 22:04:47,328 - INFO - Minimum Distance Statistics (km): +2025-05-24 22:04:47,328 - INFO - Mean: 68.0119 +2025-05-24 22:04:47,328 - INFO - Median: 4.3143 +2025-05-24 22:04:47,328 - INFO - Min: 1.2497 +2025-05-24 22:04:47,328 - INFO - Max: 862.7749 +2025-05-24 22:04:47,328 - INFO - Std: 119.7962 +2025-05-24 22:04:47,328 - INFO - OC Distribution Statistics: +2025-05-24 22:04:47,328 - INFO - Validation Mean: 16.6505 +2025-05-24 22:04:47,328 - INFO - Validation Std: 7.5910 +2025-05-24 22:04:47,328 - INFO - Training Mean: 23.0643 +2025-05-24 22:04:47,328 - INFO - Training Std: 20.3342 +2025-05-24 22:04:47,328 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 22:04:47,328 - INFO - KS Statistic: 0.2124 +2025-05-24 22:04:47,328 - INFO - P-value: 0.0000 +2025-05-24 22:04:47,328 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 22:04:47,328 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 22:04:47,539 - INFO - Validation and Training Set Split Statistics +2025-05-24 22:04:47,539 - INFO - Timestamp: 2025-05-24 22:04:47 +2025-05-24 22:04:47,539 - INFO - Validation set size: 1330 (8.05%) +2025-05-24 22:04:47,539 - INFO - Training set size: 15184 (91.95%) +2025-05-24 22:04:47,539 - INFO - Minimum Distance Statistics (km): +2025-05-24 22:04:47,539 - INFO - Mean: 70.5493 +2025-05-24 22:04:47,539 - INFO - Median: 4.4468 +2025-05-24 22:04:47,539 - INFO - Min: 1.1288 +2025-05-24 22:04:47,539 - INFO - Max: 772.2996 +2025-05-24 22:04:47,539 - INFO - Std: 121.5686 +2025-05-24 22:04:47,539 - INFO - OC Distribution Statistics: +2025-05-24 22:04:47,539 - INFO - Validation Mean: 16.7085 +2025-05-24 22:04:47,539 - INFO - Validation Std: 8.0476 +2025-05-24 22:04:47,539 - INFO - Training Mean: 23.0364 +2025-05-24 22:04:47,539 - INFO - Training Std: 20.2928 +2025-05-24 22:04:47,539 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 22:04:47,539 - INFO - KS Statistic: 0.2078 +2025-05-24 22:04:47,539 - INFO - P-value: 0.0000 +2025-05-24 22:04:47,539 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 22:04:47,539 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-24 22:04:53,293 - INFO - Validation and Training Set Split Statistics +2025-05-24 22:04:53,293 - INFO - Timestamp: 2025-05-24 22:04:53 +2025-05-24 22:04:53,293 - INFO - Validation set size: 1329 (8.05%) +2025-05-24 22:04:53,293 - INFO - Training set size: 15185 (91.95%) +2025-05-24 22:04:53,293 - INFO - Minimum Distance Statistics (km): +2025-05-24 22:04:53,293 - INFO - Mean: 71.6767 +2025-05-24 22:04:53,293 - INFO - Median: 5.5952 +2025-05-24 22:04:53,293 - INFO - Min: 0.8258 +2025-05-24 22:04:53,293 - INFO - Max: 706.9584 +2025-05-24 22:04:53,293 - INFO - Std: 114.5388 +2025-05-24 22:04:53,293 - INFO - OC Distribution Statistics: +2025-05-24 22:04:53,293 - INFO - Validation Mean: 17.1254 +2025-05-24 22:04:53,293 - INFO - Validation Std: 8.6110 +2025-05-24 22:04:53,293 - INFO - Training Mean: 22.9995 +2025-05-24 22:04:53,293 - INFO - Training Std: 20.2830 +2025-05-24 22:04:53,293 - INFO - Kolmogorov-Smirnov Test: +2025-05-24 22:04:53,293 - INFO - KS Statistic: 0.1978 +2025-05-24 22:04:53,293 - INFO - P-value: 0.0000 +2025-05-24 22:04:53,293 - INFO - Distribution Fit: Inverse Gamma +2025-05-24 22:04:53,293 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 00:23:59,809 - INFO - Validation and Training Set Split Statistics +2025-05-25 00:23:59,809 - INFO - Timestamp: 2025-05-25 00:23:59 +2025-05-25 00:23:59,809 - INFO - Validation set size: 1321 (8.00%) +2025-05-25 00:23:59,809 - INFO - Training set size: 15193 (92.00%) +2025-05-25 00:23:59,809 - INFO - Minimum Distance Statistics (km): +2025-05-25 00:23:59,809 - INFO - Mean: 64.3244 +2025-05-25 00:23:59,809 - INFO - Median: 4.8521 +2025-05-25 00:23:59,809 - INFO - Min: 1.2791 +2025-05-25 00:23:59,809 - INFO - Max: 702.5656 +2025-05-25 00:23:59,809 - INFO - Std: 101.0049 +2025-05-25 00:23:59,809 - INFO - OC Distribution Statistics: +2025-05-25 00:23:59,809 - INFO - Validation Mean: 16.8047 +2025-05-25 00:23:59,809 - INFO - Validation Std: 8.1528 +2025-05-25 00:23:59,809 - INFO - Training Mean: 23.0243 +2025-05-25 00:23:59,809 - INFO - Training Std: 20.2873 +2025-05-25 00:23:59,809 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 00:23:59,809 - INFO - KS Statistic: 0.2091 +2025-05-25 00:23:59,809 - INFO - P-value: 0.0000 +2025-05-25 00:23:59,809 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 00:23:59,809 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 00:24:00,094 - INFO - Validation and Training Set Split Statistics +2025-05-25 00:24:00,094 - INFO - Timestamp: 2025-05-25 00:24:00 +2025-05-25 00:24:00,094 - INFO - Validation set size: 1401 (8.48%) +2025-05-25 00:24:00,094 - INFO - Training set size: 15113 (91.52%) +2025-05-25 00:24:00,094 - INFO - Minimum Distance Statistics (km): +2025-05-25 00:24:00,094 - INFO - Mean: 72.9598 +2025-05-25 00:24:00,094 - INFO - Median: 5.1703 +2025-05-25 00:24:00,094 - INFO - Min: 0.8608 +2025-05-25 00:24:00,094 - INFO - Max: 875.2369 +2025-05-25 00:24:00,094 - INFO - Std: 124.8027 +2025-05-25 00:24:00,094 - INFO - OC Distribution Statistics: +2025-05-25 00:24:00,094 - INFO - Validation Mean: 16.3682 +2025-05-25 00:24:00,094 - INFO - Validation Std: 7.2812 +2025-05-25 00:24:00,094 - INFO - Training Mean: 23.0977 +2025-05-25 00:24:00,094 - INFO - Training Std: 20.3449 +2025-05-25 00:24:00,094 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 00:24:00,095 - INFO - KS Statistic: 0.2357 +2025-05-25 00:24:00,095 - INFO - P-value: 0.0000 +2025-05-25 00:24:00,095 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 00:24:00,095 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 00:24:01,149 - INFO - Validation and Training Set Split Statistics +2025-05-25 00:24:01,149 - INFO - Timestamp: 2025-05-25 00:24:01 +2025-05-25 00:24:01,149 - INFO - Validation set size: 1556 (9.42%) +2025-05-25 00:24:01,149 - INFO - Training set size: 14958 (90.58%) +2025-05-25 00:24:01,150 - INFO - Minimum Distance Statistics (km): +2025-05-25 00:24:01,150 - INFO - Mean: 76.3338 +2025-05-25 00:24:01,150 - INFO - Median: 4.7569 +2025-05-25 00:24:01,150 - INFO - Min: 1.0890 +2025-05-25 00:24:01,150 - INFO - Max: 743.7101 +2025-05-25 00:24:01,150 - INFO - Std: 128.4814 +2025-05-25 00:24:01,150 - INFO - OC Distribution Statistics: +2025-05-25 00:24:01,150 - INFO - Validation Mean: 16.5175 +2025-05-25 00:24:01,150 - INFO - Validation Std: 7.5573 +2025-05-25 00:24:01,150 - INFO - Training Mean: 23.1519 +2025-05-25 00:24:01,150 - INFO - Training Std: 20.4197 +2025-05-25 00:24:01,150 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 00:24:01,150 - INFO - KS Statistic: 0.2001 +2025-05-25 00:24:01,150 - INFO - P-value: 0.0000 +2025-05-25 00:24:01,150 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 00:24:01,150 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 00:24:01,410 - INFO - Validation and Training Set Split Statistics +2025-05-25 00:24:01,410 - INFO - Timestamp: 2025-05-25 00:24:01 +2025-05-25 00:24:01,410 - INFO - Validation set size: 1445 (8.75%) +2025-05-25 00:24:01,410 - INFO - Training set size: 15069 (91.25%) +2025-05-25 00:24:01,410 - INFO - Minimum Distance Statistics (km): +2025-05-25 00:24:01,410 - INFO - Mean: 65.1437 +2025-05-25 00:24:01,410 - INFO - Median: 5.2688 +2025-05-25 00:24:01,410 - INFO - Min: 1.2773 +2025-05-25 00:24:01,410 - INFO - Max: 702.5656 +2025-05-25 00:24:01,410 - INFO - Std: 101.0378 +2025-05-25 00:24:01,410 - INFO - OC Distribution Statistics: +2025-05-25 00:24:01,411 - INFO - Validation Mean: 16.9675 +2025-05-25 00:24:01,411 - INFO - Validation Std: 8.3798 +2025-05-25 00:24:01,411 - INFO - Training Mean: 23.0599 +2025-05-25 00:24:01,411 - INFO - Training Std: 20.3452 +2025-05-25 00:24:01,411 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 00:24:01,411 - INFO - KS Statistic: 0.1959 +2025-05-25 00:24:01,411 - INFO - P-value: 0.0000 +2025-05-25 00:24:01,411 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 00:24:01,411 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 00:24:02,794 - INFO - Validation and Training Set Split Statistics +2025-05-25 00:24:02,794 - INFO - Timestamp: 2025-05-25 00:24:02 +2025-05-25 00:24:02,794 - INFO - Validation set size: 1438 (8.71%) +2025-05-25 00:24:02,794 - INFO - Training set size: 15076 (91.29%) +2025-05-25 00:24:02,794 - INFO - Minimum Distance Statistics (km): +2025-05-25 00:24:02,794 - INFO - Mean: 59.8198 +2025-05-25 00:24:02,794 - INFO - Median: 4.0758 +2025-05-25 00:24:02,794 - INFO - Min: 1.1319 +2025-05-25 00:24:02,794 - INFO - Max: 888.3558 +2025-05-25 00:24:02,794 - INFO - Std: 100.4632 +2025-05-25 00:24:02,794 - INFO - OC Distribution Statistics: +2025-05-25 00:24:02,794 - INFO - Validation Mean: 16.3717 +2025-05-25 00:24:02,794 - INFO - Validation Std: 7.3393 +2025-05-25 00:24:02,794 - INFO - Training Mean: 23.1139 +2025-05-25 00:24:02,794 - INFO - Training Std: 20.3621 +2025-05-25 00:24:02,794 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 00:24:02,794 - INFO - KS Statistic: 0.2248 +2025-05-25 00:24:02,794 - INFO - P-value: 0.0000 +2025-05-25 00:24:02,794 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 00:24:02,794 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 00:24:03,302 - INFO - Validation and Training Set Split Statistics +2025-05-25 00:24:03,302 - INFO - Timestamp: 2025-05-25 00:24:03 +2025-05-25 00:24:03,303 - INFO - Validation set size: 1368 (8.28%) +2025-05-25 00:24:03,303 - INFO - Training set size: 15146 (91.72%) +2025-05-25 00:24:03,303 - INFO - Minimum Distance Statistics (km): +2025-05-25 00:24:03,303 - INFO - Mean: 75.2090 +2025-05-25 00:24:03,303 - INFO - Median: 5.3115 +2025-05-25 00:24:03,303 - INFO - Min: 1.2156 +2025-05-25 00:24:03,303 - INFO - Max: 1106.1626 +2025-05-25 00:24:03,303 - INFO - Std: 133.0121 +2025-05-25 00:24:03,303 - INFO - OC Distribution Statistics: +2025-05-25 00:24:03,303 - INFO - Validation Mean: 16.4078 +2025-05-25 00:24:03,303 - INFO - Validation Std: 7.9226 +2025-05-25 00:24:03,303 - INFO - Training Mean: 23.0795 +2025-05-25 00:24:03,303 - INFO - Training Std: 20.3075 +2025-05-25 00:24:03,303 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 00:24:03,303 - INFO - KS Statistic: 0.2356 +2025-05-25 00:24:03,303 - INFO - P-value: 0.0000 +2025-05-25 00:24:03,303 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 00:24:03,303 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 00:24:03,729 - INFO - Validation and Training Set Split Statistics +2025-05-25 00:24:03,729 - INFO - Timestamp: 2025-05-25 00:24:03 +2025-05-25 00:24:03,729 - INFO - Validation set size: 1354 (8.20%) +2025-05-25 00:24:03,729 - INFO - Training set size: 15160 (91.80%) +2025-05-25 00:24:03,729 - INFO - Minimum Distance Statistics (km): +2025-05-25 00:24:03,729 - INFO - Mean: 70.7681 +2025-05-25 00:24:03,729 - INFO - Median: 4.2711 +2025-05-25 00:24:03,730 - INFO - Min: 1.3400 +2025-05-25 00:24:03,730 - INFO - Max: 743.7101 +2025-05-25 00:24:03,730 - INFO - Std: 121.5924 +2025-05-25 00:24:03,730 - INFO - OC Distribution Statistics: +2025-05-25 00:24:03,730 - INFO - Validation Mean: 16.0247 +2025-05-25 00:24:03,730 - INFO - Validation Std: 7.4483 +2025-05-25 00:24:03,730 - INFO - Training Mean: 23.1075 +2025-05-25 00:24:03,730 - INFO - Training Std: 20.3050 +2025-05-25 00:24:03,730 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 00:24:03,730 - INFO - KS Statistic: 0.2424 +2025-05-25 00:24:03,730 - INFO - P-value: 0.0000 +2025-05-25 00:24:03,730 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 00:24:03,730 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 00:24:05,807 - INFO - Validation and Training Set Split Statistics +2025-05-25 00:24:05,807 - INFO - Timestamp: 2025-05-25 00:24:05 +2025-05-25 00:24:05,808 - INFO - Validation set size: 1396 (8.45%) +2025-05-25 00:24:05,808 - INFO - Training set size: 15118 (91.55%) +2025-05-25 00:24:05,808 - INFO - Minimum Distance Statistics (km): +2025-05-25 00:24:05,808 - INFO - Mean: 73.5839 +2025-05-25 00:24:05,808 - INFO - Median: 5.3420 +2025-05-25 00:24:05,808 - INFO - Min: 0.8608 +2025-05-25 00:24:05,808 - INFO - Max: 743.7101 +2025-05-25 00:24:05,808 - INFO - Std: 117.7905 +2025-05-25 00:24:05,808 - INFO - OC Distribution Statistics: +2025-05-25 00:24:05,808 - INFO - Validation Mean: 16.3508 +2025-05-25 00:24:05,808 - INFO - Validation Std: 7.6307 +2025-05-25 00:24:05,808 - INFO - Training Mean: 23.0971 +2025-05-25 00:24:05,808 - INFO - Training Std: 20.3300 +2025-05-25 00:24:05,808 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 00:24:05,808 - INFO - KS Statistic: 0.2255 +2025-05-25 00:24:05,808 - INFO - P-value: 0.0000 +2025-05-25 00:24:05,808 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 00:24:05,808 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:35:02,234 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:35:02,234 - INFO - Timestamp: 2025-05-25 01:35:02 +2025-05-25 01:35:02,235 - INFO - Validation set size: 1492 (9.03%) +2025-05-25 01:35:02,235 - INFO - Training set size: 15022 (90.97%) +2025-05-25 01:35:02,235 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:35:02,235 - INFO - Mean: 65.9619 +2025-05-25 01:35:02,235 - INFO - Median: 4.4528 +2025-05-25 01:35:02,235 - INFO - Min: 0.9954 +2025-05-25 01:35:02,235 - INFO - Max: 706.9584 +2025-05-25 01:35:02,235 - INFO - Std: 116.8783 +2025-05-25 01:35:02,235 - INFO - OC Distribution Statistics: +2025-05-25 01:35:02,235 - INFO - Validation Mean: 16.1714 +2025-05-25 01:35:02,235 - INFO - Validation Std: 7.3281 +2025-05-25 01:35:02,235 - INFO - Training Mean: 23.1580 +2025-05-25 01:35:02,235 - INFO - Training Std: 20.3836 +2025-05-25 01:35:02,235 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:35:02,235 - INFO - KS Statistic: 0.2329 +2025-05-25 01:35:02,235 - INFO - P-value: 0.0000 +2025-05-25 01:35:02,235 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:35:02,235 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:35:03,199 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:35:03,199 - INFO - Timestamp: 2025-05-25 01:35:03 +2025-05-25 01:35:03,199 - INFO - Validation set size: 1384 (8.38%) +2025-05-25 01:35:03,199 - INFO - Training set size: 15130 (91.62%) +2025-05-25 01:35:03,199 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:35:03,199 - INFO - Mean: 72.5463 +2025-05-25 01:35:03,199 - INFO - Median: 5.2639 +2025-05-25 01:35:03,199 - INFO - Min: 0.6067 +2025-05-25 01:35:03,199 - INFO - Max: 706.9584 +2025-05-25 01:35:03,199 - INFO - Std: 116.4508 +2025-05-25 01:35:03,199 - INFO - OC Distribution Statistics: +2025-05-25 01:35:03,199 - INFO - Validation Mean: 16.5684 +2025-05-25 01:35:03,199 - INFO - Validation Std: 7.7191 +2025-05-25 01:35:03,199 - INFO - Training Mean: 23.0718 +2025-05-25 01:35:03,199 - INFO - Training Std: 20.3274 +2025-05-25 01:35:03,199 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:35:03,199 - INFO - KS Statistic: 0.2074 +2025-05-25 01:35:03,199 - INFO - P-value: 0.0000 +2025-05-25 01:35:03,199 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:35:03,199 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:35:04,235 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:35:04,235 - INFO - Timestamp: 2025-05-25 01:35:04 +2025-05-25 01:35:04,235 - INFO - Validation set size: 1360 (8.24%) +2025-05-25 01:35:04,235 - INFO - Training set size: 15154 (91.76%) +2025-05-25 01:35:04,235 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:35:04,235 - INFO - Mean: 72.5773 +2025-05-25 01:35:04,235 - INFO - Median: 4.6353 +2025-05-25 01:35:04,235 - INFO - Min: 1.2065 +2025-05-25 01:35:04,235 - INFO - Max: 862.7749 +2025-05-25 01:35:04,235 - INFO - Std: 123.9919 +2025-05-25 01:35:04,235 - INFO - OC Distribution Statistics: +2025-05-25 01:35:04,235 - INFO - Validation Mean: 16.7410 +2025-05-25 01:35:04,235 - INFO - Validation Std: 7.6708 +2025-05-25 01:35:04,235 - INFO - Training Mean: 23.0460 +2025-05-25 01:35:04,235 - INFO - Training Std: 20.3218 +2025-05-25 01:35:04,235 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:35:04,235 - INFO - KS Statistic: 0.2100 +2025-05-25 01:35:04,235 - INFO - P-value: 0.0000 +2025-05-25 01:35:04,235 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:35:04,235 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:35:04,348 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:35:04,349 - INFO - Timestamp: 2025-05-25 01:35:04 +2025-05-25 01:35:04,349 - INFO - Validation set size: 1666 (10.09%) +2025-05-25 01:35:04,349 - INFO - Training set size: 14848 (89.91%) +2025-05-25 01:35:04,349 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:35:04,349 - INFO - Mean: 72.9337 +2025-05-25 01:35:04,349 - INFO - Median: 5.0468 +2025-05-25 01:35:04,349 - INFO - Min: 0.6477 +2025-05-25 01:35:04,349 - INFO - Max: 716.7388 +2025-05-25 01:35:04,349 - INFO - Std: 124.7778 +2025-05-25 01:35:04,349 - INFO - OC Distribution Statistics: +2025-05-25 01:35:04,349 - INFO - Validation Mean: 16.4186 +2025-05-25 01:35:04,349 - INFO - Validation Std: 7.7994 +2025-05-25 01:35:04,349 - INFO - Training Mean: 23.2121 +2025-05-25 01:35:04,349 - INFO - Training Std: 20.4630 +2025-05-25 01:35:04,350 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:35:04,350 - INFO - KS Statistic: 0.2148 +2025-05-25 01:35:04,350 - INFO - P-value: 0.0000 +2025-05-25 01:35:04,350 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:35:04,350 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:35:04,554 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:35:04,554 - INFO - Timestamp: 2025-05-25 01:35:04 +2025-05-25 01:35:04,554 - INFO - Validation set size: 1346 (8.15%) +2025-05-25 01:35:04,554 - INFO - Training set size: 15168 (91.85%) +2025-05-25 01:35:04,554 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:35:04,554 - INFO - Mean: 69.7066 +2025-05-25 01:35:04,554 - INFO - Median: 4.0472 +2025-05-25 01:35:04,554 - INFO - Min: 1.2065 +2025-05-25 01:35:04,554 - INFO - Max: 706.9584 +2025-05-25 01:35:04,554 - INFO - Std: 114.2846 +2025-05-25 01:35:04,554 - INFO - OC Distribution Statistics: +2025-05-25 01:35:04,554 - INFO - Validation Mean: 16.7346 +2025-05-25 01:35:04,554 - INFO - Validation Std: 8.0661 +2025-05-25 01:35:04,554 - INFO - Training Mean: 23.0408 +2025-05-25 01:35:04,554 - INFO - Training Std: 20.3009 +2025-05-25 01:35:04,554 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:35:04,554 - INFO - KS Statistic: 0.2024 +2025-05-25 01:35:04,554 - INFO - P-value: 0.0000 +2025-05-25 01:35:04,554 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:35:04,554 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:35:05,058 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:35:05,058 - INFO - Timestamp: 2025-05-25 01:35:05 +2025-05-25 01:35:05,058 - INFO - Validation set size: 1393 (8.44%) +2025-05-25 01:35:05,058 - INFO - Training set size: 15121 (91.56%) +2025-05-25 01:35:05,058 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:35:05,058 - INFO - Mean: 77.0572 +2025-05-25 01:35:05,058 - INFO - Median: 5.8113 +2025-05-25 01:35:05,058 - INFO - Min: 1.2301 +2025-05-25 01:35:05,058 - INFO - Max: 1106.1626 +2025-05-25 01:35:05,058 - INFO - Std: 129.1392 +2025-05-25 01:35:05,058 - INFO - OC Distribution Statistics: +2025-05-25 01:35:05,059 - INFO - Validation Mean: 16.6423 +2025-05-25 01:35:05,059 - INFO - Validation Std: 7.8556 +2025-05-25 01:35:05,059 - INFO - Training Mean: 23.0689 +2025-05-25 01:35:05,059 - INFO - Training Std: 20.3293 +2025-05-25 01:35:05,059 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:35:05,059 - INFO - KS Statistic: 0.1953 +2025-05-25 01:35:05,059 - INFO - P-value: 0.0000 +2025-05-25 01:35:05,059 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:35:05,059 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:35:06,809 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:35:06,809 - INFO - Timestamp: 2025-05-25 01:35:06 +2025-05-25 01:35:06,809 - INFO - Validation set size: 1366 (8.27%) +2025-05-25 01:35:06,809 - INFO - Training set size: 15148 (91.73%) +2025-05-25 01:35:06,810 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:35:06,810 - INFO - Mean: 71.5834 +2025-05-25 01:35:06,810 - INFO - Median: 6.4434 +2025-05-25 01:35:06,810 - INFO - Min: 0.8394 +2025-05-25 01:35:06,810 - INFO - Max: 706.9584 +2025-05-25 01:35:06,810 - INFO - Std: 110.9422 +2025-05-25 01:35:06,810 - INFO - OC Distribution Statistics: +2025-05-25 01:35:06,810 - INFO - Validation Mean: 16.7429 +2025-05-25 01:35:06,810 - INFO - Validation Std: 8.0973 +2025-05-25 01:35:06,810 - INFO - Training Mean: 23.0484 +2025-05-25 01:35:06,810 - INFO - Training Std: 20.3100 +2025-05-25 01:35:06,810 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:35:06,810 - INFO - KS Statistic: 0.1888 +2025-05-25 01:35:06,810 - INFO - P-value: 0.0000 +2025-05-25 01:35:06,810 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:35:06,810 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:35:11,396 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:35:11,396 - INFO - Timestamp: 2025-05-25 01:35:11 +2025-05-25 01:35:11,396 - INFO - Validation set size: 1341 (8.12%) +2025-05-25 01:35:11,396 - INFO - Training set size: 15173 (91.88%) +2025-05-25 01:35:11,396 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:35:11,396 - INFO - Mean: 71.8402 +2025-05-25 01:35:11,396 - INFO - Median: 5.7863 +2025-05-25 01:35:11,396 - INFO - Min: 0.6724 +2025-05-25 01:35:11,396 - INFO - Max: 702.5656 +2025-05-25 01:35:11,396 - INFO - Std: 114.6376 +2025-05-25 01:35:11,396 - INFO - OC Distribution Statistics: +2025-05-25 01:35:11,396 - INFO - Validation Mean: 16.6405 +2025-05-25 01:35:11,396 - INFO - Validation Std: 7.6632 +2025-05-25 01:35:11,396 - INFO - Training Mean: 23.0470 +2025-05-25 01:35:11,396 - INFO - Training Std: 20.3096 +2025-05-25 01:35:11,396 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:35:11,396 - INFO - KS Statistic: 0.1885 +2025-05-25 01:35:11,396 - INFO - P-value: 0.0000 +2025-05-25 01:35:11,396 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:35:11,396 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:46:17,812 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:46:17,812 - INFO - Timestamp: 2025-05-25 01:46:17 +2025-05-25 01:46:17,812 - INFO - Validation set size: 1393 (8.44%) +2025-05-25 01:46:17,812 - INFO - Training set size: 15121 (91.56%) +2025-05-25 01:46:17,812 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:46:17,812 - INFO - Mean: 74.0862 +2025-05-25 01:46:17,813 - INFO - Median: 6.1488 +2025-05-25 01:46:17,813 - INFO - Min: 0.5501 +2025-05-25 01:46:17,813 - INFO - Max: 866.4875 +2025-05-25 01:46:17,813 - INFO - Std: 120.9581 +2025-05-25 01:46:17,813 - INFO - OC Distribution Statistics: +2025-05-25 01:46:17,813 - INFO - Validation Mean: 16.8364 +2025-05-25 01:46:17,813 - INFO - Validation Std: 8.0795 +2025-05-25 01:46:17,813 - INFO - Training Mean: 23.0510 +2025-05-25 01:46:17,813 - INFO - Training Std: 20.3268 +2025-05-25 01:46:17,813 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:46:17,813 - INFO - KS Statistic: 0.2002 +2025-05-25 01:46:17,813 - INFO - P-value: 0.0000 +2025-05-25 01:46:17,813 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:46:17,813 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:46:18,086 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:46:18,086 - INFO - Timestamp: 2025-05-25 01:46:18 +2025-05-25 01:46:18,086 - INFO - Validation set size: 1405 (8.51%) +2025-05-25 01:46:18,086 - INFO - Training set size: 15109 (91.49%) +2025-05-25 01:46:18,086 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:46:18,086 - INFO - Mean: 61.9534 +2025-05-25 01:46:18,086 - INFO - Median: 4.2711 +2025-05-25 01:46:18,086 - INFO - Min: 1.2156 +2025-05-25 01:46:18,086 - INFO - Max: 553.9996 +2025-05-25 01:46:18,086 - INFO - Std: 99.2581 +2025-05-25 01:46:18,086 - INFO - OC Distribution Statistics: +2025-05-25 01:46:18,086 - INFO - Validation Mean: 16.4959 +2025-05-25 01:46:18,086 - INFO - Validation Std: 8.1053 +2025-05-25 01:46:18,086 - INFO - Training Mean: 23.0876 +2025-05-25 01:46:18,086 - INFO - Training Std: 20.3219 +2025-05-25 01:46:18,086 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:46:18,086 - INFO - KS Statistic: 0.2166 +2025-05-25 01:46:18,086 - INFO - P-value: 0.0000 +2025-05-25 01:46:18,086 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:46:18,086 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:46:18,841 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:46:18,841 - INFO - Timestamp: 2025-05-25 01:46:18 +2025-05-25 01:46:18,841 - INFO - Validation set size: 1467 (8.88%) +2025-05-25 01:46:18,841 - INFO - Training set size: 15047 (91.12%) +2025-05-25 01:46:18,841 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:46:18,841 - INFO - Mean: 62.1475 +2025-05-25 01:46:18,841 - INFO - Median: 4.2962 +2025-05-25 01:46:18,841 - INFO - Min: 1.0596 +2025-05-25 01:46:18,841 - INFO - Max: 680.6553 +2025-05-25 01:46:18,841 - INFO - Std: 107.1166 +2025-05-25 01:46:18,841 - INFO - OC Distribution Statistics: +2025-05-25 01:46:18,841 - INFO - Validation Mean: 16.2349 +2025-05-25 01:46:18,841 - INFO - Validation Std: 7.2935 +2025-05-25 01:46:18,841 - INFO - Training Mean: 23.1402 +2025-05-25 01:46:18,841 - INFO - Training Std: 20.3741 +2025-05-25 01:46:18,841 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:46:18,841 - INFO - KS Statistic: 0.2212 +2025-05-25 01:46:18,841 - INFO - P-value: 0.0000 +2025-05-25 01:46:18,841 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:46:18,841 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:46:19,185 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:46:19,185 - INFO - Timestamp: 2025-05-25 01:46:19 +2025-05-25 01:46:19,185 - INFO - Validation set size: 1394 (8.44%) +2025-05-25 01:46:19,185 - INFO - Training set size: 15120 (91.56%) +2025-05-25 01:46:19,185 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:46:19,185 - INFO - Mean: 59.0905 +2025-05-25 01:46:19,185 - INFO - Median: 4.4085 +2025-05-25 01:46:19,185 - INFO - Min: 0.5501 +2025-05-25 01:46:19,185 - INFO - Max: 702.5656 +2025-05-25 01:46:19,185 - INFO - Std: 100.4181 +2025-05-25 01:46:19,185 - INFO - OC Distribution Statistics: +2025-05-25 01:46:19,185 - INFO - Validation Mean: 16.6539 +2025-05-25 01:46:19,185 - INFO - Validation Std: 7.9555 +2025-05-25 01:46:19,185 - INFO - Training Mean: 23.0682 +2025-05-25 01:46:19,185 - INFO - Training Std: 20.3266 +2025-05-25 01:46:19,185 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:46:19,185 - INFO - KS Statistic: 0.2028 +2025-05-25 01:46:19,185 - INFO - P-value: 0.0000 +2025-05-25 01:46:19,185 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:46:19,185 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:46:21,425 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:46:21,425 - INFO - Timestamp: 2025-05-25 01:46:21 +2025-05-25 01:46:21,425 - INFO - Validation set size: 1345 (8.14%) +2025-05-25 01:46:21,425 - INFO - Training set size: 15169 (91.86%) +2025-05-25 01:46:21,425 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:46:21,425 - INFO - Mean: 68.9988 +2025-05-25 01:46:21,425 - INFO - Median: 5.8113 +2025-05-25 01:46:21,425 - INFO - Min: 1.2497 +2025-05-25 01:46:21,425 - INFO - Max: 755.0757 +2025-05-25 01:46:21,425 - INFO - Std: 115.4127 +2025-05-25 01:46:21,425 - INFO - OC Distribution Statistics: +2025-05-25 01:46:21,425 - INFO - Validation Mean: 16.8991 +2025-05-25 01:46:21,425 - INFO - Validation Std: 8.3833 +2025-05-25 01:46:21,425 - INFO - Training Mean: 23.0258 +2025-05-25 01:46:21,425 - INFO - Training Std: 20.2934 +2025-05-25 01:46:21,425 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:46:21,425 - INFO - KS Statistic: 0.1924 +2025-05-25 01:46:21,425 - INFO - P-value: 0.0000 +2025-05-25 01:46:21,425 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:46:21,425 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:46:21,913 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:46:21,913 - INFO - Timestamp: 2025-05-25 01:46:21 +2025-05-25 01:46:21,913 - INFO - Validation set size: 1324 (8.02%) +2025-05-25 01:46:21,913 - INFO - Training set size: 15190 (91.98%) +2025-05-25 01:46:21,913 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:46:21,913 - INFO - Mean: 64.9152 +2025-05-25 01:46:21,913 - INFO - Median: 4.0175 +2025-05-25 01:46:21,913 - INFO - Min: 1.2065 +2025-05-25 01:46:21,913 - INFO - Max: 743.7101 +2025-05-25 01:46:21,913 - INFO - Std: 111.4759 +2025-05-25 01:46:21,913 - INFO - OC Distribution Statistics: +2025-05-25 01:46:21,913 - INFO - Validation Mean: 16.5713 +2025-05-25 01:46:21,913 - INFO - Validation Std: 7.7253 +2025-05-25 01:46:21,913 - INFO - Training Mean: 23.0459 +2025-05-25 01:46:21,914 - INFO - Training Std: 20.2970 +2025-05-25 01:46:21,914 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:46:21,914 - INFO - KS Statistic: 0.2189 +2025-05-25 01:46:21,914 - INFO - P-value: 0.0000 +2025-05-25 01:46:21,914 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:46:21,914 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:46:22,669 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:46:22,669 - INFO - Timestamp: 2025-05-25 01:46:22 +2025-05-25 01:46:22,669 - INFO - Validation set size: 1463 (8.86%) +2025-05-25 01:46:22,669 - INFO - Training set size: 15051 (91.14%) +2025-05-25 01:46:22,669 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:46:22,669 - INFO - Mean: 60.9949 +2025-05-25 01:46:22,669 - INFO - Median: 3.7987 +2025-05-25 01:46:22,669 - INFO - Min: 0.6724 +2025-05-25 01:46:22,669 - INFO - Max: 706.9584 +2025-05-25 01:46:22,669 - INFO - Std: 101.4382 +2025-05-25 01:46:22,669 - INFO - OC Distribution Statistics: +2025-05-25 01:46:22,669 - INFO - Validation Mean: 16.6685 +2025-05-25 01:46:22,669 - INFO - Validation Std: 7.8956 +2025-05-25 01:46:22,669 - INFO - Training Mean: 23.0962 +2025-05-25 01:46:22,669 - INFO - Training Std: 20.3640 +2025-05-25 01:46:22,669 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:46:22,669 - INFO - KS Statistic: 0.2077 +2025-05-25 01:46:22,669 - INFO - P-value: 0.0000 +2025-05-25 01:46:22,669 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:46:22,669 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 01:46:25,456 - INFO - Validation and Training Set Split Statistics +2025-05-25 01:46:25,456 - INFO - Timestamp: 2025-05-25 01:46:25 +2025-05-25 01:46:25,456 - INFO - Validation set size: 1487 (9.00%) +2025-05-25 01:46:25,456 - INFO - Training set size: 15027 (91.00%) +2025-05-25 01:46:25,456 - INFO - Minimum Distance Statistics (km): +2025-05-25 01:46:25,456 - INFO - Mean: 67.1368 +2025-05-25 01:46:25,456 - INFO - Median: 4.2460 +2025-05-25 01:46:25,456 - INFO - Min: 1.2156 +2025-05-25 01:46:25,456 - INFO - Max: 706.9584 +2025-05-25 01:46:25,456 - INFO - Std: 116.2752 +2025-05-25 01:46:25,456 - INFO - OC Distribution Statistics: +2025-05-25 01:46:25,456 - INFO - Validation Mean: 16.4350 +2025-05-25 01:46:25,457 - INFO - Validation Std: 7.2470 +2025-05-25 01:46:25,457 - INFO - Training Mean: 23.1296 +2025-05-25 01:46:25,457 - INFO - Training Std: 20.3926 +2025-05-25 01:46:25,457 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 01:46:25,457 - INFO - KS Statistic: 0.2203 +2025-05-25 01:46:25,457 - INFO - P-value: 0.0000 +2025-05-25 01:46:25,457 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 01:46:25,457 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:26:39,118 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:26:39,118 - INFO - Timestamp: 2025-05-25 02:26:39 +2025-05-25 02:26:39,118 - INFO - Validation set size: 1413 (8.56%) +2025-05-25 02:26:39,118 - INFO - Training set size: 15101 (91.44%) +2025-05-25 02:26:39,118 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:26:39,118 - INFO - Mean: 65.7419 +2025-05-25 02:26:39,118 - INFO - Median: 4.4155 +2025-05-25 02:26:39,118 - INFO - Min: 1.2156 +2025-05-25 02:26:39,118 - INFO - Max: 706.9584 +2025-05-25 02:26:39,118 - INFO - Std: 112.3661 +2025-05-25 02:26:39,118 - INFO - OC Distribution Statistics: +2025-05-25 02:26:39,118 - INFO - Validation Mean: 16.7420 +2025-05-25 02:26:39,118 - INFO - Validation Std: 8.0631 +2025-05-25 02:26:39,118 - INFO - Training Mean: 23.0681 +2025-05-25 02:26:39,118 - INFO - Training Std: 20.3347 +2025-05-25 02:26:39,118 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:26:39,119 - INFO - KS Statistic: 0.1832 +2025-05-25 02:26:39,119 - INFO - P-value: 0.0000 +2025-05-25 02:26:39,119 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:26:39,119 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:26:40,398 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:26:40,398 - INFO - Timestamp: 2025-05-25 02:26:40 +2025-05-25 02:26:40,398 - INFO - Validation set size: 1394 (8.44%) +2025-05-25 02:26:40,398 - INFO - Training set size: 15120 (91.56%) +2025-05-25 02:26:40,398 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:26:40,398 - INFO - Mean: 73.1231 +2025-05-25 02:26:40,398 - INFO - Median: 4.9212 +2025-05-25 02:26:40,398 - INFO - Min: 1.2065 +2025-05-25 02:26:40,398 - INFO - Max: 888.3558 +2025-05-25 02:26:40,399 - INFO - Std: 120.7323 +2025-05-25 02:26:40,399 - INFO - OC Distribution Statistics: +2025-05-25 02:26:40,399 - INFO - Validation Mean: 16.8592 +2025-05-25 02:26:40,399 - INFO - Validation Std: 7.6635 +2025-05-25 02:26:40,399 - INFO - Training Mean: 23.0493 +2025-05-25 02:26:40,399 - INFO - Training Std: 20.3428 +2025-05-25 02:26:40,399 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:26:40,399 - INFO - KS Statistic: 0.1963 +2025-05-25 02:26:40,399 - INFO - P-value: 0.0000 +2025-05-25 02:26:40,399 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:26:40,399 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:26:41,712 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:26:41,712 - INFO - Timestamp: 2025-05-25 02:26:41 +2025-05-25 02:26:41,712 - INFO - Validation set size: 1350 (8.17%) +2025-05-25 02:26:41,712 - INFO - Training set size: 15164 (91.83%) +2025-05-25 02:26:41,712 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:26:41,712 - INFO - Mean: 66.8048 +2025-05-25 02:26:41,712 - INFO - Median: 4.3277 +2025-05-25 02:26:41,712 - INFO - Min: 0.7371 +2025-05-25 02:26:41,712 - INFO - Max: 969.1721 +2025-05-25 02:26:41,712 - INFO - Std: 113.8224 +2025-05-25 02:26:41,712 - INFO - OC Distribution Statistics: +2025-05-25 02:26:41,712 - INFO - Validation Mean: 16.8133 +2025-05-25 02:26:41,712 - INFO - Validation Std: 8.4919 +2025-05-25 02:26:41,712 - INFO - Training Mean: 23.0354 +2025-05-25 02:26:41,712 - INFO - Training Std: 20.2896 +2025-05-25 02:26:41,712 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:26:41,712 - INFO - KS Statistic: 0.2011 +2025-05-25 02:26:41,712 - INFO - P-value: 0.0000 +2025-05-25 02:26:41,712 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:26:41,712 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:26:43,320 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:26:43,320 - INFO - Timestamp: 2025-05-25 02:26:43 +2025-05-25 02:26:43,320 - INFO - Validation set size: 1438 (8.71%) +2025-05-25 02:26:43,320 - INFO - Training set size: 15076 (91.29%) +2025-05-25 02:26:43,320 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:26:43,320 - INFO - Mean: 69.6045 +2025-05-25 02:26:43,320 - INFO - Median: 4.7210 +2025-05-25 02:26:43,320 - INFO - Min: 1.0596 +2025-05-25 02:26:43,320 - INFO - Max: 706.9584 +2025-05-25 02:26:43,320 - INFO - Std: 107.4988 +2025-05-25 02:26:43,320 - INFO - OC Distribution Statistics: +2025-05-25 02:26:43,320 - INFO - Validation Mean: 16.7976 +2025-05-25 02:26:43,320 - INFO - Validation Std: 7.7222 +2025-05-25 02:26:43,320 - INFO - Training Mean: 23.0732 +2025-05-25 02:26:43,320 - INFO - Training Std: 20.3616 +2025-05-25 02:26:43,320 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:26:43,320 - INFO - KS Statistic: 0.1862 +2025-05-25 02:26:43,320 - INFO - P-value: 0.0000 +2025-05-25 02:26:43,320 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:26:43,320 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:26:43,763 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:26:43,763 - INFO - Timestamp: 2025-05-25 02:26:43 +2025-05-25 02:26:43,763 - INFO - Validation set size: 1466 (8.88%) +2025-05-25 02:26:43,763 - INFO - Training set size: 15048 (91.12%) +2025-05-25 02:26:43,763 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:26:43,763 - INFO - Mean: 65.1988 +2025-05-25 02:26:43,763 - INFO - Median: 4.5675 +2025-05-25 02:26:43,763 - INFO - Min: 1.2065 +2025-05-25 02:26:43,763 - INFO - Max: 655.9249 +2025-05-25 02:26:43,763 - INFO - Std: 108.1585 +2025-05-25 02:26:43,763 - INFO - OC Distribution Statistics: +2025-05-25 02:26:43,763 - INFO - Validation Mean: 15.8437 +2025-05-25 02:26:43,763 - INFO - Validation Std: 6.6714 +2025-05-25 02:26:43,763 - INFO - Training Mean: 23.1779 +2025-05-25 02:26:43,763 - INFO - Training Std: 20.3811 +2025-05-25 02:26:43,764 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:26:43,764 - INFO - KS Statistic: 0.2559 +2025-05-25 02:26:43,764 - INFO - P-value: 0.0000 +2025-05-25 02:26:43,764 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:26:43,764 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:26:43,765 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:26:43,765 - INFO - Timestamp: 2025-05-25 02:26:43 +2025-05-25 02:26:43,765 - INFO - Validation set size: 1366 (8.27%) +2025-05-25 02:26:43,765 - INFO - Training set size: 15148 (91.73%) +2025-05-25 02:26:43,765 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:26:43,765 - INFO - Mean: 76.8924 +2025-05-25 02:26:43,765 - INFO - Median: 5.9124 +2025-05-25 02:26:43,765 - INFO - Min: 1.1342 +2025-05-25 02:26:43,765 - INFO - Max: 743.7101 +2025-05-25 02:26:43,765 - INFO - Std: 125.3318 +2025-05-25 02:26:43,765 - INFO - OC Distribution Statistics: +2025-05-25 02:26:43,765 - INFO - Validation Mean: 16.8225 +2025-05-25 02:26:43,765 - INFO - Validation Std: 8.0350 +2025-05-25 02:26:43,765 - INFO - Training Mean: 23.0412 +2025-05-25 02:26:43,765 - INFO - Training Std: 20.3144 +2025-05-25 02:26:43,765 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:26:43,765 - INFO - KS Statistic: 0.2035 +2025-05-25 02:26:43,765 - INFO - P-value: 0.0000 +2025-05-25 02:26:43,765 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:26:43,765 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:26:44,235 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:26:44,235 - INFO - Timestamp: 2025-05-25 02:26:44 +2025-05-25 02:26:44,235 - INFO - Validation set size: 1649 (9.99%) +2025-05-25 02:26:44,235 - INFO - Training set size: 14865 (90.01%) +2025-05-25 02:26:44,235 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:26:44,235 - INFO - Mean: 68.5074 +2025-05-25 02:26:44,235 - INFO - Median: 4.3689 +2025-05-25 02:26:44,235 - INFO - Min: 1.2156 +2025-05-25 02:26:44,235 - INFO - Max: 1106.1626 +2025-05-25 02:26:44,235 - INFO - Std: 130.8488 +2025-05-25 02:26:44,235 - INFO - OC Distribution Statistics: +2025-05-25 02:26:44,235 - INFO - Validation Mean: 16.5769 +2025-05-25 02:26:44,235 - INFO - Validation Std: 7.5038 +2025-05-25 02:26:44,235 - INFO - Training Mean: 23.1868 +2025-05-25 02:26:44,235 - INFO - Training Std: 20.4723 +2025-05-25 02:26:44,235 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:26:44,235 - INFO - KS Statistic: 0.2191 +2025-05-25 02:26:44,235 - INFO - P-value: 0.0000 +2025-05-25 02:26:44,235 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:26:44,235 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:26:45,195 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:26:45,195 - INFO - Timestamp: 2025-05-25 02:26:45 +2025-05-25 02:26:45,195 - INFO - Validation set size: 1337 (8.10%) +2025-05-25 02:26:45,195 - INFO - Training set size: 15177 (91.90%) +2025-05-25 02:26:45,195 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:26:45,195 - INFO - Mean: 73.5787 +2025-05-25 02:26:45,195 - INFO - Median: 5.4228 +2025-05-25 02:26:45,195 - INFO - Min: 1.2757 +2025-05-25 02:26:45,195 - INFO - Max: 743.7101 +2025-05-25 02:26:45,195 - INFO - Std: 117.7713 +2025-05-25 02:26:45,195 - INFO - OC Distribution Statistics: +2025-05-25 02:26:45,195 - INFO - Validation Mean: 16.5181 +2025-05-25 02:26:45,195 - INFO - Validation Std: 8.1824 +2025-05-25 02:26:45,195 - INFO - Training Mean: 23.0561 +2025-05-25 02:26:45,195 - INFO - Training Std: 20.2862 +2025-05-25 02:26:45,195 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:26:45,195 - INFO - KS Statistic: 0.2273 +2025-05-25 02:26:45,195 - INFO - P-value: 0.0000 +2025-05-25 02:26:45,195 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:26:45,195 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:36:10,855 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:36:10,855 - INFO - Timestamp: 2025-05-25 02:36:10 +2025-05-25 02:36:10,855 - INFO - Validation set size: 1678 (10.16%) +2025-05-25 02:36:10,855 - INFO - Training set size: 14836 (89.84%) +2025-05-25 02:36:10,855 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:36:10,856 - INFO - Mean: 61.7478 +2025-05-25 02:36:10,856 - INFO - Median: 3.8844 +2025-05-25 02:36:10,856 - INFO - Min: 1.0321 +2025-05-25 02:36:10,856 - INFO - Max: 706.9584 +2025-05-25 02:36:10,856 - INFO - Std: 112.5653 +2025-05-25 02:36:10,856 - INFO - OC Distribution Statistics: +2025-05-25 02:36:10,856 - INFO - Validation Mean: 16.4206 +2025-05-25 02:36:10,856 - INFO - Validation Std: 7.5544 +2025-05-25 02:36:10,856 - INFO - Training Mean: 23.2174 +2025-05-25 02:36:10,856 - INFO - Training Std: 20.4796 +2025-05-25 02:36:10,856 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:36:10,856 - INFO - KS Statistic: 0.2240 +2025-05-25 02:36:10,856 - INFO - P-value: 0.0000 +2025-05-25 02:36:10,856 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:36:10,856 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:36:10,893 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:36:10,893 - INFO - Timestamp: 2025-05-25 02:36:10 +2025-05-25 02:36:10,893 - INFO - Validation set size: 1365 (8.27%) +2025-05-25 02:36:10,893 - INFO - Training set size: 15149 (91.73%) +2025-05-25 02:36:10,893 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:36:10,893 - INFO - Mean: 67.6959 +2025-05-25 02:36:10,893 - INFO - Median: 4.5931 +2025-05-25 02:36:10,893 - INFO - Min: 0.6067 +2025-05-25 02:36:10,893 - INFO - Max: 917.2720 +2025-05-25 02:36:10,893 - INFO - Std: 114.3065 +2025-05-25 02:36:10,893 - INFO - OC Distribution Statistics: +2025-05-25 02:36:10,893 - INFO - Validation Mean: 16.4839 +2025-05-25 02:36:10,893 - INFO - Validation Std: 7.7923 +2025-05-25 02:36:10,893 - INFO - Training Mean: 23.0713 +2025-05-25 02:36:10,893 - INFO - Training Std: 20.3128 +2025-05-25 02:36:10,893 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:36:10,893 - INFO - KS Statistic: 0.2110 +2025-05-25 02:36:10,893 - INFO - P-value: 0.0000 +2025-05-25 02:36:10,893 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:36:10,893 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:36:11,921 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:36:11,921 - INFO - Timestamp: 2025-05-25 02:36:11 +2025-05-25 02:36:11,922 - INFO - Validation set size: 1629 (9.86%) +2025-05-25 02:36:11,922 - INFO - Training set size: 14885 (90.14%) +2025-05-25 02:36:11,922 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:36:11,922 - INFO - Mean: 61.8828 +2025-05-25 02:36:11,922 - INFO - Median: 4.0771 +2025-05-25 02:36:11,922 - INFO - Min: 0.5501 +2025-05-25 02:36:11,922 - INFO - Max: 655.9249 +2025-05-25 02:36:11,922 - INFO - Std: 105.3014 +2025-05-25 02:36:11,922 - INFO - OC Distribution Statistics: +2025-05-25 02:36:11,922 - INFO - Validation Mean: 15.7588 +2025-05-25 02:36:11,922 - INFO - Validation Std: 7.0595 +2025-05-25 02:36:11,922 - INFO - Training Mean: 23.2675 +2025-05-25 02:36:11,922 - INFO - Training Std: 20.4483 +2025-05-25 02:36:11,922 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:36:11,922 - INFO - KS Statistic: 0.2451 +2025-05-25 02:36:11,922 - INFO - P-value: 0.0000 +2025-05-25 02:36:11,922 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:36:11,922 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:36:13,386 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:36:13,386 - INFO - Timestamp: 2025-05-25 02:36:13 +2025-05-25 02:36:13,386 - INFO - Validation set size: 1365 (8.27%) +2025-05-25 02:36:13,386 - INFO - Training set size: 15149 (91.73%) +2025-05-25 02:36:13,386 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:36:13,386 - INFO - Mean: 75.7999 +2025-05-25 02:36:13,386 - INFO - Median: 4.4155 +2025-05-25 02:36:13,386 - INFO - Min: 0.9397 +2025-05-25 02:36:13,386 - INFO - Max: 716.7388 +2025-05-25 02:36:13,386 - INFO - Std: 128.0781 +2025-05-25 02:36:13,386 - INFO - OC Distribution Statistics: +2025-05-25 02:36:13,386 - INFO - Validation Mean: 16.5834 +2025-05-25 02:36:13,386 - INFO - Validation Std: 7.8672 +2025-05-25 02:36:13,386 - INFO - Training Mean: 23.0623 +2025-05-25 02:36:13,386 - INFO - Training Std: 20.3131 +2025-05-25 02:36:13,386 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:36:13,386 - INFO - KS Statistic: 0.1984 +2025-05-25 02:36:13,386 - INFO - P-value: 0.0000 +2025-05-25 02:36:13,386 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:36:13,386 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:36:14,017 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:36:14,017 - INFO - Timestamp: 2025-05-25 02:36:14 +2025-05-25 02:36:14,017 - INFO - Validation set size: 1410 (8.54%) +2025-05-25 02:36:14,017 - INFO - Training set size: 15104 (91.46%) +2025-05-25 02:36:14,017 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:36:14,017 - INFO - Mean: 71.0636 +2025-05-25 02:36:14,017 - INFO - Median: 5.3462 +2025-05-25 02:36:14,017 - INFO - Min: 1.0475 +2025-05-25 02:36:14,017 - INFO - Max: 655.9249 +2025-05-25 02:36:14,017 - INFO - Std: 107.1502 +2025-05-25 02:36:14,017 - INFO - OC Distribution Statistics: +2025-05-25 02:36:14,017 - INFO - Validation Mean: 16.8059 +2025-05-25 02:36:14,017 - INFO - Validation Std: 8.5803 +2025-05-25 02:36:14,017 - INFO - Training Mean: 23.0608 +2025-05-25 02:36:14,017 - INFO - Training Std: 20.3153 +2025-05-25 02:36:14,017 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:36:14,017 - INFO - KS Statistic: 0.1821 +2025-05-25 02:36:14,018 - INFO - P-value: 0.0000 +2025-05-25 02:36:14,018 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:36:14,018 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:36:14,049 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:36:14,049 - INFO - Timestamp: 2025-05-25 02:36:14 +2025-05-25 02:36:14,049 - INFO - Validation set size: 1432 (8.67%) +2025-05-25 02:36:14,049 - INFO - Training set size: 15082 (91.33%) +2025-05-25 02:36:14,049 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:36:14,049 - INFO - Mean: 74.1662 +2025-05-25 02:36:14,049 - INFO - Median: 4.9225 +2025-05-25 02:36:14,049 - INFO - Min: 1.2301 +2025-05-25 02:36:14,049 - INFO - Max: 875.2369 +2025-05-25 02:36:14,049 - INFO - Std: 126.6613 +2025-05-25 02:36:14,049 - INFO - OC Distribution Statistics: +2025-05-25 02:36:14,049 - INFO - Validation Mean: 16.6741 +2025-05-25 02:36:14,049 - INFO - Validation Std: 7.3312 +2025-05-25 02:36:14,049 - INFO - Training Mean: 23.0825 +2025-05-25 02:36:14,049 - INFO - Training Std: 20.3685 +2025-05-25 02:36:14,049 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:36:14,049 - INFO - KS Statistic: 0.2191 +2025-05-25 02:36:14,049 - INFO - P-value: 0.0000 +2025-05-25 02:36:14,049 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:36:14,049 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:36:14,586 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:36:14,586 - INFO - Timestamp: 2025-05-25 02:36:14 +2025-05-25 02:36:14,586 - INFO - Validation set size: 1678 (10.16%) +2025-05-25 02:36:14,586 - INFO - Training set size: 14836 (89.84%) +2025-05-25 02:36:14,586 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:36:14,586 - INFO - Mean: 56.6018 +2025-05-25 02:36:14,586 - INFO - Median: 4.2015 +2025-05-25 02:36:14,586 - INFO - Min: 1.1523 +2025-05-25 02:36:14,586 - INFO - Max: 917.1429 +2025-05-25 02:36:14,586 - INFO - Std: 108.6507 +2025-05-25 02:36:14,586 - INFO - OC Distribution Statistics: +2025-05-25 02:36:14,586 - INFO - Validation Mean: 15.8844 +2025-05-25 02:36:14,586 - INFO - Validation Std: 7.4468 +2025-05-25 02:36:14,586 - INFO - Training Mean: 23.2781 +2025-05-25 02:36:14,586 - INFO - Training Std: 20.4631 +2025-05-25 02:36:14,586 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:36:14,586 - INFO - KS Statistic: 0.2519 +2025-05-25 02:36:14,586 - INFO - P-value: 0.0000 +2025-05-25 02:36:14,586 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:36:14,586 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 02:36:15,658 - INFO - Validation and Training Set Split Statistics +2025-05-25 02:36:15,658 - INFO - Timestamp: 2025-05-25 02:36:15 +2025-05-25 02:36:15,658 - INFO - Validation set size: 1558 (9.43%) +2025-05-25 02:36:15,658 - INFO - Training set size: 14956 (90.57%) +2025-05-25 02:36:15,658 - INFO - Minimum Distance Statistics (km): +2025-05-25 02:36:15,658 - INFO - Mean: 66.2623 +2025-05-25 02:36:15,658 - INFO - Median: 4.4045 +2025-05-25 02:36:15,658 - INFO - Min: 1.2065 +2025-05-25 02:36:15,658 - INFO - Max: 917.1429 +2025-05-25 02:36:15,658 - INFO - Std: 113.4466 +2025-05-25 02:36:15,658 - INFO - OC Distribution Statistics: +2025-05-25 02:36:15,658 - INFO - Validation Mean: 16.4860 +2025-05-25 02:36:15,658 - INFO - Validation Std: 7.2141 +2025-05-25 02:36:15,658 - INFO - Training Mean: 23.1561 +2025-05-25 02:36:15,658 - INFO - Training Std: 20.4326 +2025-05-25 02:36:15,658 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 02:36:15,658 - INFO - KS Statistic: 0.2189 +2025-05-25 02:36:15,658 - INFO - P-value: 0.0000 +2025-05-25 02:36:15,658 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 02:36:15,658 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 03:10:17,381 - INFO - Validation and Training Set Split Statistics +2025-05-25 03:10:17,381 - INFO - Timestamp: 2025-05-25 03:10:17 +2025-05-25 03:10:17,381 - INFO - Validation set size: 1406 (8.51%) +2025-05-25 03:10:17,381 - INFO - Training set size: 15108 (91.49%) +2025-05-25 03:10:17,381 - INFO - Minimum Distance Statistics (km): +2025-05-25 03:10:17,381 - INFO - Mean: 61.6780 +2025-05-25 03:10:17,382 - INFO - Median: 4.3754 +2025-05-25 03:10:17,382 - INFO - Min: 1.2497 +2025-05-25 03:10:17,382 - INFO - Max: 622.3819 +2025-05-25 03:10:17,382 - INFO - Std: 101.0656 +2025-05-25 03:10:17,382 - INFO - OC Distribution Statistics: +2025-05-25 03:10:17,382 - INFO - Validation Mean: 16.7680 +2025-05-25 03:10:17,382 - INFO - Validation Std: 7.0904 +2025-05-25 03:10:17,382 - INFO - Training Mean: 23.0627 +2025-05-25 03:10:17,382 - INFO - Training Std: 20.3657 +2025-05-25 03:10:17,382 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 03:10:17,382 - INFO - KS Statistic: 0.1783 +2025-05-25 03:10:17,382 - INFO - P-value: 0.0000 +2025-05-25 03:10:17,382 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 03:10:17,382 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 03:10:21,870 - INFO - Validation and Training Set Split Statistics +2025-05-25 03:10:21,870 - INFO - Timestamp: 2025-05-25 03:10:21 +2025-05-25 03:10:21,870 - INFO - Validation set size: 1359 (8.23%) +2025-05-25 03:10:21,870 - INFO - Training set size: 15155 (91.77%) +2025-05-25 03:10:21,870 - INFO - Minimum Distance Statistics (km): +2025-05-25 03:10:21,870 - INFO - Mean: 73.3892 +2025-05-25 03:10:21,870 - INFO - Median: 5.1680 +2025-05-25 03:10:21,870 - INFO - Min: 1.2156 +2025-05-25 03:10:21,870 - INFO - Max: 601.2913 +2025-05-25 03:10:21,870 - INFO - Std: 110.0171 +2025-05-25 03:10:21,870 - INFO - OC Distribution Statistics: +2025-05-25 03:10:21,870 - INFO - Validation Mean: 16.9026 +2025-05-25 03:10:21,870 - INFO - Validation Std: 8.1686 +2025-05-25 03:10:21,870 - INFO - Training Mean: 23.0311 +2025-05-25 03:10:21,870 - INFO - Training Std: 20.3083 +2025-05-25 03:10:21,870 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 03:10:21,870 - INFO - KS Statistic: 0.1858 +2025-05-25 03:10:21,870 - INFO - P-value: 0.0000 +2025-05-25 03:10:21,870 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 03:10:21,870 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 11:40:59,824 - INFO - Validation and Training Set Split Statistics +2025-05-25 11:40:59,824 - INFO - Timestamp: 2025-05-25 11:40:59 +2025-05-25 11:40:59,824 - INFO - Validation set size: 1403 (8.50%) +2025-05-25 11:40:59,825 - INFO - Training set size: 15111 (91.50%) +2025-05-25 11:40:59,825 - INFO - Minimum Distance Statistics (km): +2025-05-25 11:40:59,825 - INFO - Mean: 67.2686 +2025-05-25 11:40:59,825 - INFO - Median: 4.4077 +2025-05-25 11:40:59,825 - INFO - Min: 0.5501 +2025-05-25 11:40:59,825 - INFO - Max: 835.9450 +2025-05-25 11:40:59,825 - INFO - Std: 112.6956 +2025-05-25 11:40:59,825 - INFO - OC Distribution Statistics: +2025-05-25 11:40:59,825 - INFO - Validation Mean: 16.4490 +2025-05-25 11:40:59,825 - INFO - Validation Std: 8.7262 +2025-05-25 11:40:59,825 - INFO - Training Mean: 23.0911 +2025-05-25 11:40:59,825 - INFO - Training Std: 20.2956 +2025-05-25 11:40:59,825 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 11:40:59,825 - INFO - KS Statistic: 0.2346 +2025-05-25 11:40:59,825 - INFO - P-value: 0.0000 +2025-05-25 11:40:59,825 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 11:40:59,825 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 11:41:01,982 - INFO - Validation and Training Set Split Statistics +2025-05-25 11:41:01,983 - INFO - Timestamp: 2025-05-25 11:41:01 +2025-05-25 11:41:01,983 - INFO - Validation set size: 1423 (8.62%) +2025-05-25 11:41:01,983 - INFO - Training set size: 15091 (91.38%) +2025-05-25 11:41:01,983 - INFO - Minimum Distance Statistics (km): +2025-05-25 11:41:01,983 - INFO - Mean: 70.9411 +2025-05-25 11:41:01,983 - INFO - Median: 4.8053 +2025-05-25 11:41:01,983 - INFO - Min: 1.2497 +2025-05-25 11:41:01,983 - INFO - Max: 609.1864 +2025-05-25 11:41:01,983 - INFO - Std: 114.3076 +2025-05-25 11:41:01,983 - INFO - OC Distribution Statistics: +2025-05-25 11:41:01,983 - INFO - Validation Mean: 16.9536 +2025-05-25 11:41:01,983 - INFO - Validation Std: 8.4763 +2025-05-25 11:41:01,983 - INFO - Training Mean: 23.0523 +2025-05-25 11:41:01,983 - INFO - Training Std: 20.3300 +2025-05-25 11:41:01,983 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 11:41:01,983 - INFO - KS Statistic: 0.1832 +2025-05-25 11:41:01,983 - INFO - P-value: 0.0000 +2025-05-25 11:41:01,983 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 11:41:01,983 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 11:41:02,927 - INFO - Validation and Training Set Split Statistics +2025-05-25 11:41:02,928 - INFO - Timestamp: 2025-05-25 11:41:02 +2025-05-25 11:41:02,928 - INFO - Validation set size: 1501 (9.09%) +2025-05-25 11:41:02,928 - INFO - Training set size: 15013 (90.91%) +2025-05-25 11:41:02,928 - INFO - Minimum Distance Statistics (km): +2025-05-25 11:41:02,928 - INFO - Mean: 68.6891 +2025-05-25 11:41:02,928 - INFO - Median: 3.9951 +2025-05-25 11:41:02,928 - INFO - Min: 0.9995 +2025-05-25 11:41:02,928 - INFO - Max: 1106.1626 +2025-05-25 11:41:02,928 - INFO - Std: 128.6595 +2025-05-25 11:41:02,928 - INFO - OC Distribution Statistics: +2025-05-25 11:41:02,928 - INFO - Validation Mean: 16.9026 +2025-05-25 11:41:02,928 - INFO - Validation Std: 9.1408 +2025-05-25 11:41:02,928 - INFO - Training Mean: 23.0891 +2025-05-25 11:41:02,928 - INFO - Training Std: 20.3385 +2025-05-25 11:41:02,928 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 11:41:02,928 - INFO - KS Statistic: 0.2089 +2025-05-25 11:41:02,928 - INFO - P-value: 0.0000 +2025-05-25 11:41:02,928 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 11:41:02,928 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 11:41:03,360 - INFO - Validation and Training Set Split Statistics +2025-05-25 11:41:03,360 - INFO - Timestamp: 2025-05-25 11:41:03 +2025-05-25 11:41:03,360 - INFO - Validation set size: 1358 (8.22%) +2025-05-25 11:41:03,360 - INFO - Training set size: 15156 (91.78%) +2025-05-25 11:41:03,360 - INFO - Minimum Distance Statistics (km): +2025-05-25 11:41:03,360 - INFO - Mean: 61.6679 +2025-05-25 11:41:03,360 - INFO - Median: 4.3036 +2025-05-25 11:41:03,360 - INFO - Min: 1.2757 +2025-05-25 11:41:03,360 - INFO - Max: 716.7388 +2025-05-25 11:41:03,360 - INFO - Std: 109.0348 +2025-05-25 11:41:03,360 - INFO - OC Distribution Statistics: +2025-05-25 11:41:03,360 - INFO - Validation Mean: 16.7238 +2025-05-25 11:41:03,360 - INFO - Validation Std: 8.8066 +2025-05-25 11:41:03,360 - INFO - Training Mean: 23.0467 +2025-05-25 11:41:03,360 - INFO - Training Std: 20.2790 +2025-05-25 11:41:03,360 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 11:41:03,360 - INFO - KS Statistic: 0.2116 +2025-05-25 11:41:03,360 - INFO - P-value: 0.0000 +2025-05-25 11:41:03,360 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 11:41:03,360 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 11:41:04,644 - INFO - Validation and Training Set Split Statistics +2025-05-25 11:41:04,644 - INFO - Timestamp: 2025-05-25 11:41:04 +2025-05-25 11:41:04,644 - INFO - Validation set size: 1594 (9.65%) +2025-05-25 11:41:04,644 - INFO - Training set size: 14920 (90.35%) +2025-05-25 11:41:04,645 - INFO - Minimum Distance Statistics (km): +2025-05-25 11:41:04,645 - INFO - Mean: 65.6956 +2025-05-25 11:41:04,645 - INFO - Median: 4.0770 +2025-05-25 11:41:04,645 - INFO - Min: 1.2067 +2025-05-25 11:41:04,645 - INFO - Max: 606.8447 +2025-05-25 11:41:04,645 - INFO - Std: 110.0127 +2025-05-25 11:41:04,645 - INFO - OC Distribution Statistics: +2025-05-25 11:41:04,645 - INFO - Validation Mean: 16.5068 +2025-05-25 11:41:04,645 - INFO - Validation Std: 7.9682 +2025-05-25 11:41:04,645 - INFO - Training Mean: 23.1699 +2025-05-25 11:41:04,645 - INFO - Training Std: 20.4223 +2025-05-25 11:41:04,645 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 11:41:04,645 - INFO - KS Statistic: 0.2167 +2025-05-25 11:41:04,645 - INFO - P-value: 0.0000 +2025-05-25 11:41:04,645 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 11:41:04,645 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 11:41:05,059 - INFO - Validation and Training Set Split Statistics +2025-05-25 11:41:05,059 - INFO - Timestamp: 2025-05-25 11:41:05 +2025-05-25 11:41:05,059 - INFO - Validation set size: 1332 (8.07%) +2025-05-25 11:41:05,059 - INFO - Training set size: 15182 (91.93%) +2025-05-25 11:41:05,059 - INFO - Minimum Distance Statistics (km): +2025-05-25 11:41:05,059 - INFO - Mean: 70.5757 +2025-05-25 11:41:05,059 - INFO - Median: 5.2755 +2025-05-25 11:41:05,059 - INFO - Min: 1.2329 +2025-05-25 11:41:05,059 - INFO - Max: 743.7101 +2025-05-25 11:41:05,059 - INFO - Std: 115.7099 +2025-05-25 11:41:05,059 - INFO - OC Distribution Statistics: +2025-05-25 11:41:05,059 - INFO - Validation Mean: 16.3050 +2025-05-25 11:41:05,059 - INFO - Validation Std: 7.6931 +2025-05-25 11:41:05,059 - INFO - Training Mean: 23.0727 +2025-05-25 11:41:05,059 - INFO - Training Std: 20.2945 +2025-05-25 11:41:05,059 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 11:41:05,059 - INFO - KS Statistic: 0.2197 +2025-05-25 11:41:05,059 - INFO - P-value: 0.0000 +2025-05-25 11:41:05,059 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 11:41:05,059 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 11:41:05,517 - INFO - Validation and Training Set Split Statistics +2025-05-25 11:41:05,517 - INFO - Timestamp: 2025-05-25 11:41:05 +2025-05-25 11:41:05,518 - INFO - Validation set size: 1486 (9.00%) +2025-05-25 11:41:05,518 - INFO - Training set size: 15028 (91.00%) +2025-05-25 11:41:05,518 - INFO - Minimum Distance Statistics (km): +2025-05-25 11:41:05,518 - INFO - Mean: 67.0596 +2025-05-25 11:41:05,518 - INFO - Median: 4.8053 +2025-05-25 11:41:05,518 - INFO - Min: 0.4760 +2025-05-25 11:41:05,518 - INFO - Max: 684.2079 +2025-05-25 11:41:05,518 - INFO - Std: 110.5969 +2025-05-25 11:41:05,518 - INFO - OC Distribution Statistics: +2025-05-25 11:41:05,518 - INFO - Validation Mean: 16.5719 +2025-05-25 11:41:05,518 - INFO - Validation Std: 8.4963 +2025-05-25 11:41:05,518 - INFO - Training Mean: 23.1156 +2025-05-25 11:41:05,518 - INFO - Training Std: 20.3488 +2025-05-25 11:41:05,518 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 11:41:05,518 - INFO - KS Statistic: 0.2196 +2025-05-25 11:41:05,518 - INFO - P-value: 0.0000 +2025-05-25 11:41:05,518 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 11:41:05,518 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 11:41:05,828 - INFO - Validation and Training Set Split Statistics +2025-05-25 11:41:05,828 - INFO - Timestamp: 2025-05-25 11:41:05 +2025-05-25 11:41:05,828 - INFO - Validation set size: 1608 (9.74%) +2025-05-25 11:41:05,828 - INFO - Training set size: 14906 (90.26%) +2025-05-25 11:41:05,828 - INFO - Minimum Distance Statistics (km): +2025-05-25 11:41:05,828 - INFO - Mean: 64.8504 +2025-05-25 11:41:05,828 - INFO - Median: 4.2800 +2025-05-25 11:41:05,828 - INFO - Min: 1.2247 +2025-05-25 11:41:05,828 - INFO - Max: 706.1646 +2025-05-25 11:41:05,828 - INFO - Std: 111.9304 +2025-05-25 11:41:05,828 - INFO - OC Distribution Statistics: +2025-05-25 11:41:05,828 - INFO - Validation Mean: 15.9855 +2025-05-25 11:41:05,828 - INFO - Validation Std: 7.3224 +2025-05-25 11:41:05,828 - INFO - Training Mean: 23.2324 +2025-05-25 11:41:05,828 - INFO - Training Std: 20.4364 +2025-05-25 11:41:05,828 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 11:41:05,828 - INFO - KS Statistic: 0.2458 +2025-05-25 11:41:05,828 - INFO - P-value: 0.0000 +2025-05-25 11:41:05,828 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 11:41:05,828 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 12:54:57,299 - INFO - Validation and Training Set Split Statistics +2025-05-25 12:54:57,300 - INFO - Timestamp: 2025-05-25 12:54:57 +2025-05-25 12:54:57,300 - INFO - Validation set size: 1474 (8.93%) +2025-05-25 12:54:57,300 - INFO - Training set size: 15040 (91.07%) +2025-05-25 12:54:57,300 - INFO - Minimum Distance Statistics (km): +2025-05-25 12:54:57,300 - INFO - Mean: 66.3601 +2025-05-25 12:54:57,300 - INFO - Median: 4.4619 +2025-05-25 12:54:57,300 - INFO - Min: 1.2156 +2025-05-25 12:54:57,300 - INFO - Max: 1106.1626 +2025-05-25 12:54:57,300 - INFO - Std: 119.5568 +2025-05-25 12:54:57,300 - INFO - OC Distribution Statistics: +2025-05-25 12:54:57,300 - INFO - Validation Mean: 16.1488 +2025-05-25 12:54:57,300 - INFO - Validation Std: 7.6727 +2025-05-25 12:54:57,300 - INFO - Training Mean: 23.1519 +2025-05-25 12:54:57,300 - INFO - Training Std: 20.3612 +2025-05-25 12:54:57,300 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 12:54:57,300 - INFO - KS Statistic: 0.2335 +2025-05-25 12:54:57,300 - INFO - P-value: 0.0000 +2025-05-25 12:54:57,300 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 12:54:57,300 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 12:54:57,518 - INFO - Validation and Training Set Split Statistics +2025-05-25 12:54:57,518 - INFO - Timestamp: 2025-05-25 12:54:57 +2025-05-25 12:54:57,518 - INFO - Validation set size: 1418 (8.59%) +2025-05-25 12:54:57,518 - INFO - Training set size: 15096 (91.41%) +2025-05-25 12:54:57,518 - INFO - Minimum Distance Statistics (km): +2025-05-25 12:54:57,518 - INFO - Mean: 65.9659 +2025-05-25 12:54:57,518 - INFO - Median: 4.6421 +2025-05-25 12:54:57,518 - INFO - Min: 0.5501 +2025-05-25 12:54:57,518 - INFO - Max: 706.9584 +2025-05-25 12:54:57,518 - INFO - Std: 107.6441 +2025-05-25 12:54:57,518 - INFO - OC Distribution Statistics: +2025-05-25 12:54:57,518 - INFO - Validation Mean: 16.1617 +2025-05-25 12:54:57,518 - INFO - Validation Std: 7.4703 +2025-05-25 12:54:57,518 - INFO - Training Mean: 23.1247 +2025-05-25 12:54:57,518 - INFO - Training Std: 20.3407 +2025-05-25 12:54:57,518 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 12:54:57,518 - INFO - KS Statistic: 0.2422 +2025-05-25 12:54:57,518 - INFO - P-value: 0.0000 +2025-05-25 12:54:57,518 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 12:54:57,518 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 12:54:57,881 - INFO - Validation and Training Set Split Statistics +2025-05-25 12:54:57,882 - INFO - Timestamp: 2025-05-25 12:54:57 +2025-05-25 12:54:57,882 - INFO - Validation set size: 1623 (9.83%) +2025-05-25 12:54:57,882 - INFO - Training set size: 14891 (90.17%) +2025-05-25 12:54:57,882 - INFO - Minimum Distance Statistics (km): +2025-05-25 12:54:57,882 - INFO - Mean: 64.7342 +2025-05-25 12:54:57,882 - INFO - Median: 4.1444 +2025-05-25 12:54:57,882 - INFO - Min: 1.2065 +2025-05-25 12:54:57,882 - INFO - Max: 1106.1626 +2025-05-25 12:54:57,882 - INFO - Std: 124.1370 +2025-05-25 12:54:57,882 - INFO - OC Distribution Statistics: +2025-05-25 12:54:57,882 - INFO - Validation Mean: 16.3455 +2025-05-25 12:54:57,882 - INFO - Validation Std: 7.4153 +2025-05-25 12:54:57,882 - INFO - Training Mean: 23.2005 +2025-05-25 12:54:57,882 - INFO - Training Std: 20.4539 +2025-05-25 12:54:57,882 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 12:54:57,882 - INFO - KS Statistic: 0.2306 +2025-05-25 12:54:57,882 - INFO - P-value: 0.0000 +2025-05-25 12:54:57,882 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 12:54:57,882 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 12:55:00,135 - INFO - Validation and Training Set Split Statistics +2025-05-25 12:55:00,135 - INFO - Timestamp: 2025-05-25 12:55:00 +2025-05-25 12:55:00,135 - INFO - Validation set size: 1411 (8.54%) +2025-05-25 12:55:00,135 - INFO - Training set size: 15103 (91.46%) +2025-05-25 12:55:00,135 - INFO - Minimum Distance Statistics (km): +2025-05-25 12:55:00,135 - INFO - Mean: 61.1925 +2025-05-25 12:55:00,135 - INFO - Median: 4.0758 +2025-05-25 12:55:00,135 - INFO - Min: 1.1596 +2025-05-25 12:55:00,135 - INFO - Max: 592.8937 +2025-05-25 12:55:00,135 - INFO - Std: 102.1327 +2025-05-25 12:55:00,135 - INFO - OC Distribution Statistics: +2025-05-25 12:55:00,135 - INFO - Validation Mean: 16.5188 +2025-05-25 12:55:00,135 - INFO - Validation Std: 8.0478 +2025-05-25 12:55:00,135 - INFO - Training Mean: 23.0881 +2025-05-25 12:55:00,135 - INFO - Training Std: 20.3277 +2025-05-25 12:55:00,135 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 12:55:00,135 - INFO - KS Statistic: 0.2148 +2025-05-25 12:55:00,135 - INFO - P-value: 0.0000 +2025-05-25 12:55:00,135 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 12:55:00,135 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 12:55:01,677 - INFO - Validation and Training Set Split Statistics +2025-05-25 12:55:01,677 - INFO - Timestamp: 2025-05-25 12:55:01 +2025-05-25 12:55:01,677 - INFO - Validation set size: 1413 (8.56%) +2025-05-25 12:55:01,677 - INFO - Training set size: 15101 (91.44%) +2025-05-25 12:55:01,677 - INFO - Minimum Distance Statistics (km): +2025-05-25 12:55:01,677 - INFO - Mean: 72.0368 +2025-05-25 12:55:01,677 - INFO - Median: 4.4085 +2025-05-25 12:55:01,677 - INFO - Min: 1.2247 +2025-05-25 12:55:01,677 - INFO - Max: 743.7101 +2025-05-25 12:55:01,677 - INFO - Std: 121.1268 +2025-05-25 12:55:01,677 - INFO - OC Distribution Statistics: +2025-05-25 12:55:01,677 - INFO - Validation Mean: 16.4096 +2025-05-25 12:55:01,677 - INFO - Validation Std: 7.2559 +2025-05-25 12:55:01,677 - INFO - Training Mean: 23.0992 +2025-05-25 12:55:01,677 - INFO - Training Std: 20.3532 +2025-05-25 12:55:01,677 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 12:55:01,677 - INFO - KS Statistic: 0.2159 +2025-05-25 12:55:01,677 - INFO - P-value: 0.0000 +2025-05-25 12:55:01,677 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 12:55:01,677 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 12:55:01,686 - INFO - Validation and Training Set Split Statistics +2025-05-25 12:55:01,686 - INFO - Timestamp: 2025-05-25 12:55:01 +2025-05-25 12:55:01,686 - INFO - Validation set size: 1539 (9.32%) +2025-05-25 12:55:01,686 - INFO - Training set size: 14975 (90.68%) +2025-05-25 12:55:01,686 - INFO - Minimum Distance Statistics (km): +2025-05-25 12:55:01,686 - INFO - Mean: 62.6393 +2025-05-25 12:55:01,686 - INFO - Median: 4.5236 +2025-05-25 12:55:01,686 - INFO - Min: 0.4760 +2025-05-25 12:55:01,686 - INFO - Max: 705.4078 +2025-05-25 12:55:01,686 - INFO - Std: 103.6711 +2025-05-25 12:55:01,686 - INFO - OC Distribution Statistics: +2025-05-25 12:55:01,686 - INFO - Validation Mean: 16.8480 +2025-05-25 12:55:01,686 - INFO - Validation Std: 8.4796 +2025-05-25 12:55:01,686 - INFO - Training Mean: 23.1104 +2025-05-25 12:55:01,686 - INFO - Training Std: 20.3844 +2025-05-25 12:55:01,686 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 12:55:01,686 - INFO - KS Statistic: 0.2136 +2025-05-25 12:55:01,686 - INFO - P-value: 0.0000 +2025-05-25 12:55:01,686 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 12:55:01,686 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 12:55:02,674 - INFO - Validation and Training Set Split Statistics +2025-05-25 12:55:02,674 - INFO - Timestamp: 2025-05-25 12:55:02 +2025-05-25 12:55:02,674 - INFO - Validation set size: 1360 (8.24%) +2025-05-25 12:55:02,674 - INFO - Training set size: 15154 (91.76%) +2025-05-25 12:55:02,674 - INFO - Minimum Distance Statistics (km): +2025-05-25 12:55:02,674 - INFO - Mean: 70.6995 +2025-05-25 12:55:02,674 - INFO - Median: 4.9161 +2025-05-25 12:55:02,674 - INFO - Min: 0.5501 +2025-05-25 12:55:02,674 - INFO - Max: 807.4973 +2025-05-25 12:55:02,674 - INFO - Std: 120.4663 +2025-05-25 12:55:02,674 - INFO - OC Distribution Statistics: +2025-05-25 12:55:02,674 - INFO - Validation Mean: 16.3720 +2025-05-25 12:55:02,674 - INFO - Validation Std: 7.6621 +2025-05-25 12:55:02,674 - INFO - Training Mean: 23.0791 +2025-05-25 12:55:02,674 - INFO - Training Std: 20.3115 +2025-05-25 12:55:02,674 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 12:55:02,674 - INFO - KS Statistic: 0.2183 +2025-05-25 12:55:02,674 - INFO - P-value: 0.0000 +2025-05-25 12:55:02,674 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 12:55:02,674 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 12:55:03,892 - INFO - Validation and Training Set Split Statistics +2025-05-25 12:55:03,893 - INFO - Timestamp: 2025-05-25 12:55:03 +2025-05-25 12:55:03,893 - INFO - Validation set size: 1356 (8.21%) +2025-05-25 12:55:03,893 - INFO - Training set size: 15158 (91.79%) +2025-05-25 12:55:03,893 - INFO - Minimum Distance Statistics (km): +2025-05-25 12:55:03,893 - INFO - Mean: 66.3518 +2025-05-25 12:55:03,893 - INFO - Median: 4.5821 +2025-05-25 12:55:03,893 - INFO - Min: 1.1342 +2025-05-25 12:55:03,893 - INFO - Max: 835.9450 +2025-05-25 12:55:03,893 - INFO - Std: 110.3534 +2025-05-25 12:55:03,893 - INFO - OC Distribution Statistics: +2025-05-25 12:55:03,893 - INFO - Validation Mean: 16.7697 +2025-05-25 12:55:03,893 - INFO - Validation Std: 8.2471 +2025-05-25 12:55:03,893 - INFO - Training Mean: 23.0418 +2025-05-25 12:55:03,893 - INFO - Training Std: 20.3003 +2025-05-25 12:55:03,893 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 12:55:03,893 - INFO - KS Statistic: 0.2206 +2025-05-25 12:55:03,893 - INFO - P-value: 0.0000 +2025-05-25 12:55:03,893 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 12:55:03,893 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 14:10:43,758 - INFO - Validation and Training Set Split Statistics +2025-05-25 14:10:43,758 - INFO - Timestamp: 2025-05-25 14:10:43 +2025-05-25 14:10:43,758 - INFO - Validation set size: 1372 (8.31%) +2025-05-25 14:10:43,758 - INFO - Training set size: 15142 (91.69%) +2025-05-25 14:10:43,758 - INFO - Minimum Distance Statistics (km): +2025-05-25 14:10:43,758 - INFO - Mean: 62.6708 +2025-05-25 14:10:43,758 - INFO - Median: 4.5760 +2025-05-25 14:10:43,758 - INFO - Min: 0.8728 +2025-05-25 14:10:43,758 - INFO - Max: 655.9249 +2025-05-25 14:10:43,758 - INFO - Std: 98.7282 +2025-05-25 14:10:43,758 - INFO - OC Distribution Statistics: +2025-05-25 14:10:43,759 - INFO - Validation Mean: 16.2830 +2025-05-25 14:10:43,759 - INFO - Validation Std: 7.8682 +2025-05-25 14:10:43,759 - INFO - Training Mean: 23.0925 +2025-05-25 14:10:43,759 - INFO - Training Std: 20.3077 +2025-05-25 14:10:43,759 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 14:10:43,759 - INFO - KS Statistic: 0.2355 +2025-05-25 14:10:43,759 - INFO - P-value: 0.0000 +2025-05-25 14:10:43,759 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 14:10:43,759 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 14:10:45,877 - INFO - Validation and Training Set Split Statistics +2025-05-25 14:10:45,877 - INFO - Timestamp: 2025-05-25 14:10:45 +2025-05-25 14:10:45,877 - INFO - Validation set size: 1458 (8.83%) +2025-05-25 14:10:45,877 - INFO - Training set size: 15056 (91.17%) +2025-05-25 14:10:45,877 - INFO - Minimum Distance Statistics (km): +2025-05-25 14:10:45,877 - INFO - Mean: 75.1922 +2025-05-25 14:10:45,877 - INFO - Median: 5.2688 +2025-05-25 14:10:45,877 - INFO - Min: 1.2773 +2025-05-25 14:10:45,877 - INFO - Max: 824.9624 +2025-05-25 14:10:45,877 - INFO - Std: 123.2938 +2025-05-25 14:10:45,877 - INFO - OC Distribution Statistics: +2025-05-25 14:10:45,877 - INFO - Validation Mean: 16.5719 +2025-05-25 14:10:45,877 - INFO - Validation Std: 7.7989 +2025-05-25 14:10:45,877 - INFO - Training Mean: 23.1035 +2025-05-25 14:10:45,877 - INFO - Training Std: 20.3621 +2025-05-25 14:10:45,877 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 14:10:45,877 - INFO - KS Statistic: 0.2293 +2025-05-25 14:10:45,877 - INFO - P-value: 0.0000 +2025-05-25 14:10:45,877 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 14:10:45,877 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 14:10:47,336 - INFO - Validation and Training Set Split Statistics +2025-05-25 14:10:47,337 - INFO - Timestamp: 2025-05-25 14:10:47 +2025-05-25 14:10:47,337 - INFO - Validation set size: 1331 (8.06%) +2025-05-25 14:10:47,337 - INFO - Training set size: 15183 (91.94%) +2025-05-25 14:10:47,337 - INFO - Minimum Distance Statistics (km): +2025-05-25 14:10:47,337 - INFO - Mean: 72.8798 +2025-05-25 14:10:47,337 - INFO - Median: 5.0097 +2025-05-25 14:10:47,337 - INFO - Min: 1.2156 +2025-05-25 14:10:47,337 - INFO - Max: 875.2369 +2025-05-25 14:10:47,337 - INFO - Std: 115.4047 +2025-05-25 14:10:47,337 - INFO - OC Distribution Statistics: +2025-05-25 14:10:47,337 - INFO - Validation Mean: 16.4464 +2025-05-25 14:10:47,337 - INFO - Validation Std: 7.9350 +2025-05-25 14:10:47,337 - INFO - Training Mean: 23.0598 +2025-05-25 14:10:47,337 - INFO - Training Std: 20.2899 +2025-05-25 14:10:47,337 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 14:10:47,337 - INFO - KS Statistic: 0.2180 +2025-05-25 14:10:47,337 - INFO - P-value: 0.0000 +2025-05-25 14:10:47,337 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 14:10:47,337 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 14:10:47,452 - INFO - Validation and Training Set Split Statistics +2025-05-25 14:10:47,453 - INFO - Timestamp: 2025-05-25 14:10:47 +2025-05-25 14:10:47,453 - INFO - Validation set size: 1341 (8.12%) +2025-05-25 14:10:47,453 - INFO - Training set size: 15173 (91.88%) +2025-05-25 14:10:47,453 - INFO - Minimum Distance Statistics (km): +2025-05-25 14:10:47,453 - INFO - Mean: 58.7581 +2025-05-25 14:10:47,453 - INFO - Median: 4.2430 +2025-05-25 14:10:47,453 - INFO - Min: 1.2757 +2025-05-25 14:10:47,453 - INFO - Max: 888.3558 +2025-05-25 14:10:47,453 - INFO - Std: 105.2714 +2025-05-25 14:10:47,453 - INFO - OC Distribution Statistics: +2025-05-25 14:10:47,453 - INFO - Validation Mean: 16.3460 +2025-05-25 14:10:47,453 - INFO - Validation Std: 7.5706 +2025-05-25 14:10:47,453 - INFO - Training Mean: 23.0730 +2025-05-25 14:10:47,453 - INFO - Training Std: 20.3042 +2025-05-25 14:10:47,453 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 14:10:47,453 - INFO - KS Statistic: 0.2179 +2025-05-25 14:10:47,453 - INFO - P-value: 0.0000 +2025-05-25 14:10:47,453 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 14:10:47,453 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 14:10:47,909 - INFO - Validation and Training Set Split Statistics +2025-05-25 14:10:47,909 - INFO - Timestamp: 2025-05-25 14:10:47 +2025-05-25 14:10:47,909 - INFO - Validation set size: 1334 (8.08%) +2025-05-25 14:10:47,909 - INFO - Training set size: 15180 (91.92%) +2025-05-25 14:10:47,909 - INFO - Minimum Distance Statistics (km): +2025-05-25 14:10:47,909 - INFO - Mean: 68.3363 +2025-05-25 14:10:47,909 - INFO - Median: 4.9161 +2025-05-25 14:10:47,909 - INFO - Min: 1.2067 +2025-05-25 14:10:47,909 - INFO - Max: 705.4078 +2025-05-25 14:10:47,909 - INFO - Std: 110.8084 +2025-05-25 14:10:47,909 - INFO - OC Distribution Statistics: +2025-05-25 14:10:47,909 - INFO - Validation Mean: 16.8139 +2025-05-25 14:10:47,909 - INFO - Validation Std: 8.1876 +2025-05-25 14:10:47,909 - INFO - Training Mean: 23.0288 +2025-05-25 14:10:47,909 - INFO - Training Std: 20.2928 +2025-05-25 14:10:47,909 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 14:10:47,909 - INFO - KS Statistic: 0.2355 +2025-05-25 14:10:47,909 - INFO - P-value: 0.0000 +2025-05-25 14:10:47,910 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 14:10:47,910 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 14:10:48,339 - INFO - Validation and Training Set Split Statistics +2025-05-25 14:10:48,340 - INFO - Timestamp: 2025-05-25 14:10:48 +2025-05-25 14:10:48,340 - INFO - Validation set size: 1398 (8.47%) +2025-05-25 14:10:48,340 - INFO - Training set size: 15116 (91.53%) +2025-05-25 14:10:48,340 - INFO - Minimum Distance Statistics (km): +2025-05-25 14:10:48,340 - INFO - Mean: 73.1713 +2025-05-25 14:10:48,340 - INFO - Median: 5.2369 +2025-05-25 14:10:48,340 - INFO - Min: 1.2247 +2025-05-25 14:10:48,340 - INFO - Max: 969.1721 +2025-05-25 14:10:48,340 - INFO - Std: 123.8517 +2025-05-25 14:10:48,340 - INFO - OC Distribution Statistics: +2025-05-25 14:10:48,340 - INFO - Validation Mean: 16.6394 +2025-05-25 14:10:48,340 - INFO - Validation Std: 8.0002 +2025-05-25 14:10:48,340 - INFO - Training Mean: 23.0713 +2025-05-25 14:10:48,340 - INFO - Training Std: 20.3265 +2025-05-25 14:10:48,340 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 14:10:48,340 - INFO - KS Statistic: 0.2026 +2025-05-25 14:10:48,340 - INFO - P-value: 0.0000 +2025-05-25 14:10:48,340 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 14:10:48,340 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 14:10:49,380 - INFO - Validation and Training Set Split Statistics +2025-05-25 14:10:49,381 - INFO - Timestamp: 2025-05-25 14:10:49 +2025-05-25 14:10:49,381 - INFO - Validation set size: 1396 (8.45%) +2025-05-25 14:10:49,381 - INFO - Training set size: 15118 (91.55%) +2025-05-25 14:10:49,381 - INFO - Minimum Distance Statistics (km): +2025-05-25 14:10:49,381 - INFO - Mean: 82.9829 +2025-05-25 14:10:49,381 - INFO - Median: 7.6480 +2025-05-25 14:10:49,381 - INFO - Min: 1.2156 +2025-05-25 14:10:49,381 - INFO - Max: 917.1429 +2025-05-25 14:10:49,381 - INFO - Std: 122.7680 +2025-05-25 14:10:49,381 - INFO - OC Distribution Statistics: +2025-05-25 14:10:49,381 - INFO - Validation Mean: 16.6285 +2025-05-25 14:10:49,381 - INFO - Validation Std: 7.9956 +2025-05-25 14:10:49,381 - INFO - Training Mean: 23.0714 +2025-05-25 14:10:49,381 - INFO - Training Std: 20.3254 +2025-05-25 14:10:49,381 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 14:10:49,381 - INFO - KS Statistic: 0.1969 +2025-05-25 14:10:49,381 - INFO - P-value: 0.0000 +2025-05-25 14:10:49,381 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 14:10:49,381 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 14:10:51,957 - INFO - Validation and Training Set Split Statistics +2025-05-25 14:10:51,957 - INFO - Timestamp: 2025-05-25 14:10:51 +2025-05-25 14:10:51,957 - INFO - Validation set size: 1346 (8.15%) +2025-05-25 14:10:51,957 - INFO - Training set size: 15168 (91.85%) +2025-05-25 14:10:51,957 - INFO - Minimum Distance Statistics (km): +2025-05-25 14:10:51,957 - INFO - Mean: 69.0770 +2025-05-25 14:10:51,957 - INFO - Median: 4.4045 +2025-05-25 14:10:51,957 - INFO - Min: 1.2904 +2025-05-25 14:10:51,957 - INFO - Max: 917.1429 +2025-05-25 14:10:51,957 - INFO - Std: 119.9514 +2025-05-25 14:10:51,958 - INFO - OC Distribution Statistics: +2025-05-25 14:10:51,958 - INFO - Validation Mean: 16.5906 +2025-05-25 14:10:51,958 - INFO - Validation Std: 7.4456 +2025-05-25 14:10:51,958 - INFO - Training Mean: 23.0536 +2025-05-25 14:10:51,958 - INFO - Training Std: 20.3179 +2025-05-25 14:10:51,958 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 14:10:51,958 - INFO - KS Statistic: 0.1992 +2025-05-25 14:10:51,958 - INFO - P-value: 0.0000 +2025-05-25 14:10:51,958 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 14:10:51,958 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 15:26:37,468 - INFO - Validation and Training Set Split Statistics +2025-05-25 15:26:37,468 - INFO - Timestamp: 2025-05-25 15:26:37 +2025-05-25 15:26:37,468 - INFO - Validation set size: 1379 (8.35%) +2025-05-25 15:26:37,468 - INFO - Training set size: 15135 (91.65%) +2025-05-25 15:26:37,468 - INFO - Minimum Distance Statistics (km): +2025-05-25 15:26:37,468 - INFO - Mean: 60.3374 +2025-05-25 15:26:37,468 - INFO - Median: 4.2711 +2025-05-25 15:26:37,468 - INFO - Min: 1.2156 +2025-05-25 15:26:37,468 - INFO - Max: 655.9249 +2025-05-25 15:26:37,468 - INFO - Std: 98.6470 +2025-05-25 15:26:37,468 - INFO - OC Distribution Statistics: +2025-05-25 15:26:37,468 - INFO - Validation Mean: 16.4451 +2025-05-25 15:26:37,469 - INFO - Validation Std: 7.6612 +2025-05-25 15:26:37,469 - INFO - Training Mean: 23.0809 +2025-05-25 15:26:37,469 - INFO - Training Std: 20.3232 +2025-05-25 15:26:37,469 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 15:26:37,469 - INFO - KS Statistic: 0.2324 +2025-05-25 15:26:37,469 - INFO - P-value: 0.0000 +2025-05-25 15:26:37,469 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 15:26:37,469 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 15:26:38,227 - INFO - Validation and Training Set Split Statistics +2025-05-25 15:26:38,227 - INFO - Timestamp: 2025-05-25 15:26:38 +2025-05-25 15:26:38,227 - INFO - Validation set size: 1377 (8.34%) +2025-05-25 15:26:38,227 - INFO - Training set size: 15137 (91.66%) +2025-05-25 15:26:38,227 - INFO - Minimum Distance Statistics (km): +2025-05-25 15:26:38,227 - INFO - Mean: 67.5029 +2025-05-25 15:26:38,227 - INFO - Median: 5.0828 +2025-05-25 15:26:38,227 - INFO - Min: 1.0475 +2025-05-25 15:26:38,227 - INFO - Max: 706.9584 +2025-05-25 15:26:38,227 - INFO - Std: 107.4227 +2025-05-25 15:26:38,227 - INFO - OC Distribution Statistics: +2025-05-25 15:26:38,227 - INFO - Validation Mean: 16.5059 +2025-05-25 15:26:38,227 - INFO - Validation Std: 8.2522 +2025-05-25 15:26:38,227 - INFO - Training Mean: 23.0745 +2025-05-25 15:26:38,227 - INFO - Training Std: 20.3030 +2025-05-25 15:26:38,227 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 15:26:38,227 - INFO - KS Statistic: 0.2228 +2025-05-25 15:26:38,227 - INFO - P-value: 0.0000 +2025-05-25 15:26:38,227 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 15:26:38,227 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 15:26:39,392 - INFO - Validation and Training Set Split Statistics +2025-05-25 15:26:39,392 - INFO - Timestamp: 2025-05-25 15:26:39 +2025-05-25 15:26:39,392 - INFO - Validation set size: 1562 (9.46%) +2025-05-25 15:26:39,392 - INFO - Training set size: 14952 (90.54%) +2025-05-25 15:26:39,392 - INFO - Minimum Distance Statistics (km): +2025-05-25 15:26:39,392 - INFO - Mean: 73.1448 +2025-05-25 15:26:39,393 - INFO - Median: 5.2369 +2025-05-25 15:26:39,393 - INFO - Min: 1.2156 +2025-05-25 15:26:39,393 - INFO - Max: 862.7749 +2025-05-25 15:26:39,393 - INFO - Std: 125.9593 +2025-05-25 15:26:39,393 - INFO - OC Distribution Statistics: +2025-05-25 15:26:39,393 - INFO - Validation Mean: 16.6209 +2025-05-25 15:26:39,393 - INFO - Validation Std: 8.6906 +2025-05-25 15:26:39,393 - INFO - Training Mean: 23.1438 +2025-05-25 15:26:39,393 - INFO - Training Std: 20.3791 +2025-05-25 15:26:39,393 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 15:26:39,393 - INFO - KS Statistic: 0.2228 +2025-05-25 15:26:39,393 - INFO - P-value: 0.0000 +2025-05-25 15:26:39,393 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 15:26:39,393 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 15:26:39,621 - INFO - Validation and Training Set Split Statistics +2025-05-25 15:26:39,621 - INFO - Timestamp: 2025-05-25 15:26:39 +2025-05-25 15:26:39,622 - INFO - Validation set size: 1335 (8.08%) +2025-05-25 15:26:39,622 - INFO - Training set size: 15179 (91.92%) +2025-05-25 15:26:39,622 - INFO - Minimum Distance Statistics (km): +2025-05-25 15:26:39,622 - INFO - Mean: 72.7963 +2025-05-25 15:26:39,622 - INFO - Median: 5.3115 +2025-05-25 15:26:39,622 - INFO - Min: 0.8728 +2025-05-25 15:26:39,622 - INFO - Max: 655.9249 +2025-05-25 15:26:39,622 - INFO - Std: 110.4320 +2025-05-25 15:26:39,622 - INFO - OC Distribution Statistics: +2025-05-25 15:26:39,622 - INFO - Validation Mean: 16.5877 +2025-05-25 15:26:39,622 - INFO - Validation Std: 8.3606 +2025-05-25 15:26:39,622 - INFO - Training Mean: 23.0491 +2025-05-25 15:26:39,622 - INFO - Training Std: 20.2808 +2025-05-25 15:26:39,622 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 15:26:39,622 - INFO - KS Statistic: 0.2253 +2025-05-25 15:26:39,622 - INFO - P-value: 0.0000 +2025-05-25 15:26:39,622 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 15:26:39,622 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 15:26:40,553 - INFO - Validation and Training Set Split Statistics +2025-05-25 15:26:40,554 - INFO - Timestamp: 2025-05-25 15:26:40 +2025-05-25 15:26:40,554 - INFO - Validation set size: 1462 (8.85%) +2025-05-25 15:26:40,554 - INFO - Training set size: 15052 (91.15%) +2025-05-25 15:26:40,554 - INFO - Minimum Distance Statistics (km): +2025-05-25 15:26:40,554 - INFO - Mean: 63.8482 +2025-05-25 15:26:40,554 - INFO - Median: 3.8329 +2025-05-25 15:26:40,554 - INFO - Min: 1.0890 +2025-05-25 15:26:40,554 - INFO - Max: 706.9584 +2025-05-25 15:26:40,554 - INFO - Std: 109.2530 +2025-05-25 15:26:40,554 - INFO - OC Distribution Statistics: +2025-05-25 15:26:40,554 - INFO - Validation Mean: 16.1896 +2025-05-25 15:26:40,554 - INFO - Validation Std: 7.4767 +2025-05-25 15:26:40,554 - INFO - Training Mean: 23.1423 +2025-05-25 15:26:40,554 - INFO - Training Std: 20.3636 +2025-05-25 15:26:40,554 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 15:26:40,554 - INFO - KS Statistic: 0.2320 +2025-05-25 15:26:40,554 - INFO - P-value: 0.0000 +2025-05-25 15:26:40,554 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 15:26:40,554 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 15:26:40,587 - INFO - Validation and Training Set Split Statistics +2025-05-25 15:26:40,588 - INFO - Timestamp: 2025-05-25 15:26:40 +2025-05-25 15:26:40,588 - INFO - Validation set size: 1356 (8.21%) +2025-05-25 15:26:40,588 - INFO - Training set size: 15158 (91.79%) +2025-05-25 15:26:40,588 - INFO - Minimum Distance Statistics (km): +2025-05-25 15:26:40,588 - INFO - Mean: 66.7163 +2025-05-25 15:26:40,588 - INFO - Median: 4.4045 +2025-05-25 15:26:40,588 - INFO - Min: 0.5501 +2025-05-25 15:26:40,588 - INFO - Max: 702.5656 +2025-05-25 15:26:40,588 - INFO - Std: 111.1178 +2025-05-25 15:26:40,588 - INFO - OC Distribution Statistics: +2025-05-25 15:26:40,588 - INFO - Validation Mean: 16.2970 +2025-05-25 15:26:40,588 - INFO - Validation Std: 7.9680 +2025-05-25 15:26:40,588 - INFO - Training Mean: 23.0841 +2025-05-25 15:26:40,588 - INFO - Training Std: 20.2967 +2025-05-25 15:26:40,588 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 15:26:40,588 - INFO - KS Statistic: 0.2321 +2025-05-25 15:26:40,588 - INFO - P-value: 0.0000 +2025-05-25 15:26:40,588 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 15:26:40,588 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 15:26:42,630 - INFO - Validation and Training Set Split Statistics +2025-05-25 15:26:42,630 - INFO - Timestamp: 2025-05-25 15:26:42 +2025-05-25 15:26:42,630 - INFO - Validation set size: 1333 (8.07%) +2025-05-25 15:26:42,630 - INFO - Training set size: 15181 (91.93%) +2025-05-25 15:26:42,630 - INFO - Minimum Distance Statistics (km): +2025-05-25 15:26:42,630 - INFO - Mean: 72.4112 +2025-05-25 15:26:42,630 - INFO - Median: 5.8327 +2025-05-25 15:26:42,630 - INFO - Min: 0.7391 +2025-05-25 15:26:42,630 - INFO - Max: 702.5656 +2025-05-25 15:26:42,630 - INFO - Std: 115.3326 +2025-05-25 15:26:42,630 - INFO - OC Distribution Statistics: +2025-05-25 15:26:42,630 - INFO - Validation Mean: 16.9515 +2025-05-25 15:26:42,630 - INFO - Validation Std: 8.0741 +2025-05-25 15:26:42,630 - INFO - Training Mean: 23.0163 +2025-05-25 15:26:42,630 - INFO - Training Std: 20.2999 +2025-05-25 15:26:42,630 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 15:26:42,630 - INFO - KS Statistic: 0.2092 +2025-05-25 15:26:42,630 - INFO - P-value: 0.0000 +2025-05-25 15:26:42,630 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 15:26:42,630 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 15:26:43,054 - INFO - Validation and Training Set Split Statistics +2025-05-25 15:26:43,054 - INFO - Timestamp: 2025-05-25 15:26:43 +2025-05-25 15:26:43,054 - INFO - Validation set size: 1558 (9.43%) +2025-05-25 15:26:43,054 - INFO - Training set size: 14956 (90.57%) +2025-05-25 15:26:43,054 - INFO - Minimum Distance Statistics (km): +2025-05-25 15:26:43,054 - INFO - Mean: 61.8414 +2025-05-25 15:26:43,054 - INFO - Median: 4.0399 +2025-05-25 15:26:43,054 - INFO - Min: 0.7534 +2025-05-25 15:26:43,054 - INFO - Max: 706.9584 +2025-05-25 15:26:43,054 - INFO - Std: 102.3312 +2025-05-25 15:26:43,054 - INFO - OC Distribution Statistics: +2025-05-25 15:26:43,054 - INFO - Validation Mean: 16.6997 +2025-05-25 15:26:43,054 - INFO - Validation Std: 8.9050 +2025-05-25 15:26:43,054 - INFO - Training Mean: 23.1338 +2025-05-25 15:26:43,054 - INFO - Training Std: 20.3702 +2025-05-25 15:26:43,054 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 15:26:43,054 - INFO - KS Statistic: 0.2073 +2025-05-25 15:26:43,054 - INFO - P-value: 0.0000 +2025-05-25 15:26:43,055 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 15:26:43,055 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 19:51:03,703 - INFO - Validation and Training Set Split Statistics +2025-05-25 19:51:03,703 - INFO - Timestamp: 2025-05-25 19:51:03 +2025-05-25 19:51:03,703 - INFO - Validation set size: 1321 (8.00%) +2025-05-25 19:51:03,703 - INFO - Training set size: 15193 (92.00%) +2025-05-25 19:51:03,703 - INFO - Minimum Distance Statistics (km): +2025-05-25 19:51:03,703 - INFO - Mean: 69.2193 +2025-05-25 19:51:03,703 - INFO - Median: 4.2475 +2025-05-25 19:51:03,703 - INFO - Min: 1.2156 +2025-05-25 19:51:03,703 - INFO - Max: 1106.1626 +2025-05-25 19:51:03,703 - INFO - Std: 121.0323 +2025-05-25 19:51:03,703 - INFO - OC Distribution Statistics: +2025-05-25 19:51:03,703 - INFO - Validation Mean: 16.8829 +2025-05-25 19:51:03,703 - INFO - Validation Std: 8.3181 +2025-05-25 19:51:03,703 - INFO - Training Mean: 23.0175 +2025-05-25 19:51:03,703 - INFO - Training Std: 20.2835 +2025-05-25 19:51:03,703 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 19:51:03,703 - INFO - KS Statistic: 0.1981 +2025-05-25 19:51:03,703 - INFO - P-value: 0.0000 +2025-05-25 19:51:03,703 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 19:51:03,703 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 19:51:04,095 - INFO - Validation and Training Set Split Statistics +2025-05-25 19:51:04,095 - INFO - Timestamp: 2025-05-25 19:51:04 +2025-05-25 19:51:04,095 - INFO - Validation set size: 1456 (8.82%) +2025-05-25 19:51:04,095 - INFO - Training set size: 15058 (91.18%) +2025-05-25 19:51:04,095 - INFO - Minimum Distance Statistics (km): +2025-05-25 19:51:04,095 - INFO - Mean: 62.8888 +2025-05-25 19:51:04,095 - INFO - Median: 4.2800 +2025-05-25 19:51:04,095 - INFO - Min: 1.2156 +2025-05-25 19:51:04,095 - INFO - Max: 706.9584 +2025-05-25 19:51:04,095 - INFO - Std: 111.6619 +2025-05-25 19:51:04,095 - INFO - OC Distribution Statistics: +2025-05-25 19:51:04,095 - INFO - Validation Mean: 16.1751 +2025-05-25 19:51:04,095 - INFO - Validation Std: 7.9599 +2025-05-25 19:51:04,095 - INFO - Training Mean: 23.1409 +2025-05-25 19:51:04,095 - INFO - Training Std: 20.3424 +2025-05-25 19:51:04,095 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 19:51:04,095 - INFO - KS Statistic: 0.2378 +2025-05-25 19:51:04,095 - INFO - P-value: 0.0000 +2025-05-25 19:51:04,095 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 19:51:04,095 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 19:51:04,741 - INFO - Validation and Training Set Split Statistics +2025-05-25 19:51:04,742 - INFO - Timestamp: 2025-05-25 19:51:04 +2025-05-25 19:51:04,742 - INFO - Validation set size: 1375 (8.33%) +2025-05-25 19:51:04,742 - INFO - Training set size: 15139 (91.67%) +2025-05-25 19:51:04,742 - INFO - Minimum Distance Statistics (km): +2025-05-25 19:51:04,742 - INFO - Mean: 65.7559 +2025-05-25 19:51:04,742 - INFO - Median: 4.8053 +2025-05-25 19:51:04,742 - INFO - Min: 1.0475 +2025-05-25 19:51:04,742 - INFO - Max: 826.2195 +2025-05-25 19:51:04,742 - INFO - Std: 112.0157 +2025-05-25 19:51:04,742 - INFO - OC Distribution Statistics: +2025-05-25 19:51:04,742 - INFO - Validation Mean: 16.2044 +2025-05-25 19:51:04,742 - INFO - Validation Std: 7.2921 +2025-05-25 19:51:04,742 - INFO - Training Mean: 23.1010 +2025-05-25 19:51:04,742 - INFO - Training Std: 20.3263 +2025-05-25 19:51:04,742 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 19:51:04,742 - INFO - KS Statistic: 0.2148 +2025-05-25 19:51:04,742 - INFO - P-value: 0.0000 +2025-05-25 19:51:04,742 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 19:51:04,742 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 19:51:05,238 - INFO - Validation and Training Set Split Statistics +2025-05-25 19:51:05,238 - INFO - Timestamp: 2025-05-25 19:51:05 +2025-05-25 19:51:05,238 - INFO - Validation set size: 1410 (8.54%) +2025-05-25 19:51:05,238 - INFO - Training set size: 15104 (91.46%) +2025-05-25 19:51:05,238 - INFO - Minimum Distance Statistics (km): +2025-05-25 19:51:05,238 - INFO - Mean: 65.2216 +2025-05-25 19:51:05,238 - INFO - Median: 5.4941 +2025-05-25 19:51:05,238 - INFO - Min: 1.2301 +2025-05-25 19:51:05,238 - INFO - Max: 622.3819 +2025-05-25 19:51:05,238 - INFO - Std: 101.5130 +2025-05-25 19:51:05,238 - INFO - OC Distribution Statistics: +2025-05-25 19:51:05,238 - INFO - Validation Mean: 16.4367 +2025-05-25 19:51:05,238 - INFO - Validation Std: 7.7327 +2025-05-25 19:51:05,238 - INFO - Training Mean: 23.0953 +2025-05-25 19:51:05,238 - INFO - Training Std: 20.3361 +2025-05-25 19:51:05,238 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 19:51:05,238 - INFO - KS Statistic: 0.2273 +2025-05-25 19:51:05,238 - INFO - P-value: 0.0000 +2025-05-25 19:51:05,238 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 19:51:05,238 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 19:51:05,828 - INFO - Validation and Training Set Split Statistics +2025-05-25 19:51:05,828 - INFO - Timestamp: 2025-05-25 19:51:05 +2025-05-25 19:51:05,829 - INFO - Validation set size: 1407 (8.52%) +2025-05-25 19:51:05,829 - INFO - Training set size: 15107 (91.48%) +2025-05-25 19:51:05,829 - INFO - Minimum Distance Statistics (km): +2025-05-25 19:51:05,829 - INFO - Mean: 67.8772 +2025-05-25 19:51:05,829 - INFO - Median: 4.2506 +2025-05-25 19:51:05,829 - INFO - Min: 0.8258 +2025-05-25 19:51:05,829 - INFO - Max: 751.3159 +2025-05-25 19:51:05,829 - INFO - Std: 113.8846 +2025-05-25 19:51:05,829 - INFO - OC Distribution Statistics: +2025-05-25 19:51:05,829 - INFO - Validation Mean: 16.8835 +2025-05-25 19:51:05,829 - INFO - Validation Std: 7.5856 +2025-05-25 19:51:05,829 - INFO - Training Mean: 23.0524 +2025-05-25 19:51:05,829 - INFO - Training Std: 20.3529 +2025-05-25 19:51:05,829 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 19:51:05,829 - INFO - KS Statistic: 0.1976 +2025-05-25 19:51:05,829 - INFO - P-value: 0.0000 +2025-05-25 19:51:05,829 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 19:51:05,829 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 19:51:07,351 - INFO - Validation and Training Set Split Statistics +2025-05-25 19:51:07,351 - INFO - Timestamp: 2025-05-25 19:51:07 +2025-05-25 19:51:07,352 - INFO - Validation set size: 1544 (9.35%) +2025-05-25 19:51:07,352 - INFO - Training set size: 14970 (90.65%) +2025-05-25 19:51:07,352 - INFO - Minimum Distance Statistics (km): +2025-05-25 19:51:07,352 - INFO - Mean: 70.8905 +2025-05-25 19:51:07,352 - INFO - Median: 4.2460 +2025-05-25 19:51:07,352 - INFO - Min: 0.9102 +2025-05-25 19:51:07,352 - INFO - Max: 1106.1626 +2025-05-25 19:51:07,352 - INFO - Std: 129.6995 +2025-05-25 19:51:07,352 - INFO - OC Distribution Statistics: +2025-05-25 19:51:07,352 - INFO - Validation Mean: 16.6298 +2025-05-25 19:51:07,352 - INFO - Validation Std: 7.5030 +2025-05-25 19:51:07,352 - INFO - Training Mean: 23.1350 +2025-05-25 19:51:07,352 - INFO - Training Std: 20.4193 +2025-05-25 19:51:07,352 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 19:51:07,352 - INFO - KS Statistic: 0.2011 +2025-05-25 19:51:07,352 - INFO - P-value: 0.0000 +2025-05-25 19:51:07,352 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 19:51:07,352 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 19:51:08,310 - INFO - Validation and Training Set Split Statistics +2025-05-25 19:51:08,310 - INFO - Timestamp: 2025-05-25 19:51:08 +2025-05-25 19:51:08,310 - INFO - Validation set size: 1376 (8.33%) +2025-05-25 19:51:08,311 - INFO - Training set size: 15138 (91.67%) +2025-05-25 19:51:08,311 - INFO - Minimum Distance Statistics (km): +2025-05-25 19:51:08,311 - INFO - Mean: 67.9327 +2025-05-25 19:51:08,311 - INFO - Median: 4.2475 +2025-05-25 19:51:08,311 - INFO - Min: 1.2329 +2025-05-25 19:51:08,311 - INFO - Max: 702.5656 +2025-05-25 19:51:08,311 - INFO - Std: 120.6426 +2025-05-25 19:51:08,311 - INFO - OC Distribution Statistics: +2025-05-25 19:51:08,311 - INFO - Validation Mean: 16.2453 +2025-05-25 19:51:08,311 - INFO - Validation Std: 7.6008 +2025-05-25 19:51:08,311 - INFO - Training Mean: 23.0978 +2025-05-25 19:51:08,311 - INFO - Training Std: 20.3177 +2025-05-25 19:51:08,311 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 19:51:08,311 - INFO - KS Statistic: 0.2378 +2025-05-25 19:51:08,311 - INFO - P-value: 0.0000 +2025-05-25 19:51:08,311 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 19:51:08,311 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 19:51:09,622 - INFO - Validation and Training Set Split Statistics +2025-05-25 19:51:09,623 - INFO - Timestamp: 2025-05-25 19:51:09 +2025-05-25 19:51:09,623 - INFO - Validation set size: 1505 (9.11%) +2025-05-25 19:51:09,623 - INFO - Training set size: 15009 (90.89%) +2025-05-25 19:51:09,623 - INFO - Minimum Distance Statistics (km): +2025-05-25 19:51:09,623 - INFO - Mean: 65.8088 +2025-05-25 19:51:09,623 - INFO - Median: 4.2196 +2025-05-25 19:51:09,623 - INFO - Min: 1.2904 +2025-05-25 19:51:09,623 - INFO - Max: 917.1429 +2025-05-25 19:51:09,623 - INFO - Std: 116.1545 +2025-05-25 19:51:09,623 - INFO - OC Distribution Statistics: +2025-05-25 19:51:09,623 - INFO - Validation Mean: 16.3132 +2025-05-25 19:51:09,623 - INFO - Validation Std: 7.3804 +2025-05-25 19:51:09,623 - INFO - Training Mean: 23.1498 +2025-05-25 19:51:09,623 - INFO - Training Std: 20.3931 +2025-05-25 19:51:09,623 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 19:51:09,623 - INFO - KS Statistic: 0.2190 +2025-05-25 19:51:09,623 - INFO - P-value: 0.0000 +2025-05-25 19:51:09,623 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 19:51:09,623 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 20:57:44,837 - INFO - Validation and Training Set Split Statistics +2025-05-25 20:57:44,837 - INFO - Timestamp: 2025-05-25 20:57:44 +2025-05-25 20:57:44,838 - INFO - Validation set size: 1424 (8.62%) +2025-05-25 20:57:44,838 - INFO - Training set size: 15090 (91.38%) +2025-05-25 20:57:44,838 - INFO - Minimum Distance Statistics (km): +2025-05-25 20:57:44,838 - INFO - Mean: 74.4069 +2025-05-25 20:57:44,838 - INFO - Median: 4.1193 +2025-05-25 20:57:44,838 - INFO - Min: 0.2243 +2025-05-25 20:57:44,838 - INFO - Max: 743.7101 +2025-05-25 20:57:44,838 - INFO - Std: 128.1540 +2025-05-25 20:57:44,838 - INFO - OC Distribution Statistics: +2025-05-25 20:57:44,838 - INFO - Validation Mean: 16.4778 +2025-05-25 20:57:44,838 - INFO - Validation Std: 8.6219 +2025-05-25 20:57:44,838 - INFO - Training Mean: 23.0976 +2025-05-25 20:57:44,838 - INFO - Training Std: 20.3107 +2025-05-25 20:57:44,839 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 20:57:44,839 - INFO - KS Statistic: 0.2345 +2025-05-25 20:57:44,839 - INFO - P-value: 0.0000 +2025-05-25 20:57:44,839 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 20:57:44,839 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 20:57:45,968 - INFO - Validation and Training Set Split Statistics +2025-05-25 20:57:45,968 - INFO - Timestamp: 2025-05-25 20:57:45 +2025-05-25 20:57:45,969 - INFO - Validation set size: 1415 (8.57%) +2025-05-25 20:57:45,969 - INFO - Training set size: 15099 (91.43%) +2025-05-25 20:57:45,969 - INFO - Minimum Distance Statistics (km): +2025-05-25 20:57:45,969 - INFO - Mean: 63.9632 +2025-05-25 20:57:45,969 - INFO - Median: 4.3323 +2025-05-25 20:57:45,969 - INFO - Min: 1.0979 +2025-05-25 20:57:45,969 - INFO - Max: 917.1429 +2025-05-25 20:57:45,969 - INFO - Std: 114.2260 +2025-05-25 20:57:45,969 - INFO - OC Distribution Statistics: +2025-05-25 20:57:45,969 - INFO - Validation Mean: 16.6724 +2025-05-25 20:57:45,969 - INFO - Validation Std: 8.5036 +2025-05-25 20:57:45,969 - INFO - Training Mean: 23.0754 +2025-05-25 20:57:45,969 - INFO - Training Std: 20.3169 +2025-05-25 20:57:45,969 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 20:57:45,969 - INFO - KS Statistic: 0.2153 +2025-05-25 20:57:45,969 - INFO - P-value: 0.0000 +2025-05-25 20:57:45,969 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 20:57:45,969 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 20:57:46,503 - INFO - Validation and Training Set Split Statistics +2025-05-25 20:57:46,503 - INFO - Timestamp: 2025-05-25 20:57:46 +2025-05-25 20:57:46,503 - INFO - Validation set size: 1389 (8.41%) +2025-05-25 20:57:46,504 - INFO - Training set size: 15125 (91.59%) +2025-05-25 20:57:46,504 - INFO - Minimum Distance Statistics (km): +2025-05-25 20:57:46,504 - INFO - Mean: 70.1118 +2025-05-25 20:57:46,504 - INFO - Median: 4.1551 +2025-05-25 20:57:46,504 - INFO - Min: 0.2243 +2025-05-25 20:57:46,504 - INFO - Max: 743.7101 +2025-05-25 20:57:46,504 - INFO - Std: 120.7221 +2025-05-25 20:57:46,504 - INFO - OC Distribution Statistics: +2025-05-25 20:57:46,504 - INFO - Validation Mean: 16.6336 +2025-05-25 20:57:46,504 - INFO - Validation Std: 8.2785 +2025-05-25 20:57:46,504 - INFO - Training Mean: 23.0680 +2025-05-25 20:57:46,504 - INFO - Training Std: 20.3116 +2025-05-25 20:57:46,504 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 20:57:46,504 - INFO - KS Statistic: 0.2218 +2025-05-25 20:57:46,504 - INFO - P-value: 0.0000 +2025-05-25 20:57:46,504 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 20:57:46,504 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 20:57:47,095 - INFO - Validation and Training Set Split Statistics +2025-05-25 20:57:47,095 - INFO - Timestamp: 2025-05-25 20:57:47 +2025-05-25 20:57:47,095 - INFO - Validation set size: 1401 (8.48%) +2025-05-25 20:57:47,095 - INFO - Training set size: 15113 (91.52%) +2025-05-25 20:57:47,095 - INFO - Minimum Distance Statistics (km): +2025-05-25 20:57:47,095 - INFO - Mean: 67.8855 +2025-05-25 20:57:47,095 - INFO - Median: 4.2475 +2025-05-25 20:57:47,095 - INFO - Min: 1.2156 +2025-05-25 20:57:47,095 - INFO - Max: 835.9450 +2025-05-25 20:57:47,095 - INFO - Std: 117.5890 +2025-05-25 20:57:47,095 - INFO - OC Distribution Statistics: +2025-05-25 20:57:47,095 - INFO - Validation Mean: 16.2365 +2025-05-25 20:57:47,095 - INFO - Validation Std: 7.3125 +2025-05-25 20:57:47,095 - INFO - Training Mean: 23.1099 +2025-05-25 20:57:47,095 - INFO - Training Std: 20.3398 +2025-05-25 20:57:47,095 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 20:57:47,095 - INFO - KS Statistic: 0.2226 +2025-05-25 20:57:47,095 - INFO - P-value: 0.0000 +2025-05-25 20:57:47,095 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 20:57:47,095 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 20:57:47,736 - INFO - Validation and Training Set Split Statistics +2025-05-25 20:57:47,736 - INFO - Timestamp: 2025-05-25 20:57:47 +2025-05-25 20:57:47,736 - INFO - Validation set size: 1349 (8.17%) +2025-05-25 20:57:47,736 - INFO - Training set size: 15165 (91.83%) +2025-05-25 20:57:47,736 - INFO - Minimum Distance Statistics (km): +2025-05-25 20:57:47,736 - INFO - Mean: 65.9776 +2025-05-25 20:57:47,736 - INFO - Median: 4.6106 +2025-05-25 20:57:47,736 - INFO - Min: 1.2065 +2025-05-25 20:57:47,736 - INFO - Max: 706.9584 +2025-05-25 20:57:47,736 - INFO - Std: 107.7667 +2025-05-25 20:57:47,736 - INFO - OC Distribution Statistics: +2025-05-25 20:57:47,736 - INFO - Validation Mean: 16.9069 +2025-05-25 20:57:47,736 - INFO - Validation Std: 8.0236 +2025-05-25 20:57:47,736 - INFO - Training Mean: 23.0267 +2025-05-25 20:57:47,736 - INFO - Training Std: 20.3086 +2025-05-25 20:57:47,736 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 20:57:47,736 - INFO - KS Statistic: 0.1753 +2025-05-25 20:57:47,736 - INFO - P-value: 0.0000 +2025-05-25 20:57:47,736 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 20:57:47,736 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 20:57:49,580 - INFO - Validation and Training Set Split Statistics +2025-05-25 20:57:49,580 - INFO - Timestamp: 2025-05-25 20:57:49 +2025-05-25 20:57:49,580 - INFO - Validation set size: 1440 (8.72%) +2025-05-25 20:57:49,580 - INFO - Training set size: 15074 (91.28%) +2025-05-25 20:57:49,580 - INFO - Minimum Distance Statistics (km): +2025-05-25 20:57:49,580 - INFO - Mean: 69.0915 +2025-05-25 20:57:49,580 - INFO - Median: 4.2124 +2025-05-25 20:57:49,580 - INFO - Min: 1.2102 +2025-05-25 20:57:49,580 - INFO - Max: 772.2996 +2025-05-25 20:57:49,580 - INFO - Std: 121.4656 +2025-05-25 20:57:49,580 - INFO - OC Distribution Statistics: +2025-05-25 20:57:49,580 - INFO - Validation Mean: 16.4475 +2025-05-25 20:57:49,580 - INFO - Validation Std: 7.2297 +2025-05-25 20:57:49,581 - INFO - Training Mean: 23.1075 +2025-05-25 20:57:49,581 - INFO - Training Std: 20.3692 +2025-05-25 20:57:49,581 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 20:57:49,581 - INFO - KS Statistic: 0.1976 +2025-05-25 20:57:49,581 - INFO - P-value: 0.0000 +2025-05-25 20:57:49,581 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 20:57:49,581 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 20:57:49,926 - INFO - Validation and Training Set Split Statistics +2025-05-25 20:57:49,926 - INFO - Timestamp: 2025-05-25 20:57:49 +2025-05-25 20:57:49,926 - INFO - Validation set size: 1361 (8.24%) +2025-05-25 20:57:49,926 - INFO - Training set size: 15153 (91.76%) +2025-05-25 20:57:49,926 - INFO - Minimum Distance Statistics (km): +2025-05-25 20:57:49,926 - INFO - Mean: 70.9740 +2025-05-25 20:57:49,926 - INFO - Median: 4.9468 +2025-05-25 20:57:49,926 - INFO - Min: 1.2247 +2025-05-25 20:57:49,926 - INFO - Max: 824.9624 +2025-05-25 20:57:49,926 - INFO - Std: 121.6696 +2025-05-25 20:57:49,926 - INFO - OC Distribution Statistics: +2025-05-25 20:57:49,926 - INFO - Validation Mean: 16.3792 +2025-05-25 20:57:49,926 - INFO - Validation Std: 8.0255 +2025-05-25 20:57:49,926 - INFO - Training Mean: 23.0789 +2025-05-25 20:57:49,926 - INFO - Training Std: 20.2996 +2025-05-25 20:57:49,926 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 20:57:49,926 - INFO - KS Statistic: 0.2300 +2025-05-25 20:57:49,926 - INFO - P-value: 0.0000 +2025-05-25 20:57:49,926 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 20:57:49,927 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 20:57:53,495 - INFO - Validation and Training Set Split Statistics +2025-05-25 20:57:53,496 - INFO - Timestamp: 2025-05-25 20:57:53 +2025-05-25 20:57:53,496 - INFO - Validation set size: 1357 (8.22%) +2025-05-25 20:57:53,496 - INFO - Training set size: 15157 (91.78%) +2025-05-25 20:57:53,496 - INFO - Minimum Distance Statistics (km): +2025-05-25 20:57:53,496 - INFO - Mean: 70.4126 +2025-05-25 20:57:53,496 - INFO - Median: 5.1703 +2025-05-25 20:57:53,496 - INFO - Min: 1.0596 +2025-05-25 20:57:53,496 - INFO - Max: 702.5656 +2025-05-25 20:57:53,496 - INFO - Std: 117.1255 +2025-05-25 20:57:53,496 - INFO - OC Distribution Statistics: +2025-05-25 20:57:53,496 - INFO - Validation Mean: 16.4455 +2025-05-25 20:57:53,496 - INFO - Validation Std: 7.3038 +2025-05-25 20:57:53,496 - INFO - Training Mean: 23.0712 +2025-05-25 20:57:53,496 - INFO - Training Std: 20.3239 +2025-05-25 20:57:53,496 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 20:57:53,496 - INFO - KS Statistic: 0.2312 +2025-05-25 20:57:53,496 - INFO - P-value: 0.0000 +2025-05-25 20:57:53,496 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 20:57:53,496 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 22:06:18,392 - INFO - Validation and Training Set Split Statistics +2025-05-25 22:06:18,392 - INFO - Timestamp: 2025-05-25 22:06:18 +2025-05-25 22:06:18,392 - INFO - Validation set size: 1423 (8.62%) +2025-05-25 22:06:18,393 - INFO - Training set size: 15091 (91.38%) +2025-05-25 22:06:18,393 - INFO - Minimum Distance Statistics (km): +2025-05-25 22:06:18,393 - INFO - Mean: 64.3357 +2025-05-25 22:06:18,393 - INFO - Median: 4.6106 +2025-05-25 22:06:18,393 - INFO - Min: 0.7391 +2025-05-25 22:06:18,393 - INFO - Max: 835.9450 +2025-05-25 22:06:18,393 - INFO - Std: 109.8687 +2025-05-25 22:06:18,393 - INFO - OC Distribution Statistics: +2025-05-25 22:06:18,393 - INFO - Validation Mean: 16.0584 +2025-05-25 22:06:18,393 - INFO - Validation Std: 7.3919 +2025-05-25 22:06:18,393 - INFO - Training Mean: 23.1367 +2025-05-25 22:06:18,393 - INFO - Training Std: 20.3426 +2025-05-25 22:06:18,393 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 22:06:18,393 - INFO - KS Statistic: 0.2340 +2025-05-25 22:06:18,393 - INFO - P-value: 0.0000 +2025-05-25 22:06:18,393 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 22:06:18,393 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 22:06:19,403 - INFO - Validation and Training Set Split Statistics +2025-05-25 22:06:19,404 - INFO - Timestamp: 2025-05-25 22:06:19 +2025-05-25 22:06:19,404 - INFO - Validation set size: 1416 (8.57%) +2025-05-25 22:06:19,404 - INFO - Training set size: 15098 (91.43%) +2025-05-25 22:06:19,404 - INFO - Minimum Distance Statistics (km): +2025-05-25 22:06:19,404 - INFO - Mean: 69.9026 +2025-05-25 22:06:19,404 - INFO - Median: 4.1945 +2025-05-25 22:06:19,404 - INFO - Min: 0.9587 +2025-05-25 22:06:19,404 - INFO - Max: 743.7101 +2025-05-25 22:06:19,404 - INFO - Std: 118.5521 +2025-05-25 22:06:19,404 - INFO - OC Distribution Statistics: +2025-05-25 22:06:19,404 - INFO - Validation Mean: 16.5297 +2025-05-25 22:06:19,404 - INFO - Validation Std: 8.2425 +2025-05-25 22:06:19,404 - INFO - Training Mean: 23.0892 +2025-05-25 22:06:19,404 - INFO - Training Std: 20.3232 +2025-05-25 22:06:19,404 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 22:06:19,404 - INFO - KS Statistic: 0.2162 +2025-05-25 22:06:19,404 - INFO - P-value: 0.0000 +2025-05-25 22:06:19,404 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 22:06:19,404 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 22:06:19,467 - INFO - Validation and Training Set Split Statistics +2025-05-25 22:06:19,467 - INFO - Timestamp: 2025-05-25 22:06:19 +2025-05-25 22:06:19,467 - INFO - Validation set size: 1431 (8.67%) +2025-05-25 22:06:19,467 - INFO - Training set size: 15083 (91.33%) +2025-05-25 22:06:19,467 - INFO - Minimum Distance Statistics (km): +2025-05-25 22:06:19,467 - INFO - Mean: 63.2125 +2025-05-25 22:06:19,467 - INFO - Median: 4.2475 +2025-05-25 22:06:19,467 - INFO - Min: 1.2301 +2025-05-25 22:06:19,467 - INFO - Max: 716.7388 +2025-05-25 22:06:19,467 - INFO - Std: 107.3672 +2025-05-25 22:06:19,467 - INFO - OC Distribution Statistics: +2025-05-25 22:06:19,467 - INFO - Validation Mean: 16.4857 +2025-05-25 22:06:19,467 - INFO - Validation Std: 8.2653 +2025-05-25 22:06:19,467 - INFO - Training Mean: 23.0999 +2025-05-25 22:06:19,467 - INFO - Training Std: 20.3283 +2025-05-25 22:06:19,467 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 22:06:19,467 - INFO - KS Statistic: 0.2203 +2025-05-25 22:06:19,468 - INFO - P-value: 0.0000 +2025-05-25 22:06:19,468 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 22:06:19,468 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 22:06:19,850 - INFO - Validation and Training Set Split Statistics +2025-05-25 22:06:19,851 - INFO - Timestamp: 2025-05-25 22:06:19 +2025-05-25 22:06:19,851 - INFO - Validation set size: 1584 (9.59%) +2025-05-25 22:06:19,851 - INFO - Training set size: 14930 (90.41%) +2025-05-25 22:06:19,851 - INFO - Minimum Distance Statistics (km): +2025-05-25 22:06:19,851 - INFO - Mean: 65.3056 +2025-05-25 22:06:19,851 - INFO - Median: 4.4155 +2025-05-25 22:06:19,851 - INFO - Min: 0.8728 +2025-05-25 22:06:19,851 - INFO - Max: 743.7101 +2025-05-25 22:06:19,851 - INFO - Std: 118.3729 +2025-05-25 22:06:19,851 - INFO - OC Distribution Statistics: +2025-05-25 22:06:19,851 - INFO - Validation Mean: 16.1409 +2025-05-25 22:06:19,851 - INFO - Validation Std: 7.3308 +2025-05-25 22:06:19,851 - INFO - Training Mean: 23.2043 +2025-05-25 22:06:19,851 - INFO - Training Std: 20.4295 +2025-05-25 22:06:19,851 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 22:06:19,851 - INFO - KS Statistic: 0.2362 +2025-05-25 22:06:19,851 - INFO - P-value: 0.0000 +2025-05-25 22:06:19,851 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 22:06:19,851 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 22:06:20,200 - INFO - Validation and Training Set Split Statistics +2025-05-25 22:06:20,200 - INFO - Timestamp: 2025-05-25 22:06:20 +2025-05-25 22:06:20,200 - INFO - Validation set size: 1426 (8.64%) +2025-05-25 22:06:20,200 - INFO - Training set size: 15088 (91.36%) +2025-05-25 22:06:20,200 - INFO - Minimum Distance Statistics (km): +2025-05-25 22:06:20,200 - INFO - Mean: 65.4164 +2025-05-25 22:06:20,200 - INFO - Median: 4.1395 +2025-05-25 22:06:20,200 - INFO - Min: 1.0311 +2025-05-25 22:06:20,200 - INFO - Max: 706.9584 +2025-05-25 22:06:20,200 - INFO - Std: 107.8052 +2025-05-25 22:06:20,200 - INFO - OC Distribution Statistics: +2025-05-25 22:06:20,200 - INFO - Validation Mean: 16.3014 +2025-05-25 22:06:20,200 - INFO - Validation Std: 7.6122 +2025-05-25 22:06:20,200 - INFO - Training Mean: 23.1152 +2025-05-25 22:06:20,200 - INFO - Training Std: 20.3442 +2025-05-25 22:06:20,200 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 22:06:20,200 - INFO - KS Statistic: 0.2251 +2025-05-25 22:06:20,200 - INFO - P-value: 0.0000 +2025-05-25 22:06:20,200 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 22:06:20,201 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 22:06:21,513 - INFO - Validation and Training Set Split Statistics +2025-05-25 22:06:21,514 - INFO - Timestamp: 2025-05-25 22:06:21 +2025-05-25 22:06:21,514 - INFO - Validation set size: 1376 (8.33%) +2025-05-25 22:06:21,514 - INFO - Training set size: 15138 (91.67%) +2025-05-25 22:06:21,514 - INFO - Minimum Distance Statistics (km): +2025-05-25 22:06:21,514 - INFO - Mean: 63.0449 +2025-05-25 22:06:21,514 - INFO - Median: 4.2475 +2025-05-25 22:06:21,514 - INFO - Min: 1.2757 +2025-05-25 22:06:21,514 - INFO - Max: 655.9249 +2025-05-25 22:06:21,514 - INFO - Std: 103.2849 +2025-05-25 22:06:21,514 - INFO - OC Distribution Statistics: +2025-05-25 22:06:21,514 - INFO - Validation Mean: 16.8014 +2025-05-25 22:06:21,514 - INFO - Validation Std: 8.7885 +2025-05-25 22:06:21,514 - INFO - Training Mean: 23.0472 +2025-05-25 22:06:21,514 - INFO - Training Std: 20.2905 +2025-05-25 22:06:21,514 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 22:06:21,514 - INFO - KS Statistic: 0.2098 +2025-05-25 22:06:21,514 - INFO - P-value: 0.0000 +2025-05-25 22:06:21,514 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 22:06:21,514 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 22:06:23,256 - INFO - Validation and Training Set Split Statistics +2025-05-25 22:06:23,256 - INFO - Timestamp: 2025-05-25 22:06:23 +2025-05-25 22:06:23,256 - INFO - Validation set size: 1438 (8.71%) +2025-05-25 22:06:23,256 - INFO - Training set size: 15076 (91.29%) +2025-05-25 22:06:23,256 - INFO - Minimum Distance Statistics (km): +2025-05-25 22:06:23,256 - INFO - Mean: 68.6636 +2025-05-25 22:06:23,256 - INFO - Median: 5.8013 +2025-05-25 22:06:23,256 - INFO - Min: 1.1596 +2025-05-25 22:06:23,256 - INFO - Max: 705.4078 +2025-05-25 22:06:23,256 - INFO - Std: 100.2346 +2025-05-25 22:06:23,256 - INFO - OC Distribution Statistics: +2025-05-25 22:06:23,256 - INFO - Validation Mean: 16.6454 +2025-05-25 22:06:23,256 - INFO - Validation Std: 8.1205 +2025-05-25 22:06:23,256 - INFO - Training Mean: 23.0878 +2025-05-25 22:06:23,256 - INFO - Training Std: 20.3422 +2025-05-25 22:06:23,256 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 22:06:23,256 - INFO - KS Statistic: 0.2253 +2025-05-25 22:06:23,257 - INFO - P-value: 0.0000 +2025-05-25 22:06:23,257 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 22:06:23,257 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 22:06:28,628 - INFO - Validation and Training Set Split Statistics +2025-05-25 22:06:28,628 - INFO - Timestamp: 2025-05-25 22:06:28 +2025-05-25 22:06:28,628 - INFO - Validation set size: 1661 (10.06%) +2025-05-25 22:06:28,628 - INFO - Training set size: 14853 (89.94%) +2025-05-25 22:06:28,628 - INFO - Minimum Distance Statistics (km): +2025-05-25 22:06:28,628 - INFO - Mean: 65.7126 +2025-05-25 22:06:28,628 - INFO - Median: 3.9792 +2025-05-25 22:06:28,628 - INFO - Min: 1.2065 +2025-05-25 22:06:28,628 - INFO - Max: 862.7749 +2025-05-25 22:06:28,628 - INFO - Std: 117.6809 +2025-05-25 22:06:28,628 - INFO - OC Distribution Statistics: +2025-05-25 22:06:28,628 - INFO - Validation Mean: 16.4120 +2025-05-25 22:06:28,628 - INFO - Validation Std: 7.7839 +2025-05-25 22:06:28,628 - INFO - Training Mean: 23.2106 +2025-05-25 22:06:28,628 - INFO - Training Std: 20.4609 +2025-05-25 22:06:28,628 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 22:06:28,628 - INFO - KS Statistic: 0.2264 +2025-05-25 22:06:28,628 - INFO - P-value: 0.0000 +2025-05-25 22:06:28,628 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 22:06:28,628 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 23:14:36,436 - INFO - Validation and Training Set Split Statistics +2025-05-25 23:14:36,436 - INFO - Timestamp: 2025-05-25 23:14:36 +2025-05-25 23:14:36,436 - INFO - Validation set size: 1353 (8.19%) +2025-05-25 23:14:36,436 - INFO - Training set size: 15161 (91.81%) +2025-05-25 23:14:36,436 - INFO - Minimum Distance Statistics (km): +2025-05-25 23:14:36,436 - INFO - Mean: 60.6055 +2025-05-25 23:14:36,436 - INFO - Median: 3.8354 +2025-05-25 23:14:36,436 - INFO - Min: 0.7534 +2025-05-25 23:14:36,436 - INFO - Max: 969.1721 +2025-05-25 23:14:36,436 - INFO - Std: 107.8069 +2025-05-25 23:14:36,436 - INFO - OC Distribution Statistics: +2025-05-25 23:14:36,436 - INFO - Validation Mean: 16.3096 +2025-05-25 23:14:36,436 - INFO - Validation Std: 7.6985 +2025-05-25 23:14:36,436 - INFO - Training Mean: 23.0816 +2025-05-25 23:14:36,436 - INFO - Training Std: 20.3049 +2025-05-25 23:14:36,436 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 23:14:36,436 - INFO - KS Statistic: 0.2172 +2025-05-25 23:14:36,436 - INFO - P-value: 0.0000 +2025-05-25 23:14:36,436 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 23:14:36,436 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 23:14:37,531 - INFO - Validation and Training Set Split Statistics +2025-05-25 23:14:37,531 - INFO - Timestamp: 2025-05-25 23:14:37 +2025-05-25 23:14:37,531 - INFO - Validation set size: 1336 (8.09%) +2025-05-25 23:14:37,531 - INFO - Training set size: 15178 (91.91%) +2025-05-25 23:14:37,531 - INFO - Minimum Distance Statistics (km): +2025-05-25 23:14:37,531 - INFO - Mean: 75.0513 +2025-05-25 23:14:37,531 - INFO - Median: 5.7863 +2025-05-25 23:14:37,531 - INFO - Min: 1.2156 +2025-05-25 23:14:37,531 - INFO - Max: 862.7749 +2025-05-25 23:14:37,531 - INFO - Std: 121.8160 +2025-05-25 23:14:37,531 - INFO - OC Distribution Statistics: +2025-05-25 23:14:37,531 - INFO - Validation Mean: 16.6704 +2025-05-25 23:14:37,531 - INFO - Validation Std: 7.8627 +2025-05-25 23:14:37,531 - INFO - Training Mean: 23.0423 +2025-05-25 23:14:37,531 - INFO - Training Std: 20.3011 +2025-05-25 23:14:37,531 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 23:14:37,531 - INFO - KS Statistic: 0.1871 +2025-05-25 23:14:37,531 - INFO - P-value: 0.0000 +2025-05-25 23:14:37,531 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 23:14:37,531 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 23:14:37,969 - INFO - Validation and Training Set Split Statistics +2025-05-25 23:14:37,969 - INFO - Timestamp: 2025-05-25 23:14:37 +2025-05-25 23:14:37,969 - INFO - Validation set size: 1327 (8.04%) +2025-05-25 23:14:37,969 - INFO - Training set size: 15187 (91.96%) +2025-05-25 23:14:37,969 - INFO - Minimum Distance Statistics (km): +2025-05-25 23:14:37,969 - INFO - Mean: 64.7878 +2025-05-25 23:14:37,969 - INFO - Median: 4.5217 +2025-05-25 23:14:37,970 - INFO - Min: 1.2247 +2025-05-25 23:14:37,970 - INFO - Max: 702.5656 +2025-05-25 23:14:37,970 - INFO - Std: 110.9145 +2025-05-25 23:14:37,970 - INFO - OC Distribution Statistics: +2025-05-25 23:14:37,970 - INFO - Validation Mean: 16.8672 +2025-05-25 23:14:37,970 - INFO - Validation Std: 8.3368 +2025-05-25 23:14:37,970 - INFO - Training Mean: 23.0213 +2025-05-25 23:14:37,970 - INFO - Training Std: 20.2854 +2025-05-25 23:14:37,970 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 23:14:37,970 - INFO - KS Statistic: 0.1912 +2025-05-25 23:14:37,970 - INFO - P-value: 0.0000 +2025-05-25 23:14:37,970 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 23:14:37,970 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 23:14:38,927 - INFO - Validation and Training Set Split Statistics +2025-05-25 23:14:38,928 - INFO - Timestamp: 2025-05-25 23:14:38 +2025-05-25 23:14:38,928 - INFO - Validation set size: 1382 (8.37%) +2025-05-25 23:14:38,928 - INFO - Training set size: 15132 (91.63%) +2025-05-25 23:14:38,928 - INFO - Minimum Distance Statistics (km): +2025-05-25 23:14:38,928 - INFO - Mean: 69.1770 +2025-05-25 23:14:38,928 - INFO - Median: 4.7665 +2025-05-25 23:14:38,928 - INFO - Min: 0.4760 +2025-05-25 23:14:38,928 - INFO - Max: 824.9624 +2025-05-25 23:14:38,928 - INFO - Std: 118.7227 +2025-05-25 23:14:38,928 - INFO - OC Distribution Statistics: +2025-05-25 23:14:38,928 - INFO - Validation Mean: 16.2104 +2025-05-25 23:14:38,928 - INFO - Validation Std: 7.8077 +2025-05-25 23:14:38,928 - INFO - Training Mean: 23.1037 +2025-05-25 23:14:38,928 - INFO - Training Std: 20.3125 +2025-05-25 23:14:38,928 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 23:14:38,928 - INFO - KS Statistic: 0.2187 +2025-05-25 23:14:38,928 - INFO - P-value: 0.0000 +2025-05-25 23:14:38,928 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 23:14:38,928 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 23:14:40,199 - INFO - Validation and Training Set Split Statistics +2025-05-25 23:14:40,199 - INFO - Timestamp: 2025-05-25 23:14:40 +2025-05-25 23:14:40,199 - INFO - Validation set size: 1326 (8.03%) +2025-05-25 23:14:40,199 - INFO - Training set size: 15188 (91.97%) +2025-05-25 23:14:40,199 - INFO - Minimum Distance Statistics (km): +2025-05-25 23:14:40,200 - INFO - Mean: 74.4754 +2025-05-25 23:14:40,200 - INFO - Median: 5.4941 +2025-05-25 23:14:40,200 - INFO - Min: 1.2065 +2025-05-25 23:14:40,200 - INFO - Max: 917.1429 +2025-05-25 23:14:40,200 - INFO - Std: 119.0634 +2025-05-25 23:14:40,200 - INFO - OC Distribution Statistics: +2025-05-25 23:14:40,200 - INFO - Validation Mean: 16.8601 +2025-05-25 23:14:40,200 - INFO - Validation Std: 9.0889 +2025-05-25 23:14:40,200 - INFO - Training Mean: 23.0215 +2025-05-25 23:14:40,200 - INFO - Training Std: 20.2565 +2025-05-25 23:14:40,200 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 23:14:40,200 - INFO - KS Statistic: 0.2119 +2025-05-25 23:14:40,200 - INFO - P-value: 0.0000 +2025-05-25 23:14:40,200 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 23:14:40,200 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 23:14:41,288 - INFO - Validation and Training Set Split Statistics +2025-05-25 23:14:41,289 - INFO - Timestamp: 2025-05-25 23:14:41 +2025-05-25 23:14:41,289 - INFO - Validation set size: 1384 (8.38%) +2025-05-25 23:14:41,289 - INFO - Training set size: 15130 (91.62%) +2025-05-25 23:14:41,289 - INFO - Minimum Distance Statistics (km): +2025-05-25 23:14:41,289 - INFO - Mean: 68.6492 +2025-05-25 23:14:41,289 - INFO - Median: 5.4799 +2025-05-25 23:14:41,289 - INFO - Min: 1.2904 +2025-05-25 23:14:41,289 - INFO - Max: 601.2913 +2025-05-25 23:14:41,289 - INFO - Std: 108.2108 +2025-05-25 23:14:41,289 - INFO - OC Distribution Statistics: +2025-05-25 23:14:41,289 - INFO - Validation Mean: 16.6887 +2025-05-25 23:14:41,289 - INFO - Validation Std: 8.6057 +2025-05-25 23:14:41,289 - INFO - Training Mean: 23.0608 +2025-05-25 23:14:41,289 - INFO - Training Std: 20.2983 +2025-05-25 23:14:41,289 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 23:14:41,289 - INFO - KS Statistic: 0.1901 +2025-05-25 23:14:41,289 - INFO - P-value: 0.0000 +2025-05-25 23:14:41,289 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 23:14:41,289 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 23:14:42,094 - INFO - Validation and Training Set Split Statistics +2025-05-25 23:14:42,094 - INFO - Timestamp: 2025-05-25 23:14:42 +2025-05-25 23:14:42,094 - INFO - Validation set size: 1376 (8.33%) +2025-05-25 23:14:42,094 - INFO - Training set size: 15138 (91.67%) +2025-05-25 23:14:42,094 - INFO - Minimum Distance Statistics (km): +2025-05-25 23:14:42,094 - INFO - Mean: 69.1432 +2025-05-25 23:14:42,094 - INFO - Median: 4.6281 +2025-05-25 23:14:42,094 - INFO - Min: 0.8728 +2025-05-25 23:14:42,094 - INFO - Max: 702.5656 +2025-05-25 23:14:42,094 - INFO - Std: 115.8550 +2025-05-25 23:14:42,094 - INFO - OC Distribution Statistics: +2025-05-25 23:14:42,094 - INFO - Validation Mean: 16.4697 +2025-05-25 23:14:42,094 - INFO - Validation Std: 8.0560 +2025-05-25 23:14:42,094 - INFO - Training Mean: 23.0774 +2025-05-25 23:14:42,094 - INFO - Training Std: 20.3086 +2025-05-25 23:14:42,094 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 23:14:42,094 - INFO - KS Statistic: 0.2101 +2025-05-25 23:14:42,094 - INFO - P-value: 0.0000 +2025-05-25 23:14:42,094 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 23:14:42,094 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-05-25 23:14:42,545 - INFO - Validation and Training Set Split Statistics +2025-05-25 23:14:42,545 - INFO - Timestamp: 2025-05-25 23:14:42 +2025-05-25 23:14:42,545 - INFO - Validation set size: 1360 (8.24%) +2025-05-25 23:14:42,545 - INFO - Training set size: 15154 (91.76%) +2025-05-25 23:14:42,545 - INFO - Minimum Distance Statistics (km): +2025-05-25 23:14:42,545 - INFO - Mean: 74.2640 +2025-05-25 23:14:42,545 - INFO - Median: 5.7863 +2025-05-25 23:14:42,545 - INFO - Min: 0.4760 +2025-05-25 23:14:42,545 - INFO - Max: 745.6552 +2025-05-25 23:14:42,545 - INFO - Std: 118.7535 +2025-05-25 23:14:42,545 - INFO - OC Distribution Statistics: +2025-05-25 23:14:42,545 - INFO - Validation Mean: 17.0558 +2025-05-25 23:14:42,545 - INFO - Validation Std: 8.2593 +2025-05-25 23:14:42,545 - INFO - Training Mean: 23.0178 +2025-05-25 23:14:42,545 - INFO - Training Std: 20.3096 +2025-05-25 23:14:42,545 - INFO - Kolmogorov-Smirnov Test: +2025-05-25 23:14:42,545 - INFO - KS Statistic: 0.1911 +2025-05-25 23:14:42,545 - INFO - P-value: 0.0000 +2025-05-25 23:14:42,545 - INFO - Distribution Fit: Inverse Gamma +2025-05-25 23:14:42,545 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:05:32,021 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:05:32,022 - INFO - Timestamp: 2025-06-17 13:05:32 +2025-06-17 13:05:32,022 - INFO - Validation set size: 1422 (8.61%) +2025-06-17 13:05:32,022 - INFO - Training set size: 15092 (91.39%) +2025-06-17 13:05:32,022 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:05:32,022 - INFO - Mean: 59.5309 +2025-06-17 13:05:32,022 - INFO - Median: 4.6338 +2025-06-17 13:05:32,022 - INFO - Min: 1.0979 +2025-06-17 13:05:32,022 - INFO - Max: 917.1429 +2025-06-17 13:05:32,022 - INFO - Std: 104.1488 +2025-06-17 13:05:32,022 - INFO - OC Distribution Statistics: +2025-06-17 13:05:32,022 - INFO - Validation Mean: 16.3612 +2025-06-17 13:05:32,022 - INFO - Validation Std: 7.9095 +2025-06-17 13:05:32,022 - INFO - Training Mean: 23.1077 +2025-06-17 13:05:32,022 - INFO - Training Std: 20.3334 +2025-06-17 13:05:32,022 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:05:32,022 - INFO - KS Statistic: 0.2326 +2025-06-17 13:05:32,022 - INFO - P-value: 0.0000 +2025-06-17 13:05:32,022 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:05:32,022 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:05:32,057 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:05:32,058 - INFO - Timestamp: 2025-06-17 13:05:32 +2025-06-17 13:05:32,058 - INFO - Validation set size: 1376 (8.33%) +2025-06-17 13:05:32,058 - INFO - Training set size: 15138 (91.67%) +2025-06-17 13:05:32,058 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:05:32,058 - INFO - Mean: 73.1333 +2025-06-17 13:05:32,058 - INFO - Median: 4.0944 +2025-06-17 13:05:32,058 - INFO - Min: 1.2930 +2025-06-17 13:05:32,058 - INFO - Max: 743.7101 +2025-06-17 13:05:32,058 - INFO - Std: 122.2936 +2025-06-17 13:05:32,058 - INFO - OC Distribution Statistics: +2025-06-17 13:05:32,058 - INFO - Validation Mean: 16.4810 +2025-06-17 13:05:32,058 - INFO - Validation Std: 8.6034 +2025-06-17 13:05:32,058 - INFO - Training Mean: 23.0763 +2025-06-17 13:05:32,058 - INFO - Training Std: 20.2885 +2025-06-17 13:05:32,058 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:05:32,058 - INFO - KS Statistic: 0.2394 +2025-06-17 13:05:32,058 - INFO - P-value: 0.0000 +2025-06-17 13:05:32,058 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:05:32,058 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:06:03,626 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:06:03,626 - INFO - Timestamp: 2025-06-17 13:06:03 +2025-06-17 13:06:03,626 - INFO - Validation set size: 1503 (9.10%) +2025-06-17 13:06:03,626 - INFO - Training set size: 15011 (90.90%) +2025-06-17 13:06:03,626 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:06:03,626 - INFO - Mean: 61.5436 +2025-06-17 13:06:03,626 - INFO - Median: 4.5420 +2025-06-17 13:06:03,626 - INFO - Min: 1.2065 +2025-06-17 13:06:03,626 - INFO - Max: 743.7101 +2025-06-17 13:06:03,626 - INFO - Std: 108.8938 +2025-06-17 13:06:03,626 - INFO - OC Distribution Statistics: +2025-06-17 13:06:03,626 - INFO - Validation Mean: 16.3596 +2025-06-17 13:06:03,626 - INFO - Validation Std: 7.4321 +2025-06-17 13:06:03,626 - INFO - Training Mean: 23.1443 +2025-06-17 13:06:03,626 - INFO - Training Std: 20.3918 +2025-06-17 13:06:03,626 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:06:03,626 - INFO - KS Statistic: 0.2148 +2025-06-17 13:06:03,626 - INFO - P-value: 0.0000 +2025-06-17 13:06:03,626 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:06:03,626 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:06:06,974 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:06:06,975 - INFO - Timestamp: 2025-06-17 13:06:06 +2025-06-17 13:06:06,975 - INFO - Validation set size: 1343 (8.13%) +2025-06-17 13:06:06,975 - INFO - Training set size: 15171 (91.87%) +2025-06-17 13:06:06,975 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:06:06,975 - INFO - Mean: 86.3647 +2025-06-17 13:06:06,975 - INFO - Median: 5.6686 +2025-06-17 13:06:06,975 - INFO - Min: 1.2156 +2025-06-17 13:06:06,975 - INFO - Max: 1106.1626 +2025-06-17 13:06:06,975 - INFO - Std: 152.5699 +2025-06-17 13:06:06,975 - INFO - OC Distribution Statistics: +2025-06-17 13:06:06,975 - INFO - Validation Mean: 16.7363 +2025-06-17 13:06:06,975 - INFO - Validation Std: 7.9307 +2025-06-17 13:06:06,975 - INFO - Training Mean: 23.0394 +2025-06-17 13:06:06,975 - INFO - Training Std: 20.3041 +2025-06-17 13:06:06,975 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:06:06,975 - INFO - KS Statistic: 0.2042 +2025-06-17 13:06:06,975 - INFO - P-value: 0.0000 +2025-06-17 13:06:06,975 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:06:06,975 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:06:08,946 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:06:08,947 - INFO - Timestamp: 2025-06-17 13:06:08 +2025-06-17 13:06:08,947 - INFO - Validation set size: 1394 (8.44%) +2025-06-17 13:06:08,947 - INFO - Training set size: 15120 (91.56%) +2025-06-17 13:06:08,947 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:06:08,947 - INFO - Mean: 67.9509 +2025-06-17 13:06:08,947 - INFO - Median: 4.5280 +2025-06-17 13:06:08,947 - INFO - Min: 0.7667 +2025-06-17 13:06:08,947 - INFO - Max: 1106.1626 +2025-06-17 13:06:08,947 - INFO - Std: 120.7230 +2025-06-17 13:06:08,947 - INFO - OC Distribution Statistics: +2025-06-17 13:06:08,947 - INFO - Validation Mean: 16.2210 +2025-06-17 13:06:08,947 - INFO - Validation Std: 6.6961 +2025-06-17 13:06:08,947 - INFO - Training Mean: 23.1082 +2025-06-17 13:06:08,947 - INFO - Training Std: 20.3553 +2025-06-17 13:06:08,947 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:06:08,947 - INFO - KS Statistic: 0.2291 +2025-06-17 13:06:08,947 - INFO - P-value: 0.0000 +2025-06-17 13:06:08,947 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:06:08,947 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:06:09,372 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:06:09,372 - INFO - Timestamp: 2025-06-17 13:06:09 +2025-06-17 13:06:09,372 - INFO - Validation set size: 1355 (8.21%) +2025-06-17 13:06:09,372 - INFO - Training set size: 15159 (91.79%) +2025-06-17 13:06:09,372 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:06:09,372 - INFO - Mean: 65.8512 +2025-06-17 13:06:09,372 - INFO - Median: 4.5729 +2025-06-17 13:06:09,372 - INFO - Min: 1.1496 +2025-06-17 13:06:09,372 - INFO - Max: 702.5656 +2025-06-17 13:06:09,372 - INFO - Std: 108.7787 +2025-06-17 13:06:09,372 - INFO - OC Distribution Statistics: +2025-06-17 13:06:09,372 - INFO - Validation Mean: 16.4744 +2025-06-17 13:06:09,372 - INFO - Validation Std: 7.8504 +2025-06-17 13:06:09,372 - INFO - Training Mean: 23.0678 +2025-06-17 13:06:09,372 - INFO - Training Std: 20.3055 +2025-06-17 13:06:09,372 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:06:09,372 - INFO - KS Statistic: 0.2201 +2025-06-17 13:06:09,372 - INFO - P-value: 0.0000 +2025-06-17 13:06:09,372 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:06:09,372 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:06:12,460 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:06:12,460 - INFO - Timestamp: 2025-06-17 13:06:12 +2025-06-17 13:06:12,460 - INFO - Validation set size: 1437 (8.70%) +2025-06-17 13:06:12,460 - INFO - Training set size: 15077 (91.30%) +2025-06-17 13:06:12,460 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:06:12,460 - INFO - Mean: 66.6314 +2025-06-17 13:06:12,460 - INFO - Median: 4.7619 +2025-06-17 13:06:12,460 - INFO - Min: 1.0311 +2025-06-17 13:06:12,460 - INFO - Max: 655.9249 +2025-06-17 13:06:12,460 - INFO - Std: 106.6361 +2025-06-17 13:06:12,460 - INFO - OC Distribution Statistics: +2025-06-17 13:06:12,460 - INFO - Validation Mean: 16.5668 +2025-06-17 13:06:12,460 - INFO - Validation Std: 8.7080 +2025-06-17 13:06:12,460 - INFO - Training Mean: 23.0948 +2025-06-17 13:06:12,460 - INFO - Training Std: 20.3162 +2025-06-17 13:06:12,460 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:06:12,460 - INFO - KS Statistic: 0.2160 +2025-06-17 13:06:12,460 - INFO - P-value: 0.0000 +2025-06-17 13:06:12,460 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:06:12,460 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:06:15,046 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:06:15,046 - INFO - Timestamp: 2025-06-17 13:06:15 +2025-06-17 13:06:15,046 - INFO - Validation set size: 1370 (8.30%) +2025-06-17 13:06:15,046 - INFO - Training set size: 15144 (91.70%) +2025-06-17 13:06:15,046 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:06:15,046 - INFO - Mean: 72.5306 +2025-06-17 13:06:15,046 - INFO - Median: 4.0771 +2025-06-17 13:06:15,046 - INFO - Min: 1.2301 +2025-06-17 13:06:15,046 - INFO - Max: 875.2369 +2025-06-17 13:06:15,046 - INFO - Std: 126.0567 +2025-06-17 13:06:15,046 - INFO - OC Distribution Statistics: +2025-06-17 13:06:15,046 - INFO - Validation Mean: 16.7800 +2025-06-17 13:06:15,046 - INFO - Validation Std: 8.1427 +2025-06-17 13:06:15,046 - INFO - Training Mean: 23.0467 +2025-06-17 13:06:15,046 - INFO - Training Std: 20.3114 +2025-06-17 13:06:15,046 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:06:15,046 - INFO - KS Statistic: 0.2011 +2025-06-17 13:06:15,046 - INFO - P-value: 0.0000 +2025-06-17 13:06:15,046 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:06:15,046 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:35:28,806 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:35:28,807 - INFO - Timestamp: 2025-06-17 13:35:28 +2025-06-17 13:35:28,807 - INFO - Validation set size: 1323 (8.01%) +2025-06-17 13:35:28,807 - INFO - Training set size: 15191 (91.99%) +2025-06-17 13:35:28,807 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:35:28,807 - INFO - Mean: 62.6831 +2025-06-17 13:35:28,807 - INFO - Median: 4.9161 +2025-06-17 13:35:28,807 - INFO - Min: 1.2904 +2025-06-17 13:35:28,807 - INFO - Max: 743.7101 +2025-06-17 13:35:28,807 - INFO - Std: 106.3173 +2025-06-17 13:35:28,807 - INFO - OC Distribution Statistics: +2025-06-17 13:35:28,807 - INFO - Validation Mean: 16.0118 +2025-06-17 13:35:28,807 - INFO - Validation Std: 7.8910 +2025-06-17 13:35:28,807 - INFO - Training Mean: 23.0942 +2025-06-17 13:35:28,807 - INFO - Training Std: 20.2746 +2025-06-17 13:35:28,807 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:35:28,807 - INFO - KS Statistic: 0.2309 +2025-06-17 13:35:28,807 - INFO - P-value: 0.0000 +2025-06-17 13:35:28,807 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:35:28,807 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:35:30,601 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:35:30,601 - INFO - Timestamp: 2025-06-17 13:35:30 +2025-06-17 13:35:30,601 - INFO - Validation set size: 1599 (9.68%) +2025-06-17 13:35:30,601 - INFO - Training set size: 14915 (90.32%) +2025-06-17 13:35:30,601 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:35:30,601 - INFO - Mean: 71.6318 +2025-06-17 13:35:30,601 - INFO - Median: 4.3143 +2025-06-17 13:35:30,601 - INFO - Min: 1.2065 +2025-06-17 13:35:30,601 - INFO - Max: 917.1429 +2025-06-17 13:35:30,601 - INFO - Std: 123.7547 +2025-06-17 13:35:30,601 - INFO - OC Distribution Statistics: +2025-06-17 13:35:30,601 - INFO - Validation Mean: 16.8781 +2025-06-17 13:35:30,601 - INFO - Validation Std: 8.8146 +2025-06-17 13:35:30,601 - INFO - Training Mean: 23.1324 +2025-06-17 13:35:30,601 - INFO - Training Std: 20.4002 +2025-06-17 13:35:30,601 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:35:30,601 - INFO - KS Statistic: 0.1852 +2025-06-17 13:35:30,601 - INFO - P-value: 0.0000 +2025-06-17 13:35:30,601 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:35:30,601 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:35:40,006 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:35:40,006 - INFO - Timestamp: 2025-06-17 13:35:40 +2025-06-17 13:35:40,006 - INFO - Validation set size: 1438 (8.71%) +2025-06-17 13:35:40,006 - INFO - Training set size: 15076 (91.29%) +2025-06-17 13:35:40,006 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:35:40,006 - INFO - Mean: 63.6017 +2025-06-17 13:35:40,006 - INFO - Median: 4.9878 +2025-06-17 13:35:40,006 - INFO - Min: 0.5501 +2025-06-17 13:35:40,006 - INFO - Max: 705.4078 +2025-06-17 13:35:40,006 - INFO - Std: 107.2931 +2025-06-17 13:35:40,006 - INFO - OC Distribution Statistics: +2025-06-17 13:35:40,006 - INFO - Validation Mean: 16.4540 +2025-06-17 13:35:40,006 - INFO - Validation Std: 7.5371 +2025-06-17 13:35:40,006 - INFO - Training Mean: 23.1060 +2025-06-17 13:35:40,006 - INFO - Training Std: 20.3578 +2025-06-17 13:35:40,006 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:35:40,006 - INFO - KS Statistic: 0.2014 +2025-06-17 13:35:40,006 - INFO - P-value: 0.0000 +2025-06-17 13:35:40,007 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:35:40,007 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:35:49,261 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:35:49,262 - INFO - Timestamp: 2025-06-17 13:35:49 +2025-06-17 13:35:49,262 - INFO - Validation set size: 1440 (8.72%) +2025-06-17 13:35:49,262 - INFO - Training set size: 15074 (91.28%) +2025-06-17 13:35:49,262 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:35:49,262 - INFO - Mean: 66.2944 +2025-06-17 13:35:49,262 - INFO - Median: 4.6101 +2025-06-17 13:35:49,262 - INFO - Min: 0.6067 +2025-06-17 13:35:49,262 - INFO - Max: 862.7749 +2025-06-17 13:35:49,262 - INFO - Std: 107.9948 +2025-06-17 13:35:49,262 - INFO - OC Distribution Statistics: +2025-06-17 13:35:49,262 - INFO - Validation Mean: 16.4182 +2025-06-17 13:35:49,262 - INFO - Validation Std: 7.8803 +2025-06-17 13:35:49,262 - INFO - Training Mean: 23.1103 +2025-06-17 13:35:49,262 - INFO - Training Std: 20.3452 +2025-06-17 13:35:49,262 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:35:49,262 - INFO - KS Statistic: 0.2204 +2025-06-17 13:35:49,262 - INFO - P-value: 0.0000 +2025-06-17 13:35:49,262 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:35:49,262 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:35:49,279 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:35:49,279 - INFO - Timestamp: 2025-06-17 13:35:49 +2025-06-17 13:35:49,279 - INFO - Validation set size: 1427 (8.64%) +2025-06-17 13:35:49,279 - INFO - Training set size: 15087 (91.36%) +2025-06-17 13:35:49,279 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:35:49,279 - INFO - Mean: 64.4169 +2025-06-17 13:35:49,279 - INFO - Median: 4.5343 +2025-06-17 13:35:49,279 - INFO - Min: 1.2497 +2025-06-17 13:35:49,279 - INFO - Max: 772.4342 +2025-06-17 13:35:49,279 - INFO - Std: 114.7582 +2025-06-17 13:35:49,279 - INFO - OC Distribution Statistics: +2025-06-17 13:35:49,279 - INFO - Validation Mean: 16.3103 +2025-06-17 13:35:49,279 - INFO - Validation Std: 7.3648 +2025-06-17 13:35:49,279 - INFO - Training Mean: 23.1148 +2025-06-17 13:35:49,279 - INFO - Training Std: 20.3536 +2025-06-17 13:35:49,279 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:35:49,279 - INFO - KS Statistic: 0.2101 +2025-06-17 13:35:49,279 - INFO - P-value: 0.0000 +2025-06-17 13:35:49,279 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:35:49,279 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:35:51,953 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:35:51,953 - INFO - Timestamp: 2025-06-17 13:35:51 +2025-06-17 13:35:51,953 - INFO - Validation set size: 1369 (8.29%) +2025-06-17 13:35:51,953 - INFO - Training set size: 15145 (91.71%) +2025-06-17 13:35:51,953 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:35:51,953 - INFO - Mean: 57.7884 +2025-06-17 13:35:51,953 - INFO - Median: 3.9657 +2025-06-17 13:35:51,953 - INFO - Min: 1.2156 +2025-06-17 13:35:51,953 - INFO - Max: 568.4258 +2025-06-17 13:35:51,953 - INFO - Std: 98.2013 +2025-06-17 13:35:51,953 - INFO - OC Distribution Statistics: +2025-06-17 13:35:51,953 - INFO - Validation Mean: 16.1734 +2025-06-17 13:35:51,953 - INFO - Validation Std: 7.3703 +2025-06-17 13:35:51,953 - INFO - Training Mean: 23.1011 +2025-06-17 13:35:51,953 - INFO - Training Std: 20.3197 +2025-06-17 13:35:51,953 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:35:51,953 - INFO - KS Statistic: 0.2312 +2025-06-17 13:35:51,953 - INFO - P-value: 0.0000 +2025-06-17 13:35:51,953 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:35:51,953 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:35:52,230 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:35:52,230 - INFO - Timestamp: 2025-06-17 13:35:52 +2025-06-17 13:35:52,230 - INFO - Validation set size: 1353 (8.19%) +2025-06-17 13:35:52,230 - INFO - Training set size: 15161 (91.81%) +2025-06-17 13:35:52,230 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:35:52,230 - INFO - Mean: 71.8335 +2025-06-17 13:35:52,230 - INFO - Median: 4.9468 +2025-06-17 13:35:52,230 - INFO - Min: 1.2065 +2025-06-17 13:35:52,231 - INFO - Max: 706.9584 +2025-06-17 13:35:52,231 - INFO - Std: 118.0630 +2025-06-17 13:35:52,231 - INFO - OC Distribution Statistics: +2025-06-17 13:35:52,231 - INFO - Validation Mean: 16.8063 +2025-06-17 13:35:52,231 - INFO - Validation Std: 8.3856 +2025-06-17 13:35:52,231 - INFO - Training Mean: 23.0373 +2025-06-17 13:35:52,231 - INFO - Training Std: 20.2948 +2025-06-17 13:35:52,231 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:35:52,231 - INFO - KS Statistic: 0.2123 +2025-06-17 13:35:52,231 - INFO - P-value: 0.0000 +2025-06-17 13:35:52,231 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:35:52,231 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:36:00,877 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:36:00,877 - INFO - Timestamp: 2025-06-17 13:36:00 +2025-06-17 13:36:00,877 - INFO - Validation set size: 1561 (9.45%) +2025-06-17 13:36:00,877 - INFO - Training set size: 14953 (90.55%) +2025-06-17 13:36:00,877 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:36:00,877 - INFO - Mean: 63.9099 +2025-06-17 13:36:00,877 - INFO - Median: 4.1444 +2025-06-17 13:36:00,877 - INFO - Min: 1.2301 +2025-06-17 13:36:00,877 - INFO - Max: 862.7749 +2025-06-17 13:36:00,877 - INFO - Std: 110.6874 +2025-06-17 13:36:00,878 - INFO - OC Distribution Statistics: +2025-06-17 13:36:00,878 - INFO - Validation Mean: 16.5259 +2025-06-17 13:36:00,878 - INFO - Validation Std: 8.1250 +2025-06-17 13:36:00,878 - INFO - Training Mean: 23.1532 +2025-06-17 13:36:00,878 - INFO - Training Std: 20.3998 +2025-06-17 13:36:00,878 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:36:00,878 - INFO - KS Statistic: 0.2097 +2025-06-17 13:36:00,878 - INFO - P-value: 0.0000 +2025-06-17 13:36:00,878 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:36:00,878 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:43:34,965 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:43:34,966 - INFO - Timestamp: 2025-06-17 13:43:34 +2025-06-17 13:43:34,966 - INFO - Validation set size: 1341 (8.12%) +2025-06-17 13:43:34,966 - INFO - Training set size: 15173 (91.88%) +2025-06-17 13:43:34,966 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:43:34,966 - INFO - Mean: 73.2014 +2025-06-17 13:43:34,966 - INFO - Median: 5.1078 +2025-06-17 13:43:34,966 - INFO - Min: 1.2301 +2025-06-17 13:43:34,966 - INFO - Max: 875.2369 +2025-06-17 13:43:34,966 - INFO - Std: 124.2869 +2025-06-17 13:43:34,966 - INFO - OC Distribution Statistics: +2025-06-17 13:43:34,966 - INFO - Validation Mean: 16.9401 +2025-06-17 13:43:34,966 - INFO - Validation Std: 8.4525 +2025-06-17 13:43:34,966 - INFO - Training Mean: 23.0205 +2025-06-17 13:43:34,966 - INFO - Training Std: 20.2900 +2025-06-17 13:43:34,966 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:43:34,966 - INFO - KS Statistic: 0.1903 +2025-06-17 13:43:34,966 - INFO - P-value: 0.0000 +2025-06-17 13:43:34,966 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:43:34,966 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:43:35,168 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:43:35,169 - INFO - Timestamp: 2025-06-17 13:43:35 +2025-06-17 13:43:35,169 - INFO - Validation set size: 1328 (8.04%) +2025-06-17 13:43:35,169 - INFO - Training set size: 15186 (91.96%) +2025-06-17 13:43:35,169 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:43:35,169 - INFO - Mean: 71.8127 +2025-06-17 13:43:35,169 - INFO - Median: 4.4993 +2025-06-17 13:43:35,169 - INFO - Min: 1.2329 +2025-06-17 13:43:35,169 - INFO - Max: 917.1429 +2025-06-17 13:43:35,169 - INFO - Std: 114.1655 +2025-06-17 13:43:35,169 - INFO - OC Distribution Statistics: +2025-06-17 13:43:35,169 - INFO - Validation Mean: 16.8138 +2025-06-17 13:43:35,169 - INFO - Validation Std: 7.7152 +2025-06-17 13:43:35,169 - INFO - Training Mean: 23.0264 +2025-06-17 13:43:35,169 - INFO - Training Std: 20.3060 +2025-06-17 13:43:35,169 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:43:35,169 - INFO - KS Statistic: 0.1954 +2025-06-17 13:43:35,169 - INFO - P-value: 0.0000 +2025-06-17 13:43:35,169 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:43:35,169 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:43:35,419 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:43:35,419 - INFO - Timestamp: 2025-06-17 13:43:35 +2025-06-17 13:43:35,419 - INFO - Validation set size: 1486 (9.00%) +2025-06-17 13:43:35,419 - INFO - Training set size: 15028 (91.00%) +2025-06-17 13:43:35,419 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:43:35,419 - INFO - Mean: 64.8949 +2025-06-17 13:43:35,419 - INFO - Median: 4.4468 +2025-06-17 13:43:35,419 - INFO - Min: 0.8394 +2025-06-17 13:43:35,419 - INFO - Max: 835.9450 +2025-06-17 13:43:35,419 - INFO - Std: 115.9027 +2025-06-17 13:43:35,419 - INFO - OC Distribution Statistics: +2025-06-17 13:43:35,419 - INFO - Validation Mean: 16.2458 +2025-06-17 13:43:35,419 - INFO - Validation Std: 6.6887 +2025-06-17 13:43:35,419 - INFO - Training Mean: 23.1479 +2025-06-17 13:43:35,419 - INFO - Training Std: 20.4047 +2025-06-17 13:43:35,419 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:43:35,419 - INFO - KS Statistic: 0.2244 +2025-06-17 13:43:35,419 - INFO - P-value: 0.0000 +2025-06-17 13:43:35,419 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:43:35,419 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:43:36,237 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:43:36,237 - INFO - Timestamp: 2025-06-17 13:43:36 +2025-06-17 13:43:36,237 - INFO - Validation set size: 1445 (8.75%) +2025-06-17 13:43:36,237 - INFO - Training set size: 15069 (91.25%) +2025-06-17 13:43:36,237 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:43:36,237 - INFO - Mean: 61.3423 +2025-06-17 13:43:36,237 - INFO - Median: 4.3243 +2025-06-17 13:43:36,237 - INFO - Min: 1.0596 +2025-06-17 13:43:36,237 - INFO - Max: 743.7101 +2025-06-17 13:43:36,237 - INFO - Std: 105.3104 +2025-06-17 13:43:36,237 - INFO - OC Distribution Statistics: +2025-06-17 13:43:36,237 - INFO - Validation Mean: 16.4798 +2025-06-17 13:43:36,237 - INFO - Validation Std: 7.7065 +2025-06-17 13:43:36,237 - INFO - Training Mean: 23.1066 +2025-06-17 13:43:36,237 - INFO - Training Std: 20.3561 +2025-06-17 13:43:36,237 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:43:36,237 - INFO - KS Statistic: 0.2164 +2025-06-17 13:43:36,237 - INFO - P-value: 0.0000 +2025-06-17 13:43:36,237 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:43:36,237 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 13:43:42,239 - INFO - Validation and Training Set Split Statistics +2025-06-17 13:43:42,239 - INFO - Timestamp: 2025-06-17 13:43:42 +2025-06-17 13:43:42,239 - INFO - Validation set size: 1602 (9.70%) +2025-06-17 13:43:42,239 - INFO - Training set size: 14912 (90.30%) +2025-06-17 13:43:42,239 - INFO - Minimum Distance Statistics (km): +2025-06-17 13:43:42,239 - INFO - Mean: 64.3031 +2025-06-17 13:43:42,239 - INFO - Median: 4.1787 +2025-06-17 13:43:42,239 - INFO - Min: 0.8608 +2025-06-17 13:43:42,239 - INFO - Max: 888.3558 +2025-06-17 13:43:42,239 - INFO - Std: 116.7887 +2025-06-17 13:43:42,239 - INFO - OC Distribution Statistics: +2025-06-17 13:43:42,239 - INFO - Validation Mean: 16.2474 +2025-06-17 13:43:42,239 - INFO - Validation Std: 7.4098 +2025-06-17 13:43:42,239 - INFO - Training Mean: 23.2014 +2025-06-17 13:43:42,239 - INFO - Training Std: 20.4397 +2025-06-17 13:43:42,240 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 13:43:42,240 - INFO - KS Statistic: 0.2140 +2025-06-17 13:43:42,240 - INFO - P-value: 0.0000 +2025-06-17 13:43:42,240 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 13:43:42,240 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 14:43:29,176 - INFO - Validation and Training Set Split Statistics +2025-06-17 14:43:29,176 - INFO - Timestamp: 2025-06-17 14:43:29 +2025-06-17 14:43:29,176 - INFO - Validation set size: 1349 (8.17%) +2025-06-17 14:43:29,176 - INFO - Training set size: 15165 (91.83%) +2025-06-17 14:43:29,176 - INFO - Minimum Distance Statistics (km): +2025-06-17 14:43:29,176 - INFO - Mean: 65.4431 +2025-06-17 14:43:29,176 - INFO - Median: 5.1441 +2025-06-17 14:43:29,176 - INFO - Min: 1.1496 +2025-06-17 14:43:29,176 - INFO - Max: 716.7632 +2025-06-17 14:43:29,176 - INFO - Std: 109.1110 +2025-06-17 14:43:29,176 - INFO - OC Distribution Statistics: +2025-06-17 14:43:29,176 - INFO - Validation Mean: 16.8773 +2025-06-17 14:43:29,176 - INFO - Validation Std: 8.4039 +2025-06-17 14:43:29,176 - INFO - Training Mean: 23.0293 +2025-06-17 14:43:29,176 - INFO - Training Std: 20.2941 +2025-06-17 14:43:29,176 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 14:43:29,176 - INFO - KS Statistic: 0.1991 +2025-06-17 14:43:29,176 - INFO - P-value: 0.0000 +2025-06-17 14:43:29,176 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 14:43:29,176 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 14:43:30,026 - INFO - Validation and Training Set Split Statistics +2025-06-17 14:43:30,026 - INFO - Timestamp: 2025-06-17 14:43:30 +2025-06-17 14:43:30,026 - INFO - Validation set size: 1622 (9.82%) +2025-06-17 14:43:30,026 - INFO - Training set size: 14892 (90.18%) +2025-06-17 14:43:30,026 - INFO - Minimum Distance Statistics (km): +2025-06-17 14:43:30,026 - INFO - Mean: 64.3569 +2025-06-17 14:43:30,026 - INFO - Median: 4.0201 +2025-06-17 14:43:30,026 - INFO - Min: 1.0711 +2025-06-17 14:43:30,026 - INFO - Max: 716.7388 +2025-06-17 14:43:30,026 - INFO - Std: 115.4423 +2025-06-17 14:43:30,026 - INFO - OC Distribution Statistics: +2025-06-17 14:43:30,026 - INFO - Validation Mean: 16.5457 +2025-06-17 14:43:30,026 - INFO - Validation Std: 7.7373 +2025-06-17 14:43:30,026 - INFO - Training Mean: 23.1782 +2025-06-17 14:43:30,026 - INFO - Training Std: 20.4476 +2025-06-17 14:43:30,026 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 14:43:30,026 - INFO - KS Statistic: 0.2013 +2025-06-17 14:43:30,026 - INFO - P-value: 0.0000 +2025-06-17 14:43:30,026 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 14:43:30,026 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 14:43:30,552 - INFO - Validation and Training Set Split Statistics +2025-06-17 14:43:30,553 - INFO - Timestamp: 2025-06-17 14:43:30 +2025-06-17 14:43:30,553 - INFO - Validation set size: 1647 (9.97%) +2025-06-17 14:43:30,553 - INFO - Training set size: 14867 (90.03%) +2025-06-17 14:43:30,553 - INFO - Minimum Distance Statistics (km): +2025-06-17 14:43:30,553 - INFO - Mean: 59.3385 +2025-06-17 14:43:30,553 - INFO - Median: 4.1551 +2025-06-17 14:43:30,553 - INFO - Min: 1.0475 +2025-06-17 14:43:30,553 - INFO - Max: 705.4078 +2025-06-17 14:43:30,553 - INFO - Std: 104.6639 +2025-06-17 14:43:30,553 - INFO - OC Distribution Statistics: +2025-06-17 14:43:30,553 - INFO - Validation Mean: 16.3363 +2025-06-17 14:43:30,553 - INFO - Validation Std: 6.9047 +2025-06-17 14:43:30,553 - INFO - Training Mean: 23.2126 +2025-06-17 14:43:30,553 - INFO - Training Std: 20.4858 +2025-06-17 14:43:30,553 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 14:43:30,553 - INFO - KS Statistic: 0.2142 +2025-06-17 14:43:30,553 - INFO - P-value: 0.0000 +2025-06-17 14:43:30,553 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 14:43:30,553 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 14:43:40,664 - INFO - Validation and Training Set Split Statistics +2025-06-17 14:43:40,664 - INFO - Timestamp: 2025-06-17 14:43:40 +2025-06-17 14:43:40,664 - INFO - Validation set size: 1449 (8.77%) +2025-06-17 14:43:40,664 - INFO - Training set size: 15065 (91.23%) +2025-06-17 14:43:40,664 - INFO - Minimum Distance Statistics (km): +2025-06-17 14:43:40,664 - INFO - Mean: 69.2803 +2025-06-17 14:43:40,664 - INFO - Median: 4.8124 +2025-06-17 14:43:40,664 - INFO - Min: 1.2065 +2025-06-17 14:43:40,664 - INFO - Max: 702.5656 +2025-06-17 14:43:40,664 - INFO - Std: 113.4675 +2025-06-17 14:43:40,664 - INFO - OC Distribution Statistics: +2025-06-17 14:43:40,664 - INFO - Validation Mean: 16.8530 +2025-06-17 14:43:40,664 - INFO - Validation Std: 8.3363 +2025-06-17 14:43:40,664 - INFO - Training Mean: 23.0725 +2025-06-17 14:43:40,664 - INFO - Training Std: 20.3455 +2025-06-17 14:43:40,664 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 14:43:40,664 - INFO - KS Statistic: 0.2099 +2025-06-17 14:43:40,664 - INFO - P-value: 0.0000 +2025-06-17 14:43:40,664 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 14:43:40,664 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 14:43:48,534 - INFO - Validation and Training Set Split Statistics +2025-06-17 14:43:48,535 - INFO - Timestamp: 2025-06-17 14:43:48 +2025-06-17 14:43:48,535 - INFO - Validation set size: 1377 (8.34%) +2025-06-17 14:43:48,535 - INFO - Training set size: 15137 (91.66%) +2025-06-17 14:43:48,535 - INFO - Minimum Distance Statistics (km): +2025-06-17 14:43:48,535 - INFO - Mean: 62.8934 +2025-06-17 14:43:48,535 - INFO - Median: 4.4468 +2025-06-17 14:43:48,535 - INFO - Min: 0.5501 +2025-06-17 14:43:48,535 - INFO - Max: 705.4078 +2025-06-17 14:43:48,535 - INFO - Std: 105.5530 +2025-06-17 14:43:48,535 - INFO - OC Distribution Statistics: +2025-06-17 14:43:48,535 - INFO - Validation Mean: 16.2943 +2025-06-17 14:43:48,535 - INFO - Validation Std: 7.1447 +2025-06-17 14:43:48,535 - INFO - Training Mean: 23.0937 +2025-06-17 14:43:48,535 - INFO - Training Std: 20.3348 +2025-06-17 14:43:48,535 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 14:43:48,535 - INFO - KS Statistic: 0.2291 +2025-06-17 14:43:48,535 - INFO - P-value: 0.0000 +2025-06-17 14:43:48,535 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 14:43:48,535 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 14:43:56,448 - INFO - Validation and Training Set Split Statistics +2025-06-17 14:43:56,448 - INFO - Timestamp: 2025-06-17 14:43:56 +2025-06-17 14:43:56,448 - INFO - Validation set size: 1379 (8.35%) +2025-06-17 14:43:56,448 - INFO - Training set size: 15135 (91.65%) +2025-06-17 14:43:56,448 - INFO - Minimum Distance Statistics (km): +2025-06-17 14:43:56,448 - INFO - Mean: 65.2277 +2025-06-17 14:43:56,448 - INFO - Median: 4.9388 +2025-06-17 14:43:56,448 - INFO - Min: 1.1829 +2025-06-17 14:43:56,448 - INFO - Max: 706.9584 +2025-06-17 14:43:56,448 - INFO - Std: 106.5432 +2025-06-17 14:43:56,448 - INFO - OC Distribution Statistics: +2025-06-17 14:43:56,448 - INFO - Validation Mean: 16.3488 +2025-06-17 14:43:56,448 - INFO - Validation Std: 7.4739 +2025-06-17 14:43:56,448 - INFO - Training Mean: 23.0897 +2025-06-17 14:43:56,448 - INFO - Training Std: 20.3267 +2025-06-17 14:43:56,448 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 14:43:56,448 - INFO - KS Statistic: 0.2107 +2025-06-17 14:43:56,448 - INFO - P-value: 0.0000 +2025-06-17 14:43:56,448 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 14:43:56,448 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 14:43:58,720 - INFO - Validation and Training Set Split Statistics +2025-06-17 14:43:58,720 - INFO - Timestamp: 2025-06-17 14:43:58 +2025-06-17 14:43:58,720 - INFO - Validation set size: 1634 (9.89%) +2025-06-17 14:43:58,720 - INFO - Training set size: 14880 (90.11%) +2025-06-17 14:43:58,720 - INFO - Minimum Distance Statistics (km): +2025-06-17 14:43:58,720 - INFO - Mean: 61.9577 +2025-06-17 14:43:58,720 - INFO - Median: 3.7935 +2025-06-17 14:43:58,720 - INFO - Min: 1.2065 +2025-06-17 14:43:58,720 - INFO - Max: 743.7101 +2025-06-17 14:43:58,720 - INFO - Std: 111.8002 +2025-06-17 14:43:58,720 - INFO - OC Distribution Statistics: +2025-06-17 14:43:58,720 - INFO - Validation Mean: 16.6074 +2025-06-17 14:43:58,720 - INFO - Validation Std: 7.9249 +2025-06-17 14:43:58,720 - INFO - Training Mean: 23.1768 +2025-06-17 14:43:58,720 - INFO - Training Std: 20.4481 +2025-06-17 14:43:58,720 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 14:43:58,720 - INFO - KS Statistic: 0.2178 +2025-06-17 14:43:58,720 - INFO - P-value: 0.0000 +2025-06-17 14:43:58,720 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 14:43:58,720 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 14:43:58,768 - INFO - Validation and Training Set Split Statistics +2025-06-17 14:43:58,769 - INFO - Timestamp: 2025-06-17 14:43:58 +2025-06-17 14:43:58,769 - INFO - Validation set size: 1390 (8.42%) +2025-06-17 14:43:58,769 - INFO - Training set size: 15124 (91.58%) +2025-06-17 14:43:58,769 - INFO - Minimum Distance Statistics (km): +2025-06-17 14:43:58,769 - INFO - Mean: 69.4040 +2025-06-17 14:43:58,769 - INFO - Median: 4.2015 +2025-06-17 14:43:58,769 - INFO - Min: 1.1829 +2025-06-17 14:43:58,769 - INFO - Max: 706.9584 +2025-06-17 14:43:58,769 - INFO - Std: 111.2319 +2025-06-17 14:43:58,769 - INFO - OC Distribution Statistics: +2025-06-17 14:43:58,769 - INFO - Validation Mean: 16.0697 +2025-06-17 14:43:58,769 - INFO - Validation Std: 7.1080 +2025-06-17 14:43:58,769 - INFO - Training Mean: 23.1202 +2025-06-17 14:43:58,769 - INFO - Training Std: 20.3356 +2025-06-17 14:43:58,769 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 14:43:58,769 - INFO - KS Statistic: 0.2359 +2025-06-17 14:43:58,769 - INFO - P-value: 0.0000 +2025-06-17 14:43:58,769 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 14:43:58,769 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 14:53:12,701 - INFO - Validation and Training Set Split Statistics +2025-06-17 14:53:12,701 - INFO - Timestamp: 2025-06-17 14:53:12 +2025-06-17 14:53:12,701 - INFO - Validation set size: 1351 (8.18%) +2025-06-17 14:53:12,701 - INFO - Training set size: 15163 (91.82%) +2025-06-17 14:53:12,701 - INFO - Minimum Distance Statistics (km): +2025-06-17 14:53:12,701 - INFO - Mean: 69.3966 +2025-06-17 14:53:12,702 - INFO - Median: 4.3243 +2025-06-17 14:53:12,702 - INFO - Min: 0.9102 +2025-06-17 14:53:12,702 - INFO - Max: 743.7101 +2025-06-17 14:53:12,702 - INFO - Std: 114.4724 +2025-06-17 14:53:12,702 - INFO - OC Distribution Statistics: +2025-06-17 14:53:12,702 - INFO - Validation Mean: 16.6372 +2025-06-17 14:53:12,702 - INFO - Validation Std: 7.8315 +2025-06-17 14:53:12,702 - INFO - Training Mean: 23.0515 +2025-06-17 14:53:12,702 - INFO - Training Std: 20.3088 +2025-06-17 14:53:12,702 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 14:53:12,702 - INFO - KS Statistic: 0.2265 +2025-06-17 14:53:12,702 - INFO - P-value: 0.0000 +2025-06-17 14:53:12,702 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 14:53:12,702 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 14:53:12,749 - INFO - Validation and Training Set Split Statistics +2025-06-17 14:53:12,749 - INFO - Timestamp: 2025-06-17 14:53:12 +2025-06-17 14:53:12,749 - INFO - Validation set size: 1368 (8.28%) +2025-06-17 14:53:12,749 - INFO - Training set size: 15146 (91.72%) +2025-06-17 14:53:12,749 - INFO - Minimum Distance Statistics (km): +2025-06-17 14:53:12,749 - INFO - Mean: 65.1887 +2025-06-17 14:53:12,749 - INFO - Median: 4.4333 +2025-06-17 14:53:12,749 - INFO - Min: 1.2067 +2025-06-17 14:53:12,749 - INFO - Max: 835.9450 +2025-06-17 14:53:12,749 - INFO - Std: 112.1648 +2025-06-17 14:53:12,749 - INFO - OC Distribution Statistics: +2025-06-17 14:53:12,749 - INFO - Validation Mean: 16.9442 +2025-06-17 14:53:12,749 - INFO - Validation Std: 8.8653 +2025-06-17 14:53:12,749 - INFO - Training Mean: 23.0310 +2025-06-17 14:53:12,749 - INFO - Training Std: 20.2875 +2025-06-17 14:53:12,749 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 14:53:12,749 - INFO - KS Statistic: 0.1981 +2025-06-17 14:53:12,749 - INFO - P-value: 0.0000 +2025-06-17 14:53:12,749 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 14:53:12,749 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 14:53:12,763 - INFO - Validation and Training Set Split Statistics +2025-06-17 14:53:12,763 - INFO - Timestamp: 2025-06-17 14:53:12 +2025-06-17 14:53:12,763 - INFO - Validation set size: 1407 (8.52%) +2025-06-17 14:53:12,763 - INFO - Training set size: 15107 (91.48%) +2025-06-17 14:53:12,763 - INFO - Minimum Distance Statistics (km): +2025-06-17 14:53:12,763 - INFO - Mean: 66.0681 +2025-06-17 14:53:12,763 - INFO - Median: 4.5975 +2025-06-17 14:53:12,763 - INFO - Min: 0.8608 +2025-06-17 14:53:12,763 - INFO - Max: 706.9584 +2025-06-17 14:53:12,763 - INFO - Std: 106.9822 +2025-06-17 14:53:12,763 - INFO - OC Distribution Statistics: +2025-06-17 14:53:12,763 - INFO - Validation Mean: 16.3922 +2025-06-17 14:53:12,763 - INFO - Validation Std: 7.1199 +2025-06-17 14:53:12,763 - INFO - Training Mean: 23.0981 +2025-06-17 14:53:12,763 - INFO - Training Std: 20.3541 +2025-06-17 14:53:12,763 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 14:53:12,763 - INFO - KS Statistic: 0.2139 +2025-06-17 14:53:12,763 - INFO - P-value: 0.0000 +2025-06-17 14:53:12,763 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 14:53:12,763 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +2025-06-17 14:53:17,030 - INFO - Validation and Training Set Split Statistics +2025-06-17 14:53:17,030 - INFO - Timestamp: 2025-06-17 14:53:17 +2025-06-17 14:53:17,030 - INFO - Validation set size: 1554 (9.41%) +2025-06-17 14:53:17,030 - INFO - Training set size: 14960 (90.59%) +2025-06-17 14:53:17,030 - INFO - Minimum Distance Statistics (km): +2025-06-17 14:53:17,030 - INFO - Mean: 60.1599 +2025-06-17 14:53:17,031 - INFO - Median: 3.9731 +2025-06-17 14:53:17,031 - INFO - Min: 1.0475 +2025-06-17 14:53:17,031 - INFO - Max: 716.7388 +2025-06-17 14:53:17,031 - INFO - Std: 110.7677 +2025-06-17 14:53:17,031 - INFO - OC Distribution Statistics: +2025-06-17 14:53:17,031 - INFO - Validation Mean: 16.6229 +2025-06-17 14:53:17,031 - INFO - Validation Std: 7.7305 +2025-06-17 14:53:17,031 - INFO - Training Mean: 23.1401 +2025-06-17 14:53:17,031 - INFO - Training Std: 20.4155 +2025-06-17 14:53:17,031 - INFO - Kolmogorov-Smirnov Test: +2025-06-17 14:53:17,031 - INFO - KS Statistic: 0.1893 +2025-06-17 14:53:17,031 - INFO - P-value: 0.0000 +2025-06-17 14:53:17,031 - INFO - Distribution Fit: Inverse Gamma +2025-06-17 14:53:17,031 - INFO - Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} diff --git a/Archive/3DCNNoutput_archive/split_stats.txt b/Archive/3DCNNoutput_archive/split_stats.txt new file mode 100644 index 0000000000000000000000000000000000000000..051ef50e38269696c41d4105dfb74ca4fe8d399f --- /dev/null +++ b/Archive/3DCNNoutput_archive/split_stats.txt @@ -0,0 +1,20 @@ +Validation and Training Set Split Statistics +Timestamp: 2025-06-17 14:53:17 +Validation set size: 1554 (9.41%) +Training set size: 14960 (90.59%) +Minimum Distance Statistics (km): + Mean: 60.1599 + Median: 3.9731 + Min: 1.0475 + Max: 716.7388 + Std: 110.7677 +OC Distribution Statistics: + Validation Mean: 16.6229 + Validation Std: 7.7305 + Training Mean: 23.1401 + Training Std: 20.4155 +Kolmogorov-Smirnov Test: + KS Statistic: 0.1893 + P-value: 0.0000 +Distribution Fit: Inverse Gamma +Distribution Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} \ No newline at end of file diff --git a/Archive/3DCNNoutput_archive/training_metrics_3dcnn_02fbb39d.txt b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_02fbb39d.txt new file mode 100644 index 0000000000000000000000000000000000000000..2d486262fdcec924d8513780d312a3a1263b81ab --- /dev/null +++ b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_02fbb39d.txt @@ -0,0 +1,84 @@ +Training Metrics and Configuration +================================================== + +Command Line Arguments: +------------------------------ +lr: 0.0001 +loss_type: composite_l2 +loss_alpha: 0.5 +target_transform: normalize +use_validation: True +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +use_gpu: True +distance_threshold: 1.2 +target_fraction: 0.75 +num_runs: 3 +save_train_and_val: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-3dcnn + Run Name: run_1 + Run ID: ll4hm5ik + +Run 2: + Project: socmapping-3dcnn + Run Name: run_2 + Run ID: b58c9zzg + +Run 3: + Project: socmapping-3dcnn + Run Name: run_3 + Run ID: 3y89p6p1 + +Average Metrics Across Runs: +------------------------------ +train_loss: 6.4264 +val_loss: 1.8640 +correlation: 0.3729 +r_squared: 0.1463 +mse: 102.7110 +rmse: 10.1070 +mae: 8.8221 +rpiq: 0.5118 +output_std: 4.2077 +target_std: 7.3586 + +Average Min Distance Statistics: +------------------------------ +avg_mean: 68.6285 +std_mean: 2.1782 +avg_median: 4.8168 +std_median: 0.5506 +avg_min: 1.0857 +std_min: 0.1838 +avg_max: 825.7686 +std_max: 168.0231 +avg_std: 114.0578 +std_std: 6.0681 + +Best Metrics Across Runs: +------------------------------ +r_squared: + Mean: 0.1511 + Std: 0.0526 + Values: ['0.0848', '0.2133', '0.1552'] + +mae: + Mean: 8.6492 + Std: 0.8456 + Values: ['7.8297', '9.8132', '8.3049'] + +rmse: + Mean: 9.8618 + Std: 0.7260 + Values: ['9.4121', '10.8860', '9.2872'] + +rpiq: + Mean: 0.5327 + Std: 0.0359 + Values: ['0.5206', '0.4961', '0.5814'] + diff --git a/Archive/3DCNNoutput_archive/training_metrics_3dcnn_1b33be71.txt b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_1b33be71.txt new file mode 100644 index 0000000000000000000000000000000000000000..c9f7be993adfdfe41addd9f1ff1a58da09e253fd --- /dev/null +++ b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_1b33be71.txt @@ -0,0 +1,93 @@ +Training Metrics and Configuration +================================================== + +Command Line Arguments: +------------------------------ +lr: 0.0001 +loss_type: composite_l1 +loss_alpha: 0.5 +target_transform: none +use_validation: True +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +use_gpu: True +distance_threshold: 1.2 +target_fraction: 0.75 +num_runs: 5 + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-3dcnn + Run Name: run_1 + Run ID: gav25tvn + +Run 2: + Project: socmapping-3dcnn + Run Name: run_2 + Run ID: fgqtniyz + +Run 3: + Project: socmapping-3dcnn + Run Name: run_3 + Run ID: fzwj50j0 + +Run 4: + Project: socmapping-3dcnn + Run Name: run_4 + Run ID: otc8mnqn + +Run 5: + Project: socmapping-3dcnn + Run Name: run_5 + Run ID: f7tcuhan + +Average Metrics Across Runs: +------------------------------ +train_loss: 11.4008 +val_loss: 6.2997 +correlation: 0.4149 +r_squared: 0.1774 +mse: 94.7077 +rmse: 9.4840 +mae: 6.4140 +rpiq: 0.5976 +output_std: 7.7044 +target_std: 7.9292 + +Average Min Distance Statistics: +------------------------------ +avg_mean: 68.7107 +std_mean: 2.7547 +avg_median: 4.3628 +std_median: 0.4776 +avg_min: 1.0688 +std_min: 0.3023 +avg_max: 715.8405 +std_max: 68.4051 +avg_std: 113.9984 +std_std: 4.1768 + +Best Metrics Across Runs: +------------------------------ +r_squared: + Mean: 0.1981 + Std: 0.0235 + Values: ['0.2399', '0.1990', '0.1962', '0.1875', '0.1681'] + +mae: + Mean: 5.7614 + Std: 0.1775 + Values: ['5.9360', '5.5040', '5.6498', '5.7375', '5.9799'] + +rmse: + Mean: 8.8995 + Std: 0.4427 + Values: ['9.2759', '8.3109', '8.4110', '9.3125', '9.1872'] + +rpiq: + Mean: 0.6171 + Std: 0.0287 + Values: ['0.5929', '0.6497', '0.6539', '0.6013', '0.5878'] + diff --git a/Archive/3DCNNoutput_archive/training_metrics_3dcnn_1caf7b2a.txt b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_1caf7b2a.txt new file mode 100644 index 0000000000000000000000000000000000000000..31db270ef7b2045446c55333b91715e7b7148253 --- /dev/null +++ b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_1caf7b2a.txt @@ -0,0 +1,93 @@ +Training Metrics and Configuration +================================================== + +Command Line Arguments: +------------------------------ +lr: 0.0001 +loss_type: mse +loss_alpha: 0.5 +target_transform: log +use_validation: True +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +use_gpu: True +distance_threshold: 1.2 +target_fraction: 0.75 +num_runs: 5 + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-3dcnn + Run Name: run_1 + Run ID: oe63v7oc + +Run 2: + Project: socmapping-3dcnn + Run Name: run_2 + Run ID: xv9egz3v + +Run 3: + Project: socmapping-3dcnn + Run Name: run_3 + Run ID: ehr9kfeb + +Run 4: + Project: socmapping-3dcnn + Run Name: run_4 + Run ID: ty8z873a + +Run 5: + Project: socmapping-3dcnn + Run Name: run_5 + Run ID: cpq4e62c + +Average Metrics Across Runs: +------------------------------ +train_loss: 1.0003 +val_loss: 0.5817 +correlation: 0.3230 +r_squared: 0.1093 +mse: 572.3964 +rmse: 13.9881 +mae: 7.6166 +rpiq: 0.4946 +output_std: 8.2482 +target_std: 7.7923 + +Average Min Distance Statistics: +------------------------------ +avg_mean: 66.0408 +std_mean: 7.8728 +avg_median: 4.3733 +std_median: 0.4878 +avg_min: 0.9995 +std_min: 0.1359 +avg_max: 756.3923 +std_max: 61.5068 +avg_std: 114.3722 +std_std: 6.2864 + +Best Metrics Across Runs: +------------------------------ +r_squared: + Mean: 0.1065 + Std: 0.0226 + Values: ['0.1300', '0.1328', '0.0872', '0.0757', '0.1070'] + +mae: + Mean: 10.1341 + Std: 4.2859 + Values: ['6.4906', '7.0510', '15.4817', '15.2687', '6.3784'] + +rmse: + Mean: 12.6112 + Std: 3.8024 + Values: ['9.3320', '10.2214', '17.5057', '16.9714', '9.0254'] + +rpiq: + Mean: 0.4501 + Std: 0.1204 + Values: ['0.5465', '0.5185', '0.2913', '0.3182', '0.5762'] + diff --git a/Archive/3DCNNoutput_archive/training_metrics_3dcnn_2f83919f.txt b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_2f83919f.txt new file mode 100644 index 0000000000000000000000000000000000000000..2ce32dd6883f721c5bc1d5371bcba88efa6f9efc --- /dev/null +++ b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_2f83919f.txt @@ -0,0 +1,93 @@ +Training Metrics and Configuration +================================================== + +Command Line Arguments: +------------------------------ +lr: 0.0001 +loss_type: l1 +loss_alpha: 0.5 +target_transform: log +use_validation: True +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +use_gpu: True +distance_threshold: 1.2 +target_fraction: 0.75 +num_runs: 5 + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-3dcnn + Run Name: run_1 + Run ID: j5js3s1o + +Run 2: + Project: socmapping-3dcnn + Run Name: run_2 + Run ID: m3n6teld + +Run 3: + Project: socmapping-3dcnn + Run Name: run_3 + Run ID: 7dq3grpw + +Run 4: + Project: socmapping-3dcnn + Run Name: run_4 + Run ID: 1sqycyi2 + +Run 5: + Project: socmapping-3dcnn + Run Name: run_5 + Run ID: gfisp52j + +Average Metrics Across Runs: +------------------------------ +train_loss: 0.8158 +val_loss: 0.5996 +correlation: 0.2578 +r_squared: 0.0744 +mse: 116005481.4646 +rmse: 1039.5817 +mae: 42.7963 +rpiq: 0.4509 +output_std: 1034.2538 +target_std: 7.7447 + +Average Min Distance Statistics: +------------------------------ +avg_mean: 67.1032 +std_mean: 4.1278 +avg_median: 4.5363 +std_median: 0.3622 +avg_min: 0.8178 +std_min: 0.4846 +avg_max: 739.2716 +std_max: 97.4283 +avg_std: 112.9022 +std_std: 3.9528 + +Best Metrics Across Runs: +------------------------------ +r_squared: + Mean: 0.0665 + Std: 0.0409 + Values: ['0.0924', '0.0090', '0.0256', '0.0957', '0.1099'] + +mae: + Mean: 9.9784 + Std: 4.4766 + Values: ['6.9788', '15.2520', '15.6321', '6.0411', '5.9879'] + +rmse: + Mean: 12.5851 + Std: 4.0567 + Values: ['9.9380', '17.1332', '17.9168', '9.0286', '8.9088'] + +rpiq: + Mean: 0.4490 + Std: 0.1202 + Values: ['0.5232', '0.3152', '0.2916', '0.5427', '0.5725'] + diff --git a/Archive/3DCNNoutput_archive/training_metrics_3dcnn_431aca8a.txt b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_431aca8a.txt new file mode 100644 index 0000000000000000000000000000000000000000..d98c1cfc7603153c060147dc66acc2612c0940d4 --- /dev/null +++ b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_431aca8a.txt @@ -0,0 +1,73 @@ +Training Metrics and Configuration +================================================== + +Command Line Arguments: +------------------------------ +lr: 0.0001 +loss_type: mse +loss_alpha: 0.5 +target_transform: normalize +use_validation: True +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +use_gpu: True +distance_threshold: 1.2 +target_fraction: 0.75 +num_runs: 1 + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-3dcnn + Run Name: run_1 + Run ID: t9zgumlh + +Average Metrics Across Runs: +------------------------------ +train_loss: 5.7544 +val_loss: 1.7787 +correlation: 0.3725 +r_squared: 0.1393 +mse: 113.2157 +rmse: 10.6396 +mae: 9.4967 +rpiq: 0.4794 +output_std: 3.6853 +target_std: 7.7129 + +Average Min Distance Statistics: +------------------------------ +avg_mean: 64.5375 +std_mean: 0.0000 +avg_median: 5.0468 +std_median: 0.0000 +avg_min: 0.5501 +std_min: 0.0000 +avg_max: 888.3558 +std_max: 0.0000 +avg_std: 111.3474 +std_std: 0.0000 + +Best Metrics Across Runs: +------------------------------ +r_squared: + Mean: 0.1409 + Std: 0.0000 + Values: ['0.1409'] + +mae: + Mean: 9.4513 + Std: 0.0000 + Values: ['9.4513'] + +rmse: + Mean: 10.6168 + Std: 0.0000 + Values: ['10.6168'] + +rpiq: + Mean: 0.4804 + Std: 0.0000 + Values: ['0.4804'] + diff --git a/Archive/3DCNNoutput_archive/training_metrics_3dcnn_6cc50ada.txt b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_6cc50ada.txt new file mode 100644 index 0000000000000000000000000000000000000000..e9537778b61bb41ab14a1fdf03e30f3d6620e735 --- /dev/null +++ b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_6cc50ada.txt @@ -0,0 +1,73 @@ +Training Metrics and Configuration +================================================== + +Command Line Arguments: +------------------------------ +lr: 0.0001 +loss_type: mse +loss_alpha: 0.5 +target_transform: normalize +use_validation: True +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +use_gpu: True +distance_threshold: 1.2 +target_fraction: 0.75 +num_runs: 1 + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-3dcnn + Run Name: run_1 + Run ID: dkreehzi + +Average Metrics Across Runs: +------------------------------ +train_loss: 5.8070 +val_loss: 1.8080 +correlation: 0.4044 +r_squared: 0.1651 +mse: 109.6080 +rmse: 10.4644 +mae: 9.4227 +rpiq: 0.4878 +output_std: 2.8558 +target_std: 7.6244 + +Average Min Distance Statistics: +------------------------------ +avg_mean: 60.8634 +std_mean: 0.0000 +avg_median: 3.7089 +std_median: 0.0000 +avg_min: 0.9954 +std_min: 0.0000 +avg_max: 917.1429 +std_max: 0.0000 +avg_std: 119.0602 +std_std: 0.0000 + +Best Metrics Across Runs: +------------------------------ +r_squared: + Mean: 0.1763 + Std: 0.0000 + Values: ['0.1763'] + +mae: + Mean: 9.2331 + Std: 0.0000 + Values: ['9.2331'] + +rmse: + Mean: 10.3380 + Std: 0.0000 + Values: ['10.3380'] + +rpiq: + Mean: 0.4933 + Std: 0.0000 + Values: ['0.4933'] + diff --git a/Archive/3DCNNoutput_archive/training_metrics_3dcnn_a7cad827.txt b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_a7cad827.txt new file mode 100644 index 0000000000000000000000000000000000000000..4160572778f477c280d55cc81045042ebe5d2727 --- /dev/null +++ b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_a7cad827.txt @@ -0,0 +1,54 @@ +Training Metrics and Configuration +================================================== + +Command Line Arguments: +------------------------------ +lr: 0.0001 +loss_type: mse +loss_alpha: 0.5 +target_transform: normalize +use_validation: False +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +use_gpu: True +distance_threshold: 1.2 +target_fraction: 0.75 +num_runs: 1 + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-3dcnn + Run Name: run_1 + Run ID: 876juxp6 + +Average Metrics Across Runs: +------------------------------ +train_loss: 0.7339 + +Average Min Distance Statistics: +------------------------------ + +Best Metrics Across Runs: +------------------------------ +r_squared: + Mean: nan + Std: nan + Values: [] + +mae: + Mean: nan + Std: nan + Values: [] + +rmse: + Mean: nan + Std: nan + Values: [] + +rpiq: + Mean: nan + Std: nan + Values: [] + diff --git a/Archive/3DCNNoutput_archive/training_metrics_3dcnn_e5e81865.txt b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_e5e81865.txt new file mode 100644 index 0000000000000000000000000000000000000000..6e7792dbf4d5ea7ba2877067470b7d50f49cec6e --- /dev/null +++ b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_e5e81865.txt @@ -0,0 +1,89 @@ +Training Metrics and Configuration +================================================== + +Command Line Arguments: +------------------------------ +lr: 0.0001 +loss_type: mse +loss_alpha: 0.5 +target_transform: none +use_validation: True +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +use_gpu: True +distance_threshold: 1.2 +target_fraction: 0.75 +num_runs: 4 +save_train_and_val: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-3dcnn + Run Name: run_1 + Run ID: 649t7482 + +Run 2: + Project: socmapping-3dcnn + Run Name: run_2 + Run ID: zcrdosd4 + +Run 3: + Project: socmapping-3dcnn + Run Name: run_3 + Run ID: lda2pxca + +Run 4: + Project: socmapping-3dcnn + Run Name: run_4 + Run ID: qcw7ie7o + +Average Metrics Across Runs: +------------------------------ +train_loss: 398.3901 +val_loss: 96.2240 +correlation: 0.4059 +r_squared: 0.1684 +mse: 98.3516 +rmse: 9.6933 +mae: 6.7544 +rpiq: 0.5549 +output_std: 8.0404 +target_std: 7.5269 + +Average Min Distance Statistics: +------------------------------ +avg_mean: 69.0495 +std_mean: 3.5597 +avg_median: 4.7888 +std_median: 0.7280 +avg_min: 1.0081 +std_min: 0.3181 +avg_max: 807.0347 +std_max: 86.4755 +avg_std: 117.4286 +std_std: 0.9400 + +Best Metrics Across Runs: +------------------------------ +r_squared: + Mean: 0.1810 + Std: 0.0222 + Values: ['0.1484', '0.1773', '0.2101', '0.1882'] + +mae: + Mean: 5.9793 + Std: 0.2121 + Values: ['6.2062', '5.9703', '5.6417', '6.0990'] + +rmse: + Mean: 9.0060 + Std: 0.4685 + Values: ['9.4969', '9.1143', '8.2344', '9.1782'] + +rpiq: + Mean: 0.5811 + Std: 0.0197 + Values: ['0.5581', '0.5925', '0.6072', '0.5666'] + diff --git a/Archive/3DCNNoutput_archive/training_metrics_3dcnn_fbd41772.txt b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_fbd41772.txt new file mode 100644 index 0000000000000000000000000000000000000000..3540fa77099cad8ec051b2cd4bcfd79237706a96 --- /dev/null +++ b/Archive/3DCNNoutput_archive/training_metrics_3dcnn_fbd41772.txt @@ -0,0 +1,84 @@ +Training Metrics and Configuration +================================================== + +Command Line Arguments: +------------------------------ +lr: 0.0001 +loss_type: composite_l2 +loss_alpha: 0.5 +target_transform: normalize +use_validation: True +num_bins: 128 +output_dir: output +target_val_ratio: 0.08 +use_gpu: True +distance_threshold: 1.2 +target_fraction: 0.75 +num_runs: 3 +save_train_and_val: True + +Wandb Runs Information: +------------------------------ +Run 1: + Project: socmapping-3dcnn + Run Name: run_1 + Run ID: h977ay70 + +Run 2: + Project: socmapping-3dcnn + Run Name: run_2 + Run ID: dm79f48a + +Run 3: + Project: socmapping-3dcnn + Run Name: run_3 + Run ID: uixoi3bz + +Average Metrics Across Runs: +------------------------------ +train_loss: 6.6376 +val_loss: 2.1456 +correlation: 0.3968 +r_squared: 0.1587 +mse: 120.0812 +rmse: 10.9542 +mae: 9.9069 +rpiq: 0.4476 +output_std: 3.2929 +target_std: 7.4246 + +Average Min Distance Statistics: +------------------------------ +avg_mean: 70.8536 +std_mean: 2.7229 +avg_median: 5.1248 +std_median: 0.6473 +avg_min: 0.9163 +std_min: 0.2252 +avg_max: 717.7447 +std_max: 18.4477 +avg_std: 116.2344 +std_std: 1.2875 + +Best Metrics Across Runs: +------------------------------ +r_squared: + Mean: 0.1532 + Std: 0.0117 + Values: ['0.1680', '0.1523', '0.1394'] + +mae: + Mean: 9.4814 + Std: 0.4132 + Values: ['10.0602', '9.2618', '9.1221'] + +rmse: + Mean: 10.6034 + Std: 0.3621 + Values: ['11.0791', '10.2015', '10.5295'] + +rpiq: + Mean: 0.4862 + Std: 0.0387 + Values: ['0.4355', '0.5293', '0.4938'] + diff --git a/Archive/AX_OC_150_TIME_BEGINNING_2007_TIME_END_2023_R2_0.8038_TRANSFORM_none_LOSS_composite_l2_BiasedDataset_10.25kmx10.25km.txt b/Archive/AX_OC_150_TIME_BEGINNING_2007_TIME_END_2023_R2_0.8038_TRANSFORM_none_LOSS_composite_l2_BiasedDataset_10.25kmx10.25km.txt new file mode 100644 index 0000000000000000000000000000000000000000..9dc06606803b9b8e60651d953f78760c8ae5c80d --- /dev/null +++ b/Archive/AX_OC_150_TIME_BEGINNING_2007_TIME_END_2023_R2_0.8038_TRANSFORM_none_LOSS_composite_l2_BiasedDataset_10.25kmx10.25km.txt @@ -0,0 +1,54 @@ +R²: 0.7954 +RMSE: 7.4822 +MAE: 3.5606 +RPIQ: 1.1928 + +Run 4 Model with best R² (0.8038) saved at: TFT_model_run_4_MAX_OC_150_TIME_BEGINNING_2007_TIME_END_2023_R2_0.8038_TRANSFORM_none_LOSS_composite_l2.pth +wandb: ⢿ uploading TFT_model_run_4_MAX_OC_150_TIME_BEGINNING_2007_TIME_END_2023_R2_0.8038_TRANSFORM_none_LOSS_composite_l2.pth 1.4MB/1.4MB (0.7s) +wandb: ⢿ uploading output.log 126.0KB/126.0KB (0.7s) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +wandb: +wandb: +wandb: Run history: +wandb: batch ▁▁▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▃▃▃▄▄▅▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇██ +wandb: correlation ▁▄▄▅▅▆▆▆▆▇▇▇▇▇▇██▇██████████████████████ +wandb: epoch ▁▁▁▁▁▂▂▂▂▂▂▂▂▃▃▄▄▄▄▄▄▄▅▅▅▆▆▆▆▇▇▇▇▇██████ +wandb: mae █▅▆▆▄▃▄▃▂▃▃▂▃▂▂▂▃▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ +wandb: mse █▄▄▃▃▃▃▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ +wandb: r_squared ▁▂▂▂▄▄▅▅▅▅▆▆▇▇▇▇▇▇████▇█████████████████ +wandb: rmse █▇▆▆▆▅▄▄▄▄▃▃▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▂▂▂▂▁▁▁▁▁▁▁▁▁ +wandb: rpiq ▁▃▄▄▄▄▄▅▅▅▆▅▆▆▇▆▆▆▆▇▇▇▇▇▇▇█▇█▇▇▇██▇█████ +wandb: train_loss █▇▆▄▇▄▃▂▃▂▂▂▃▂▂▂▂▁▂▂▂▂▂▁▂▂▂▁▂▂▂▂▂▃▂▁▁▁▁▁ +wandb: train_loss_avg █▇█▆▆▄▄▃▃▃▂▂▂▂▂▂▂▂▂▂▁▂▂▁▁▁▁▂▂▂▁▁▁▁▁▁▁▁▁▁ +wandb: val_loss █▅▆▅▅▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ +wandb: +wandb: Run summary: +wandb: batch 2800 +wandb: best_r2 0.80378 +wandb: correlation 0.89186 +wandb: epoch 200 +wandb: mae 3.5606 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a/Archive/SimpleTimeModel/RandomForest/config.py b/Archive/SimpleTimeModel/RandomForest/config.py new file mode 100644 index 0000000000000000000000000000000000000000..3da82045cf52b6dc229acbb44771d09873a0617a --- /dev/null +++ b/Archive/SimpleTimeModel/RandomForest/config.py @@ -0,0 +1,100 @@ + + +base_path_data = '/home/vfourel/SOCProject/SOCmapping/Data' + +file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC = f"{base_path_data}/LUCAS_LFU_Lfl_00to23_Bavaria_OC.xlsx" + +window_size = 33 +TIME_BEGINNING = '2002' +TIME_END = '2023' +bands_list_order = ['Elevation','LAI','LST','MODIS_NPP','SoilEvaporation','TotalEvapotranspiration'] +MAX_OC = 150 +INFERENCE_TIME = '2015' + + +def generate_seasonal_list(): + seasons = ['winter', 'spring', 'summer', 'autumn'] # Reordered with winter first + years = range(2000, 2025) # 2000 to 2024 + + seasonal_list = [] + + for year in years: + for season in seasons: + # Skip winter 2000 as it belongs to 1999 + if year == 2000 and season == 'winter': + continue + # For 2024, only include winter + if year == 2024 and season != 'winter': + continue + seasonal_list.append(f"{year}_{season}") + + return seasonal_list + +# Generate the list +seasons = generate_seasonal_list() + +years_padded = [f"{year:04d}" for year in range(2000, 2024)] + + +####################################################################### +# YEARLY PATH + +elevationBandMatrixCoordinates_Yearly = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates/StaticValue/Elevation' +LAIBandMatrixCoordinates_Yearly = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates/YearlyValue/LAI' +LSTBandMatrixCoordinates_Yearly = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates/YearlyValue/LST' +MODIS_NPPBandMatrixCoordinates_Yearly = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates/YearlyValue/MODIS_NPP' +SoilEvaporationBandMatrixCoordinates_Yearly = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates/YearlyValue/SoilEvaporation' +TotalEvapotranspirationBandMatrixCoordinates_Yearly = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates/YearlyValue/TotalEvapotranspiration' + +SamplesCoordinates_Yearly = [elevationBandMatrixCoordinates_Yearly, LAIBandMatrixCoordinates_Yearly, LSTBandMatrixCoordinates_Yearly, MODIS_NPPBandMatrixCoordinates_Yearly , SoilEvaporationBandMatrixCoordinates_Yearly, TotalEvapotranspirationBandMatrixCoordinates_Yearly ] + +elevationBandMatrixCoordinates_1milPoints_Yearly = f'{base_path_data}/Coordinates1Mil/StaticValue/Elevation' +LAIBandMatrixCoordinates_1milPoints_Yearly = f'{base_path_data}/Coordinates1Mil/YearlyValue/LAI' +LSTBandMatrixCoordinates_1milPoints_Yearly = f'{base_path_data}/Coordinates1Mil/YearlyValue/LST' +MODIS_NPPBandMatrixCoordinates_1milPoints_Yearly = f'{base_path_data}/Coordinates1Mil/YearlyValue/MODIS_NPP' +SoilEvaporationBandMatrixCoordinates_1milPoints_Yearly = f'{base_path_data}/Coordinates1Mil/YearlyValue/SoilEvaporation' +TotalEvapotranspirationBandMatrixCoordinates_1milPoints_Yearly = f'{base_path_data}/Coordinates1Mil/YearlyValue/TotalEvapotranspiration' + +MatrixCoordinates_1mil_Yearly = [elevationBandMatrixCoordinates_1milPoints_Yearly, LAIBandMatrixCoordinates_1milPoints_Yearly, LSTBandMatrixCoordinates_1milPoints_Yearly, MODIS_NPPBandMatrixCoordinates_1milPoints_Yearly, SoilEvaporationBandMatrixCoordinates_1milPoints_Yearly, TotalEvapotranspirationBandMatrixCoordinates_1milPoints_Yearly ] + +elevationTensorData = f'{base_path_data}/RasterTensorData/StaticValue/Elevation' +LAITensorDataYearly = f'{base_path_data}/RasterTensorData/YearlyValue/LAI' +LSTTensorDataYearly = f'{base_path_data}/RasterTensorData/YearlyValue/LST' +MODIS_NPPTensorDataYearly = f'{base_path_data}/RasterTensorData/YearlyValue/MODIS_NPP' +SoilEvaporationTensorDataYearly = f'{base_path_data}/RasterTensorData/YearlyValue/SoilEvaporation' +TotalEvapotranspirationTensorDataYearly = f'{base_path_data}/RasterTensorData/YearlyValue/TotalEvapotranspiration' + +DataYearly = [elevationTensorData , LAITensorDataYearly , LSTTensorDataYearly, MODIS_NPPTensorDataYearly, SoilEvaporationTensorDataYearly, TotalEvapotranspirationTensorDataYearly ] + +####################################################################### +#SEASON PATHS + +elevationBandMatrixCoordinates_Seasonally = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates/StaticValue/Elevation' +LAIBandMatrixCoordinates_Seasonally = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates/SeasonalValue/LAI' +LSTBandMatrixCoordinates_Seasonally = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates/SeasonalValue/LST' +MODIS_NPPBandMatrixCoordinates_Seasonally = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates/YearlyValue/MODIS_NPP' +SoilEvaporationBandMatrixCoordinates_Seasonally = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates/SeasonalValue/SoilEvaporation' +TotalEvapotranspirationBandMatrixCoordinates_Seasonally = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates/SeasonalValue/TotalEvapotranspiration' + +SamplesCoordinates_Seasonally = [elevationBandMatrixCoordinates_Seasonally , LAIBandMatrixCoordinates_Seasonally, LSTBandMatrixCoordinates_Seasonally, MODIS_NPPBandMatrixCoordinates_Seasonally, SoilEvaporationBandMatrixCoordinates_Seasonally, TotalEvapotranspirationBandMatrixCoordinates_Seasonally ] + +elevationBandMatrixCoordinates_1milPoints_Seasonally = f'{base_path_data}/Coordinates1Mil/StaticValue/Elevation' +LAIBandMatrixCoordinates_1milPoints_Seasonally = f'{base_path_data}/Coordinates1Mil/SeasonalValue/LAI' +LSTBandMatrixCoordinates_1milPoints_Seasonally = f'{base_path_data}/Coordinates1Mil/SeasonalValue/LST' +MODIS_NPPBandMatrixCoordinates_1milPoints_Seasonally = f'{base_path_data}/Coordinates1Mil/YearlyValue/MODIS_NPP' +SoilEvaporationBandMatrixCoordinates_1milPoints_Seasonally = f'{base_path_data}/Coordinates1Mil/SeasonalValue/SoilEvaporation' +TotalEvapotranspirationBandMatrixCoordinates_1milPoints_Seasonally = f'{base_path_data}/Coordinates1Mil/SeasonalValue/TotalEvapotranspiration' + +MatrixCoordinates_1mil_Seasonally = [elevationBandMatrixCoordinates_1milPoints_Seasonally , LAIBandMatrixCoordinates_1milPoints_Seasonally, LSTBandMatrixCoordinates_1milPoints_Seasonally, MODIS_NPPBandMatrixCoordinates_1milPoints_Seasonally, SoilEvaporationBandMatrixCoordinates_1milPoints_Seasonally, TotalEvapotranspirationBandMatrixCoordinates_1milPoints_Seasonally ] + +elevationTensorData = f'{base_path_data}/RasterTensorData/StaticValue/Elevation' +LAITensorDataSeasonally = f'{base_path_data}/RasterTensorData/SeasonalValue/LAI' +LSTTensorDataSeasonally = f'{base_path_data}/RasterTensorData/SeasonalValue/LST' +MODIS_NPPTensorSeasonally = f'{base_path_data}/RasterTensorData/YearlyValue/MODIS_NPP' +SoilEvaporationTensorSeasonally = f'{base_path_data}/RasterTensorData/SeasonalValue/SoilEvaporation' +TotalEvapotranspirationTensorSeasonally = f'{base_path_data}/RasterTensorData/SeasonalValue/TotalEvapotranspiration' + +DataSeasonally = [elevationTensorData, LAITensorDataSeasonally, LSTTensorDataSeasonally, MODIS_NPPTensorSeasonally, SoilEvaporationTensorSeasonally, TotalEvapotranspirationTensorSeasonally ] + +####################################################################### + diff --git a/Archive/SimpleTimeModel/RandomForest/coordinates_1mil.npy b/Archive/SimpleTimeModel/RandomForest/coordinates_1mil.npy new file mode 100644 index 0000000000000000000000000000000000000000..d993b25d485a49dc73564e1073bca329b8e61ccd --- /dev/null +++ b/Archive/SimpleTimeModel/RandomForest/coordinates_1mil.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3deae839e17d61a44c79fd33bf77481210264b5e726ce3cb737a7c1e0c65fbab +size 20800128 diff --git a/Archive/SimpleTimeModel/RandomForest/count_randomforest_parameters.py b/Archive/SimpleTimeModel/RandomForest/count_randomforest_parameters.py new file mode 100644 index 0000000000000000000000000000000000000000..e8f755d11d8365928d63ae56826590640aea2644 --- /dev/null +++ b/Archive/SimpleTimeModel/RandomForest/count_randomforest_parameters.py @@ -0,0 +1,60 @@ +from sklearn.ensemble import RandomForestRegressor +from config import bands_list_order, window_size # For feature size estimation +import numpy as np + +def count_rf_nodes(model): + """Count the total number of nodes across all trees in the RandomForestRegressor.""" + total_nodes = 0 + for estimator in model.estimators_: + total_nodes += estimator.tree_.node_count + return total_nodes + + +def main(): + # Model configuration matching your training script + model = RandomForestRegressor( + n_estimators=1000, # Number of trees + max_depth=10, # Maximum depth of each tree + n_jobs=-1, # Use all available cores + random_state=42 # For reproducibility + ) + + # Since RF requires training data to build trees, we'll simulate a small fit + # to get a realistic node count. In practice, this depends on the data. + # Here, we use a placeholder dataset based on your config. + n_samples = 100 # Small sample size for demonstration + n_features = len(bands_list_order) * window_size * window_size # Flattened feature size + X_dummy = np.random.rand(n_samples, n_features) + y_dummy = np.random.rand(n_samples) + model.fit(X_dummy, y_dummy) + + # Count total nodes across all trees + total_nodes = count_rf_nodes(model) + + # Hyperparameters from your training script + hyperparameters = { + "n_estimators": model.n_estimators, + "max_depth": model.max_depth, + "min_samples_split": model.min_samples_split, + "min_samples_leaf": model.min_samples_leaf, + "random_state": model.random_state + } + + # Print hyperparameter summary + print("RandomForestRegressor Hyperparameters:") + for key, value in hyperparameters.items(): + print(f"{key}: {value}") + + # Print feature size (input dimensionality) + print(f"\nInput Features (Flattened): {n_features} (bands={len(bands_list_order)}, window_size={window_size})") + + # Print total number of nodes + print(f"\nTotal Number of Nodes Across All Trees: {total_nodes:,}") + + # Note on "parameters" + print("\nNote: RandomForest doesn't have 'trainable parameters' like neural networks. " + "Complexity is reflected in the number of trees, nodes, and feature size.") + + +if __name__ == "__main__": + main() diff --git a/Archive/SimpleTimeModel/RandomForest/dataloader/__init__.py b/Archive/SimpleTimeModel/RandomForest/dataloader/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/__init__.cpython-310.pyc b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c02074139986a06e3929bb2b0598587730b3dc9f Binary files /dev/null and b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/__init__.cpython-310.pyc differ diff --git a/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/__init__.cpython-38.pyc b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..404bc3e220f140718bd583c1483c8cbfe3eb5e30 Binary files /dev/null and b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/__init__.cpython-38.pyc differ diff --git a/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataframe_loader.cpython-310.pyc b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataframe_loader.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..49d2e477dc3794cae32097a0d6b059f37c071611 Binary files /dev/null and b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataframe_loader.cpython-310.pyc differ diff --git a/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataframe_loader.cpython-38.pyc b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataframe_loader.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..26642f6671b0ca6b9fc77b2a703d87e658548c53 Binary files /dev/null and b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataframe_loader.cpython-38.pyc differ diff --git a/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataloader.cpython-310.pyc b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataloader.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1a57763b59423e3a13ba933cd12ab34ada862826 Binary files /dev/null and b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataloader.cpython-310.pyc differ diff --git a/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataloader.cpython-38.pyc b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataloader.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6263fbab334a29c3688e9c0a9d82987cf0d73ee8 Binary files /dev/null and b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataloader.cpython-38.pyc differ diff --git a/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataloaderMapping.cpython-310.pyc b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataloaderMapping.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ec258d8b8fb90f4cae717c40194fb2db69279834 Binary files /dev/null and b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataloaderMapping.cpython-310.pyc differ diff --git a/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataloaderMapping.cpython-38.pyc b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataloaderMapping.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..db33a0d805a7afe2be7322f6582ab9fca60a6345 Binary files /dev/null and b/Archive/SimpleTimeModel/RandomForest/dataloader/__pycache__/dataloaderMapping.cpython-38.pyc differ diff --git a/Archive/SimpleTimeModel/RandomForest/dataloader/dataframe_loader.py b/Archive/SimpleTimeModel/RandomForest/dataloader/dataframe_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..069c8d4c17247509d44f224416b3cda8ce3f147e --- /dev/null +++ b/Archive/SimpleTimeModel/RandomForest/dataloader/dataframe_loader.py @@ -0,0 +1,255 @@ + +from config import TIME_BEGINNING ,TIME_END , seasons, years_padded , SamplesCoordinates_Yearly, MatrixCoordinates_1mil_Yearly, DataYearly, SamplesCoordinates_Seasonally, MatrixCoordinates_1mil_Seasonally, DataSeasonally ,file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC +import pandas as pd + +def get_time_range(TIME_BEGINNING= TIME_BEGINNING, TIME_END=TIME_END, seasons=seasons, years_padded=years_padded): + # Define seasons list for matching + seasons_list = ['winter', 'spring', 'summer', 'autumn'] + + # Check if TIME_BEGINNING is a season + is_season = any(season in TIME_BEGINNING.lower() for season in seasons_list) + + if is_season: + # Handle seasons case + start_idx = next(i for i, season in enumerate(seasons) + if TIME_BEGINNING.lower() in season.lower()) + end_idx = next(i for i, season in enumerate(seasons) + if TIME_END.lower() in season.lower()) + + # Get the subset including both start and end + return seasons[start_idx:end_idx + 1] + else: + # Handle years case + start_idx = years_padded.index(TIME_BEGINNING) + end_idx = years_padded.index(TIME_END) + + # Get the subset including both start and end + return years_padded[start_idx:end_idx + 1] + + +def process_paths_yearly(path, year, seen_years): + if 'Elevation' in path: + return path + elif 'MODIS_NPP' in path: + paths = [] + # Add current year + if year not in seen_years: + seen_years.add(year) + paths.append(f"{path}/{year}") + # Add previous year + prev_year = str(int(year) - 1) + if prev_year not in seen_years: + seen_years.add(prev_year) + paths.append(f"{path}/{prev_year}") + return paths + else: + return f"{path}/{year}" + +def create_path_arrays_yearly(SamplesCoordinates_Yearly, DataYearly, selected_years): + seen_years_samples = set() + seen_years_data = set() + + samples_coordinates_array_path = [ + processed_path + for idx, base_path in enumerate(SamplesCoordinates_Yearly) + for year in selected_years + if idx < len(SamplesCoordinates_Yearly) + if (processed_path := process_paths_yearly(base_path, year, seen_years_samples)) is not None + ] + + data_yearly_array_path = [ + processed_path + for idx, base_path in enumerate(DataYearly) + for year in selected_years + if idx < len(DataYearly) + if (processed_path := process_paths_yearly(base_path, year, seen_years_data)) is not None + ] + + return samples_coordinates_array_path, data_yearly_array_path + + +def process_paths(path, season, seen_years): + if 'Elevation' in path: + return path + elif 'MODIS_NPP' in path: + year = season.split('_')[0][:4] # Get year from season + paths = [] + # Add current year + if year not in seen_years: + seen_years.add(year) + paths.append(f"{path}/{year}") + # Add previous year + prev_year = str(int(year) - 1) + if prev_year not in seen_years: + seen_years.add(prev_year) + paths.append(f"{path}/{prev_year}") + return paths + else: + return f"{path}/{season}" + +def create_path_arrays(SamplesCoordinates_Seasonally, DataSeasonally, selected_seasons): + seen_years_samples = set() + seen_years_data = set() + + samples_coordinates_array_path = [ + processed_path + for idx, base_path in enumerate(SamplesCoordinates_Seasonally) + for season in selected_seasons + if idx < len(SamplesCoordinates_Seasonally) + if (processed_path := process_paths(base_path, season, seen_years_samples)) is not None + ] + + data_seasons_array_path = [ + processed_path + for idx, base_path in enumerate(DataSeasonally) + for season in selected_seasons + if idx < len(DataSeasonally) + if (processed_path := process_paths(base_path, season, seen_years_data)) is not None + ] + + return samples_coordinates_array_path, data_seasons_array_path + + + +def separate_and_add_data(TIME_BEGINNING=TIME_BEGINNING, TIME_END=TIME_END, seasons=seasons, years_padded=years_padded, + SamplesCoordinates_Yearly=SamplesCoordinates_Yearly, DataYearly=DataYearly, + SamplesCoordinates_Seasonally=SamplesCoordinates_Seasonally, DataSeasonally=DataSeasonally): + + # Define seasons list for matching + seasons_list = ['winter', 'spring', 'summer', 'autumn'] + + # Check if TIME_BEGINNING is a season + is_season = any(season in TIME_BEGINNING.lower() for season in seasons_list) + + if is_season: + # Handle seasons case + start_idx = next(i for i, season in enumerate(seasons) + if TIME_BEGINNING.lower() in season.lower()) + end_idx = next(i for i, season in enumerate(seasons) + if TIME_END.lower() in season.lower()) + + # Get the seasonal range + selected_seasons = seasons[start_idx:end_idx + 1] + + + # Add seasonal data pairs + return create_path_arrays(SamplesCoordinates_Seasonally, DataSeasonally, selected_seasons) + else: + start_idx = years_padded.index(TIME_BEGINNING) + end_idx = years_padded.index(TIME_END) + selected_years = years_padded[start_idx:end_idx + 1] + return create_path_arrays_yearly(SamplesCoordinates_Yearly, DataYearly, selected_years) + +def add_season_column(dataframe): + seasons_months = { + 'winter': [12, 1, 2], + 'spring': [3, 4, 5], + 'summer': [6, 7, 8], + 'autumn': [9, 10, 11] + } + + month_to_season = { + month: season + for season, months in seasons_months.items() + for month in months + } + + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) + + def get_season_year(row): + if pd.isna(row['survey_date']): + return None + + month = row['survey_date'].month + year = row['survey_date'].year + + if month == 12: + year += 1 + + season = month_to_season.get(month) + if season: + return f"{year}_{season}" + return None + + valid_dates_mask = dataframe['survey_date'] >= '2000-01-01' + dataframe['season'] = None + dataframe.loc[valid_dates_mask, 'season'] = ( + dataframe[valid_dates_mask].apply(get_season_year, axis=1) + ) + + return dataframe + +def filter_dataframe(time_beginning, time_end, max_oc=150): + # Read and prepare data + df = pd.read_excel(file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC) + df = add_season_column(df) + + # Convert columns to numeric + df['GPS_LONG'] = pd.to_numeric(df['GPS_LONG'], errors='coerce') + df['GPS_LAT'] = pd.to_numeric(df['GPS_LAT'], errors='coerce') + df['OC'] = pd.to_numeric(df['OC'], errors='coerce') + + # Basic data quality mask + quality_mask = ( + (df['OC'] <= max_oc) & + df['GPS_LONG'].notna() & + df['GPS_LAT'].notna() & + df['OC'].notna() + ) + + # Check if time_beginning contains a season + seasons = ['winter', 'spring', 'summer', 'autumn'] + is_season = any(season in time_beginning.lower() for season in seasons) + + if is_season: + # Create a list of all valid seasons between time_beginning and time_end + start_year, start_season = time_beginning.split('_') + end_year, end_season = time_end.split('_') + start_year = int(start_year) + end_year = int(end_year) + + valid_seasons = [] + current_year = start_year + season_order = ['winter', 'spring', 'summer', 'autumn'] + start_idx = season_order.index(start_season) + end_idx = season_order.index(end_season) + + while current_year <= end_year: + if current_year == start_year: + season_start = start_idx + else: + season_start = 0 + + if current_year == end_year: + season_end = end_idx + else: + season_end = len(season_order) - 1 + + for season in season_order[season_start:season_end + 1]: + valid_seasons.append(f"{current_year}_{season}") + + current_year += 1 + + # Filter using the valid seasons list + filtered_df = df[ + df['season'].isin(valid_seasons) & + quality_mask + ] + else: + # Filter by year range + start_year = int(time_beginning) + end_year = int(time_end) + filtered_df = df[ + (df['year'].between(start_year, end_year, inclusive='both')) & + quality_mask + ] + + print(f"Initial shape: {df.shape}") + print(f"Final filtered shape: {filtered_df.shape}") + + if filtered_df.empty: + print("\nDebug information:") + print("NaN counts:", df[['GPS_LONG', 'GPS_LAT', 'OC', 'survey_date']].isna().sum()) + print(f"OC range: {df['OC'].min()} to {df['OC'].max()}") + + return filtered_df diff --git a/Archive/SimpleTimeModel/RandomForest/dataloader/dataloader.py b/Archive/SimpleTimeModel/RandomForest/dataloader/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..df37ac0c6bfedc359164fd62d9427dbe9bc955e4 --- /dev/null +++ b/Archive/SimpleTimeModel/RandomForest/dataloader/dataloader.py @@ -0,0 +1,260 @@ + + +import numpy as np +import torch +from torch.utils.data import Dataset, DataLoader +import os +from pathlib import Path +import re +import glob +import pandas as pd + + +def get_metadata(self, id_num): + """Get metadata from filename""" + if id_num not in self.id_to_file: + raise ValueError(f"ID {id_num} not found") + + filename = self.id_to_file[id_num].name + pattern = r'ID(\d+)N(\d+\.\d+)S(\d+\.\d+)W(\d+\.\d+)E(\d+\.\d+)' + match = re.search(pattern, filename) + + if match: + return { + 'id': int(match.group(1)), + 'north': float(match.group(2)), + 'south': float(match.group(3)), + 'west': float(match.group(4)), + 'east': float(match.group(5)) + } + return None + +def get_available_ids(self): + """Return list of available IDs""" + return list(self.id_to_file.keys()) + + +class RasterTensorDataset(Dataset): + def __init__(self, base_path): + """ + Initialize the dataset + + Parameters: + base_path: str, base path to RasterTensorData directory + subfolder: str, name of the subfolder (e.g., 'Elevation') + """ + + self.folder_path = base_path + + # Create ID to filename mapping + self.id_to_file = self._create_id_mapping() + + # Load all numpy arrays into memory (optional, can be modified to load on demand) + self.data_cache = {} + for id_num, filepath in self.id_to_file.items(): + self.data_cache[id_num] = np.load(filepath) + + def _create_id_mapping(self): + """Create a dictionary mapping IDs to their corresponding file paths""" + id_to_file = {} + for file_path in glob.glob(os.path.join(self.folder_path, "*.npy")): + # Extract ID number from filename + match = re.search(r'ID(\d+)N', file_path) + if match: + id_num = int(match.group(1)) + id_to_file[id_num] = file_path + + return id_to_file + + def get_tensor_by_location(self, id_num, x, y, window_size=17): + """ + Get a window_size x window_size square around the specified x,y coordinates + + Parameters: + id_num: int, ID number from filename + x: int, x coordinate + y: int, y coordinate + window_size: int, size of the square window (default 17) + + Returns: + torch.Tensor: window_size x window_size tensor + """ + if id_num not in self.id_to_file: + raise ValueError(f"ID {id_num} not found in dataset") + + # Get the data array + if id_num in self.data_cache: + data = self.data_cache[id_num] + else: + data = np.load(self.id_to_file[id_num]) + + # Calculate window boundaries + half_window = window_size // 2 + x_start = int(max(0, x - half_window)) + x_end = int(min(data.shape[0], x + half_window + 1)) + y_start = int(max(0, y - half_window)) + y_end = int(min(data.shape[1], y + half_window + 1)) + + # Extract window + window = data[x_start:x_end, y_start:y_end] + + # Pad if necessary + if window.shape != (window_size, window_size): + padded_window = np.zeros((window_size, window_size)) + x_offset = half_window - (x - x_start) + y_offset = half_window - (y - y_start) + padded_window[ + x_offset:x_offset+window.shape[0], + y_offset:y_offset+window.shape[1] + ] = window + window = padded_window + + return torch.from_numpy(window).float() + + def __len__(self): + return len(self.id_to_file) + + def __getitem__(self, idx): + # This is a placeholder implementation + # Modify according to your specific needs + id_num = list(self.id_to_file.keys())[idx] + return self.data_cache[id_num] + +# Example usage: +""" +# Initialize the dataset +base_path = "/content/drive/MyDrive/Colab Notebooks/MappingSOC/Data/RasterTensorData" +dataset = RasterTensorDataset(base_path, "Elevation") + +# Get the dictionary mapping IDs to filenames +id_mapping = dataset.id_to_file +print("ID to filename mapping:", id_mapping) + +# Get a 17x17 window for a specific location +id_num = 10 # example ID +x, y = 100, 100 # example coordinates +window = dataset.get_tensor_by_location(id_num, x, y) +print("Window shape:", window.shape) + +# Create a DataLoader if needed +from torch.utils.data import DataLoader +dataloader = DataLoader(dataset, batch_size=4, shuffle=True) +""" + + +class MultiRasterDataset(Dataset): + def __init__(self, samples_coordinates_array_subfolders , data_array_subfolders , dataframe): + """ + Parameters: + subfolders: list of str, names of subfolders to include + dataframe: pandas.DataFrame, contains columns GPS_LONG, GPS_LAT, and OC (target variable) + """ + self.data_array_subfolders = data_array_subfolders + self.seasonalityBased = self.check_seasonality(data_array_subfolders) + + self.samples_coordinates_array_subfolders = samples_coordinates_array_subfolders + self.dataframe = dataframe + self.datasets = { + self.get_last_three_folders(subfolder): RasterTensorDataset(subfolder) + for subfolder in self.data_array_subfolders + } + + self.coordinates = { + self.get_last_three_folders(subfolder): np.load(f"{subfolder}/coordinates.npy")#[np.isfinite(np.load(f"{subfolder}/coordinates.npy"))] + for subfolder in self.samples_coordinates_array_subfolders + } + + def check_seasonality(self,data_array_subfolders): + seasons = ['winter', 'spring', 'summer', 'autumn'] + + # Check if any subfolder contains a season name + is_seasonal = any( + any(season in subfolder.lower() for season in seasons) + for subfolder in data_array_subfolders + ) + + return 1 if is_seasonal else 0 + + + def get_last_three_folders(self,path): + # Split the path into components + parts = path.rstrip('/').split('/') + # Return last 3 components, or all if less than 3 + return '/'.join(parts[-2:]) + + def find_coordinates_index(self, subfolder, longitude, latitude): + """ + Finds the index of the matching coordinates in the subfolder's coordinates.npy file. + + Parameters: + subfolder: str, name of the subfolder + longitude: float, longitude to match + latitude: float, latitude to match + + Returns: + tuple: (id_num, x, y) if match is found, otherwise raises an error + """ + coords = self.coordinates[subfolder] + # Assuming the first two columns of `coordinates.npy` are longitude and latitude + match = np.where((coords[:, 1] == longitude) & (coords[:, 0] == latitude))[0] + if match.size == 0: + raise ValueError(f"{coords} Coordinates ({longitude}, {latitude}) not found in {subfolder}") + + # Return id_num, x, y from the same row + return coords[match[0], 2], coords[match[0], 3], coords[match[0], 4] + + def __getitem__(self, index): + """ + Retrieve tensor and target value for a given index. + + Parameters: + index: int, index of the row in the dataframe + + Returns: + tuple: (tensor, OC), where tensor is the data and OC is the target variable + """ + row = self.dataframe.iloc[index] + longitude, latitude, oc = row["GPS_LONG"], row["GPS_LAT"], row["OC"] + tensors = [] + + filtered_array = self.filter_by_season_or_year(row['season'],row['year'],self.seasonalityBased) + + for subfolder in filtered_array: + subfolder = self.get_last_three_folders(subfolder) + id_num, x, y = self.find_coordinates_index(subfolder, longitude, latitude) + tensor = self.datasets[subfolder].get_tensor_by_location(id_num, x, y) + if tensor is not None: + tensors.append(tensor) + # Combine all tensors into a single tensor + return longitude, latitude, torch.stack(tensors), oc + + + def filter_by_season_or_year(self, season,year,Season_or_year): + + if Season_or_year: + filtered_array = [ + path for path in self.samples_coordinates_array_subfolders + if ('Elevation' in path) or + ('MODIS_NPP' in path and path.endswith(str(year))) or + (not 'Elevation' in path and not 'MODIS_NPP' in path and path.endswith(season)) + ] + + else: + filtered_array = [ + path for path in self.samples_coordinates_array_subfolders + if ('Elevation' in path) or + (not 'Elevation' in path and path.endswith(str(year))) + ] + + return filtered_array + + def __len__(self): + """ + Return the number of samples in the dataset. + """ + return len(self.dataframe) + + def get_tensor_by_location(self, subfolder, id_num, x, y): + """Get tensor from specific subfolder dataset""" + return self.datasets[subfolder].get_tensor_by_location(id_num, x, y) + diff --git a/Archive/SimpleTimeModel/RandomForest/dataloader/dataloaderMapping.py b/Archive/SimpleTimeModel/RandomForest/dataloader/dataloaderMapping.py new file mode 100644 index 0000000000000000000000000000000000000000..50e9de0881e5ce746a0d628b1869850e7b294754 --- /dev/null +++ b/Archive/SimpleTimeModel/RandomForest/dataloader/dataloaderMapping.py @@ -0,0 +1,188 @@ + +import numpy as np +import torch +from torch.utils.data import Dataset, DataLoader +import os +from pathlib import Path +import re +import glob + + +class RasterTensorDatasetMapping(Dataset): + def __init__(self, base_path): + """ + Initialize the dataset + + Parameters: + base_path: str, base path to RasterTensorData directory + subfolder: str, name of the subfolder (e.g., 'Elevation') + """ + # Replace "OC_LUCAS_LFU_LfL_Coordinates" with "RasterTensorData" in the base path + self.base_path = Path(base_path.replace("Coordinates1Mil", "RasterTensorData")) + self.folder_path = self.base_path + + # Create ID to filename mapping + self.id_to_file = self._create_id_mapping() + + # Load all numpy arrays into memory (optional, can be modified to load on demand) + self.data_cache = {} + for id_num, filepath in self.id_to_file.items(): + self.data_cache[id_num] = np.load(filepath) + + def _create_id_mapping(self): + """Create a dictionary mapping IDs to their corresponding file paths""" + id_to_file = {} + + for file_path in self.folder_path.glob("*.npy"): + # Extract ID number from filename + match = re.search(r'ID(\d+)N', file_path.name) + if match: + id_num = int(match.group(1)) + id_to_file[id_num] = file_path + + return id_to_file + + def get_tensor_by_location(self, id_num, x, y, window_size=17): + """ + Get a window_size x window_size square around the specified x,y coordinates + + Parameters: + id_num: int, ID number from filename + x: int, x coordinate + y: int, y coordinate + window_size: int, size of the square window (default 17) + + Returns: + torch.Tensor: window_size x window_size tensor + """ + if id_num not in self.id_to_file: + raise ValueError(f"ID {id_num} not found in dataset") + + # Get the data array + if id_num in self.data_cache: + data = self.data_cache[id_num] + else: + data = np.load(self.id_to_file[id_num]) + + # Calculate window boundaries + half_window = window_size // 2 + x_start = int(max(0, x - half_window)) + x_end = int(min(data.shape[0], x + half_window + 1)) + y_start = int(max(0, y - half_window)) + y_end = int(min(data.shape[1], y + half_window + 1)) + + # Extract window + window = data[x_start:x_end, y_start:y_end] + + # Pad if necessary + if window.shape != (window_size, window_size): + padded_window = np.zeros((window_size, window_size)) + x_offset = half_window - (x - x_start) + y_offset = half_window - (y - y_start) + padded_window[ + x_offset:x_offset+window.shape[0], + y_offset:y_offset+window.shape[1] + ] = window + window = padded_window + + return torch.from_numpy(window).float() + + def __len__(self): + return len(self.id_to_file) + + def __getitem__(self, idx): + # This is a placeholder implementation + # Modify according to your specific needs + id_num = list(self.id_to_file.keys())[idx] + return self.data_cache[id_num] + +# Example usage: +""" +# Initialize the dataset +base_path = "/content/drive/MyDrive/Colab Notebooks/MappingSOC/Data/RasterTensorData" +dataset = RasterTensorDataset(base_path, "Elevation") + +# Get the dictionary mapping IDs to filenames +id_mapping = dataset.id_to_file +print("ID to filename mapping:", id_mapping) + +# Get a 17x17 window for a specific location +id_num = 10 # example ID +x, y = 100, 100 # example coordinates +window = dataset.get_tensor_by_location(id_num, x, y) +print("Window shape:", window.shape) + +# Create a DataLoader if needed +from torch.utils.data import DataLoader +dataloader = DataLoader(dataset, batch_size=4, shuffle=True) +""" + + +class MultiRasterDatasetMapping(Dataset): + def __init__(self, subfolders, dataframe): + """ + Parameters: + subfolders: list of str, names of subfolders to include + dataframe: pandas.DataFrame, contains columns GPS_LONG, GPS_LAT, and OC (target variable) + """ + self.subfolders = subfolders + self.dataframe = dataframe + self.datasets = { + subfolder: RasterTensorDatasetMapping(subfolder) + for subfolder in subfolders + } + self.coordinates = { + subfolder: np.load(f"{subfolder}/coordinates.npy") + for subfolder in subfolders + } + + def find_coordinates_index(self, subfolder, longitude, latitude): + """ + Finds the index of the matching coordinates in the subfolder's coordinates.npy file. + + Parameters: + subfolder: str, name of the subfolder + longitude: float, longitude to match + latitude: float, latitude to match + + Returns: + tuple: (id_num, x, y) if match is found, otherwise raises an error + """ + coords = self.coordinates[subfolder] + # Assuming the first two columns of `coordinates.npy` are longitude and latitude + match = np.where((coords[:, 1] == longitude) & (coords[:, 0] == latitude))[0] + if match.size == 0: + raise ValueError(f"Coordinates ({longitude}, {latitude}) not found in {subfolder}") + + # Return id_num, x, y from the same row + return coords[match[0], 2], coords[match[0], 3], coords[match[0], 4] + + def __getitem__(self, index): + """ + Retrieve tensor and target value for a given index. + + Parameters: + index: int, index of the row in the dataframe + + Returns: + tuple: (tensor, OC), where tensor is the data and OC is the target variable + """ + row = self.dataframe.iloc[index] + longitude, latitude = row["longitude"], row["latitude"] + + tensors = {} + for subfolder in self.subfolders: + id_num, x, y = self.find_coordinates_index(subfolder, longitude, latitude) + tensors[subfolder] = self.datasets[subfolder].get_tensor_by_location(id_num, x, y) + + return longitude, latitude, tensors + + def __len__(self): + """ + Return the number of samples in the dataset. + """ + return len(self.dataframe) + + def get_tensor_by_location(self, subfolder, id_num, x, y): + """Get tensor from specific subfolder dataset""" + return self.datasets[subfolder].get_tensor_by_location(id_num, x, y) diff --git a/Archive/SimpleTimeModel/RandomForest/launcher.md b/Archive/SimpleTimeModel/RandomForest/launcher.md new file mode 100644 index 0000000000000000000000000000000000000000..1ef24d4f0bb92591b86b6161b835fb8491cf19c8 --- /dev/null +++ b/Archive/SimpleTimeModel/RandomForest/launcher.md @@ -0,0 +1,10 @@ + +IN ORDER TO REGRESS THIS MODEL we can 100 cpus: + +''' +condor_submit_bid 1000 -i -append request_memory=481920 -append request_cpus=100 -append request_disk=200G -append request_gpus=1 -append 'requirements = CUDADeviceName == "NVIDIA A100-SXM4-40GB"' +''' + + +We then use: +$ python running.py diff --git a/Archive/SimpleTimeModel/RandomForest/mapping.py b/Archive/SimpleTimeModel/RandomForest/mapping.py new file mode 100644 index 0000000000000000000000000000000000000000..eca44e504e0df685dbefe1923e0ffd8f7e94ff2e --- /dev/null +++ b/Archive/SimpleTimeModel/RandomForest/mapping.py @@ -0,0 +1,225 @@ +import pandas as pd +import numpy as np +import xgboost as xgb +import matplotlib.pyplot as plt +from scipy.interpolate import griddata +import geopandas as gpd +from config import (base_path_data , file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC , MAX_OC , + TIME_BEGINNING , TIME_END , INFERENCE_TIME) +import concurrent.futures +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataloader.dataloaderMapping import MultiRasterDatasetMapping +from sklearn.utils import shuffle +import copy +from torch.utils.data import DataLoader, Subset +import torch +import tqdm +################################################################## + +# Loading the Data + + + +################################################################## + + +def create_prediction_visualizations(year,coordinates, predictions, save_path): + """ + Create and save three separate map visualizations of predictions in Bavaria plus a triptych, + with timestamps in filenames. + + Parameters: + coordinates (numpy.array): Array of coordinates (longitude, latitude) + predictions (numpy.array): Array of prediction values + save_path (str): Directory path where the images should be saved + """ + import os + import numpy as np + import matplotlib.pyplot as plt + from scipy.interpolate import griddata + import geopandas as gpd + from datetime import datetime + + # Get current timestamp + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + + # Create directories if they don't exist + os.makedirs(save_path, exist_ok=True) + individual_path = os.path.join(save_path, 'individual') + os.makedirs(individual_path, exist_ok=True) + + # Load Bavaria boundaries + bavaria = gpd.read_file('https://raw.githubusercontent.com/isellsoap/deutschlandGeoJSON/main/2_bundeslaender/4_niedrig.geo.json') + bavaria = bavaria[bavaria['name'] == 'Bayern'] + + # Create interpolation grid with higher resolution + grid_x = np.linspace(coordinates[:, 0].min(), coordinates[:, 0].max(), 300) + grid_y = np.linspace(coordinates[:, 1].min(), coordinates[:, 1].max(), 300) + grid_x, grid_y = np.meshgrid(grid_x, grid_y) + + # Interpolate values with cubic interpolation + grid_z = griddata(coordinates, predictions, (grid_x, grid_y), method='linear') + + # Common plotting parameters + plot_params = { + 'figsize': (12, 10), + 'dpi': 300 + } + + # Function to set common elements for all plots + def set_common_elements(ax, title): + bavaria.boundary.plot(ax=ax, color='black', linewidth=1) + ax.set_title(title, fontsize=12, pad=20) + ax.set_xlabel('Longitude') + ax.set_ylabel('Latitude') + ax.grid(True) + + # Function to generate filename with timestamp + def get_filename(base_name): + return f"{base_name}_MAX_OC_{str(MAX_OC)}_Beginning_{TIME_BEGINNING}_End_{TIME_END}__InferenceTime{INFERENCE_TIME}_{timestamp}.png" + + # 1. Interpolated surface + fig_interp, ax_interp = plt.subplots(**plot_params) + contour = ax_interp.contourf(grid_x, grid_y, grid_z, + levels=50, + cmap='viridis', + alpha=0.8) + set_common_elements(ax_interp, 'Interpolated Predicted Values') + plt.colorbar(contour, ax=ax_interp, label='Predicted Values') + plt.savefig(os.path.join(individual_path, get_filename(f'{year}_bavaria_interpolated')), + bbox_inches='tight') + plt.close() + + # 2. Scatter plot + fig_scatter, ax_scatter = plt.subplots(**plot_params) + scatter = ax_scatter.scatter(coordinates[:, 0], coordinates[:, 1], + c=predictions, + cmap='viridis', + alpha=0.6, + s=50) + set_common_elements(ax_scatter, 'Scatter Plot of Predicted Values') + plt.colorbar(scatter, ax=ax_scatter, label='Predicted Values') + plt.savefig(os.path.join(individual_path, get_filename(f'{year}_bavaria_scatter')), + bbox_inches='tight') + plt.close() + + # 3. Discrete points + fig_discrete, ax_discrete = plt.subplots(**plot_params) + discrete = ax_discrete.scatter(coordinates[:, 0], coordinates[:, 1], + c=predictions, + cmap='viridis', + alpha=1.0, + s=20) + set_common_elements(ax_discrete, 'Discrete Points of Predicted Values') + plt.colorbar(discrete, ax=ax_discrete, label='Predicted Values') + plt.savefig(os.path.join(individual_path, get_filename(f'{year}_bavaria_discrete')), + bbox_inches='tight') + plt.close() + + # Create triptych + fig_triptych = plt.figure(figsize=(30, 10)) + + # Interpolated plot + ax1 = plt.subplot(131) + contour = ax1.contourf(grid_x, grid_y, grid_z, + levels=50, + cmap='viridis', + alpha=0.8) + set_common_elements(ax1, 'Interpolated Predicted Values') + plt.colorbar(contour, ax=ax1, label='Predicted Values') + + # Scatter plot + ax2 = plt.subplot(132) + scatter = ax2.scatter(coordinates[:, 0], coordinates[:, 1], + c=predictions, + cmap='viridis', + alpha=0.6, + s=50) + set_common_elements(ax2, 'Scatter Plot of Predicted Values') + plt.colorbar(scatter, ax=ax2, label='Predicted Values') + + # Discrete points + ax3 = plt.subplot(133) + discrete = ax3.scatter(coordinates[:, 0], coordinates[:, 1], + c=predictions, + cmap='viridis', + alpha=1.0, + s=20) + set_common_elements(ax3, 'Discrete Points of Predicted Values') + plt.colorbar(discrete, ax=ax3, label='Predicted Values') + + plt.tight_layout() + plt.savefig(os.path.join(save_path, get_filename(f'{year}_bavaria_triptych')), + dpi=300, + bbox_inches='tight') + plt.close() + +# Example usage: +# create_prediction_map(bavaria, coordinates, predictions, save_path='output/maps') + + + + +# Define the worker function +def process_batch(df_chunk, model_copy, bands_yearly, batch_size): + # Create dataset and dataloader for this chunk + chunk_dataset = MultiRasterDatasetMapping(bands_yearly, df_chunk) + chunk_dataloader = DataLoader(chunk_dataset, batch_size=batch_size, shuffle=True) + + chunk_coordinates = [] + chunk_features = [] + + for longitudes, latitudes, batch_features in chunk_dataloader: + # Store coordinates for plotting + chunk_coordinates.append(np.column_stack((longitudes.numpy(), latitudes.numpy()))) + + # Concatenate all values in the batch_features dictionary + concatenated_features = np.concatenate([value.numpy() for value in batch_features.values()], axis=1) + # Flatten the features + flattened_features = concatenated_features.reshape(concatenated_features.shape[0], -1) + chunk_features.extend(flattened_features) + + # Convert to arrays + chunk_features = np.array(chunk_features) + chunk_coordinates = np.vstack(chunk_coordinates) + + # Make predictions using the model copy + chunk_predictions = model_copy.predict(chunk_features) + + return chunk_coordinates, chunk_predictions + +def parallel_predict(df_full, model, bands_yearly, batch_size=4, num_threads=4): + # Shuffle the DataFrame + df_shuffled = df_full.sample(frac=1, random_state=42).reset_index(drop=True) + + # Split DataFrame into chunks for each thread + chunk_size = len(df_shuffled) // num_threads + df_chunks = [df_shuffled[i:i + chunk_size] for i in range(0, len(df_shuffled), chunk_size)] + + # Ensure that predictions and coordinates match + all_coordinates = [] + all_predictions = [] + + # Use ThreadPoolExecutor for multithreading + print(num_threads) + + + with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor: + futures = [ + executor.submit( + process_batch, + chunk, + copy.deepcopy(model), + bands_yearly, + batch_size + ) for chunk in df_chunks + ] + for future in futures: + coordinates, predictions = future.result() + all_coordinates.append(coordinates) + all_predictions.append(predictions) + # Combine results from all threads + all_coordinates = np.vstack(all_coordinates) + all_predictions = np.concatenate(all_predictions) + + return all_coordinates, all_predictions \ No newline at end of file diff --git a/Archive/SimpleTimeModel/RandomForest/predictions_1mil.npy b/Archive/SimpleTimeModel/RandomForest/predictions_1mil.npy new file mode 100644 index 0000000000000000000000000000000000000000..22cde76a1294873725c4c3cbb6503a71fdb7139b --- /dev/null +++ b/Archive/SimpleTimeModel/RandomForest/predictions_1mil.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef3dfea373615bf00840a0ff9a35bc448f72cb288ef47b247ea35ce13e30bf4d +size 10400128 diff --git a/Archive/SimpleTimeModel/RandomForest/running.py b/Archive/SimpleTimeModel/RandomForest/running.py new file mode 100644 index 0000000000000000000000000000000000000000..8f130573557dad9279e9812e084992ed3b38809f --- /dev/null +++ b/Archive/SimpleTimeModel/RandomForest/running.py @@ -0,0 +1,173 @@ +import numpy as np +import matplotlib.pyplot as plt +from dataloader.dataloader import MultiRasterDataset +from dataloader.dataloaderMapping import MultiRasterDatasetMapping +from dataloader.dataframe_loader import filter_dataframe, separate_and_add_data +from sklearn.ensemble import RandomForestRegressor +import pandas as pd +from tqdm import tqdm +from pathlib import Path +from config import (TIME_BEGINNING, TIME_END, INFERENCE_TIME, MAX_OC, seasons, + SamplesCoordinates_Yearly, MatrixCoordinates_1mil_Yearly, DataYearly, + SamplesCoordinates_Seasonally, MatrixCoordinates_1mil_Seasonally, + DataSeasonally, file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC, years_padded) +from mapping import create_prediction_visualizations, parallel_predict +from torch.utils.data import Dataset, DataLoader +import multiprocessing +import argparse + +def modify_matrix_coordinates(MatrixCoordinates_1mil_Yearly=MatrixCoordinates_1mil_Yearly, + MatrixCoordinates_1mil_Seasonally=MatrixCoordinates_1mil_Seasonally, + INFERENCE_TIME=INFERENCE_TIME): + # Update MatrixCoordinates_1mil_Seasonally + for i, path in enumerate(MatrixCoordinates_1mil_Seasonally): + folders = path.split('/') + last_folder = folders[-1] + if last_folder == 'Elevation': + continue + elif last_folder == 'MODIS_NPP': + new_path = f"{path}/{INFERENCE_TIME[:4]}" + else: + new_path = f"{path}/{INFERENCE_TIME}" + MatrixCoordinates_1mil_Seasonally[i] = new_path + + # Update MatrixCoordinates_1mil_Yearly + for i, path in enumerate(MatrixCoordinates_1mil_Yearly): + if 'Elevation' in path: + continue + new_path = f"{path}/{INFERENCE_TIME[:4]}" + MatrixCoordinates_1mil_Yearly[i] = new_path + + return MatrixCoordinates_1mil_Yearly, MatrixCoordinates_1mil_Seasonally + +def parse_arguments(): + parser = argparse.ArgumentParser(description='Random Forest Regression for SOC Mapping') + parser.add_argument('--model', type=str, choices=['rf'], + default='rf', + help='Model type: rf (Random Forest)') + return parser.parse_args() + +def get_top_sampling_years(file_path, top_n=3): + """Get the top n years with most samples from Excel file""" + try: + df = pd.read_excel(file_path) + year_counts = df['year'].value_counts() + top_years = year_counts.head(top_n) + print(f"\nTop {top_n} years with the most samples:") + for year, count in top_years.items(): + print(f"Year {year}: {count} samples") + return df, top_years + except Exception as e: + print(f"Error reading file: {str(e)}") + return None, None + +def flatten_paths(path_list): + flattened = [] + for item in path_list: + if isinstance(item, list): + flattened.extend(flatten_paths(item)) + else: + flattened.append(item) + return flattened + +def main(): + args = parse_arguments() + df = filter_dataframe(TIME_BEGINNING, TIME_END, MAX_OC) + + # Prepare data paths + samples_coordinates_array_path, data_array_path = separate_and_add_data() + samples_coordinates_array_path = list(dict.fromkeys(flatten_paths(samples_coordinates_array_path))) + data_array_path = list(dict.fromkeys(flatten_paths(data_array_path))) + + # Create dataset and dataloader + dataset = MultiRasterDataset(samples_coordinates_array_path, data_array_path, df) + print("Dataset length:", len(df)) + dataloader = DataLoader(dataset, batch_size=2, shuffle=True) + + # Prepare training data + X_train, y_train = [], [] + coordinates = [] + + for longitudes, latitudes, batch_features, batch_targets in dataloader: + longs = longitudes.numpy() + lats = latitudes.numpy() + valid_mask = ~(np.isnan(longs) | np.isnan(lats)) + + if not np.any(valid_mask): + continue + + coordinates.append(np.column_stack((longs[valid_mask], lats[valid_mask]))) + features_np = batch_features.numpy() + flattened_features = features_np.reshape(features_np.shape[0], -1) + filtered_features = flattened_features[valid_mask] + filtered_targets = batch_targets.numpy()[valid_mask] + + X_train.extend(filtered_features) + y_train.extend(filtered_targets) + + X_train = np.array(X_train) + y_train = np.array(y_train) + coordinates = np.vstack(coordinates) + + # Train RandomForest model + model = RandomForestRegressor(n_estimators=1000, max_depth=10, n_jobs=-1, random_state=42) + model.fit(X_train, y_train) + print("RandomForest model trained successfully!") + + # Make predictions + predictions = model.predict(X_train) + + # Training set visualization + plt.figure(figsize=(10, 8)) + scatter = plt.scatter(coordinates[:, 0], coordinates[:, 1], + c=predictions, cmap='viridis', alpha=0.6) + plt.colorbar(scatter, label='Predicted Values') + plt.xlabel('Longitude') + plt.ylabel('Latitude') + plt.title('Training Set Predictions') + plt.grid(True) + plt.show() + + # Load full prediction coordinates + file_path_coords = "/home/vfourel/SOCProject/SOCmapping/Data/Coordinates1Mil/coordinates_Bavaria_1mil.csv" + try: + df_full = pd.read_csv(file_path_coords) + print(df_full.head()) + except Exception as e: + print(f"Error loading coordinates file: {e}") + return + + # Modify paths for inference + BandsYearly_1milPoints, _ = modify_matrix_coordinates() + num_cpus = multiprocessing.cpu_count() + + # Parallel prediction + coordinates, predictions = parallel_predict( + df_full=df_full, + model=model, + bands_yearly=BandsYearly_1milPoints, + batch_size=8, + num_threads=num_cpus + ) + save_path_coords = "coordinates_1mil.npy" + save_path_preds = "predictions_1mil.npy" + + np.save(save_path_coords, coordinates) + np.save(save_path_preds, predictions) + # Final visualization + plt.figure(figsize=(10, 8)) + scatter = plt.scatter(coordinates[:, 0], coordinates[:, 1], + c=predictions, cmap='viridis', alpha=0.6) + plt.colorbar(scatter, label='Predicted Values') + plt.xlabel('Longitude') + plt.ylabel('Latitude') + plt.title('Full Map Predictions') + plt.grid(True) + plt.show() + + # Save predictions + save_path = '/home/vfourel/SOCProject/SOCmapping/predictions_plots/randomForest_plots' + create_prediction_visualizations(INFERENCE_TIME, coordinates, predictions, save_path) + +if __name__ == "__main__": + main() diff --git a/Archive/SimpleTimeModel/RandomForest/tmp.py b/Archive/SimpleTimeModel/RandomForest/tmp.py new file mode 100644 index 0000000000000000000000000000000000000000..8047f11890ee7f018dab8a80fcf0109efb39ce64 --- /dev/null +++ b/Archive/SimpleTimeModel/RandomForest/tmp.py @@ -0,0 +1,23 @@ +import numpy as np + +def check_shapes(): + try: + # Load the saved numpy arrays + predictions = np.load("predictions_1mil.npy") + coordinates = np.load("coordinates_1mil.npy") + + # Print their shapes + print("Predictions shape:", predictions.shape) + print("Coordinates shape:", coordinates.shape) + + # Print total number of elements for comparison + print("Number of predictions:", predictions.size) + print("Number of coordinate pairs:", coordinates.shape[0]) + + except FileNotFoundError as e: + print(f"Error: One or both files not found - {e}") + except Exception as e: + print(f"Error loading files: {e}") + +if __name__ == "__main__": + check_shapes() diff --git a/Archive/SimpleTimeModel/SimpleCNN/Window_size41/simpletimecnn_model_MAX_OC_150_TIME_BEGINNING_2007_TIME_END_2023_LOSS_composite_l1_TRANSFORM_none_BEST_R2_0.5107.pth 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['Elevation','LAI','LST','MODIS_NPP','SoilEvaporation','TotalEvapotranspiration'] +MAX_OC = 150 +num_epochs = 200 + + +def generate_seasonal_list(): + seasons = ['winter', 'spring', 'summer', 'autumn'] # Reordered with winter first + years = range(2000, 2025) # 2000 to 2024 + + seasonal_list = [] + + for year in years: + for season in seasons: + # Skip winter 2000 as it belongs to 1999 + if year == 2000 and season == 'winter': + continue + # For 2024, only include winter + if year == 2024 and season != 'winter': + continue + seasonal_list.append(f"{year}_{season}") + + return seasonal_list + +# Generate the list +seasons = generate_seasonal_list() + +years_padded = [f"{year:04d}" for year in range(2000, 2024)] + + +####################################################################### +# YEARLY PATH + +elevationBandMatrixCoordinates_Yearly = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates_v2/StaticValue/Elevation' +LAIBandMatrixCoordinates_Yearly = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates_v2/YearlyValue/LAI' +LSTBandMatrixCoordinates_Yearly = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates_v2/YearlyValue/LST' +MODIS_NPPBandMatrixCoordinates_Yearly = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates_v2/YearlyValue/MODIS_NPP' +SoilEvaporationBandMatrixCoordinates_Yearly = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates_v2/YearlyValue/SoilEvaporation' +TotalEvapotranspirationBandMatrixCoordinates_Yearly = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates_v2/YearlyValue/TotalEvapotranspiration' + +SamplesCoordinates_Yearly = [elevationBandMatrixCoordinates_Yearly, LAIBandMatrixCoordinates_Yearly, LSTBandMatrixCoordinates_Yearly, MODIS_NPPBandMatrixCoordinates_Yearly , SoilEvaporationBandMatrixCoordinates_Yearly, TotalEvapotranspirationBandMatrixCoordinates_Yearly ] + +elevationBandMatrixCoordinates_1milPoints_Yearly = f'{base_path_data}/Coordinates1Mil/StaticValue/Elevation' +LAIBandMatrixCoordinates_1milPoints_Yearly = f'{base_path_data}/Coordinates1Mil/YearlyValue/LAI' +LSTBandMatrixCoordinates_1milPoints_Yearly = f'{base_path_data}/Coordinates1Mil/YearlyValue/LST' +MODIS_NPPBandMatrixCoordinates_1milPoints_Yearly = f'{base_path_data}/Coordinates1Mil/YearlyValue/MODIS_NPP' +SoilEvaporationBandMatrixCoordinates_1milPoints_Yearly = f'{base_path_data}/Coordinates1Mil/YearlyValue/SoilEvaporation' +TotalEvapotranspirationBandMatrixCoordinates_1milPoints_Yearly = f'{base_path_data}/Coordinates1Mil/YearlyValue/TotalEvapotranspiration' + +MatrixCoordinates_1mil_Yearly = [elevationBandMatrixCoordinates_1milPoints_Yearly, LAIBandMatrixCoordinates_1milPoints_Yearly, LSTBandMatrixCoordinates_1milPoints_Yearly, MODIS_NPPBandMatrixCoordinates_1milPoints_Yearly, SoilEvaporationBandMatrixCoordinates_1milPoints_Yearly, TotalEvapotranspirationBandMatrixCoordinates_1milPoints_Yearly ] + +elevationTensorData = f'{base_path_data}/RasterTensorData/StaticValue/Elevation' +LAITensorDataYearly = f'{base_path_data}/RasterTensorData/YearlyValue/LAI' +LSTTensorDataYearly = f'{base_path_data}/RasterTensorData/YearlyValue/LST' +MODIS_NPPTensorDataYearly = f'{base_path_data}/RasterTensorData/YearlyValue/MODIS_NPP' +SoilEvaporationTensorDataYearly = f'{base_path_data}/RasterTensorData/YearlyValue/SoilEvaporation' +TotalEvapotranspirationTensorDataYearly = f'{base_path_data}/RasterTensorData/YearlyValue/TotalEvapotranspiration' + +DataYearly = [elevationTensorData , LAITensorDataYearly , LSTTensorDataYearly, MODIS_NPPTensorDataYearly, SoilEvaporationTensorDataYearly, TotalEvapotranspirationTensorDataYearly ] + +####################################################################### +#SEASON PATHS + +elevationBandMatrixCoordinates_Seasonally = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates_v2/StaticValue/Elevation' +LAIBandMatrixCoordinates_Seasonally = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates_v2/SeasonalValue/LAI' +LSTBandMatrixCoordinates_Seasonally = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates_v2/SeasonalValue/LST' +MODIS_NPPBandMatrixCoordinates_Seasonally = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates_v2/YearlyValue/MODIS_NPP' +SoilEvaporationBandMatrixCoordinates_Seasonally = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates_v2/SeasonalValue/SoilEvaporation' +TotalEvapotranspirationBandMatrixCoordinates_Seasonally = f'{base_path_data}/OC_LUCAS_LFU_LfL_Coordinates_v2/SeasonalValue/TotalEvapotranspiration' + +SamplesCoordinates_Seasonally = [elevationBandMatrixCoordinates_Seasonally , LAIBandMatrixCoordinates_Seasonally, LSTBandMatrixCoordinates_Seasonally, MODIS_NPPBandMatrixCoordinates_Seasonally, SoilEvaporationBandMatrixCoordinates_Seasonally, TotalEvapotranspirationBandMatrixCoordinates_Seasonally ] + +elevationBandMatrixCoordinates_1milPoints_Seasonally = f'{base_path_data}/Coordinates1Mil/StaticValue/Elevation' +LAIBandMatrixCoordinates_1milPoints_Seasonally = f'{base_path_data}/Coordinates1Mil/SeasonalValue/LAI' +LSTBandMatrixCoordinates_1milPoints_Seasonally = f'{base_path_data}/Coordinates1Mil/SeasonalValue/LST' +MODIS_NPPBandMatrixCoordinates_1milPoints_Seasonally = f'{base_path_data}/Coordinates1Mil/YearlyValue/MODIS_NPP' +SoilEvaporationBandMatrixCoordinates_1milPoints_Seasonally = f'{base_path_data}/Coordinates1Mil/SeasonalValue/SoilEvaporation' +TotalEvapotranspirationBandMatrixCoordinates_1milPoints_Seasonally = f'{base_path_data}/Coordinates1Mil/SeasonalValue/TotalEvapotranspiration' + +MatrixCoordinates_1mil_Seasonally = [elevationBandMatrixCoordinates_1milPoints_Seasonally , LAIBandMatrixCoordinates_1milPoints_Seasonally, LSTBandMatrixCoordinates_1milPoints_Seasonally, MODIS_NPPBandMatrixCoordinates_1milPoints_Seasonally, SoilEvaporationBandMatrixCoordinates_1milPoints_Seasonally, TotalEvapotranspirationBandMatrixCoordinates_1milPoints_Seasonally ] + +elevationTensorData = f'{base_path_data}/RasterTensorData/StaticValue/Elevation' +LAITensorDataSeasonally = f'{base_path_data}/RasterTensorData/SeasonalValue/LAI' +LSTTensorDataSeasonally = f'{base_path_data}/RasterTensorData/SeasonalValue/LST' +MODIS_NPPTensorSeasonally = f'{base_path_data}/RasterTensorData/YearlyValue/MODIS_NPP' +SoilEvaporationTensorSeasonally = f'{base_path_data}/RasterTensorData/SeasonalValue/SoilEvaporation' +TotalEvapotranspirationTensorSeasonally = f'{base_path_data}/RasterTensorData/SeasonalValue/TotalEvapotranspiration' + +DataSeasonally = [elevationTensorData, LAITensorDataSeasonally, LSTTensorDataSeasonally, MODIS_NPPTensorSeasonally, SoilEvaporationTensorSeasonally, TotalEvapotranspirationTensorSeasonally ] + +####################################################################### + diff --git a/Archive/SimpleTimeModel/SimpleCNN/count_multiyearcnn_parameters.py b/Archive/SimpleTimeModel/SimpleCNN/count_multiyearcnn_parameters.py new file mode 100644 index 0000000000000000000000000000000000000000..a3f13c2d579233162cec90cb1e83b19c5c6df90d --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/count_multiyearcnn_parameters.py @@ -0,0 +1,33 @@ +import torch +from modelCNNMultiYear import Small3DCNN +from config import bands_list_order, window_size, time_before + + +def count_parameters(model): + """Count the total number of trainable parameters in a PyTorch model.""" + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + +def main(): + # Model configuration matching your training script + model = Small3DCNN( + input_channels=len(bands_list_order), # Number of input bands + input_height=window_size, # Spatial height + input_width=window_size, # Spatial width + input_time=time_before # Temporal dimension + ) + + # Count parameters + total_params = count_parameters(model) + + # Print detailed breakdown + print("MultiYearCNN (Small3DCNN) Parameter Breakdown:") + for name, param in model.named_parameters(): + if param.requires_grad: + print(f"{name}: {param.numel()} parameters") + + print(f"\nTotal Trainable Parameters: {total_params:,}") + + +if __name__ == "__main__": + main() diff --git a/Archive/SimpleTimeModel/SimpleCNN/count_simpletimecnn_parameters.py b/Archive/SimpleTimeModel/SimpleCNN/count_simpletimecnn_parameters.py new file mode 100644 index 0000000000000000000000000000000000000000..2893a1459f44ef7e0f8d5ed3ac5455d3fb585173 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/count_simpletimecnn_parameters.py @@ -0,0 +1,29 @@ +import torch +from modelCNN import SmallCNN # Assuming this is where SimpleTimeCNN is defined +from config import bands_list_order # Using bands_list_order for input channels + + +def count_parameters(model): + """Count the total number of trainable parameters in a PyTorch model.""" + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + +def main(): + # Model configuration matching your training script + # Assuming input_channels corresponds to the number of bands + model = SmallCNN(input_channels=len(bands_list_order)) + + # Count parameters + total_params = count_parameters(model) + + # Print detailed breakdown + print("SimpleTimeCNN (SmallCNN) Parameter Breakdown:") + for name, param in model.named_parameters(): + if param.requires_grad: + print(f"{name}: {param.numel()} parameters") + + print(f"\nTotal Trainable Parameters: {total_params:,}") + + +if __name__ == "__main__": + main() diff --git a/Archive/SimpleTimeModel/SimpleCNN/dataloader/__init__.py b/Archive/SimpleTimeModel/SimpleCNN/dataloader/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Archive/SimpleTimeModel/SimpleCNN/dataloader/__pycache__/__init__.cpython-310.pyc b/Archive/SimpleTimeModel/SimpleCNN/dataloader/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..99ef12eb08c84326df4a9266fe54d65505cd6b09 Binary files /dev/null and b/Archive/SimpleTimeModel/SimpleCNN/dataloader/__pycache__/__init__.cpython-310.pyc differ diff 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SamplesCoordinates_Yearly, MatrixCoordinates_1mil_Yearly, DataYearly, SamplesCoordinates_Seasonally, MatrixCoordinates_1mil_Seasonally, DataSeasonally ,file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC +import pandas as pd +import numpy as np + +def get_time_range(TIME_BEGINNING= TIME_BEGINNING, TIME_END=TIME_END, seasons=seasons, years_padded=years_padded): + # Define seasons list for matching + seasons_list = ['winter', 'spring', 'summer', 'autumn'] + + # Check if TIME_BEGINNING is a season + is_season = any(season in TIME_BEGINNING.lower() for season in seasons_list) + + if is_season: + # Handle seasons case + start_idx = next(i for i, season in enumerate(seasons) + if TIME_BEGINNING.lower() in season.lower()) + end_idx = next(i for i, season in enumerate(seasons) + if TIME_END.lower() in season.lower()) + + # Get the subset including both start and end + return seasons[start_idx:end_idx + 1] + else: + # Handle years case + start_idx = years_padded.index(TIME_BEGINNING) + end_idx = years_padded.index(TIME_END) + + # Get the subset including both start and end + return years_padded[start_idx:end_idx + 1] + + +def process_paths_yearly(path, year, seen_years): + if 'Elevation' in path: + return path + elif 'MODIS_NPP' in path: + paths = [] + # Add current year + if year not in seen_years: + seen_years.add(year) + paths.append(f"{path}/{year}") + # Add previous year + prev_year = str(int(year) - 1) + if prev_year not in seen_years: + seen_years.add(prev_year) + paths.append(f"{path}/{prev_year}") + return paths + else: + return f"{path}/{year}" + +def create_path_arrays_yearly(SamplesCoordinates_Yearly, DataYearly, selected_years): + seen_years_samples = set() + seen_years_data = set() + + samples_coordinates_array_path = [ + processed_path + for idx, base_path in enumerate(SamplesCoordinates_Yearly) + for year in selected_years + if idx < len(SamplesCoordinates_Yearly) + if (processed_path := process_paths_yearly(base_path, year, seen_years_samples)) is not None + ] + + data_yearly_array_path = [ + processed_path + for idx, base_path in enumerate(DataYearly) + for year in selected_years + if idx < len(DataYearly) + if (processed_path := process_paths_yearly(base_path, year, seen_years_data)) is not None + ] + + return samples_coordinates_array_path, data_yearly_array_path + + +def process_paths(path, season, seen_years): + if 'Elevation' in path: + return path + elif 'MODIS_NPP' in path: + year = season.split('_')[0][:4] # Get year from season + paths = [] + # Add current year + if year not in seen_years: + seen_years.add(year) + paths.append(f"{path}/{year}") + # Add previous year + prev_year = str(int(year) - 1) + if prev_year not in seen_years: + seen_years.add(prev_year) + paths.append(f"{path}/{prev_year}") + return paths + else: + return f"{path}/{season}" + +def create_path_arrays(SamplesCoordinates_Seasonally, DataSeasonally, selected_seasons): + seen_years_samples = set() + seen_years_data = set() + + samples_coordinates_array_path = [ + processed_path + for idx, base_path in enumerate(SamplesCoordinates_Seasonally) + for season in selected_seasons + if idx < len(SamplesCoordinates_Seasonally) + if (processed_path := process_paths(base_path, season, seen_years_samples)) is not None + ] + + data_seasons_array_path = [ + processed_path + for idx, base_path in enumerate(DataSeasonally) + for season in selected_seasons + if idx < len(DataSeasonally) + if (processed_path := process_paths(base_path, season, seen_years_data)) is not None + ] + + return samples_coordinates_array_path, data_seasons_array_path + + + +def separate_and_add_data(LOADING_TIME_BEGINNING=LOADING_TIME_BEGINNING, TIME_END=TIME_END, seasons=seasons, years_padded=years_padded, + SamplesCoordinates_Yearly=SamplesCoordinates_Yearly, DataYearly=DataYearly, + SamplesCoordinates_Seasonally=SamplesCoordinates_Seasonally, DataSeasonally=DataSeasonally): + + # Define seasons list for matching + seasons_list = ['winter', 'spring', 'summer', 'autumn'] + + # Check if LOADING_TIME_BEGINNING is a season + is_season = any(season in LOADING_TIME_BEGINNING.lower() for season in seasons_list) + + if is_season: + # Handle seasons case + start_idx = next(i for i, season in enumerate(seasons) + if LOADING_TIME_BEGINNING.lower() in season.lower()) + end_idx = next(i for i, season in enumerate(seasons) + if TIME_END.lower() in season.lower()) + + # Get the seasonal range + selected_seasons = seasons[start_idx:end_idx + 1] + + + # Add seasonal data pairs + return create_path_arrays(SamplesCoordinates_Seasonally, DataSeasonally, selected_seasons) + else: + start_idx = years_padded.index(LOADING_TIME_BEGINNING) + end_idx = years_padded.index(TIME_END) + selected_years = years_padded[start_idx:end_idx + 1] + return create_path_arrays_yearly(SamplesCoordinates_Yearly, DataYearly, selected_years) + +def add_season_column(dataframe): + seasons_months = { + 'winter': [12, 1, 2], + 'spring': [3, 4, 5], + 'summer': [6, 7, 8], + 'autumn': [9, 10, 11] + } + + month_to_season = { + month: season + for season, months in seasons_months.items() + for month in months + } + + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) + + def get_season_year(row): + if pd.isna(row['survey_date']): + return None + + month = row['survey_date'].month + year = row['survey_date'].year + + if month == 12: + year += 1 + + season = month_to_season.get(month) + if season: + return f"{year}_{season}" + return None + + valid_dates_mask = dataframe['survey_date'] >= '2000-01-01' + dataframe['season'] = None + dataframe.loc[valid_dates_mask, 'season'] = ( + dataframe[valid_dates_mask].apply(get_season_year, axis=1) + ) + + return dataframe + +def filter_dataframe(time_beginning, time_end, max_oc=150): + # Read and prepare data + df = pd.read_excel(file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC) + df = add_season_column(df) + + # Convert columns to numeric + df['GPS_LONG'] = pd.to_numeric(df['GPS_LONG'], errors='coerce') + df['GPS_LAT'] = pd.to_numeric(df['GPS_LAT'], errors='coerce') + df['OC'] = pd.to_numeric(df['OC'], errors='coerce') + + # Basic data quality mask + quality_mask = ( + (df['OC'] <= max_oc) & + df['GPS_LONG'].notna() & + df['GPS_LAT'].notna() & + df['OC'].notna() + ) + + # Check if time_beginning contains a season + seasons = ['winter', 'spring', 'summer', 'autumn'] + is_season = any(season in time_beginning.lower() for season in seasons) + + if is_season: + # Create a list of all valid seasons between time_beginning and time_end + start_year, start_season = time_beginning.split('_') + end_year, end_season = time_end.split('_') + start_year = int(start_year) + end_year = int(end_year) + + valid_seasons = [] + current_year = start_year + season_order = ['winter', 'spring', 'summer', 'autumn'] + start_idx = season_order.index(start_season) + end_idx = season_order.index(end_season) + + while current_year <= end_year: + if current_year == start_year: + season_start = start_idx + else: + season_start = 0 + + if current_year == end_year: + season_end = end_idx + else: + season_end = len(season_order) - 1 + + for season in season_order[season_start:season_end + 1]: + valid_seasons.append(f"{current_year}_{season}") + + current_year += 1 + + # Filter using the valid seasons list + filtered_df = df[ + df['season'].isin(valid_seasons) & + quality_mask + ] + else: + # Filter by year range + start_year = int(time_beginning) + end_year = int(time_end) + filtered_df = df[ + (df['year'].between(start_year, end_year, inclusive='both')) & + quality_mask + ] + + print(f"Initial shape: {df.shape}") + print(f"Final filtered shape: {filtered_df.shape}") + + if filtered_df.empty: + print("\nDebug information:") + print("NaN counts:", df[['GPS_LONG', 'GPS_LAT', 'OC', 'survey_date']].isna().sum()) + print(f"OC range: {df['OC'].min()} to {df['OC'].max()}") + + return filtered_df diff --git a/Archive/SimpleTimeModel/SimpleCNN/dataloader/dataloader.py b/Archive/SimpleTimeModel/SimpleCNN/dataloader/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..8e10737da47b0f649f8110617fc5633869331245 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/dataloader/dataloader.py @@ -0,0 +1,262 @@ + + +import numpy as np +import torch +from torch.utils.data import Dataset, DataLoader +import os +from pathlib import Path +import re +import glob +import pandas as pd +from config import bands_list_order, window_size + + +def get_metadata(self, id_num): + """Get metadata from filename""" + if id_num not in self.id_to_file: + raise ValueError(f"ID {id_num} not found") + + filename = self.id_to_file[id_num].name + pattern = r'ID(\d+)N(\d+\.\d+)S(\d+\.\d+)W(\d+\.\d+)E(\d+\.\d+)' + match = re.search(pattern, filename) + + if match: + return { + 'id': int(match.group(1)), + 'north': float(match.group(2)), + 'south': float(match.group(3)), + 'west': float(match.group(4)), + 'east': float(match.group(5)) + } + return None + +def get_available_ids(self): + """Return list of available IDs""" + return list(self.id_to_file.keys()) + + +class RasterTensorDataset(Dataset): + def __init__(self, base_path): + """ + Initialize the dataset + + Parameters: + base_path: str, base path to RasterTensorData directory + subfolder: str, name of the subfolder (e.g., 'Elevation') + """ + + self.folder_path = base_path + + # Create ID to filename mapping + self.id_to_file = self._create_id_mapping() + + # Load all numpy arrays into memory (optional, can be modified to load on demand) + self.data_cache = {} + for id_num, filepath in self.id_to_file.items(): + self.data_cache[id_num] = np.load(filepath) + + def _create_id_mapping(self): + """Create a dictionary mapping IDs to their corresponding file paths""" + id_to_file = {} + for file_path in glob.glob(os.path.join(self.folder_path, "*.npy")): + # Extract ID number from filename + match = re.search(r'ID(\d+)N', file_path) + if match: + id_num = int(match.group(1)) + id_to_file[id_num] = file_path + + return id_to_file + + def get_tensor_by_location(self, id_num, x, y, window_size=window_size): + """ + Get a window_size x window_size square around the specified x,y coordinates + + Parameters: + id_num: int, ID number from filename + x: int, x coordinate + y: int, y coordinate + window_size: int, size of the square window (default 17) + + Returns: + torch.Tensor: window_size x window_size tensor + """ + if id_num not in self.id_to_file: + raise ValueError(f"ID {id_num} not found in dataset") + + # Get the data array + if id_num in self.data_cache: + data = self.data_cache[id_num] + else: + data = np.load(self.id_to_file[id_num]) + + # Calculate window boundaries + half_window = window_size // 2 + x_start = int(max(0, x - half_window)) + x_end = int(min(data.shape[0], x + half_window + 1)) + y_start = int(max(0, y - half_window)) + y_end = int(min(data.shape[1], y + half_window + 1)) + + # Extract window + window = data[x_start:x_end, y_start:y_end] + + # Pad if necessary + if window.shape != (window_size, window_size): + padded_window = np.zeros((window_size, window_size)) + x_offset = half_window - (x - x_start) + y_offset = half_window - (y - y_start) + padded_window[ + x_offset:x_offset+window.shape[0], + y_offset:y_offset+window.shape[1] + ] = window + window = padded_window + + #window = np.asarray(window) + return torch.from_numpy(window).float() + + def __len__(self): + return len(self.id_to_file) + + def __getitem__(self, idx): + # This is a placeholder implementation + # Modify according to your specific needs + id_num = list(self.id_to_file.keys())[idx] + return self.data_cache[id_num] + +# Example usage: +""" +# Initialize the dataset +base_path = "/content/drive/MyDrive/Colab Notebooks/MappingSOC/Data/RasterTensorData" +dataset = RasterTensorDataset(base_path, "Elevation") + +# Get the dictionary mapping IDs to filenames +id_mapping = dataset.id_to_file +print("ID to filename mapping:", id_mapping) + +# Get a 17x17 window for a specific location +id_num = 10 # example ID +x, y = 100, 100 # example coordinates +window = dataset.get_tensor_by_location(id_num, x, y) +print("Window shape:", window.shape) + +# Create a DataLoader if needed +from torch.utils.data import DataLoader +dataloader = DataLoader(dataset, batch_size=4, shuffle=True) +""" + + +class MultiRasterDataset(Dataset): + def __init__(self, samples_coordinates_array_subfolders , data_array_subfolders , dataframe): + """ + Parameters: + subfolders: list of str, names of subfolders to include + dataframe: pandas.DataFrame, contains columns GPS_LONG, GPS_LAT, and OC (target variable) + """ + self.data_array_subfolders = data_array_subfolders + self.seasonalityBased = self.check_seasonality(data_array_subfolders) + + self.samples_coordinates_array_subfolders = samples_coordinates_array_subfolders + self.dataframe = dataframe + self.datasets = { + self.get_last_three_folders(subfolder): RasterTensorDataset(subfolder) + for subfolder in self.data_array_subfolders + } + + self.coordinates = { + self.get_last_three_folders(subfolder): np.load(f"{subfolder}/coordinates.npy")#[np.isfinite(np.load(f"{subfolder}/coordinates.npy"))] + for subfolder in self.samples_coordinates_array_subfolders + } + + def check_seasonality(self,data_array_subfolders): + seasons = ['winter', 'spring', 'summer', 'autumn'] + + # Check if any subfolder contains a season name + is_seasonal = any( + any(season in subfolder.lower() for season in seasons) + for subfolder in data_array_subfolders + ) + + return 1 if is_seasonal else 0 + + + def get_last_three_folders(self,path): + # Split the path into components + parts = path.rstrip('/').split('/') + # Return last 3 components, or all if less than 3 + return '/'.join(parts[-2:]) + + def find_coordinates_index(self, subfolder, longitude, latitude): + """ + Finds the index of the matching coordinates in the subfolder's coordinates.npy file. + + Parameters: + subfolder: str, name of the subfolder + longitude: float, longitude to match + latitude: float, latitude to match + + Returns: + tuple: (id_num, x, y) if match is found, otherwise raises an error + """ + coords = self.coordinates[subfolder] + # Assuming the first two columns of `coordinates.npy` are longitude and latitude + match = np.where((coords[:, 1] == longitude) & (coords[:, 0] == latitude))[0] + if match.size == 0: + raise ValueError(f"{coords} Coordinates ({longitude}, {latitude}) not found in {subfolder}") + + # Return id_num, x, y from the same row + return coords[match[0], 2], coords[match[0], 3], coords[match[0], 4] + + def __getitem__(self, index): + """ + Retrieve tensor and target value for a given index. + + Parameters: + index: int, index of the row in the dataframe + + Returns: + tuple: (tensor, OC), where tensor is the data and OC is the target variable + """ + row = self.dataframe.iloc[index] + longitude, latitude, oc = row["GPS_LONG"], row["GPS_LAT"], row["OC"] + tensors = [] + + filtered_array = self.filter_by_season_or_year(row['season'],row['year'],self.seasonalityBased) + + for subfolder in filtered_array: + subfolder = self.get_last_three_folders(subfolder) + id_num, x, y = self.find_coordinates_index(subfolder, longitude, latitude) + tensor = self.datasets[subfolder].get_tensor_by_location(id_num, x, y) + if tensor is not None: + tensors.append(tensor) + # Combine all tensors into a single tensor + return longitude, latitude, torch.stack(tensors), oc + + + def filter_by_season_or_year(self, season,year,Season_or_year): + + if Season_or_year: + filtered_array = [ + path for path in self.samples_coordinates_array_subfolders + if ('Elevation' in path) or + ('MODIS_NPP' in path and path.endswith(str(year))) or + (not 'Elevation' in path and not 'MODIS_NPP' in path and path.endswith(season)) + ] + + else: + filtered_array = [ + path for path in self.samples_coordinates_array_subfolders + if ('Elevation' in path) or + (not 'Elevation' in path and path.endswith(str(year))) + ] + + return filtered_array + + def __len__(self): + """ + Return the number of samples in the dataset. + """ + return len(self.dataframe) + + def get_tensor_by_location(self, subfolder, id_num, x, y): + """Get tensor from specific subfolder dataset""" + return self.datasets[subfolder].get_tensor_by_location(id_num, x, y) + diff --git a/Archive/SimpleTimeModel/SimpleCNN/dataloader/dataloaderMapping.py b/Archive/SimpleTimeModel/SimpleCNN/dataloader/dataloaderMapping.py new file mode 100644 index 0000000000000000000000000000000000000000..18068a526cb160d7b22157a8ef2560e504fcc77c --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/dataloader/dataloaderMapping.py @@ -0,0 +1,236 @@ + +import numpy as np +import torch +from torch.utils.data import Dataset, DataLoader +import os +from pathlib import Path +import re +import glob + + +class RasterTensorDatasetMapping(Dataset): + def __init__(self, base_path): + """ + Initialize the dataset + + Parameters: + base_path: str, base path to RasterTensorData directory + subfolder: str, name of the subfolder (e.g., 'Elevation') + """ + # Replace "OC_LUCAS_LFU_LfL_Coordinates" with "RasterTensorData" in the base path + self.base_path = Path(base_path.replace("Coordinates1Mil", "RasterTensorData")) + self.folder_path = self.base_path + + # Create ID to filename mapping + self.id_to_file = self._create_id_mapping() + + # Load all numpy arrays into memory (optional, can be modified to load on demand) + self.data_cache = {} + for id_num, filepath in self.id_to_file.items(): + self.data_cache[id_num] = np.load(filepath) + + def _create_id_mapping(self): + """Create a dictionary mapping IDs to their corresponding file paths""" + id_to_file = {} + + for file_path in self.folder_path.glob("*.npy"): + # Extract ID number from filename + match = re.search(r'ID(\d+)N', file_path.name) + if match: + id_num = int(match.group(1)) + id_to_file[id_num] = file_path + + return id_to_file + + def get_tensor_by_location(self, id_num, x, y, window_size=17): + """ + Get a window_size x window_size square around the specified x,y coordinates + + Parameters: + id_num: int, ID number from filename + x: int, x coordinate + y: int, y coordinate + window_size: int, size of the square window (default 17) + + Returns: + torch.Tensor: window_size x window_size tensor + """ + if id_num not in self.id_to_file: + raise ValueError(f"ID {id_num} not found in dataset") + + # Get the data array + if id_num in self.data_cache: + data = self.data_cache[id_num] + else: + data = np.load(self.id_to_file[id_num]) + + # Calculate window boundaries + half_window = window_size // 2 + x_start = int(max(0, x - half_window)) + x_end = int(min(data.shape[0], x + half_window + 1)) + y_start = int(max(0, y - half_window)) + y_end = int(min(data.shape[1], y + half_window + 1)) + + # Extract window + window = data[x_start:x_end, y_start:y_end] + + # Pad if necessary + if window.shape != (window_size, window_size): + padded_window = np.zeros((window_size, window_size)) + x_offset = half_window - (x - x_start) + y_offset = half_window - (y - y_start) + padded_window[ + x_offset:x_offset+window.shape[0], + y_offset:y_offset+window.shape[1] + ] = window + window = padded_window + + return torch.from_numpy(window).float() + + def __len__(self): + return len(self.id_to_file) + + def __getitem__(self, idx): + # This is a placeholder implementation + # Modify according to your specific needs + id_num = list(self.id_to_file.keys())[idx] + return self.data_cache[id_num] + +# Example usage: +""" +# Initialize the dataset +base_path = "/content/drive/MyDrive/Colab Notebooks/MappingSOC/Data/RasterTensorData" +dataset = RasterTensorDataset(base_path, "Elevation") + +# Get the dictionary mapping IDs to filenames +id_mapping = dataset.id_to_file +print("ID to filename mapping:", id_mapping) + +# Get a 17x17 window for a specific location +id_num = 10 # example ID +x, y = 100, 100 # example coordinates +window = dataset.get_tensor_by_location(id_num, x, y) +print("Window shape:", window.shape) + +# Create a DataLoader if needed +from torch.utils.data import DataLoader +dataloader = DataLoader(dataset, batch_size=4, shuffle=True) +""" + + +from torch.utils.data import Dataset +import numpy as np +import torch +from pathlib import Path +import os + +class MultiRasterDatasetMapping(Dataset): + def __init__(self, subfolders, dataframe, bands_list_order=None): + """ + Parameters: + subfolders: list of str, paths to directories containing raster data and coordinates.npy (e.g., /path/to/LAI/2015) + dataframe: pandas.DataFrame, contains columns 'longitude' and 'latitude' + bands_list_order: list of str, order of bands (e.g., ['Elevation', 'LAI', ...]) + """ + self.subfolders = subfolders + self.dataframe = dataframe + self.bands_list_order = bands_list_order if bands_list_order is not None else [] + self.datasets = {} + self.coordinates = {} + + # Initialize datasets and coordinates with error checking + for subfolder in subfolders: + coord_file = f"{subfolder}/coordinates.npy" + if not Path(coord_file).exists(): + raise FileNotFoundError(f"Coordinates file not found: {coord_file}") + self.coordinates[subfolder] = np.load(coord_file) + self.datasets[subfolder] = RasterTensorDatasetMapping(subfolder) # Assuming this class exists + + def find_coordinates_index(self, subfolder, longitude, latitude): + """ + Finds the index of the matching coordinates in the subfolder's coordinates.npy file. + + Parameters: + subfolder: str, path to the subfolder (e.g., /path/to/LAI/2015) + longitude: float, longitude to match + latitude: float, latitude to match + + Returns: + tuple: (id_num, x, y) if match is found, otherwise raises an error + """ + coords = self.coordinates[subfolder] + match = np.where((coords[:, 1] == longitude) & (coords[:, 0] == latitude))[0] + if match.size == 0: + raise ValueError(f"Coordinates ({longitude}, {latitude}) not found in {subfolder}") + return coords[match[0], 2], coords[match[0], 3], coords[match[0], 4] + + def filter_by_year(self, inference_year): + """ + Filter subfolders for a single inference year. + + Parameters: + inference_year: str, the year for inference (e.g., '2015') + + Returns: + dict: {band: subfolder path for the specified year} + """ + filtered_paths = {band: None for band in self.bands_list_order} + for subfolder in self.subfolders: + parts = subfolder.split('/') + band = parts[-2] if 'Elevation' not in subfolder else 'Elevation' + subfolder_year = parts[-1] if 'Elevation' not in subfolder else None + + if band in self.bands_list_order: + if band == 'Elevation': + filtered_paths['Elevation'] = subfolder + elif subfolder_year == inference_year: + filtered_paths[band] = subfolder + return filtered_paths + + def __getitem__(self, index): + """ + Retrieve tensor and coordinates for a given index for a single year. + + Parameters: + index: int, index of the row in the dataframe + + Returns: + tuple: (tensor, coordinates), where tensor is (num_bands, height, width) + and coordinates are (longitude, latitude) + """ + row = self.dataframe.iloc[index] + longitude, latitude = row["longitude"], row["latitude"] + inference_year = "2015" # Hardcoded to INFERENCE_TIME; adjust if needed in dataframe + + # Filter subfolders by year + filtered_paths = self.filter_by_year(inference_year) + + # Initialize band tensors + band_tensors = [] + window_size = 33 # From your config + + # Collect tensors for each band for the single year + for band in self.bands_list_order: + subfolder = filtered_paths[band] + if subfolder: + id_num, x, y = self.find_coordinates_index(subfolder, longitude, latitude) + tensor = self.datasets[subfolder].get_tensor_by_location(id_num, x, y) + band_tensors.append(tensor if tensor is not None else torch.zeros(window_size, window_size)) + else: + band_tensors.append(torch.zeros(window_size, window_size)) + + if len(band_tensors) != len(self.bands_list_order): + raise ValueError(f"Expected {len(self.bands_list_order)} bands, but got {len(band_tensors)}") + + final_tensor = torch.stack(band_tensors) # Shape: (num_bands, height, width) + return final_tensor, torch.tensor([longitude, latitude], dtype=torch.float32) + + def __len__(self): + """ + Return the number of samples in the dataset. + """ + return len(self.dataframe) + + def get_tensor_by_location(self, subfolder, id_num, x, y): + """Get tensor from specific subfolder dataset""" + return self.datasets[subfolder].get_tensor_by_location(id_num, x, y) \ No newline at end of file diff --git a/Archive/SimpleTimeModel/SimpleCNN/dataloader/dataloaderMultiYears.py b/Archive/SimpleTimeModel/SimpleCNN/dataloader/dataloaderMultiYears.py new file mode 100644 index 0000000000000000000000000000000000000000..887f9fb51b55fdb61a436f763957fd46404b94d0 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/dataloader/dataloaderMultiYears.py @@ -0,0 +1,301 @@ + + +import numpy as np +import torch +from torch.utils.data import Dataset, DataLoader +import os +from pathlib import Path +import re +import glob +import pandas as pd +from config import bands_list_order , time_before, LOADING_TIME_BEGINNING , window_size + + +def get_metadata(self, id_num): + """Get metadata from filename""" + if id_num not in self.id_to_file: + raise ValueError(f"ID {id_num} not found") + + filename = self.id_to_file[id_num].name + pattern = r'ID(\d+)N(\d+\.\d+)S(\d+\.\d+)W(\d+\.\d+)E(\d+\.\d+)' + match = re.search(pattern, filename) + + if match: + return { + 'id': int(match.group(1)), + 'north': float(match.group(2)), + 'south': float(match.group(3)), + 'west': float(match.group(4)), + 'east': float(match.group(5)) + } + return None + +def get_available_ids(self): + """Return list of available IDs""" + return list(self.id_to_file.keys()) + + +class RasterTensorDataset(Dataset): + def __init__(self, base_path): + """ + Initialize the dataset + + Parameters: + base_path: str, base path to RasterTensorData directory + subfolder: str, name of the subfolder (e.g., 'Elevation') + """ + + self.folder_path = base_path + + # Create ID to filename mapping + self.id_to_file = self._create_id_mapping() + + # Load all numpy arrays into memory (optional, can be modified to load on demand) + self.data_cache = {} + for id_num, filepath in self.id_to_file.items(): + self.data_cache[id_num] = np.load(filepath) + + def _create_id_mapping(self): + """Create a dictionary mapping IDs to their corresponding file paths""" + id_to_file = {} + for file_path in glob.glob(os.path.join(self.folder_path, "*.npy")): + # Extract ID number from filename + match = re.search(r'ID(\d+)N', file_path) + if match: + id_num = int(match.group(1)) + id_to_file[id_num] = file_path + + return id_to_file + + def get_tensor_by_location(self, id_num, x, y, window_size=window_size): + """ + Get a window_size x window_size square around the specified x,y coordinates + + Parameters: + id_num: int, ID number from filename + x: int, x coordinate + y: int, y coordinate + window_size: int, size of the square window (default 17) + + Returns: + torch.Tensor: window_size x window_size tensor + """ + if id_num not in self.id_to_file: + raise ValueError(f"ID {id_num} not found in dataset") + + # Get the data array + if id_num in self.data_cache: + data = self.data_cache[id_num] + else: + data = np.load(self.id_to_file[id_num]) + + # Calculate window boundaries + half_window = window_size // 2 + x_start = int(max(0, x - half_window)) + x_end = int(min(data.shape[0], x + half_window + 1)) + y_start = int(max(0, y - half_window)) + y_end = int(min(data.shape[1], y + half_window + 1)) + + # Extract window + window = data[x_start:x_end, y_start:y_end] + + # Pad if necessary + if window.shape != (window_size, window_size): + padded_window = np.zeros((window_size, window_size)) + x_offset = half_window - (x - x_start) + y_offset = half_window - (y - y_start) + padded_window[ + x_offset:x_offset+window.shape[0], + y_offset:y_offset+window.shape[1] + ] = window + window = padded_window + + #window = np.asarray(window) + return torch.from_numpy(window).float() + + def __len__(self): + return len(self.id_to_file) + + def __getitem__(self, idx): + # This is a placeholder implementation + # Modify according to your specific needs + id_num = list(self.id_to_file.keys())[idx] + return self.data_cache[id_num] + +# Example usage: +""" +# Initialize the dataset +base_path = "/content/drive/MyDrive/Colab Notebooks/MappingSOC/Data/RasterTensorData" +dataset = RasterTensorDataset(base_path, "Elevation") + +# Get the dictionary mapping IDs to filenames +id_mapping = dataset.id_to_file +print("ID to filename mapping:", id_mapping) + +# Get a 17x17 window for a specific location +id_num = 10 # example ID +x, y = 100, 100 # example coordinates +window = dataset.get_tensor_by_location(id_num, x, y) +print("Window shape:", window.shape) + +# Create a DataLoader if needed +from torch.utils.data import DataLoader +dataloader = DataLoader(dataset, batch_size=4, shuffle=True) +""" + + +class MultiRasterDatasetMultiYears(Dataset): + def __init__(self, samples_coordinates_array_subfolders , data_array_subfolders , dataframe, time_before = time_before): + """ + Parameters: + subfolders: list of str, names of subfolders to include + dataframe: pandas.DataFrame, contains columns GPS_LONG, GPS_LAT, and OC (target variable) + """ + self.data_array_subfolders = data_array_subfolders + self.seasonalityBased = self.check_seasonality(data_array_subfolders) + self.time_before = time_before + self.samples_coordinates_array_subfolders = samples_coordinates_array_subfolders + self.dataframe = dataframe + self.datasets = { + self.get_last_three_folders(subfolder): RasterTensorDataset(subfolder) + for subfolder in self.data_array_subfolders + } + + self.coordinates = { + self.get_last_three_folders(subfolder): np.load(f"{subfolder}/coordinates.npy")#[np.isfinite(np.load(f"{subfolder}/coordinates.npy"))] + for subfolder in self.samples_coordinates_array_subfolders + } + + def check_seasonality(self,data_array_subfolders): + seasons = ['winter', 'spring', 'summer', 'autumn'] + + # Check if any subfolder contains a season name + is_seasonal = any( + any(season in subfolder.lower() for season in seasons) + for subfolder in data_array_subfolders + ) + + return 1 if is_seasonal else 0 + + + def get_last_three_folders(self,path): + # Split the path into components + parts = path.rstrip('/').split('/') + # Return last 3 components, or all if less than 3 + return '/'.join(parts[-2:]) + + def find_coordinates_index(self, subfolder, longitude, latitude): + """ + Finds the index of the matching coordinates in the subfolder's coordinates.npy file. + + Parameters: + subfolder: str, name of the subfolder + longitude: float, longitude to match + latitude: float, latitude to match + + Returns: + tuple: (id_num, x, y) if match is found, otherwise raises an error + """ + coords = self.coordinates[subfolder] + # Assuming the first two columns of `coordinates.npy` are longitude and latitude + match = np.where((coords[:, 1] == longitude) & (coords[:, 0] == latitude))[0] + if match.size == 0: + raise ValueError(f"{coords} Coordinates ({longitude}, {latitude}) not found in {subfolder}") + + # Return id_num, x, y from the same row + return coords[match[0], 2], coords[match[0], 3], coords[match[0], 4] + + def __getitem__(self, index): + """ + Retrieve tensor and target value for a given index. + + Parameters: + index: int, index of the row in the dataframe + + Returns: + tuple: (tensor, OC), where tensor is the data and OC is the target variable + """ + row = self.dataframe.iloc[index] + longitude, latitude, oc = row["GPS_LONG"], row["GPS_LAT"], row["OC"] + tensors = [] + + filtered_array = self.filter_by_season_or_year(row['season'],row['year'],self.seasonalityBased) + # Initialize a dictionary to hold the tensors for each band + band_tensors = {band: [] for band in bands_list_order} + + for subfolder in filtered_array: + subfolder = self.get_last_three_folders(subfolder) + + # Check if the forelast subfolder is 'Elevation' + if subfolder.split(os.path.sep)[-1] == 'Elevation': + # Get the tensor for 'Elevation' + id_num, x, y = self.find_coordinates_index(subfolder, longitude, latitude) + elevation_tensor = self.datasets[subfolder].get_tensor_by_location(id_num, x, y) + + if elevation_tensor is not None: + # Repeat the 'Elevation' tensor self.time_before times + for _ in range(self.time_before): + band_tensors['Elevation'].append(elevation_tensor) + else: + # Get the year from the last subfolder + year = int(subfolder.split(os.path.sep)[-1]) + + # Decrement the year by self.time_before + for decrement in range(self.time_before): + current_year = year - decrement + + # Construct the subfolder with the decremented year + decremented_subfolder = os.path.sep.join(subfolder.split(os.path.sep)[:-1] + [str(current_year)]) + + id_num, x, y = self.find_coordinates_index(decremented_subfolder, longitude, latitude) + tensor = self.datasets[decremented_subfolder].get_tensor_by_location(id_num, x, y) + + if tensor is not None: + # Append the tensor to the corresponding band in the dictionary + band = subfolder.split(os.path.sep)[-2] + band_tensors[band].append(tensor) + + # Stack the tensors for each band + stacked_tensors = [] + for band in bands_list_order: + if band_tensors[band]: + # Stack the tensors for the current band + stacked_tensor = torch.stack(band_tensors[band]) + stacked_tensors.append(stacked_tensor) + + # Concatenate all stacked tensors along the band dimension + final_tensor = torch.stack(stacked_tensors) + final_tensor = final_tensor.permute(0, 2, 3, 1) + + return longitude, latitude, final_tensor, oc + + + + def filter_by_season_or_year(self, season,year,Season_or_year): + + if Season_or_year: + filtered_array = [ + path for path in self.samples_coordinates_array_subfolders + if ('Elevation' in path) or + ('MODIS_NPP' in path and path.endswith(str(year))) or + (not 'Elevation' in path and not 'MODIS_NPP' in path and path.endswith(season)) + ] + + else: + filtered_array = [ + path for path in self.samples_coordinates_array_subfolders + if ('Elevation' in path) or + (not 'Elevation' in path and path.endswith(str(year))) + ] + return filtered_array + + def __len__(self): + """ + Return the number of samples in the dataset. + """ + return len(self.dataframe) + + def get_tensor_by_location(self, subfolder, id_num, x, y): + """Get tensor from specific subfolder dataset""" + return self.datasets[subfolder].get_tensor_by_location(id_num, x, y) + diff --git a/Archive/SimpleTimeModel/SimpleCNN/launcher.md b/Archive/SimpleTimeModel/SimpleCNN/launcher.md new file mode 100644 index 0000000000000000000000000000000000000000..a9470ca07a70e30b3d8ddef26874c804e1c74dac --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/launcher.md @@ -0,0 +1,11 @@ + + +You can use the following commands to launch the training: +''' +accelerate launch --multi_gpu train.py +''' + +For multi years, we can use: +''' +accelerate launch --multi_gpu trainMultiYear.py +''' diff --git a/Archive/SimpleTimeModel/SimpleCNN/mapping.py b/Archive/SimpleTimeModel/SimpleCNN/mapping.py new file mode 100644 index 0000000000000000000000000000000000000000..d28a5b2c391214bfa731824fefeb333676cf206b --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/mapping.py @@ -0,0 +1,210 @@ +import pandas as pd +import numpy as np +import xgboost as xgb +import matplotlib.pyplot as plt +from scipy.interpolate import griddata +import geopandas as gpd +from config import (base_path_data , file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC , MAX_OC , + TIME_BEGINNING , TIME_END , INFERENCE_TIME) +import concurrent.futures +from concurrent.futures import ThreadPoolExecutor, as_completed +from dataloader.dataloaderMapping import MultiRasterDatasetMapping +from sklearn.utils import shuffle +import copy +from torch.utils.data import DataLoader, Subset +import torch +from accelerate import Accelerator + +from tqdm import tqdm + +################################################################## + +# Loading the Data + + + +################################################################## + + +def create_prediction_visualizations(year,coordinates, predictions, save_path): + """ + Create and save three separate map visualizations of predictions in Bavaria plus a triptych, + with timestamps in filenames. + + Parameters: + coordinates (numpy.array): Array of coordinates (longitude, latitude) + predictions (numpy.array): Array of prediction values + save_path (str): Directory path where the images should be saved + """ + import os + import numpy as np + import matplotlib.pyplot as plt + from scipy.interpolate import griddata + import geopandas as gpd + from datetime import datetime + + # Get current timestamp + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + + # Create directories if they don't exist + os.makedirs(save_path, exist_ok=True) + individual_path = os.path.join(save_path, 'individual') + os.makedirs(individual_path, exist_ok=True) + + # Load Bavaria boundaries + bavaria = gpd.read_file('https://raw.githubusercontent.com/isellsoap/deutschlandGeoJSON/main/2_bundeslaender/4_niedrig.geo.json') + bavaria = bavaria[bavaria['name'] == 'Bayern'] + + # Create interpolation grid with higher resolution + grid_x = np.linspace(coordinates[:, 0].min(), coordinates[:, 0].max(), 300) + grid_y = np.linspace(coordinates[:, 1].min(), coordinates[:, 1].max(), 300) + grid_x, grid_y = np.meshgrid(grid_x, grid_y) + + # Interpolate values with cubic interpolation + grid_z = griddata(coordinates, predictions, (grid_x, grid_y), method='linear') + + # Common plotting parameters + plot_params = { + 'figsize': (12, 10), + 'dpi': 300 + } + + # Function to set common elements for all plots + def set_common_elements(ax, title): + bavaria.boundary.plot(ax=ax, color='black', linewidth=1) + ax.set_title(title, fontsize=12, pad=20) + ax.set_xlabel('Longitude') + ax.set_ylabel('Latitude') + ax.grid(True) + + # Function to generate filename with timestamp + def get_filename(base_name): + return f"{base_name}_MAX_OC_{str(MAX_OC)}_Beginning_{TIME_BEGINNING}_End_{TIME_END}__InferenceTime{INFERENCE_TIME}_{timestamp}.png" + + # 1. Interpolated surface + fig_interp, ax_interp = plt.subplots(**plot_params) + contour = ax_interp.contourf(grid_x, grid_y, grid_z, + levels=50, + cmap='viridis', + alpha=0.8) + set_common_elements(ax_interp, 'Interpolated Predicted Values') + plt.colorbar(contour, ax=ax_interp, label='Predicted Values') + plt.savefig(os.path.join(individual_path, get_filename(f'{year}_bavaria_interpolated')), + bbox_inches='tight') + plt.close() + + # 2. Scatter plot + fig_scatter, ax_scatter = plt.subplots(**plot_params) + scatter = ax_scatter.scatter(coordinates[:, 0], coordinates[:, 1], + c=predictions, + cmap='viridis', + alpha=0.6, + s=50) + set_common_elements(ax_scatter, 'Scatter Plot of Predicted Values') + plt.colorbar(scatter, ax=ax_scatter, label='Predicted Values') + plt.savefig(os.path.join(individual_path, get_filename(f'{year}_bavaria_scatter')), + bbox_inches='tight') + plt.close() + + # 3. Discrete points + fig_discrete, ax_discrete = plt.subplots(**plot_params) + discrete = ax_discrete.scatter(coordinates[:, 0], coordinates[:, 1], + c=predictions, + cmap='viridis', + alpha=1.0, + s=20) + set_common_elements(ax_discrete, 'Discrete Points of Predicted Values') + plt.colorbar(discrete, ax=ax_discrete, label='Predicted Values') + plt.savefig(os.path.join(individual_path, get_filename(f'{year}_bavaria_discrete')), + bbox_inches='tight') + plt.close() + + # Create triptych + fig_triptych = plt.figure(figsize=(30, 10)) + + # Interpolated plot + ax1 = plt.subplot(131) + contour = ax1.contourf(grid_x, grid_y, grid_z, + levels=50, + cmap='viridis', + alpha=0.8) + set_common_elements(ax1, 'Interpolated Predicted Values') + plt.colorbar(contour, ax=ax1, label='Predicted Values') + + # Scatter plot + ax2 = plt.subplot(132) + scatter = ax2.scatter(coordinates[:, 0], coordinates[:, 1], + c=predictions, + cmap='viridis', + alpha=0.6, + s=50) + set_common_elements(ax2, 'Scatter Plot of Predicted Values') + plt.colorbar(scatter, ax=ax2, label='Predicted Values') + + # Discrete points + ax3 = plt.subplot(133) + discrete = ax3.scatter(coordinates[:, 0], coordinates[:, 1], + c=predictions, + cmap='viridis', + alpha=1.0, + s=20) + set_common_elements(ax3, 'Discrete Points of Predicted Values') + plt.colorbar(discrete, ax=ax3, label='Predicted Values') + + plt.tight_layout() + plt.savefig(os.path.join(save_path, get_filename(f'{year}_bavaria_triptych')), + dpi=300, + bbox_inches='tight') + plt.close() + +# Example usage: +# create_prediction_map(bavaria, coordinates, predictions, save_path='output/maps') + + + + +def process_batch(df_chunk, model, bands_yearly, batch_size, device): + model = model.to(device) + chunk_dataset = MultiRasterDatasetMapping(bands_yearly, df_chunk) + chunk_dataloader = DataLoader(chunk_dataset, batch_size=batch_size, shuffle=True) + + chunk_coordinates = [] + chunk_predictions = [] + + model.eval() + with torch.no_grad(): + for longitudes, latitudes, batch_features in chunk_dataloader: + # Store coordinates + chunk_coordinates.append(np.column_stack((longitudes.numpy(), latitudes.numpy()))) + + # Move features to GPU + # features_gpu = {k: v.to(device) for k, v in batch_features.items()} + + # Run inference + predictions = model(batch_features).cpu().numpy() + chunk_predictions.extend(predictions) + + return np.vstack(chunk_coordinates), np.array(chunk_predictions) + +def parallel_predict(df_full, cnn_model, bands_yearly, batch_size=256): + accelerator = Accelerator() + print(bands_yearly) + dataset = MultiRasterDatasetMapping(bands_yearly, df_full) + dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True) + + cnn_model, dataloader = accelerator.prepare(cnn_model, dataloader) + cnn_model.eval() + + coordinates, predictions = [], [] + + with torch.no_grad(): + progress_bar = tqdm(dataloader, desc='Predicting', leave=True) + for longitudes, latitudes, features in progress_bar: + coordinates.append(np.column_stack((longitudes.cpu().numpy(), latitudes.cpu().numpy()))) + # Convert dictionary of features into stacked tensor + features_stacked = torch.stack(list(features.values()), dim=1) + features_stacked = features_stacked.to(accelerator.device) + batch_preds = cnn_model(features_stacked).cpu().numpy() + predictions.extend(batch_preds) + + return np.vstack(coordinates), np.array(predictions) diff --git a/Archive/SimpleTimeModel/SimpleCNN/modelCNN.py b/Archive/SimpleTimeModel/SimpleCNN/modelCNN.py new file mode 100644 index 0000000000000000000000000000000000000000..7938af8549e829c6ef7fdd980e00b9c7574d92e9 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/modelCNN.py @@ -0,0 +1,87 @@ +import numpy as np +import torch +import torch.nn as nn + + +class SmallCNN(nn.Module): + def __init__(self, input_channels=6, dropout_rate=0.3): + super(SmallCNN, self).__init__() + + # Convolutional layers + self.conv1 = nn.Conv2d(input_channels, 16, kernel_size=3, padding=1) + self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1) + self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1) + + # Additional convolutional layers + self.conv4 = nn.Conv2d(64, 64, kernel_size=3, padding=1) + self.conv5 = nn.Conv2d(64, 32, kernel_size=3, padding=1) + + # Skip connection layers (1x1 convolutions for dimension matching) + self.skip1 = nn.Conv2d(16, 32, kernel_size=1) + self.skip2 = nn.Conv2d(32, 64, kernel_size=1) + self.skip3 = nn.Conv2d(64, 32, kernel_size=1) + + # Pooling layer + self.pool = nn.MaxPool2d(2, 2) + + # Dropout layers + self.dropout_conv = nn.Dropout2d(p=dropout_rate) + self.dropout_fc = nn.Dropout(p=dropout_rate) + + # Fully connected layers + self.fc1 = nn.Linear(128, 64) + self.fc2 = nn.Linear(64, 1) + + # Activation + self.relu = nn.ReLU() + + def forward(self, x): + # First convolutional block + x1 = self.conv1(x) + x1 = self.relu(x1) + x1 = self.dropout_conv(x1) + x1_pooled = self.pool(x1) + + # Second convolutional block with skip connection + x2 = self.conv2(x1_pooled) + x2 = self.relu(x2) + x2 = self.dropout_conv(x2) + # Skip connection from first block (with dimension matching) + skip_x1 = self.skip1(self.pool(x1)) + x2 = x2 + skip_x1 + x2_pooled = self.pool(x2) + + # Third convolutional block with skip connection + x3 = self.conv3(x2_pooled) + x3 = self.relu(x3) + x3 = self.dropout_conv(x3) + # Skip connection from second block + skip_x2 = self.skip2(self.pool(x2)) + x3 = x3 + skip_x2 + x3_pooled = self.pool(x3) + + # Fourth convolutional block (additional depth) + x4 = self.conv4(x3_pooled) + x4 = self.relu(x4) + x4 = self.dropout_conv(x4) + x4 = x4 + x3_pooled # Residual connection + + # Fifth convolutional block (additional depth) + x5 = self.conv5(x4) + x5 = self.relu(x5) + x5 = self.dropout_conv(x5) + # Skip connection from third block + skip_x3 = self.skip3(x3_pooled) + x5 = x5 + skip_x3 + + # Flatten and fully connected layers + x = x5.view(x5.size(0), -1) + x = self.fc1(x) + x = self.relu(x) + x = self.dropout_fc(x) + x = self.fc2(x) + + return x.squeeze() + + def count_parameters(self): + return sum(p.numel() for p in self.parameters() if p.requires_grad) diff --git a/Archive/SimpleTimeModel/SimpleCNN/model_predictions.csv b/Archive/SimpleTimeModel/SimpleCNN/model_predictions.csv new file mode 100644 index 0000000000000000000000000000000000000000..59de11edaf001c3d1f897423d8af01a6a0616e77 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/model_predictions.csv @@ -0,0 +1,10524 @@ +True_Values,Predictions +18.1,19.023436 +11.7,10.378908 +19.3,82.46518 +15.9,22.083487 +16.8,18.184397 +17.4,16.504822 +12.9,15.284321 +23.4,22.461887 +25.8,24.261425 +27.3,23.22056 +14.0,21.824217 +17.3,21.867449 +28.6,25.558971 +17.8,17.912754 +24.4,22.243704 +9.8,13.463484 +24.3,24.769775 +15.7,16.582094 +22.9,21.114586 +15.5,16.853502 +13.9,15.464842 +30.4,33.235138 +10.5,16.021986 +14.5,20.377766 +12.41,13.409935 +11.2,17.581467 +16.1,15.653582 +10.5,8.507015 +25.4,28.274769 +8.4,12.677789 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@@ -0,0 +1,181 @@ +import numpy as np +import xgboost as xgb +import matplotlib.pyplot as plt +import pandas as pd +import torch +from tqdm import tqdm +from pathlib import Path +from accelerate import Accelerator +from torch.utils.data import Dataset, DataLoader, TensorDataset +import os + +from dataloader.dataloader import MultiRasterDataset +from dataloader.dataloaderMapping import MultiRasterDatasetMapping +from dataloader.dataframe_loader import filter_dataframe, separate_and_add_data +from config import (TIME_BEGINNING, TIME_END, INFERENCE_TIME, seasons, years_padded, + SamplesCoordinates_Yearly, MatrixCoordinates_1mil_Yearly, + DataYearly, SamplesCoordinates_Seasonally, + MatrixCoordinates_1mil_Seasonally, DataSeasonally, + file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC, + window_size, bands_list_order) +from mapping import create_prediction_visualizations +from modelCNN import SmallCNN + +def load_cnn_model(model_path="/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/simpletimecnn_model_MAX_OC_150_TIME_BEGINNING_2007_TIME_END_2023.pth"): + print("1. Entering load_cnn_model") + try: + model = SmallCNN() + print("2. Model initialized:", model.__class__.__name__) + state_dict = torch.load(model_path, map_location="cpu", weights_only=True) + print("3. State dict loaded with keys:", list(state_dict.keys())[:5]) + + if all(k.startswith('module.') for k in state_dict.keys()): + print("4. Removing 'module.' prefix from state_dict keys") + state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()} + elif not any(k.startswith('module.') for k in state_dict.keys()) and hasattr(model, 'module'): + print("5. Adding 'module.' prefix to state_dict keys") + state_dict = {'module.' + k: v for k, v in state_dict.items()} + + model.load_state_dict(state_dict) + print("6. State dict loaded into model") + model.eval() + print("7. Model set to evaluation mode") + return model + except Exception as e: + print(f"Error loading model: {e}") + raise + +def separate_and_add_data_1mil_inference(TIME_END=INFERENCE_TIME, + SamplesCoordinates_Yearly=MatrixCoordinates_1mil_Yearly): + print("8. Entering separate_and_add_data_1mil_inference") + + def create_path_arrays_yearly(paths, year): + samples_paths = [] + for path in paths: + if 'Elevation' in path: + samples_paths.append(path) # Elevation is static + else: + samples_paths.append(f"{path}/{year}") + return samples_paths + + samples_paths = create_path_arrays_yearly(SamplesCoordinates_Yearly, TIME_END) + + print("9. Returning samples paths:", len(samples_paths)) + return samples_paths + +def flatten_paths(path_list): + print("10. Entering flatten_paths with input length:", len(path_list)) + flattened = [item for sublist in path_list for item in (flatten_paths(sublist) if isinstance(sublist, list) else [sublist])] + print("11. Flattened paths length:", len(flattened)) + return flattened + +def accelerated_predict(df_full, cnn_model, subfolders, batch_size=512): + print("12. Entering accelerated_predict") + print("13. df_full shape:", df_full.shape) + print("14. subfolders:", subfolders[:5]) + + try: + accelerator = Accelerator() + print("15. Accelerator initialized") + + print("16. Creating MultiRasterDatasetMapping") + dataset = MultiRasterDatasetMapping(subfolders, df_full, bands_list_order=bands_list_order) + print("17. Dataset length:", len(dataset)) + dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=4) + print("18. DataLoader created with batch_size:", batch_size) + + model, dataloader = accelerator.prepare(cnn_model, dataloader) + print("19. Model and DataLoader prepared with accelerator") + + all_predictions = [] + all_coordinates = [] + + with torch.no_grad(): + print("20. Starting inference loop") + for i, batch in enumerate(tqdm(dataloader, desc="Processing batches")): + inputs, coords = batch # inputs: (batch, num_bands, h, w), coords: (batch, 2) + outputs = model(inputs) + + # Gather outputs and coordinates across all processes + gathered_outputs = accelerator.gather(outputs) + gathered_coords = accelerator.gather(coords) + + if accelerator.is_main_process: + # Flatten predictions if necessary (assuming outputs are 2D or higher) + gathered_outputs = gathered_outputs.view(-1).cpu().numpy() # Flatten to 1D + gathered_coords = gathered_coords.view(-1, 2).cpu().numpy() # Ensure coords are (N, 2) + all_predictions.append(gathered_outputs) + all_coordinates.append(gathered_coords) + + if accelerator.is_main_process: + predictions = np.concatenate(all_predictions, axis=0) + coordinates = np.concatenate(all_coordinates, axis=0) + print("27. Final predictions shape:", predictions.shape) + print("28. Final coordinates shape:", coordinates.shape) + + # Ensure lengths match + if len(predictions) != len(coordinates): + raise ValueError(f"Mismatch between predictions ({len(predictions)}) and coordinates ({len(coordinates)})") + + return coordinates, predictions + else: + print("29. Not main process, returning None") + return None, None + + except Exception as e: + print(f"Error in accelerated prediction: {e}") + return None, None + finally: + if 'accelerator' in locals(): + accelerator.free_memory() + accelerator.wait_for_everyone() + +def main(): + print("30. Entering main") + try: + subfolders = separate_and_add_data_1mil_inference() + print("31. Subfolders retrieved:", len(subfolders)) + + subfolders = flatten_paths(subfolders) + print("32. Paths flattened:", len(subfolders)) + + subfolders = list(dict.fromkeys(subfolders)) + print("33. Duplicates removed:", len(subfolders)) + + file_path = "/home/vfourel/SOCProject/SOCmapping/Data/Coordinates1Mil/coordinates_Bavaria_1mil.csv" + df_full = pd.read_csv(file_path) + print("34. Coordinates loaded, head:\n", df_full.head()) + + cnn_model = load_cnn_model() + print("35. CNN model loaded") + + coordinates, predictions = accelerated_predict( + df_full=df_full, + cnn_model=cnn_model, + subfolders=subfolders, + batch_size=512 + ) + print("36. Prediction completed") + + if coordinates is not None and predictions is not None: + save_path_coords = "coordinates_1mil.npy" + save_path_preds = "predictions_1mil.npy" + + np.save(save_path_coords, coordinates) + np.save(save_path_preds, predictions) + print("37. Results saved to:", save_path_coords, save_path_preds) + + save_path = '/home/vfourel/SOCProject/SOCmapping/predictions_plots/cnnsimple_plots' + Path(save_path).mkdir(parents=True, exist_ok=True) + print("38. Visualization directory created:", save_path) + create_prediction_visualizations(INFERENCE_TIME, coordinates, predictions, save_path) + print(f"39. Results saved and visualizations created at {save_path}") + else: + print("40. Prediction failed or not on main process") + + except Exception as e: + print(f"Error in main: {e}") + raise + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/Archive/SimpleTimeModel/SimpleCNN/train.py b/Archive/SimpleTimeModel/SimpleCNN/train.py new file mode 100644 index 0000000000000000000000000000000000000000..1417743eb91998e6f596ceef1fcb18935c62e411 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/train.py @@ -0,0 +1,329 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.optim as optim +from dataloader.dataloader import MultiRasterDataset +from dataloader.dataloaderMapping import MultiRasterDatasetMapping +from dataloader.dataframe_loader import filter_dataframe, separate_and_add_data +import pandas as pd +from tqdm import tqdm +from pathlib import Path +import wandb +from accelerate import Accelerator +from config import (TIME_BEGINNING, TIME_END, INFERENCE_TIME, MAX_OC, + seasons, years_padded, num_epochs, + SamplesCoordinates_Yearly, MatrixCoordinates_1mil_Yearly, + DataYearly, SamplesCoordinates_Seasonally, + MatrixCoordinates_1mil_Seasonally, DataSeasonally, + file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC) +from torch.utils.data import Dataset, DataLoader +from modelCNN import SmallCNN +import argparse + +def composite_l1_chi2_loss(outputs, targets, sigma=3.0, alpha=0.5): + errors = targets - outputs + l1_loss = torch.mean(torch.abs(errors)) + squared_errors = errors ** 2 + chi2_unscaled = (1/4) * squared_errors * torch.exp(-squared_errors / (2 * sigma)) + chi2_unscaled_mean = torch.mean(chi2_unscaled) + chi2_unscaled_mean = torch.clamp(chi2_unscaled_mean, min=1e-8) + scale_factor = l1_loss / chi2_unscaled_mean + chi2_scaled = scale_factor * chi2_unscaled_mean + return alpha * l1_loss + (1 - alpha) * chi2_scaled + +def composite_l2_chi2_loss(outputs, targets, sigma=3.0, alpha=0.5): + errors = targets - outputs + l2_loss = torch.mean(errors ** 2) + chi2_loss = torch.mean((errors ** 2) / (sigma ** 2)) + chi2_loss = torch.clamp(chi2_loss, min=1e-8) + scale_factor = l2_loss / chi2_loss + chi2_scaled = scale_factor * chi2_loss + return alpha * l2_loss + (1 - alpha) * chi2_scaled + +def parse_args(): + parser = argparse.ArgumentParser(description='Train SimpleCNN model with customizable parameters') + parser.add_argument('--lr', type=float, default=0.001, help='Learning rate') + parser.add_argument('--loss_type', type=str, default='mse', choices=['composite_l1', 'l1', 'mse','composite_l2'], help='Type of loss function') + parser.add_argument('--loss_alpha', type=float, default=0.5, help='Weight for L1 loss in composite loss (if used)') + parser.add_argument('--target_transform', type=str, default='log', choices=['none', 'log', 'normalize'], help='Transformation to apply to targets') + parser.add_argument('--use_validation', action='store_true', default=True, help='Whether to use validation set') + return parser.parse_args() + +def create_balanced_dataset(df, use_validation=True, n_bins=128, min_ratio=3/4): + bins = pd.qcut(df['OC'], q=n_bins, labels=False, duplicates='drop') + df['bin'] = bins + bin_counts = df['bin'].value_counts() + max_samples = bin_counts.max() + min_samples = max(int(max_samples * min_ratio), 5) + training_dfs = [] + if use_validation: + validation_indices = [] + for bin_idx in range(len(bin_counts)): + bin_data = df[df['bin'] == bin_idx] + if len(bin_data) >= 4: + val_samples = bin_data.sample(n=min(13, len(bin_data))) + validation_indices.extend(val_samples.index) + train_samples = bin_data.drop(val_samples.index) + if len(train_samples) > 0: + if len(train_samples) < min_samples: + resampled = train_samples.sample(n=min_samples, replace=True) + training_dfs.append(resampled) + else: + training_dfs.append(train_samples) + if not training_dfs or not validation_indices: + raise ValueError("No training or validation data available after binning") + training_df = pd.concat(training_dfs).drop('bin', axis=1) + validation_df = df.loc[validation_indices].drop('bin', axis=1) + print('Size of the training set: ', len(training_df)) + print('Size of the validation set: ', len(validation_df)) + return training_df, validation_df + else: + for bin_idx in range(len(bin_counts)): + bin_data = df[df['bin'] == bin_idx] + if len(bin_data) > 0: + if len(bin_data) < min_samples: + resampled = bin_data.sample(n=min_samples, replace=True) + training_dfs.append(resampled) + else: + training_dfs.append(bin_data) + if not training_dfs: + raise ValueError("No training data available after binning") + training_df = pd.concat(training_dfs).drop('bin', axis=1) + return training_df, None # Return None for validation_df when no validation + +def train_model(args, model, train_loader, val_loader, num_epochs, accelerator, loss_type='L1', target_transform='none'): + if loss_type == 'composite_l1': + criterion = lambda outputs, targets: composite_l1_chi2_loss(outputs, targets, sigma=3.0, alpha=args.loss_alpha) + elif loss_type == 'composite_l2': + criterion = lambda outputs, targets: composite_l2_chi2_loss(outputs, targets, sigma=3.0, alpha=args.loss_alpha) + elif loss_type == 'l1': + criterion = nn.L1Loss() + elif loss_type == 'mse': + criterion = nn.MSELoss() + else: + raise ValueError(f"Unknown loss type: {loss_type}") + + optimizer = optim.Adam(model.parameters(), lr=args.lr) + train_loader, model, optimizer = accelerator.prepare( + train_loader, model, optimizer + ) + if val_loader is not None: + val_loader = accelerator.prepare(val_loader) + + if target_transform == 'normalize': + all_targets = [] + for _, _, _, targets in train_loader: + all_targets.append(targets) + all_targets = torch.cat(all_targets) + target_mean = all_targets.mean().item() + target_std = all_targets.std().item() + if accelerator.is_main_process: + print(f"Target mean: {target_mean}, Target std: {target_std}") + else: + target_mean, target_std = 0.0, 1.0 + + best_r_squared = -float('inf') if args.use_validation else 1.0 + best_model_state = None + + for epoch in range(num_epochs): + model.train() + running_loss = 0.0 + + for batch_idx, (longitudes, latitudes, features, targets) in enumerate(tqdm(train_loader)): + features = features.to(accelerator.device) + targets = targets.to(accelerator.device).float() + if target_transform == 'log': + targets = torch.log(targets + 1e-10) + elif target_transform == 'normalize': + targets = (targets - target_mean) / (target_std + 1e-10) + optimizer.zero_grad() + outputs = model(features) + loss = criterion(outputs, targets) + accelerator.backward(loss) + optimizer.step() + running_loss += loss.item() + + if accelerator.is_main_process: + wandb.log({ + 'train_loss': loss.item(), + 'batch': batch_idx + 1 + epoch * len(train_loader), + 'epoch': epoch + 1 + }) + + train_loss = running_loss / len(train_loader) + + if args.use_validation and val_loader is not None: + model.eval() + val_loss = 0.0 + val_outputs_list = [] + val_targets_list = [] + + with torch.no_grad(): + for longitudes, latitudes, features, targets in val_loader: + features = features.to(accelerator.device) + targets = targets.to(accelerator.device).float() + if target_transform == 'log': + targets = torch.log(targets + 1e-10) + elif target_transform == 'normalize': + targets = (targets - target_mean) / (target_std + 1e-10) + outputs = model(features) + loss = criterion(outputs, targets) + val_loss += loss.item() + val_outputs_list.append(outputs.cpu()) + val_targets_list.append(targets.cpu()) + + val_loss = val_loss / len(val_loader) + + # Concatenate outputs and targets from all batches + val_outputs = torch.cat(val_outputs_list, dim=0).numpy() + val_targets = torch.cat(val_targets_list, dim=0).numpy() + + # Gather data from all processes + val_outputs_all = torch.from_numpy(val_outputs).to(accelerator.device) + val_targets_all = torch.from_numpy(val_targets).to(accelerator.device) + val_outputs_all = accelerator.gather(val_outputs_all).cpu().numpy() + val_targets_all = accelerator.gather(val_targets_all).cpu().numpy() + + if accelerator.is_main_process: + # Inverse transform to original scale + if target_transform == 'log': + original_val_outputs = np.exp(val_outputs_all) + original_val_targets = np.exp(val_targets_all) + elif target_transform == 'normalize': + original_val_outputs = val_outputs_all * target_std + target_mean + original_val_targets = val_targets_all * target_std + target_mean + else: + original_val_outputs = val_outputs_all + original_val_targets = val_targets_all + + # Compute metrics on original scale + if len(original_val_outputs) > 1 and np.std(original_val_outputs) > 1e-6 and np.std(original_val_targets) > 1e-6: + correlation = np.corrcoef(original_val_outputs, original_val_targets)[0, 1] + r_squared = correlation ** 2 + mse = np.mean((original_val_outputs - original_val_targets) ** 2) + rmse = np.sqrt(mse) + mae = np.mean(np.abs(original_val_outputs - original_val_targets)) + iqr = np.percentile(original_val_targets, 75) - np.percentile(original_val_targets, 25) + rpiq = iqr / rmse if rmse > 0 else float('inf') + else: + correlation = 0.0 + r_squared = 0.0 + mse = float('nan') + rmse = float('nan') + mae = float('nan') + rpiq = float('nan') + + # Update best model based on R² + if r_squared > best_r_squared: + best_r_squared = r_squared + best_model_state = {k: v.cpu() for k, v in model.state_dict().items()} + wandb.run.summary['best_r_squared'] = best_r_squared + + wandb.log({ + 'epoch': epoch + 1, + 'train_loss_avg': train_loss, + 'val_loss': val_loss, + 'correlation': correlation, + 'r_squared': r_squared, + 'mse': mse, + 'rmse': rmse, + 'mae': mae, + 'rpiq': rpiq + }) + + accelerator.print(f'Epoch {epoch+1}:') + accelerator.print(f'Training Loss: {train_loss:.4f}') + accelerator.print(f'Validation Loss: {val_loss:.4f}') + if accelerator.is_main_process: + accelerator.print(f'RPIQ: {rpiq:.4f}\n') + else: + # No validation, update model state and set R² to 1.0 + best_r_squared = 1.0 + best_model_state = {k: v.cpu() for k, v in model.state_dict().items()} + wandb.run.summary['best_r_squared'] = best_r_squared + + if accelerator.is_main_process: + wandb.log({ + 'epoch': epoch + 1, + 'train_loss_avg': train_loss, + }) + + accelerator.print(f'Epoch {epoch+1}:') + accelerator.print(f'Training Loss: {train_loss:.4f}\n') + + return model, None, None, best_model_state, best_r_squared + +if __name__ == "__main__": + args = parse_args() + accelerator = Accelerator() + + wandb.init( + project="socmapping-SimpleTimeCNN", + config={ + "max_oc": MAX_OC, + "time_beginning": TIME_BEGINNING, + "time_end": TIME_END, + "epochs": num_epochs, + "batch_size": 256, + "learning_rate": 0.001, + "input_channels": 6, + "loss_type": args.loss_type, + "target_transform": args.target_transform, + "use_validation": args.use_validation + } + ) + + df = filter_dataframe(TIME_BEGINNING, TIME_END, MAX_OC) + samples_coordinates_array_path, data_array_path = separate_and_add_data() + + def flatten_paths(path_list): + flattened = [] + for item in path_list: + if isinstance(item, list): + flattened.extend(flatten_paths(item)) + else: + flattened.append(item) + return flattened + + samples_coordinates_array_path = list(dict.fromkeys(flatten_paths(samples_coordinates_array_path))) + data_array_path = list(dict.fromkeys(flatten_paths(data_array_path))) + + if args.use_validation: + train_df, val_df = create_balanced_dataset(df, use_validation=args.use_validation) + else: + train_df, val_df = create_balanced_dataset(df, use_validation=args.use_validation) + + train_dataset = MultiRasterDataset(samples_coordinates_array_path, data_array_path, train_df) + val_dataset = MultiRasterDataset(samples_coordinates_array_path, data_array_path, val_df) if val_df is not None else None + + train_loader = DataLoader(train_dataset, batch_size=256, shuffle=True) + val_loader = DataLoader(val_dataset, batch_size=256, shuffle=False) if val_dataset is not None else None + + model = SmallCNN(input_channels=6) + + if accelerator.is_main_process: + wandb.run.summary["model_parameters"] = model.count_parameters() + wandb.run.summary["train_size"] = len(train_df) + wandb.run.summary["val_size"] = len(val_df) if val_df is not None else 0 + print(f"Model parameters: {model.count_parameters()}") + print(f"Training set size: {len(train_df)}") + print(f"Validation set size: {len(val_df) if val_df is not None else 0}") + + model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( + args, model, train_loader, val_loader, + num_epochs=num_epochs, + accelerator=accelerator, + loss_type=args.loss_type, + target_transform=args.target_transform + ) + + if accelerator.is_main_process and best_model_state is not None: + final_model_path = (f'simpletimecnn_model_MAX_OC_{MAX_OC}_TIME_BEGINNING_{TIME_BEGINNING}_' + f'TIME_END_{TIME_END}_LOSS_{args.loss_type}_TRANSFORM_{args.target_transform}_' + f'BEST_R2_{best_r_squared:.4f}.pth') + torch.save(best_model_state, final_model_path) + wandb.save(final_model_path) + print(f"Best model saved with R²: {best_r_squared:.4f}") + + wandb.finish() + # bst moel with none/l2 \ No newline at end of file diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/debug-internal.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/debug-internal.log new file mode 100644 index 0000000000000000000000000000000000000000..461e73630cf965df4d74fa5691a22d455d0b57a5 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/debug-internal.log @@ -0,0 +1,7 @@ +{"time":"2025-04-16T00:12:08.78105528+02:00","level":"INFO","msg":"stream: starting","core version":"0.19.3","symlink path":"/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001208-pexcogii/logs/debug-core.log"} +{"time":"2025-04-16T00:12:08.897078796+02:00","level":"INFO","msg":"created new stream","id":"pexcogii"} +{"time":"2025-04-16T00:12:08.897114739+02:00","level":"INFO","msg":"stream: started","id":"pexcogii"} +{"time":"2025-04-16T00:12:08.89715043+02:00","level":"INFO","msg":"writer: Do: started","stream_id":"pexcogii"} +{"time":"2025-04-16T00:12:08.897168063+02:00","level":"INFO","msg":"sender: started","stream_id":"pexcogii"} +{"time":"2025-04-16T00:12:08.897161174+02:00","level":"INFO","msg":"handler: started","stream_id":"pexcogii"} +{"time":"2025-04-16T00:12:09.159367487+02:00","level":"INFO","msg":"Starting system monitor"} diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/debug.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/debug.log new file mode 100644 index 0000000000000000000000000000000000000000..f59986c8982b6bd2d193e9cd5e6306aa47470742 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/debug.log @@ -0,0 +1,22 @@ +2025-04-16 00:12:08,482 INFO MainThread:953931 [wandb_setup.py:_flush():68] Current SDK version is 0.19.3 +2025-04-16 00:12:08,483 INFO MainThread:953931 [wandb_setup.py:_flush():68] Configure stats pid to 953931 +2025-04-16 00:12:08,483 INFO MainThread:953931 [wandb_setup.py:_flush():68] Loading settings from /home/vfourel/.config/wandb/settings +2025-04-16 00:12:08,483 INFO MainThread:953931 [wandb_setup.py:_flush():68] Loading settings from /lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/wandb/settings +2025-04-16 00:12:08,483 INFO MainThread:953931 [wandb_setup.py:_flush():68] Loading settings from environment variables +2025-04-16 00:12:08,483 INFO MainThread:953931 [wandb_init.py:_log_setup():598] Logging user logs to /lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001208-pexcogii/logs/debug.log +2025-04-16 00:12:08,483 INFO MainThread:953931 [wandb_init.py:_log_setup():599] Logging internal logs to /lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001208-pexcogii/logs/debug-internal.log +2025-04-16 00:12:08,483 INFO MainThread:953931 [wandb_init.py:init():714] calling init triggers +2025-04-16 00:12:08,483 INFO MainThread:953931 [wandb_init.py:init():719] wandb.init called with sweep_config: {} +config: {'max_oc': 150, 'time_beginning': '2007', 'time_end': '2023', 'epochs': 200, 'batch_size': 256, 'learning_rate': 0.001, 'input_channels': 6, 'loss_type': 'mse', 'target_transform': 'log', 'use_validation': True} +2025-04-16 00:12:08,483 INFO MainThread:953931 [wandb_init.py:init():745] starting backend +2025-04-16 00:12:08,721 INFO MainThread:953931 [wandb_init.py:init():749] sending inform_init request +2025-04-16 00:12:08,773 INFO MainThread:953931 [backend.py:_multiprocessing_setup():104] multiprocessing start_methods=fork,spawn,forkserver, using: spawn +2025-04-16 00:12:08,774 INFO MainThread:953931 [wandb_init.py:init():764] backend started and connected +2025-04-16 00:12:08,776 INFO MainThread:953931 [wandb_init.py:init():857] updated telemetry +2025-04-16 00:12:08,814 INFO MainThread:953931 [wandb_init.py:init():889] communicating run to backend with 90.0 second timeout +2025-04-16 00:12:09,155 INFO MainThread:953931 [wandb_init.py:init():941] starting run threads in backend +2025-04-16 00:12:09,456 INFO MainThread:953931 [wandb_run.py:_console_start():2420] atexit reg +2025-04-16 00:12:09,456 INFO MainThread:953931 [wandb_run.py:_redirect():2270] redirect: wrap_raw +2025-04-16 00:12:09,457 INFO MainThread:953931 [wandb_run.py:_redirect():2335] Wrapping output streams. +2025-04-16 00:12:09,457 INFO MainThread:953931 [wandb_run.py:_redirect():2360] Redirects installed. +2025-04-16 00:12:09,461 INFO MainThread:953931 [wandb_init.py:init():983] run started, returning control to user process diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0awc6tyy/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0awc6tyy/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f27f8f6a0e520c91f08b3c022b1162dea9d15161 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0awc6tyy/files/config.yaml @@ -0,0 +1,42 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 2 + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0awc6tyy/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0awc6tyy/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..ef75bccac61137f781de81d8d0ad8b718b1455da --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0awc6tyy/files/output.log @@ -0,0 +1,204 @@ 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0awc6tyy/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0awc6tyy/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..bfb44884dfb3d333be9f8d1c385816dd2781c020 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0awc6tyy/files/requirements.txt @@ -0,0 +1,156 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 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value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 2 + - 3 + - 14 + - 16 + - 23 + - 55 + - 62 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0dmcbofq/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0dmcbofq/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..2f618b124d9292c0afee3f7cfd4df59787455911 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0dmcbofq/files/output.log @@ -0,0 +1,1008 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20355 +Validation set size: 1024 +Model parameters: 40529 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:11<00:00, 3.60it/s] +Epoch 1: +Training Loss: 29612.2954 +Validation Loss: 169.1794 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 2: +Training Loss: 884.9280 +Validation Loss: 146.7502 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 3: +Training Loss: 702.4921 +Validation Loss: 112.4817 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 4: +Training Loss: 639.9474 +Validation Loss: 95.0772 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 5: +Training Loss: 586.8990 +Validation Loss: 82.4658 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 6: +Training Loss: 574.8708 +Validation Loss: 78.0471 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 7: +Training Loss: 554.3161 +Validation Loss: 71.7522 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.96it/s] + +Epoch 8: +Training Loss: 549.0310 +Validation Loss: 65.2490 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 9: +Training Loss: 504.0148 +Validation Loss: 64.4213 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.96it/s] + +Epoch 10: +Training Loss: 505.8259 +Validation Loss: 57.7025 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 11: +Training Loss: 490.7917 +Validation Loss: 53.8215 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 12: +Training Loss: 469.9979 +Validation Loss: 53.9135 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 13: +Training Loss: 482.3764 +Validation Loss: 55.6715 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.96it/s] + +Epoch 14: +Training Loss: 466.9206 +Validation Loss: 56.0206 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 15: +Training Loss: 476.2886 +Validation Loss: 54.5258 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 16: +Training Loss: 455.3992 +Validation Loss: 56.9194 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.89it/s] + +Epoch 17: +Training Loss: 439.6617 +Validation Loss: 61.3422 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.81it/s] + +Epoch 18: +Training Loss: 447.9087 +Validation Loss: 57.7479 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.92it/s] + +Epoch 19: +Training Loss: 455.8835 +Validation Loss: 52.1573 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 20: +Training Loss: 461.1870 +Validation Loss: 62.8512 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 21: +Training Loss: 446.3664 +Validation Loss: 65.5545 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.96it/s] + +Epoch 22: +Training Loss: 428.9426 +Validation Loss: 56.6659 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 23: +Training Loss: 454.5297 +Validation Loss: 58.1553 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 24: +Training Loss: 442.0816 +Validation Loss: 53.4436 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+100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 40: +Training Loss: 431.5923 +Validation Loss: 54.3218 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 41: +Training Loss: 418.7010 +Validation Loss: 61.6777 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 42: +Training Loss: 412.3707 +Validation Loss: 61.8064 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 43: +Training Loss: 433.9806 +Validation Loss: 53.1589 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 44: +Training Loss: 405.0242 +Validation Loss: 63.8870 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 45: +Training Loss: 410.2061 +Validation Loss: 59.9836 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 46: +Training Loss: 415.0279 +Validation Loss: 63.8563 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 47: +Training Loss: 408.3272 +Validation Loss: 57.4207 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 48: +Training Loss: 409.2480 +Validation Loss: 65.2902 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 49: +Training Loss: 441.9910 +Validation Loss: 62.0495 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 50: +Training Loss: 409.5167 +Validation Loss: 60.9891 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 51: +Training Loss: 402.3547 +Validation Loss: 60.3197 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 52: +Training Loss: 401.1177 +Validation Loss: 64.3436 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 53: +Training Loss: 433.5304 +Validation Loss: 52.6655 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 54: +Training Loss: 411.1972 +Validation Loss: 49.9258 +100%|███████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 55: +Training Loss: 407.5710 +Validation Loss: 55.5192 +100%|███████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 56: +Training Loss: 396.0586 +Validation Loss: 60.7281 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 57: +Training Loss: 382.7943 +Validation Loss: 53.1911 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 58: +Training Loss: 421.0469 +Validation Loss: 47.1090 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.96it/s] + +Epoch 59: +Training Loss: 395.2191 +Validation Loss: 53.2896 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 60: +Training Loss: 396.4215 +Validation Loss: 60.3085 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 61: +Training Loss: 406.4136 +Validation Loss: 60.3322 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.96it/s] + +Epoch 62: +Training Loss: 390.7093 +Validation Loss: 60.6256 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 63: +Training Loss: 373.3005 +Validation Loss: 62.8413 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.96it/s] + +Epoch 64: +Training Loss: 395.5229 +Validation Loss: 77.2066 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.96it/s] + +Epoch 65: +Training Loss: 397.3000 +Validation Loss: 67.3425 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.96it/s] + +Epoch 66: +Training Loss: 406.2439 +Validation Loss: 61.6999 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 67: +Training Loss: 398.8192 +Validation Loss: 61.8271 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 68: +Training Loss: 407.6412 +Validation Loss: 72.5763 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 69: +Training Loss: 388.6335 +Validation Loss: 53.5924 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 70: +Training Loss: 378.8100 +Validation Loss: 52.5163 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 71: +Training Loss: 395.6855 +Validation Loss: 50.4224 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 72: +Training Loss: 398.5670 +Validation Loss: 65.5832 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 73: +Training Loss: 381.5093 +Validation Loss: 55.5606 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 74: +Training Loss: 386.1070 +Validation Loss: 46.8524 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 75: +Training Loss: 371.8793 +Validation Loss: 55.7920 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 76: +Training Loss: 368.5634 +Validation Loss: 59.4082 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 77: +Training Loss: 374.9706 +Validation Loss: 59.4136 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 78: +Training Loss: 358.4386 +Validation Loss: 56.2081 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 79: +Training Loss: 361.1851 +Validation Loss: 63.9306 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 80: +Training Loss: 365.9129 +Validation Loss: 63.0395 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 81: +Training Loss: 348.1940 +Validation Loss: 64.9950 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 82: +Training Loss: 350.7281 +Validation Loss: 68.1406 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 83: +Training Loss: 338.7979 +Validation Loss: 75.4407 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 84: +Training Loss: 343.5379 +Validation Loss: 68.7842 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 85: +Training Loss: 362.2926 +Validation Loss: 67.1847 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 86: +Training Loss: 347.1772 +Validation Loss: 63.6181 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 87: +Training Loss: 347.1429 +Validation Loss: 74.8662 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 88: +Training Loss: 332.9276 +Validation Loss: 75.8410 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 89: +Training Loss: 360.2465 +Validation Loss: 67.7761 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 90: +Training Loss: 328.9642 +Validation Loss: 77.9015 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 91: +Training Loss: 328.8737 +Validation Loss: 64.6514 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 92: +Training Loss: 336.6882 +Validation Loss: 77.7873 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 93: +Training Loss: 315.3011 +Validation Loss: 55.2901 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 94: +Training Loss: 345.1345 +Validation Loss: 77.2731 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 95: +Training Loss: 327.8137 +Validation Loss: 55.4285 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 96: +Training Loss: 311.4311 +Validation Loss: 67.5185 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 97: +Training Loss: 323.2135 +Validation Loss: 78.0404 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 98: +Training Loss: 330.3788 +Validation Loss: 59.4223 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 99: +Training Loss: 315.6839 +Validation Loss: 79.1478 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 100: +Training Loss: 328.1680 +Validation Loss: 74.3823 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 101: +Training Loss: 322.2032 +Validation Loss: 56.6452 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 102: +Training Loss: 323.6272 +Validation Loss: 72.2975 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 103: +Training Loss: 325.1670 +Validation Loss: 65.9821 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 104: +Training Loss: 312.1814 +Validation Loss: 71.9928 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 105: +Training Loss: 303.8906 +Validation Loss: 76.1861 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 106: +Training Loss: 323.8757 +Validation Loss: 56.3777 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 107: +Training Loss: 314.0686 +Validation Loss: 72.2995 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 108: +Training Loss: 322.6773 +Validation Loss: 81.5951 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 109: +Training Loss: 313.4071 +Validation Loss: 73.2900 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 110: +Training Loss: 327.1438 +Validation Loss: 59.4378 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 111: +Training Loss: 301.7496 +Validation Loss: 64.3132 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 112: +Training Loss: 309.7906 +Validation Loss: 61.1761 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 113: +Training Loss: 303.6187 +Validation Loss: 63.5350 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 114: +Training Loss: 304.1702 +Validation Loss: 68.2603 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 115: +Training Loss: 307.8741 +Validation Loss: 64.6398 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 116: +Training Loss: 316.1163 +Validation Loss: 52.9154 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 117: +Training Loss: 301.6441 +Validation Loss: 67.1211 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 118: +Training Loss: 294.4768 +Validation Loss: 70.9089 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 119: +Training Loss: 303.8752 +Validation Loss: 74.0472 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 120: +Training Loss: 297.3782 +Validation Loss: 61.7733 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 121: +Training Loss: 274.8944 +Validation Loss: 58.9970 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 122: +Training Loss: 319.7677 +Validation Loss: 60.0092 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 123: +Training Loss: 289.0395 +Validation Loss: 64.4597 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 124: +Training Loss: 305.9552 +Validation Loss: 60.5901 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 125: +Training Loss: 294.9814 +Validation Loss: 65.9553 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 126: +Training Loss: 287.5797 +Validation Loss: 61.7854 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 127: +Training Loss: 305.9974 +Validation Loss: 67.4125 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 128: +Training Loss: 278.9549 +Validation Loss: 61.0889 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 129: +Training Loss: 311.0393 +Validation Loss: 65.6593 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 130: +Training Loss: 298.4283 +Validation Loss: 67.8299 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 131: +Training Loss: 274.6643 +Validation Loss: 70.8129 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 132: +Training Loss: 299.7494 +Validation Loss: 59.1465 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 133: +Training Loss: 273.2414 +Validation Loss: 57.8537 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 134: +Training Loss: 290.6535 +Validation Loss: 67.7524 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.96it/s] + +Epoch 135: +Training Loss: 291.8810 +Validation Loss: 60.1348 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 136: +Training Loss: 282.8515 +Validation Loss: 76.3663 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 137: +Training Loss: 286.2089 +Validation Loss: 46.3676 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 138: +Training Loss: 279.8616 +Validation Loss: 64.1014 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 139: +Training Loss: 281.5154 +Validation Loss: 63.9644 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 140: +Training Loss: 286.7082 +Validation Loss: 55.1560 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 141: +Training Loss: 283.6634 +Validation Loss: 55.9360 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 142: +Training Loss: 277.1594 +Validation Loss: 58.5243 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 143: +Training Loss: 294.7334 +Validation Loss: 55.3723 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 144: +Training Loss: 288.3914 +Validation Loss: 55.0333 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.77it/s] + +Epoch 145: +Training Loss: 291.7742 +Validation Loss: 65.0061 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.74it/s] + +Epoch 146: +Training Loss: 290.6638 +Validation Loss: 58.9959 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.77it/s] + +Epoch 147: +Training Loss: 268.5203 +Validation Loss: 66.1530 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.82it/s] + +Epoch 148: +Training Loss: 283.2946 +Validation Loss: 60.3438 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.85it/s] + +Epoch 149: +Training Loss: 279.3167 +Validation Loss: 53.5184 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.86it/s] + +Epoch 150: +Training Loss: 271.9371 +Validation Loss: 62.3886 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.87it/s] + +Epoch 151: +Training Loss: 275.0145 +Validation Loss: 64.4041 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.88it/s] + +Epoch 152: +Training Loss: 275.2427 +Validation Loss: 54.0392 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.88it/s] + +Epoch 153: +Training Loss: 269.8017 +Validation Loss: 66.3674 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.91it/s] + +Epoch 154: +Training Loss: 279.3433 +Validation Loss: 50.6704 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.91it/s] + +Epoch 155: +Training Loss: 287.4620 +Validation Loss: 57.3580 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.92it/s] + +Epoch 156: +Training Loss: 284.2679 +Validation Loss: 64.7781 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.92it/s] + +Epoch 157: +Training Loss: 275.8061 +Validation Loss: 44.4207 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.91it/s] + +Epoch 158: +Training Loss: 279.1408 +Validation Loss: 56.3897 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 159: +Training Loss: 278.9623 +Validation Loss: 63.0777 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 160: +Training Loss: 261.5794 +Validation Loss: 55.6168 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.92it/s] + +Epoch 161: +Training Loss: 263.4310 +Validation Loss: 51.6767 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 162: +Training Loss: 267.1867 +Validation Loss: 64.0840 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 163: +Training Loss: 283.4603 +Validation Loss: 64.8240 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 164: +Training Loss: 268.2805 +Validation Loss: 57.3576 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 165: +Training Loss: 263.2530 +Validation Loss: 66.8375 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 166: +Training Loss: 264.3864 +Validation Loss: 64.5092 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 167: +Training Loss: 268.1567 +Validation Loss: 52.2610 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 168: +Training Loss: 281.4885 +Validation Loss: 61.7021 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 169: +Training Loss: 255.7027 +Validation Loss: 57.0514 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 170: +Training Loss: 255.5293 +Validation Loss: 47.1523 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 171: +Training Loss: 275.7743 +Validation Loss: 73.0870 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 172: +Training Loss: 255.5846 +Validation Loss: 50.1709 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 173: +Training Loss: 277.4923 +Validation Loss: 56.7930 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 174: +Training Loss: 260.4309 +Validation Loss: 52.1126 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 175: +Training Loss: 266.4945 +Validation Loss: 55.5163 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 176: +Training Loss: 264.8042 +Validation Loss: 57.5814 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 177: +Training Loss: 266.7028 +Validation Loss: 62.5204 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.96it/s] + +Epoch 178: +Training Loss: 269.0321 +Validation Loss: 64.1221 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 179: +Training Loss: 256.5602 +Validation Loss: 65.9467 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 180: +Training Loss: 269.0303 +Validation Loss: 55.2808 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 181: +Training Loss: 258.6928 +Validation Loss: 60.7927 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 182: +Training Loss: 257.0777 +Validation Loss: 72.1812 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 183: +Training Loss: 261.4711 +Validation Loss: 54.8003 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 184: +Training Loss: 260.4314 +Validation Loss: 61.8632 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 185: +Training Loss: 252.4671 +Validation Loss: 50.2862 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 186: +Training Loss: 253.0636 +Validation Loss: 59.7833 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 187: +Training Loss: 251.3780 +Validation Loss: 51.0836 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 188: +Training Loss: 251.0757 +Validation Loss: 60.8677 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 189: +Training Loss: 254.1934 +Validation Loss: 59.3643 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 190: +Training Loss: 258.1609 +Validation Loss: 56.0600 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 191: +Training Loss: 254.0268 +Validation Loss: 51.7394 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 192: +Training Loss: 249.8183 +Validation Loss: 63.9974 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.92it/s] + +Epoch 193: +Training Loss: 257.5247 +Validation Loss: 59.2628 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 194: +Training Loss: 244.2218 +Validation Loss: 55.2670 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 195: +Training Loss: 245.8459 +Validation Loss: 56.0412 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.94it/s] + +Epoch 196: +Training Loss: 259.5055 +Validation Loss: 56.5920 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 197: +Training Loss: 252.1727 +Validation Loss: 59.7333 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 198: +Training Loss: 247.4488 +Validation Loss: 56.6326 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.93it/s] + +Epoch 199: +Training Loss: 247.1833 +Validation Loss: 55.4683 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:10<00:00, 3.95it/s] + +Epoch 200: +Training Loss: 259.0663 +Validation Loss: 46.4884 + +Model trained and saved successfully! diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0dmcbofq/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0dmcbofq/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..bfb44884dfb3d333be9f8d1c385816dd2781c020 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_014243-0dmcbofq/files/requirements.txt @@ -0,0 +1,156 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20355 +Validation set size: 1024 +Size of the first batch: torch.Size([256, 6, 33, 33, 4]) +Model parameters: 123889 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:38<00:00, 1.04it/s] +Epoch 1: +Training Loss: 15961.2257 +Validation Loss: 237.7222 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:38<00:00, 1.05it/s] + +Epoch 2: +Training Loss: 723.4335 +Validation Loss: 84.3108 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.06it/s] + +Epoch 3: +Training Loss: 574.7348 +Validation Loss: 65.6711 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.06it/s] + +Epoch 4: +Training Loss: 509.8936 +Validation Loss: 58.7213 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.07it/s] + +Epoch 5: +Training Loss: 505.1845 +Validation Loss: 63.2188 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.07it/s] + +Epoch 6: +Training Loss: 515.5017 +Validation Loss: 62.3877 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.08it/s] + +Epoch 7: +Training Loss: 505.0036 +Validation Loss: 62.6632 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 8: +Training Loss: 509.5316 +Validation Loss: 68.0809 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 9: +Training Loss: 485.3502 +Validation Loss: 60.3660 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.11it/s] + +Epoch 10: +Training Loss: 496.0062 +Validation Loss: 63.5136 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 11: +Training Loss: 493.7572 +Validation Loss: 62.2035 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 12: +Training Loss: 480.8334 +Validation Loss: 65.9818 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 13: +Training Loss: 482.4588 +Validation Loss: 62.7384 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 14: +Training Loss: 489.8958 +Validation Loss: 60.1493 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 15: +Training Loss: 485.6193 +Validation Loss: 60.6971 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 16: +Training Loss: 477.3485 +Validation Loss: 65.0157 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.08it/s] + +Epoch 17: +Training Loss: 461.9316 +Validation Loss: 67.0822 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 18: +Training Loss: 479.6275 +Validation Loss: 63.1272 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 19: +Training Loss: 491.5713 +Validation Loss: 63.8117 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 20: +Training Loss: 486.6815 +Validation Loss: 66.6190 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.11it/s] + +Epoch 21: +Training Loss: 481.0600 +Validation Loss: 72.1093 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 22: +Training Loss: 461.4241 +Validation Loss: 64.5360 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 23: +Training Loss: 484.7702 +Validation Loss: 60.3426 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 24: +Training Loss: 478.8873 +Validation Loss: 62.7727 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 25: +Training Loss: 457.6917 +Validation Loss: 57.9903 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 26: +Training Loss: 471.6977 +Validation Loss: 69.0909 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 27: +Training Loss: 462.5085 +Validation Loss: 63.1041 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.11it/s] + +Epoch 28: +Training Loss: 459.5130 +Validation Loss: 69.1396 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 29: +Training Loss: 488.6775 +Validation Loss: 74.2147 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.11it/s] + +Epoch 30: +Training Loss: 495.0533 +Validation Loss: 68.3826 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.08it/s] + +Epoch 31: +Training Loss: 470.9168 +Validation Loss: 60.7096 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 32: +Training Loss: 462.3931 +Validation Loss: 74.5799 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.11it/s] + +Epoch 33: +Training Loss: 456.9788 +Validation Loss: 55.1290 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 34: +Training Loss: 483.9317 +Validation Loss: 56.4018 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 35: +Training Loss: 456.8249 +Validation Loss: 55.2917 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 36: +Training Loss: 476.6213 +Validation Loss: 56.1908 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 37: +Training Loss: 468.7691 +Validation Loss: 62.3922 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.11it/s] + +Epoch 38: +Training Loss: 463.5582 +Validation Loss: 57.2967 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 39: +Training Loss: 478.6495 +Validation Loss: 59.3125 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 40: +Training Loss: 483.1817 +Validation Loss: 48.7735 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.11it/s] + +Epoch 41: +Training Loss: 467.7086 +Validation Loss: 65.0968 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 42: +Training Loss: 452.1527 +Validation Loss: 66.0920 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.11it/s] + +Epoch 43: +Training Loss: 475.9787 +Validation Loss: 66.8053 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 44: +Training Loss: 454.7502 +Validation Loss: 66.7500 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 45: +Training Loss: 454.5793 +Validation Loss: 72.0694 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:38<00:00, 1.04it/s] + +Epoch 46: +Training Loss: 454.3210 +Validation Loss: 68.9920 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.08it/s] + +Epoch 47: +Training Loss: 444.6656 +Validation Loss: 66.2754 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 48: +Training Loss: 454.8028 +Validation Loss: 67.0617 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 49: +Training Loss: 475.3623 +Validation Loss: 56.4041 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 50: +Training Loss: 458.2243 +Validation Loss: 83.0144 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 51: +Training Loss: 453.5234 +Validation Loss: 60.2569 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 52: +Training Loss: 435.4636 +Validation Loss: 80.7629 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 53: +Training Loss: 471.6181 +Validation Loss: 76.2985 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.07it/s] + +Epoch 54: +Training Loss: 433.4168 +Validation Loss: 66.3249 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.06it/s] + +Epoch 55: +Training Loss: 424.8172 +Validation Loss: 63.2539 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.06it/s] + +Epoch 56: +Training Loss: 415.4935 +Validation Loss: 70.8284 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.06it/s] + +Epoch 57: +Training Loss: 408.0107 +Validation Loss: 80.3984 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.06it/s] + +Epoch 58: +Training Loss: 429.5540 +Validation Loss: 68.1133 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.06it/s] + +Epoch 59: +Training Loss: 415.2184 +Validation Loss: 66.5027 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.06it/s] + +Epoch 60: +Training Loss: 412.1941 +Validation Loss: 82.4366 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.07it/s] + +Epoch 61: +Training Loss: 424.6868 +Validation Loss: 69.1012 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.07it/s] + +Epoch 62: +Training Loss: 406.5215 +Validation Loss: 76.5718 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.06it/s] + +Epoch 63: +Training Loss: 392.7631 +Validation Loss: 81.7524 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 64: +Training Loss: 409.1013 +Validation Loss: 76.6935 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 65: +Training Loss: 411.6122 +Validation Loss: 68.2784 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:38<00:00, 1.05it/s] + +Epoch 66: +Training Loss: 422.2701 +Validation Loss: 77.6808 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.05it/s] + +Epoch 67: +Training Loss: 424.1287 +Validation Loss: 59.9678 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 68: +Training Loss: 430.4147 +Validation Loss: 88.6125 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:38<00:00, 1.05it/s] + +Epoch 69: +Training Loss: 408.6583 +Validation Loss: 62.2082 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.06it/s] + +Epoch 70: +Training Loss: 404.6593 +Validation Loss: 69.7110 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 71: +Training Loss: 413.0854 +Validation Loss: 83.5883 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 72: +Training Loss: 404.8578 +Validation Loss: 67.7835 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.11it/s] + +Epoch 73: +Training Loss: 408.0904 +Validation Loss: 70.4381 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 74: +Training Loss: 417.3891 +Validation Loss: 56.6171 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 75: +Training Loss: 394.1395 +Validation Loss: 70.9436 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 76: +Training Loss: 406.1477 +Validation Loss: 71.2504 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 77: +Training Loss: 423.7034 +Validation Loss: 66.1013 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 78: +Training Loss: 391.6619 +Validation Loss: 74.8652 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 79: +Training Loss: 392.0839 +Validation Loss: 62.1362 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 80: +Training Loss: 405.4781 +Validation Loss: 84.3613 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 81: +Training Loss: 390.5781 +Validation Loss: 65.1360 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 82: +Training Loss: 412.3473 +Validation Loss: 74.2067 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 83: +Training Loss: 389.7748 +Validation Loss: 74.9104 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 84: +Training Loss: 388.2872 +Validation Loss: 69.7238 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 85: +Training Loss: 409.7883 +Validation Loss: 73.2084 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 86: +Training Loss: 382.5053 +Validation Loss: 70.9726 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.11it/s] + +Epoch 87: +Training Loss: 390.9571 +Validation Loss: 72.3668 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 88: +Training Loss: 395.7726 +Validation Loss: 69.0394 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 89: +Training Loss: 409.0377 +Validation Loss: 53.4538 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 90: +Training Loss: 389.9316 +Validation Loss: 78.7764 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 91: +Training Loss: 389.4476 +Validation Loss: 72.6399 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 92: +Training Loss: 402.2737 +Validation Loss: 77.6084 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 93: +Training Loss: 377.0500 +Validation Loss: 82.7017 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 94: +Training Loss: 397.3901 +Validation Loss: 96.7497 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 95: +Training Loss: 391.3462 +Validation Loss: 72.6282 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.11it/s] + +Epoch 96: +Training Loss: 366.8488 +Validation Loss: 92.2476 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 97: +Training Loss: 399.8186 +Validation Loss: 67.8833 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.10it/s] + +Epoch 98: +Training Loss: 380.4470 +Validation Loss: 67.2952 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 99: +Training Loss: 382.7752 +Validation Loss: 64.3468 +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 100: +Training Loss: 405.4167 +Validation Loss: 64.3828 + +Model 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████| 40/40 [00:38<00:00, 1.04it/s] +100%|██████████████████████████████████████████████████████████████| 40/40 [00:38<00:00, 1.05it/s] +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.06it/s] +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.06it/s] +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.07it/s] +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.07it/s] +100%|██████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.08it/s] +100%|██████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 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1.10it/s] +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] +100%|██████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_015243-wras00t5/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_015243-wras00t5/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..bfb44884dfb3d333be9f8d1c385816dd2781c020 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_015243-wras00t5/files/requirements.txt @@ -0,0 +1,156 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +Fiona==1.9.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_023756-khlxvihr/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_023756-khlxvihr/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..bfb44884dfb3d333be9f8d1c385816dd2781c020 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_023756-khlxvihr/files/requirements.txt @@ -0,0 +1,156 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +Fiona==1.9.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 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cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 2 + - 3 + - 14 + - 16 + - 23 + - 55 + - 62 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +bands: + value: 6 +batch_size: + value: 256 +epochs: + value: 100 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_before: + value: 4 +time_beginning: + value: "2007" +time_end: + value: "2023" +window_size: + value: 33 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_023756-ozdpu8v1/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_023756-ozdpu8v1/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..d40c48804862ddd2418c18fab0bf8fe6e83552d8 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_023756-ozdpu8v1/files/output.log @@ -0,0 +1,513 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20355 +Validation set size: 1024 +Size of the first batch: torch.Size([256, 6, 33, 33, 4]) +Model parameters: 123889 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide + c /= stddev[:, None] +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/numpy/lib/function_base.py:2855: RuntimeWarning: invalid value encountered in divide + c /= stddev[None, :] +Epoch 1: +Training Loss: 110614.9535 +Validation Loss: 246.8540 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.11it/s] + +Epoch 2: +Training Loss: 872.8764 +Validation Loss: 246.7447 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 3: +Training Loss: 889.1701 +Validation Loss: 246.6009 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 4: +Training Loss: 887.4596 +Validation Loss: 246.4305 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 5: +Training Loss: 890.2902 +Validation Loss: 246.2349 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 6: +Training Loss: 900.0834 +Validation Loss: 246.0186 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 7: +Training Loss: 890.7345 +Validation Loss: 245.7820 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 8: +Training Loss: 919.8936 +Validation Loss: 245.5251 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 9: +Training Loss: 873.2322 +Validation Loss: 245.2514 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 10: +Training Loss: 887.3672 +Validation Loss: 244.9601 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 11: +Training Loss: 885.0753 +Validation Loss: 244.6516 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 12: +Training Loss: 876.8618 +Validation Loss: 244.3279 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 13: +Training Loss: 891.2769 +Validation Loss: 243.9885 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 14: +Training Loss: 890.9643 +Validation Loss: 243.6341 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 15: +Training Loss: 903.8958 +Validation Loss: 243.2636 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 16: +Training Loss: 869.3451 +Validation Loss: 242.8803 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 17: +Training Loss: 858.8453 +Validation Loss: 242.4828 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 18: +Training Loss: 880.5942 +Validation Loss: 242.0702 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 19: +Training Loss: 890.5445 +Validation Loss: 241.6459 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 20: +Training Loss: 892.8941 +Validation Loss: 241.2065 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 21: +Training Loss: 882.2543 +Validation Loss: 240.7540 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 22: +Training Loss: 853.7930 +Validation Loss: 240.2891 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 23: +Training Loss: 889.0476 +Validation Loss: 239.8129 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.11it/s] + +Epoch 24: +Training Loss: 875.7953 +Validation Loss: 239.3232 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 25: +Training Loss: 857.5217 +Validation Loss: 238.8222 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 26: +Training Loss: 880.1342 +Validation Loss: 238.3069 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 27: +Training Loss: 851.7213 +Validation Loss: 237.7836 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 28: +Training Loss: 862.4781 +Validation Loss: 237.2450 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 29: +Training Loss: 898.3470 +Validation Loss: 236.6971 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 30: +Training Loss: 897.6459 +Validation Loss: 236.1371 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 31: +Training Loss: 870.5064 +Validation Loss: 235.5671 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 32: +Training Loss: 862.6012 +Validation Loss: 234.9849 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 33: +Training Loss: 853.3844 +Validation Loss: 234.3928 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 34: +Training Loss: 883.9337 +Validation Loss: 233.7899 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 35: +Training Loss: 845.2589 +Validation Loss: 233.1779 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 36: +Training Loss: 894.7421 +Validation Loss: 232.5564 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 37: +Training Loss: 859.7650 +Validation Loss: 231.9201 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 38: +Training Loss: 868.0729 +Validation Loss: 231.2786 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 39: +Training Loss: 878.6061 +Validation Loss: 230.6269 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 40: +Training Loss: 885.0644 +Validation Loss: 229.9640 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 41: +Training Loss: 873.7253 +Validation Loss: 229.2941 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 42: +Training Loss: 854.0982 +Validation Loss: 228.6136 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 43: +Training Loss: 881.7568 +Validation Loss: 227.9258 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 44: +Training Loss: 856.1793 +Validation Loss: 227.2262 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 45: +Training Loss: 854.0452 +Validation Loss: 226.5224 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 46: +Training Loss: 857.4590 +Validation Loss: 225.8086 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 47: +Training Loss: 849.1598 +Validation Loss: 225.0837 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 48: +Training Loss: 861.5889 +Validation Loss: 224.3548 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 49: +Training Loss: 894.2484 +Validation Loss: 223.6195 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 50: +Training Loss: 851.2884 +Validation Loss: 222.8726 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 51: +Training Loss: 848.8950 +Validation Loss: 222.1200 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 52: +Training Loss: 835.0632 +Validation Loss: 221.3611 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 53: +Training Loss: 890.0455 +Validation Loss: 220.5965 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 54: +Training Loss: 843.7778 +Validation Loss: 219.8242 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 55: +Training Loss: 848.5274 +Validation Loss: 219.0449 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 56: +Training Loss: 831.5064 +Validation Loss: 218.2587 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 57: +Training Loss: 816.2124 +Validation Loss: 217.4691 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 58: +Training Loss: 867.3295 +Validation Loss: 216.6729 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 59: +Training Loss: 833.8038 +Validation Loss: 215.8681 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 60: +Training Loss: 837.4947 +Validation Loss: 215.0624 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 61: +Training Loss: 836.4453 +Validation Loss: 214.2482 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 62: +Training Loss: 816.2121 +Validation Loss: 213.4307 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 63: +Training Loss: 801.5675 +Validation Loss: 212.6071 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:35<00:00, 1.12it/s] + +Epoch 64: +Training Loss: 832.3959 +Validation Loss: 211.7802 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.07it/s] + +Epoch 65: +Training Loss: 834.3446 +Validation Loss: 210.9472 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 66: +Training Loss: 854.7610 +Validation Loss: 210.1099 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.08it/s] + +Epoch 67: +Training Loss: 849.0597 +Validation Loss: 209.2722 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 68: +Training Loss: 858.5935 +Validation Loss: 208.4271 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 69: +Training Loss: 823.3853 +Validation Loss: 207.5793 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 70: +Training Loss: 819.1997 +Validation Loss: 206.7274 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 71: +Training Loss: 845.0127 +Validation Loss: 205.8734 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 72: +Training Loss: 843.1009 +Validation Loss: 205.0178 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.08it/s] + +Epoch 73: +Training Loss: 831.9225 +Validation Loss: 204.1538 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 74: +Training Loss: 830.0193 +Validation Loss: 203.2932 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 75: +Training Loss: 810.1979 +Validation Loss: 202.4251 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 76: +Training Loss: 822.2590 +Validation Loss: 201.5566 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 77: +Training Loss: 844.1127 +Validation Loss: 200.6874 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.08it/s] + +Epoch 78: +Training Loss: 802.1349 +Validation Loss: 199.8143 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 79: +Training Loss: 810.7275 +Validation Loss: 198.9389 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 80: +Training Loss: 827.1073 +Validation Loss: 198.0613 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 81: +Training Loss: 806.4908 +Validation Loss: 197.1828 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 82: +Training Loss: 822.0493 +Validation Loss: 196.3036 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 83: +Training Loss: 786.2554 +Validation Loss: 195.4200 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 84: +Training Loss: 801.5936 +Validation Loss: 194.5368 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 85: +Training Loss: 829.4162 +Validation Loss: 193.6540 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 86: +Training Loss: 797.9703 +Validation Loss: 192.7676 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.08it/s] + +Epoch 87: +Training Loss: 799.1649 +Validation Loss: 191.8816 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.08it/s] + +Epoch 88: +Training Loss: 793.9751 +Validation Loss: 190.9942 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.08it/s] + +Epoch 89: +Training Loss: 829.0297 +Validation Loss: 190.1072 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.08it/s] + +Epoch 90: +Training Loss: 788.8614 +Validation Loss: 189.2201 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:37<00:00, 1.08it/s] + +Epoch 91: +Training Loss: 776.3993 +Validation Loss: 188.3320 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 92: +Training Loss: 811.9631 +Validation Loss: 187.4406 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 93: +Training Loss: 768.2181 +Validation Loss: 186.5558 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 94: +Training Loss: 806.3273 +Validation Loss: 185.6652 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 95: +Training Loss: 796.1168 +Validation Loss: 184.7763 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 96: +Training Loss: 761.9280 +Validation Loss: 183.8881 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 97: +Training Loss: 790.2828 +Validation Loss: 182.9998 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 98: +Training Loss: 793.7772 +Validation Loss: 182.1109 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.09it/s] + +Epoch 99: +Training Loss: 782.8947 +Validation Loss: 181.2241 +100%|██████████████████████████████████████████████████████████████████████████████| 40/40 [00:36<00:00, 1.08it/s] + +Epoch 100: +Training Loss: 801.8349 +Validation Loss: 180.3389 + +Model trained and saved successfully! diff --git 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16614, 7) +Training set size: 23102 +Validation set size: 1024 +Size of the first batch: torch.Size([256, 6, 33, 33, 5]) +Model parameters: 123889 +100%|███████████████████████████████████████████| 46/46 [00:55<00:00, 1.21s/it] +Epoch 1: +Training Loss: 12311.9972 +Validation Loss: 204.1026 +100%|███████████████████████████████████████████| 46/46 [00:55<00:00, 1.22s/it] + +Epoch 2: +Training Loss: 832.7599 +Validation Loss: 69.5010 +100%|███████████████████████████████████████████| 46/46 [00:54<00:00, 1.18s/it] + +Epoch 3: +Training Loss: 731.0081 +Validation Loss: 70.5711 +100%|███████████████████████████████████████████| 46/46 [00:55<00:00, 1.20s/it] + +Epoch 4: +Training Loss: 733.4426 +Validation Loss: 67.8852 +100%|███████████████████████████████████████████| 46/46 [00:53<00:00, 1.17s/it] + +Epoch 5: +Training Loss: 690.9811 +Validation Loss: 62.9970 +100%|███████████████████████████████████████████| 46/46 [00:54<00:00, 1.18s/it] + +Epoch 6: +Training Loss: 695.7341 +Validation Loss: 60.0591 +100%|███████████████████████████████████████████| 46/46 [00:55<00:00, 1.20s/it] + +Epoch 7: +Training Loss: 694.3781 +Validation Loss: 59.1619 +100%|███████████████████████████████████████████| 46/46 [00:55<00:00, 1.20s/it] + +Epoch 8: +Training Loss: 709.0726 +Validation Loss: 57.2132 +100%|███████████████████████████████████████████| 46/46 [00:54<00:00, 1.19s/it] + +Epoch 9: +Training Loss: 741.0420 +Validation Loss: 59.5294 +100%|███████████████████████████████████████████| 46/46 [00:54<00:00, 1.18s/it] + +Epoch 10: +Training Loss: 673.6229 +Validation Loss: 56.8366 +100%|███████████████████████████████████████████| 46/46 [00:54<00:00, 1.19s/it] + +Epoch 11: +Training Loss: 695.0318 +Validation Loss: 60.1164 +100%|███████████████████████████████████████████| 46/46 [00:55<00:00, 1.21s/it] + +Epoch 12: +Training Loss: 691.6339 +Validation Loss: 53.7255 +100%|███████████████████████████████████████████| 46/46 [00:53<00:00, 1.16s/it] + +Epoch 13: +Training Loss: 698.4074 +Validation Loss: 54.2827 +100%|███████████████████████████████████████████| 46/46 [00:55<00:00, 1.21s/it] + +Epoch 14: +Training Loss: 675.4207 +Validation Loss: 54.0855 +100%|███████████████████████████████████████████| 46/46 [00:54<00:00, 1.19s/it] + +Epoch 15: +Training Loss: 707.0939 +Validation Loss: 54.8865 +100%|███████████████████████████████████████████| 46/46 [00:54<00:00, 1.18s/it] + +Epoch 16: +Training Loss: 651.3968 +Validation Loss: 54.7625 +100%|███████████████████████████████████████████| 46/46 [00:53<00:00, 1.17s/it] + +Epoch 17: +Training Loss: 659.7678 +Validation Loss: 56.2133 +100%|███████████████████████████████████████████| 46/46 [00:55<00:00, 1.20s/it] + +Epoch 18: +Training Loss: 674.4945 +Validation Loss: 56.5521 +100%|███████████████████████████████████████████| 46/46 [00:54<00:00, 1.19s/it] + +Epoch 19: +Training Loss: 680.4568 +Validation Loss: 57.0186 +100%|███████████████████████████████████████████| 46/46 [00:54<00:00, 1.18s/it] + +Epoch 20: +Training Loss: 697.8167 +Validation Loss: 58.0269 + +Model trained and saved successfully! diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_175502-gvtweoc1/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_175502-gvtweoc1/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..bfb44884dfb3d333be9f8d1c385816dd2781c020 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_175502-gvtweoc1/files/requirements.txt @@ -0,0 +1,156 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16614, 7) +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.66it/s] +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.66it/s] +100%|███████████████████████████████████████████| 46/46 [00:13<00:00, 3.53it/s] +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.61it/s] +100%|███████████████████████████████████████████| 46/46 [00:13<00:00, 3.53it/s] +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.60it/s] +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.58it/s] +100%|███████████████████████████████████████████| 46/46 [00:13<00:00, 3.53it/s] +100%|███████████████████████████████████████████| 46/46 [00:13<00:00, 3.52it/s] +100%|███████████████████████████████████████████| 46/46 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_180158-1p9rpdu6/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..bfb44884dfb3d333be9f8d1c385816dd2781c020 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_180158-1p9rpdu6/files/requirements.txt @@ -0,0 +1,156 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 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infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16614, 7) +Training set size: 23102 +Validation set size: 1024 +Model parameters: 40529 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.66it/s] +Epoch 1: +Training Loss: 8299.4785 +Validation Loss: 143.2275 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.66it/s] + +Epoch 2: +Training Loss: 725.6722 +Validation Loss: 99.1186 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.54it/s] + +Epoch 3: +Training Loss: 653.2760 +Validation Loss: 79.7167 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.62it/s] + +Epoch 4: +Training Loss: 644.0217 +Validation Loss: 68.0238 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.55it/s] + +Epoch 5: +Training Loss: 587.0262 +Validation Loss: 59.6051 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.61it/s] + +Epoch 6: +Training Loss: 591.5808 +Validation Loss: 56.5444 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.59it/s] + +Epoch 7: +Training Loss: 576.8101 +Validation Loss: 59.0535 +100%|███████████████████████████████████████████| 46/46 [00:13<00:00, 3.52it/s] + +Epoch 8: +Training Loss: 587.3538 +Validation Loss: 49.4031 +100%|███████████████████████████████████████████| 46/46 [00:13<00:00, 3.53it/s] + +Epoch 9: +Training Loss: 603.1464 +Validation Loss: 55.2054 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.55it/s] + +Epoch 10: +Training Loss: 547.6758 +Validation Loss: 56.3042 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.57it/s] + +Epoch 11: +Training Loss: 548.2584 +Validation Loss: 53.8141 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.60it/s] + +Epoch 12: +Training Loss: 563.0572 +Validation Loss: 55.9460 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.61it/s] + +Epoch 13: +Training Loss: 542.9441 +Validation Loss: 60.8406 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.66it/s] + +Epoch 14: +Training Loss: 522.6349 +Validation Loss: 57.1394 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.56it/s] + +Epoch 15: +Training Loss: 520.3255 +Validation Loss: 59.8377 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.61it/s] + +Epoch 16: +Training Loss: 482.4100 +Validation Loss: 57.9352 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.64it/s] + +Epoch 17: +Training Loss: 484.0899 +Validation Loss: 52.6994 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.70it/s] + +Epoch 18: +Training Loss: 485.0844 +Validation Loss: 71.5113 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.73it/s] + +Epoch 19: +Training Loss: 485.9695 +Validation Loss: 57.1383 +100%|███████████████████████████████████████████| 46/46 [00:12<00:00, 3.59it/s] + +Epoch 20: +Training Loss: 495.5643 +Validation Loss: 54.5763 + +Model trained and saved successfully! diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_180158-wy5r4jtq/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_180158-wy5r4jtq/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..bfb44884dfb3d333be9f8d1c385816dd2781c020 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_180158-wy5r4jtq/files/requirements.txt @@ -0,0 +1,156 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 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- 5 + "12": 0.19.3 + "13": linux-x86_64 +bands: + value: 6 +batch_size: + value: 256 +epochs: + value: 100 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_before: + value: 5 +time_beginning: + value: "2007" +time_end: + value: "2023" +window_size: + value: 33 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_181727-mtupao9b/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_181727-mtupao9b/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..a0e4a5dd3159b53e94bca92a88821908ff29783b --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_181727-mtupao9b/files/output.log @@ -0,0 +1,113 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20355 +Validation set size: 1024 +Size of the first batch: torch.Size([256, 6, 33, 33, 5]) +Model parameters: 123889 +100%|███████████████████████████████████████████| 40/40 [00:47<00:00, 1.19s/it] +Epoch 1: +Training Loss: 246161.8349 +Validation Loss: 246.0726 +100%|███████████████████████████████████████████| 40/40 [00:47<00:00, 1.20s/it] +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide + c /= stddev[:, None] +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/numpy/lib/function_base.py:2855: RuntimeWarning: invalid value encountered in divide + c /= stddev[None, :] + +Epoch 2: +Training Loss: 1010.2083 +Validation Loss: 245.9093 +100%|███████████████████████████████████████████| 40/40 [00:47<00:00, 1.18s/it] + +Epoch 3: +Training Loss: 945.0895 +Validation Loss: 245.8111 +100%|███████████████████████████████████████████| 40/40 [00:45<00:00, 1.15s/it] + +Epoch 4: +Training Loss: 924.5836 +Validation Loss: 245.6966 +100%|███████████████████████████████████████████| 40/40 [00:47<00:00, 1.18s/it] + +Epoch 5: +Training Loss: 921.0311 +Validation Loss: 245.4370 +100%|███████████████████████████████████████████| 40/40 [00:47<00:00, 1.19s/it] + +Epoch 6: +Training Loss: 772.6160 +Validation Loss: 98.8975 +100%|███████████████████████████████████████████| 40/40 [00:48<00:00, 1.22s/it] + +Epoch 7: +Training Loss: 606.8829 +Validation Loss: 70.0602 +100%|███████████████████████████████████████████| 40/40 [00:47<00:00, 1.19s/it] + +Epoch 8: +Training Loss: 607.3740 +Validation Loss: 87.9232 +100%|███████████████████████████████████████████| 40/40 [00:47<00:00, 1.20s/it] + +Epoch 9: +Training Loss: 550.0550 +Validation Loss: 79.8127 +100%|███████████████████████████████████████████| 40/40 [00:47<00:00, 1.18s/it] + +Epoch 10: +Training Loss: 575.7736 +Validation Loss: 79.5321 +100%|███████████████████████████████████████████| 40/40 [00:46<00:00, 1.17s/it] + +Epoch 11: +Training Loss: 559.0255 +Validation Loss: 80.7280 +100%|███████████████████████████████████████████| 40/40 [00:46<00:00, 1.16s/it] + +Epoch 12: +Training Loss: 549.1832 +Validation Loss: 86.7845 +100%|███████████████████████████████████████████| 40/40 [00:47<00:00, 1.20s/it] + +Epoch 13: +Training Loss: 556.1843 +Validation Loss: 81.1126 +100%|███████████████████████████████████████████| 40/40 [00:47<00:00, 1.19s/it] + +Epoch 14: +Training Loss: 561.6609 +Validation Loss: 84.4918 +100%|███████████████████████████████████████████| 40/40 [00:48<00:00, 1.20s/it] + +Epoch 15: +Training Loss: 576.0985 +Validation Loss: 84.4042 +100%|███████████████████████████████████████████| 40/40 [00:46<00:00, 1.16s/it] + +Epoch 16: +Training Loss: 541.4840 +Validation Loss: 83.0708 +100%|███████████████████████████████████████████| 40/40 [00:47<00:00, 1.19s/it] + +Epoch 17: +Training Loss: 539.2426 +Validation Loss: 78.7972 +100%|███████████████████████████████████████████| 40/40 [00:46<00:00, 1.17s/it] + +Epoch 18: +Training Loss: 552.0992 +Validation Loss: 84.9086 +100%|███████████████████████████████████████████| 40/40 [00:47<00:00, 1.20s/it] + +Epoch 19: +Training Loss: 550.1297 +Validation Loss: 81.8141 +100%|███████████████████████████████████████████| 40/40 [00:46<00:00, 1.17s/it] + +Epoch 20: +Training Loss: 549.3494 +Validation Loss: 85.0995 + +Model trained and saved successfully! diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_181727-mtupao9b/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_181727-mtupao9b/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..bfb44884dfb3d333be9f8d1c385816dd2781c020 --- /dev/null +++ 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- 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_182159-lw2s7s90/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_182159-lw2s7s90/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..037f6552b66beeecfffe280d1166d2ced17352a8 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_182159-lw2s7s90/files/output.log @@ -0,0 +1,108 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20355 +Validation set size: 1024 +Model parameters: 40529 +100%|███████████████████████████████████████████| 40/40 [00:12<00:00, 3.18it/s] +Epoch 1: +Training Loss: 7002.5558 +Validation Loss: 135.2009 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.57it/s] + +Epoch 2: +Training Loss: 624.6375 +Validation Loss: 75.8006 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.61it/s] + +Epoch 3: +Training Loss: 559.7974 +Validation Loss: 62.0066 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.61it/s] + +Epoch 4: +Training Loss: 514.4733 +Validation Loss: 56.2838 +100%|███████████████████████████████████████████| 40/40 [00:10<00:00, 3.66it/s] + +Epoch 5: +Training Loss: 502.9457 +Validation Loss: 60.5217 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.62it/s] + +Epoch 6: +Training Loss: 489.4487 +Validation Loss: 55.0264 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.56it/s] + +Epoch 7: +Training Loss: 474.0942 +Validation Loss: 50.7607 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.54it/s] + +Epoch 8: +Training Loss: 493.8542 +Validation Loss: 59.1029 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.53it/s] + +Epoch 9: +Training Loss: 444.1589 +Validation Loss: 48.1210 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.63it/s] + +Epoch 10: +Training Loss: 445.4087 +Validation Loss: 54.4961 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.60it/s] + +Epoch 11: +Training Loss: 432.6464 +Validation Loss: 61.1424 +100%|███████████████████████████████████████████| 40/40 [00:10<00:00, 3.77it/s] + +Epoch 12: +Training Loss: 412.6130 +Validation Loss: 59.2860 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.56it/s] + +Epoch 13: +Training Loss: 410.0108 +Validation Loss: 61.0993 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.58it/s] + +Epoch 14: +Training Loss: 411.3463 +Validation Loss: 65.1856 +100%|███████████████████████████████████████████| 40/40 [00:10<00:00, 3.66it/s] + +Epoch 15: +Training Loss: 410.1517 +Validation Loss: 64.1869 +100%|███████████████████████████████████████████| 40/40 [00:10<00:00, 3.65it/s] + +Epoch 16: +Training Loss: 386.2027 +Validation Loss: 65.3156 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.59it/s] + +Epoch 17: +Training Loss: 370.3948 +Validation Loss: 62.9976 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.62it/s] + +Epoch 18: +Training Loss: 388.2361 +Validation Loss: 68.4932 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.58it/s] + +Epoch 19: +Training Loss: 388.1632 +Validation Loss: 71.3591 +100%|███████████████████████████████████████████| 40/40 [00:11<00:00, 3.57it/s] + +Epoch 20: +Training Loss: 370.3413 +Validation Loss: 64.4861 + +Model trained and saved successfully! diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_182159-lw2s7s90/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_182159-lw2s7s90/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..bfb44884dfb3d333be9f8d1c385816dd2781c020 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_182159-lw2s7s90/files/requirements.txt @@ -0,0 +1,156 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 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[wandb_run.py:_finish():2145] finishing run valerian-fourel/socmapping-SimpleTimeCNN/lw2s7s90 +2025-03-11 18:26:08,236 INFO MainThread:1695078 [wandb_run.py:_atexit_cleanup():2385] got exitcode: 0 +2025-03-11 18:26:08,236 INFO MainThread:1695078 [wandb_run.py:_restore():2367] restore +2025-03-11 18:26:08,236 INFO MainThread:1695078 [wandb_run.py:_restore():2373] restore done +2025-03-11 18:26:10,927 INFO MainThread:1695078 [wandb_run.py:_footer_history_summary_info():3987] rendering history +2025-03-11 18:26:10,928 INFO MainThread:1695078 [wandb_run.py:_footer_history_summary_info():4019] rendering summary +2025-03-11 18:26:10,929 INFO MainThread:1695078 [wandb_run.py:_footer_sync_info():3948] logging synced files diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_182159-lw2s7s90/run-lw2s7s90.wandb b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250311_182159-lw2s7s90/run-lw2s7s90.wandb new file mode 100644 index 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16542, 7) +100%|███████████████████████████████████████████| 42/42 [00:50<00:00, 1.21s/it] +100%|███████████████████████████████████████████| 42/42 [00:50<00:00, 1.21s/it] +100%|███████████████████████████████████████████| 42/42 [00:49<00:00, 1.19s/it] +100%|███████████████████████████████████████████| 42/42 [00:49<00:00, 1.18s/it] +100%|███████████████████████████████████████████| 42/42 [00:49<00:00, 1.18s/it] +100%|███████████████████████████████████████████| 42/42 [00:49<00:00, 1.18s/it] +100%|███████████████████████████████████████████| 42/42 [00:48<00:00, 1.16s/it] +100%|███████████████████████████████████████████| 42/42 [00:50<00:00, 1.20s/it] +100%|███████████████████████████████████████████| 42/42 [00:50<00:00, 1.20s/it] 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**kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank7]: Traceback (most recent call last): +[rank7]: File "train.py", line 200, in +[rank7]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank7]: File "train.py", line 76, in train_model +[rank7]: outputs = model(features) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank7]: return self._call_impl(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank7]: return forward_call(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank7]: else self._run_ddp_forward(*inputs, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank7]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank7]: return self._call_impl(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank7]: return forward_call(*args, **kwargs) +[rank7]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank7]: x = self.fc1(x) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank7]: return self._call_impl(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank7]: return forward_call(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank7]: return F.linear(input, self.weight, self.bias) +[rank7]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-2u7c98r8/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-2u7c98r8/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-2u7c98r8/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 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+scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 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--git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-5dw71uec/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-5dw71uec/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..0d2e791f912d4eccecfaa1e2d1a89fbafda68c12 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-5dw71uec/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:02 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank1]: Traceback (most recent call last): +[rank1]: File "train.py", line 200, in +[rank1]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank1]: File "train.py", line 76, in train_model +[rank1]: outputs = model(features) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank1]: else self._run_ddp_forward(*inputs, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank1]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank1]: x = self.fc1(x) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank1]: return F.linear(input, self.weight, self.bias) +[rank1]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-5dw71uec/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-5dw71uec/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:02 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank6]: Traceback (most recent call last): +[rank6]: File "train.py", line 200, in +[rank6]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank6]: File "train.py", line 76, in train_model +[rank6]: outputs = model(features) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank6]: return self._call_impl(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank6]: return forward_call(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank6]: else self._run_ddp_forward(*inputs, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank6]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank6]: return self._call_impl(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank6]: return forward_call(*args, **kwargs) +[rank6]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank6]: x = self.fc1(x) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank6]: return self._call_impl(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank6]: return forward_call(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank6]: return F.linear(input, self.weight, self.bias) +[rank6]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-cweqbbjr/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-cweqbbjr/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:02 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank2]: Traceback (most recent call last): +[rank2]: File "train.py", line 200, in +[rank2]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank2]: File "train.py", line 76, in train_model +[rank2]: outputs = model(features) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank2]: else self._run_ddp_forward(*inputs, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank2]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank2]: x = self.fc1(x) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank2]: return F.linear(input, self.weight, self.bias) +[rank2]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-ep2q3h5j/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-ep2q3h5j/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-ixoc7trz/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..5f098394762363d23dfc56dda6b55c7c4ca916a8 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-ixoc7trz/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:02 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank3]: Traceback (most recent call last): +[rank3]: File "train.py", line 200, in +[rank3]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank3]: File "train.py", line 76, in train_model +[rank3]: outputs = model(features) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank3]: else self._run_ddp_forward(*inputs, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank3]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank3]: x = self.fc1(x) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank3]: return F.linear(input, self.weight, self.bias) +[rank3]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-ixoc7trz/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-ixoc7trz/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-jmbbd5kg/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-jmbbd5kg/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d922af3477d58d06ac10596aa7b4cce60f69866f --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-jmbbd5kg/files/config.yaml @@ -0,0 +1,41 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.01 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-jmbbd5kg/files/output.log 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:02 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank4]: Traceback (most recent call last): +[rank4]: File "train.py", line 200, in +[rank4]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank4]: File "train.py", line 76, in train_model +[rank4]: outputs = model(features) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank4]: return self._call_impl(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank4]: return forward_call(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank4]: else self._run_ddp_forward(*inputs, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank4]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank4]: return self._call_impl(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank4]: return forward_call(*args, **kwargs) +[rank4]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank4]: x = self.fc1(x) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank4]: return self._call_impl(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank4]: return forward_call(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank4]: return F.linear(input, self.weight, self.bias) +[rank4]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-jmbbd5kg/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-jmbbd5kg/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-nib8bohg/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-nib8bohg/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3b40f61e90fee9edcb90078c33dc3c031713f998 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-nib8bohg/files/config.yaml @@ -0,0 +1,42 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + - 62 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.01 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-nib8bohg/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-nib8bohg/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..1ddbe6b199c2be45d5ceeb1b63b8a0fb9efb6aca --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-nib8bohg/files/output.log @@ -0,0 +1,60 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20355 +Validation set size: 1024 +Model parameters: 146673 + 0%| | 0/10 [00:02 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank0]: Traceback (most recent call last): +[rank0]: File "train.py", line 200, in +[rank0]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank0]: File "train.py", line 76, in train_model +[rank0]: outputs = model(features) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank0]: else self._run_ddp_forward(*inputs, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank0]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank0]: x = self.fc1(x) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank0]: return F.linear(input, self.weight, self.bias) +[rank0]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-nib8bohg/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-nib8bohg/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-tpc2onv3/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-tpc2onv3/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d922af3477d58d06ac10596aa7b4cce60f69866f --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-tpc2onv3/files/config.yaml @@ -0,0 +1,41 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.01 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-tpc2onv3/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-tpc2onv3/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..28a30ac869ced7355537706301aba02836dad618 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-tpc2onv3/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:02 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank5]: Traceback (most recent call last): +[rank5]: File "train.py", line 200, in +[rank5]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank5]: File "train.py", line 76, in train_model +[rank5]: outputs = model(features) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank5]: return self._call_impl(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank5]: return forward_call(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank5]: else self._run_ddp_forward(*inputs, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank5]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank5]: return self._call_impl(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank5]: return forward_call(*args, **kwargs) +[rank5]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank5]: x = self.fc1(x) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank5]: return self._call_impl(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank5]: return forward_call(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank5]: return F.linear(input, self.weight, self.bias) +[rank5]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-tpc2onv3/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-tpc2onv3/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220015-tpc2onv3/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20355 +Validation set size: 1024 +Traceback (most recent call last): + File "train.py", line 210, in + val_loader = DataLoader(val_loader, batch_size=256, shuffle=False, num_workers=4) +NameError: name 'val_loader' is not defined +[rank0]: Traceback (most recent call last): +[rank0]: File "train.py", line 210, in +[rank0]: val_loader = DataLoader(val_loader, batch_size=256, shuffle=False, num_workers=4) +[rank0]: NameError: name 'val_loader' is not defined diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220222-9jt4t50c/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220222-9jt4t50c/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Traceback (most recent call last): + File "train.py", line 210, in + val_loader = DataLoader(val_loader, batch_size=256, shuffle=False, num_workers=4) +NameError: name 'val_loader' is not defined +[rank7]: Traceback (most recent call last): +[rank7]: File "train.py", line 210, in +[rank7]: val_loader = DataLoader(val_loader, batch_size=256, shuffle=False, num_workers=4) +[rank7]: NameError: name 'val_loader' is not defined diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220223-4tkr4gb9/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220223-4tkr4gb9/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Traceback (most recent call last): + File "train.py", line 210, in + val_loader = DataLoader(val_loader, batch_size=256, shuffle=False, num_workers=4) +NameError: name 'val_loader' is not defined +[rank4]: Traceback (most recent call last): +[rank4]: File "train.py", line 210, in +[rank4]: val_loader = DataLoader(val_loader, batch_size=256, shuffle=False, num_workers=4) +[rank4]: NameError: name 'val_loader' is not defined diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220223-d9h3nb46/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220223-d9h3nb46/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Traceback (most recent call last): + File "train.py", line 210, in + val_loader = DataLoader(val_loader, batch_size=256, shuffle=False, num_workers=4) +NameError: name 'val_loader' is not defined +[rank2]: Traceback (most recent call last): +[rank2]: File "train.py", line 210, in +[rank2]: val_loader = DataLoader(val_loader, batch_size=256, shuffle=False, num_workers=4) +[rank2]: NameError: name 'val_loader' is not defined diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220223-uoo89dsp/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220223-uoo89dsp/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-06jv4v14/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-06jv4v14/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dd4dca62421ad79c6540c92eab44f591ff0e1731 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-06jv4v14/files/config.yaml @@ -0,0 +1,41 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-06jv4v14/files/output.log 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank1]: Traceback (most recent call last): +[rank1]: File "train.py", line 200, in +[rank1]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank1]: File "train.py", line 76, in train_model +[rank1]: outputs = model(features) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank1]: else self._run_ddp_forward(*inputs, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank1]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank1]: x = self.fc1(x) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank1]: return F.linear(input, self.weight, self.bias) +[rank1]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-06jv4v14/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-06jv4v14/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-06jv4v14/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 +eval_type_backport==0.2.2 +docker-pycreds==0.4.0 +annotated-types==0.7.0 +pydantic==2.10.5 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-1sueihvk/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-1sueihvk/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dd4dca62421ad79c6540c92eab44f591ff0e1731 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-1sueihvk/files/config.yaml @@ -0,0 +1,41 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-1sueihvk/files/output.log 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank5]: Traceback (most recent call last): +[rank5]: File "train.py", line 200, in +[rank5]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank5]: File "train.py", line 76, in train_model +[rank5]: outputs = model(features) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank5]: return self._call_impl(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank5]: return forward_call(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank5]: else self._run_ddp_forward(*inputs, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank5]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank5]: return self._call_impl(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank5]: return forward_call(*args, **kwargs) +[rank5]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank5]: x = self.fc1(x) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank5]: return self._call_impl(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank5]: return forward_call(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank5]: return F.linear(input, self.weight, self.bias) +[rank5]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-1sueihvk/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-1sueihvk/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-2odqun4v/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-2odqun4v/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dd4dca62421ad79c6540c92eab44f591ff0e1731 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-2odqun4v/files/config.yaml @@ -0,0 +1,41 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-2odqun4v/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-2odqun4v/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..28e1e1672f9f9d8e52b698727faad3cbf151fb04 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-2odqun4v/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank4]: Traceback (most recent call last): +[rank4]: File "train.py", line 200, in +[rank4]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank4]: File "train.py", line 76, in train_model +[rank4]: outputs = model(features) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank4]: return self._call_impl(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank4]: return forward_call(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank4]: else self._run_ddp_forward(*inputs, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank4]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank4]: return self._call_impl(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank4]: return forward_call(*args, **kwargs) +[rank4]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank4]: x = self.fc1(x) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank4]: return self._call_impl(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank4]: return forward_call(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank4]: return F.linear(input, self.weight, self.bias) +[rank4]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-2odqun4v/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-2odqun4v/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-5sguspox/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..6a9101c946ef255dde21fd4f058298274b882574 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-5sguspox/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank6]: Traceback (most recent call last): +[rank6]: File "train.py", line 200, in +[rank6]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank6]: File "train.py", line 76, in train_model +[rank6]: outputs = model(features) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank6]: return self._call_impl(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank6]: return forward_call(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank6]: else self._run_ddp_forward(*inputs, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank6]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank6]: return self._call_impl(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank6]: return forward_call(*args, **kwargs) +[rank6]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank6]: x = self.fc1(x) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank6]: return self._call_impl(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank6]: return forward_call(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank6]: return F.linear(input, self.weight, self.bias) +[rank6]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-5sguspox/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-5sguspox/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-bz5gsmf1/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-bz5gsmf1/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dd4dca62421ad79c6540c92eab44f591ff0e1731 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-bz5gsmf1/files/config.yaml @@ -0,0 +1,41 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-bz5gsmf1/files/output.log 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank2]: Traceback (most recent call last): +[rank2]: File "train.py", line 200, in +[rank2]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank2]: File "train.py", line 76, in train_model +[rank2]: outputs = model(features) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank2]: else self._run_ddp_forward(*inputs, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank2]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank2]: x = self.fc1(x) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank2]: return F.linear(input, self.weight, self.bias) +[rank2]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-bz5gsmf1/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-bz5gsmf1/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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+seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-k8if36h1/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-k8if36h1/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dd4dca62421ad79c6540c92eab44f591ff0e1731 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-k8if36h1/files/config.yaml @@ -0,0 +1,41 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-k8if36h1/files/output.log 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank7]: Traceback (most recent call last): +[rank7]: File "train.py", line 200, in +[rank7]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank7]: File "train.py", line 76, in train_model +[rank7]: outputs = model(features) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank7]: return self._call_impl(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank7]: return forward_call(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank7]: else self._run_ddp_forward(*inputs, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank7]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank7]: return self._call_impl(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank7]: return forward_call(*args, **kwargs) +[rank7]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank7]: x = self.fc1(x) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank7]: return self._call_impl(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank7]: return forward_call(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank7]: return F.linear(input, self.weight, self.bias) +[rank7]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-k8if36h1/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-k8if36h1/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-qws9p1sb/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-qws9p1sb/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2e3ed4f40a67eb1e029316fb5b2f031ece76d174 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-qws9p1sb/files/config.yaml @@ -0,0 +1,42 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + - 62 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-qws9p1sb/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-qws9p1sb/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..c7f28f1dfa5cb89307edc2815d3b6c1a1126b9d9 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-qws9p1sb/files/output.log @@ -0,0 +1,60 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20355 +Validation set size: 1024 +Model parameters: 146673 + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank0]: Traceback (most recent call last): +[rank0]: File "train.py", line 200, in +[rank0]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank0]: File "train.py", line 76, in train_model +[rank0]: outputs = model(features) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank0]: else self._run_ddp_forward(*inputs, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank0]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank0]: x = self.fc1(x) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank0]: return F.linear(input, self.weight, self.bias) +[rank0]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-qws9p1sb/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-qws9p1sb/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank3]: Traceback (most recent call last): +[rank3]: File "train.py", line 200, in +[rank3]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank3]: File "train.py", line 76, in train_model +[rank3]: outputs = model(features) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank3]: else self._run_ddp_forward(*inputs, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank3]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank3]: x = self.fc1(x) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank3]: return F.linear(input, self.weight, self.bias) +[rank3]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-z7pq963k/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220344-z7pq963k/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220620-55z7mrv8/files/config.yaml @@ -0,0 +1,42 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + - 62 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220620-55z7mrv8/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220620-55z7mrv8/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..c7f28f1dfa5cb89307edc2815d3b6c1a1126b9d9 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220620-55z7mrv8/files/output.log @@ -0,0 +1,60 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20355 +Validation set size: 1024 +Model parameters: 146673 + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank0]: Traceback (most recent call last): +[rank0]: File "train.py", line 200, in +[rank0]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank0]: File "train.py", line 76, in train_model +[rank0]: outputs = model(features) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank0]: else self._run_ddp_forward(*inputs, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank0]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank0]: x = self.fc1(x) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank0]: return F.linear(input, self.weight, self.bias) +[rank0]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220620-55z7mrv8/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220620-55z7mrv8/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-3awmrxdq/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank2]: Traceback (most recent call last): +[rank2]: File "train.py", line 200, in +[rank2]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank2]: File "train.py", line 76, in train_model +[rank2]: outputs = model(features) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank2]: else self._run_ddp_forward(*inputs, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank2]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank2]: x = self.fc1(x) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank2]: return F.linear(input, self.weight, self.bias) +[rank2]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-3awmrxdq/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-3awmrxdq/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-kd9u0m4c/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank3]: Traceback (most recent call last): +[rank3]: File "train.py", line 200, in +[rank3]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank3]: File "train.py", line 76, in train_model +[rank3]: outputs = model(features) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank3]: else self._run_ddp_forward(*inputs, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank3]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank3]: x = self.fc1(x) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank3]: return F.linear(input, self.weight, self.bias) +[rank3]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-kd9u0m4c/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-kd9u0m4c/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-pq4fods3/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank7]: Traceback (most recent call last): +[rank7]: File "train.py", line 200, in +[rank7]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank7]: File "train.py", line 76, in train_model +[rank7]: outputs = model(features) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank7]: return self._call_impl(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank7]: return forward_call(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank7]: else self._run_ddp_forward(*inputs, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank7]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank7]: return self._call_impl(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank7]: return forward_call(*args, **kwargs) +[rank7]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank7]: x = self.fc1(x) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank7]: return self._call_impl(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank7]: return forward_call(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank7]: return F.linear(input, self.weight, self.bias) +[rank7]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-pq4fods3/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-pq4fods3/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-r2txdm0g/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank5]: Traceback (most recent call last): +[rank5]: File "train.py", line 200, in +[rank5]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank5]: File "train.py", line 76, in train_model +[rank5]: outputs = model(features) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank5]: return self._call_impl(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank5]: return forward_call(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank5]: else self._run_ddp_forward(*inputs, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank5]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank5]: return self._call_impl(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank5]: return forward_call(*args, **kwargs) +[rank5]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank5]: x = self.fc1(x) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank5]: return self._call_impl(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank5]: return forward_call(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank5]: return F.linear(input, self.weight, self.bias) +[rank5]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-r2txdm0g/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-r2txdm0g/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-t0bdf2qx/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank6]: Traceback (most recent call last): +[rank6]: File "train.py", line 200, in +[rank6]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank6]: File "train.py", line 76, in train_model +[rank6]: outputs = model(features) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank6]: return self._call_impl(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank6]: return forward_call(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank6]: else self._run_ddp_forward(*inputs, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank6]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank6]: return self._call_impl(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank6]: return forward_call(*args, **kwargs) +[rank6]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank6]: x = self.fc1(x) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank6]: return self._call_impl(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank6]: return forward_call(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank6]: return F.linear(input, self.weight, self.bias) +[rank6]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-t0bdf2qx/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-t0bdf2qx/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-t0bdf2qx/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-vmm82vik/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank1]: Traceback (most recent call last): +[rank1]: File "train.py", line 200, in +[rank1]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank1]: File "train.py", line 76, in train_model +[rank1]: outputs = model(features) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank1]: else self._run_ddp_forward(*inputs, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank1]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank1]: x = self.fc1(x) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank1]: return F.linear(input, self.weight, self.bias) +[rank1]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-vmm82vik/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-vmm82vik/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-wtovii6h/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) +[rank4]: Traceback (most recent call last): +[rank4]: File "train.py", line 200, in +[rank4]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank4]: File "train.py", line 76, in train_model +[rank4]: outputs = model(features) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank4]: return self._call_impl(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank4]: return forward_call(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank4]: else self._run_ddp_forward(*inputs, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank4]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank4]: return self._call_impl(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank4]: return forward_call(*args, **kwargs) +[rank4]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 67, in forward +[rank4]: x = self.fc1(x) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank4]: return self._call_impl(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank4]: return forward_call(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank4]: return F.linear(input, self.weight, self.bias) +[rank4]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 2048x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-wtovii6h/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_220621-wtovii6h/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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+++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-09ewajcw/files/output.log @@ -0,0 +1,92 @@ +Epoch 1 Training: 0%| | 0/11 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 89, in train_model + for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ + for obj in iterable: + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ + current_batch = next(dataloader_iter) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ + data = self._next_data() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data + return self._process_data(data) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data + data.reraise() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise + raise exception +KeyError: Caught KeyError in DataLoader worker process 0. +Original Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc + return self._engine.get_loc(casted_key) + File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item + File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +KeyError: 'longitude' + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop + data = fetcher.fetch(index) # type: ignore[possibly-undefined] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ + longitude, latitude = row["longitude"], row["latitude"] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ + return self._get_value(key) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value + loc = self.index.get_loc(label) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc + raise KeyError(key) from err +KeyError: 'longitude' + +[rank2]: Traceback (most recent call last): +[rank2]: File "train.py", line 220, in +[rank2]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank2]: File "train.py", line 89, in train_model +[rank2]: for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ +[rank2]: for obj in iterable: +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ +[rank2]: current_batch = next(dataloader_iter) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ +[rank2]: data = self._next_data() +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data +[rank2]: return self._process_data(data) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data +[rank2]: data.reraise() +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise +[rank2]: raise exception +[rank2]: KeyError: Caught KeyError in DataLoader worker process 0. +[rank2]: Original Traceback (most recent call last): +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc +[rank2]: return self._engine.get_loc(casted_key) +[rank2]: File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc +[rank2]: File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc +[rank2]: File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank2]: File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank2]: KeyError: 'longitude' + +[rank2]: The above exception was the direct cause of the following exception: + +[rank2]: Traceback (most recent call last): +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop +[rank2]: data = fetcher.fetch(index) # type: ignore[possibly-undefined] +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch +[rank2]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in +[rank2]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank2]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ +[rank2]: longitude, latitude = row["longitude"], row["latitude"] +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ +[rank2]: return self._get_value(key) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value +[rank2]: loc = self.index.get_loc(label) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc +[rank2]: raise KeyError(key) from err +[rank2]: KeyError: 'longitude' diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-09ewajcw/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-09ewajcw/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-09ewajcw/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 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+++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-h8963gik/files/output.log @@ -0,0 +1,92 @@ +Epoch 1 Training: 0%| | 0/11 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 89, in train_model + for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ + for obj in iterable: + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ + current_batch = next(dataloader_iter) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ + data = self._next_data() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data + return self._process_data(data) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data + data.reraise() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise + raise exception +KeyError: Caught KeyError in DataLoader worker process 0. +Original Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc + return self._engine.get_loc(casted_key) + File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item + File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +KeyError: 'longitude' + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop + data = fetcher.fetch(index) # type: ignore[possibly-undefined] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ + longitude, latitude = row["longitude"], row["latitude"] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ + return self._get_value(key) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value + loc = self.index.get_loc(label) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc + raise KeyError(key) from err +KeyError: 'longitude' + +[rank7]: Traceback (most recent call last): +[rank7]: File "train.py", line 220, in +[rank7]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank7]: File "train.py", line 89, in train_model +[rank7]: for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ +[rank7]: for obj in iterable: +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ +[rank7]: current_batch = next(dataloader_iter) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ +[rank7]: data = self._next_data() +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data +[rank7]: return self._process_data(data) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data +[rank7]: data.reraise() +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise +[rank7]: raise exception +[rank7]: KeyError: Caught KeyError in DataLoader worker process 0. +[rank7]: Original Traceback (most recent call last): +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc +[rank7]: return self._engine.get_loc(casted_key) +[rank7]: File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc +[rank7]: File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc +[rank7]: File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank7]: File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank7]: KeyError: 'longitude' + +[rank7]: The above exception was the direct cause of the following exception: + +[rank7]: Traceback (most recent call last): +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop +[rank7]: data = fetcher.fetch(index) # type: ignore[possibly-undefined] +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch +[rank7]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in +[rank7]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank7]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ +[rank7]: longitude, latitude = row["longitude"], row["latitude"] +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ +[rank7]: return self._get_value(key) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value +[rank7]: loc = self.index.get_loc(label) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc +[rank7]: raise KeyError(key) from err +[rank7]: KeyError: 'longitude' diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-h8963gik/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-h8963gik/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-h8963gik/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 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+++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-j49rwgen/files/output.log @@ -0,0 +1,92 @@ +Epoch 1 Training: 0%| | 0/11 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 89, in train_model + for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ + for obj in iterable: + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ + current_batch = next(dataloader_iter) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ + data = self._next_data() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data + return self._process_data(data) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data + data.reraise() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise + raise exception +KeyError: Caught KeyError in DataLoader worker process 0. +Original Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc + return self._engine.get_loc(casted_key) + File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item + File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +KeyError: 'longitude' + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop + data = fetcher.fetch(index) # type: ignore[possibly-undefined] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ + longitude, latitude = row["longitude"], row["latitude"] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ + return self._get_value(key) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value + loc = self.index.get_loc(label) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc + raise KeyError(key) from err +KeyError: 'longitude' + +[rank4]: Traceback (most recent call last): +[rank4]: File "train.py", line 220, in +[rank4]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank4]: File "train.py", line 89, in train_model +[rank4]: for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ +[rank4]: for obj in iterable: +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ +[rank4]: current_batch = next(dataloader_iter) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ +[rank4]: data = self._next_data() +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data +[rank4]: return self._process_data(data) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data +[rank4]: data.reraise() +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise +[rank4]: raise exception +[rank4]: KeyError: Caught KeyError in DataLoader worker process 0. +[rank4]: Original Traceback (most recent call last): +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc +[rank4]: return self._engine.get_loc(casted_key) +[rank4]: File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc +[rank4]: File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc +[rank4]: File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank4]: File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank4]: KeyError: 'longitude' + +[rank4]: The above exception was the direct cause of the following exception: + +[rank4]: Traceback (most recent call last): +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop +[rank4]: data = fetcher.fetch(index) # type: ignore[possibly-undefined] +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch +[rank4]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in +[rank4]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank4]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ +[rank4]: longitude, latitude = row["longitude"], row["latitude"] +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ +[rank4]: return self._get_value(key) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value +[rank4]: loc = self.index.get_loc(label) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc +[rank4]: raise KeyError(key) from err +[rank4]: KeyError: 'longitude' diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-j49rwgen/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-j49rwgen/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-j49rwgen/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 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/dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-jfb8rq45/files/output.log @@ -0,0 +1,95 @@ +Training set size: 21495 +Validation set size: 1024 +Model parameters: 146673 +Epoch 1 Training: 0%| | 0/11 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 89, in train_model + for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ + for obj in iterable: + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ + current_batch = next(dataloader_iter) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ + data = self._next_data() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data + return self._process_data(data) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data + data.reraise() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise + raise exception +KeyError: Caught KeyError in DataLoader worker process 0. +Original Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc + return self._engine.get_loc(casted_key) + File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item + File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +KeyError: 'longitude' + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop + data = fetcher.fetch(index) # type: ignore[possibly-undefined] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ + longitude, latitude = row["longitude"], row["latitude"] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ + return self._get_value(key) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value + loc = self.index.get_loc(label) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc + raise KeyError(key) from err +KeyError: 'longitude' + +[rank0]: Traceback (most recent call last): +[rank0]: File "train.py", line 220, in +[rank0]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank0]: File "train.py", line 89, in train_model +[rank0]: for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ +[rank0]: for obj in iterable: +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ +[rank0]: current_batch = next(dataloader_iter) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ +[rank0]: data = self._next_data() +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data +[rank0]: return self._process_data(data) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data +[rank0]: data.reraise() +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise +[rank0]: raise exception +[rank0]: KeyError: Caught KeyError in DataLoader worker process 0. +[rank0]: Original Traceback (most recent call last): +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc +[rank0]: return self._engine.get_loc(casted_key) +[rank0]: File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc +[rank0]: File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc +[rank0]: File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank0]: File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank0]: KeyError: 'longitude' + +[rank0]: The above exception was the direct cause of the following exception: + +[rank0]: Traceback (most recent call last): +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop +[rank0]: data = fetcher.fetch(index) # type: ignore[possibly-undefined] +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch +[rank0]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in +[rank0]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank0]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ +[rank0]: longitude, latitude = row["longitude"], row["latitude"] +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ +[rank0]: return self._get_value(key) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value +[rank0]: loc = self.index.get_loc(label) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc +[rank0]: raise KeyError(key) from err +[rank0]: KeyError: 'longitude' diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-jfb8rq45/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-jfb8rq45/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221039-jfb8rq45/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-41rrisbg/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-41rrisbg/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ad8ab5c83fb1c8d21799c5929524cb5f8ae0a59b --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-41rrisbg/files/config.yaml @@ -0,0 +1,43 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 100 +inference_time: + value: "2015" +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-41rrisbg/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-41rrisbg/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..307da2d21c66388d3ac1080bde59088ff1a75d13 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-41rrisbg/files/output.log @@ -0,0 +1,92 @@ +Epoch 1 Training: 0%| | 0/11 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 89, in train_model + for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ + for obj in iterable: + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ + current_batch = next(dataloader_iter) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ + data = self._next_data() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data + return self._process_data(data) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data + data.reraise() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise + raise exception +KeyError: Caught KeyError in DataLoader worker process 0. +Original Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc + return self._engine.get_loc(casted_key) + File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item + File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +KeyError: 'longitude' + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop + data = fetcher.fetch(index) # type: ignore[possibly-undefined] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ + longitude, latitude = row["longitude"], row["latitude"] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ + return self._get_value(key) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value + loc = self.index.get_loc(label) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc + raise KeyError(key) from err +KeyError: 'longitude' + +[rank6]: Traceback (most recent call last): +[rank6]: File "train.py", line 220, in +[rank6]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank6]: File "train.py", line 89, in train_model +[rank6]: for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ +[rank6]: for obj in iterable: +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ +[rank6]: current_batch = next(dataloader_iter) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ +[rank6]: data = self._next_data() +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data +[rank6]: return self._process_data(data) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data +[rank6]: data.reraise() +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise +[rank6]: raise exception +[rank6]: KeyError: Caught KeyError in DataLoader worker process 0. +[rank6]: Original Traceback (most recent call last): +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc +[rank6]: return self._engine.get_loc(casted_key) +[rank6]: File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc +[rank6]: File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc +[rank6]: File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank6]: File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank6]: KeyError: 'longitude' + +[rank6]: The above exception was the direct cause of the following exception: + +[rank6]: Traceback (most recent call last): +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop +[rank6]: data = fetcher.fetch(index) # type: ignore[possibly-undefined] +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch +[rank6]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in +[rank6]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank6]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ +[rank6]: longitude, latitude = row["longitude"], row["latitude"] +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ +[rank6]: return self._get_value(key) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value +[rank6]: loc = self.index.get_loc(label) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc +[rank6]: raise KeyError(key) from err +[rank6]: KeyError: 'longitude' diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-41rrisbg/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-41rrisbg/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-41rrisbg/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-jhr5o7j4/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-jhr5o7j4/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ad8ab5c83fb1c8d21799c5929524cb5f8ae0a59b --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-jhr5o7j4/files/config.yaml @@ -0,0 +1,43 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 100 +inference_time: + value: "2015" +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-jhr5o7j4/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-jhr5o7j4/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..a8d1d02c6b9477985ef1f969daa19a2b59363a83 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-jhr5o7j4/files/output.log @@ -0,0 +1,92 @@ +Epoch 1 Training: 0%| | 0/11 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 89, in train_model + for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ + for obj in iterable: + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ + current_batch = next(dataloader_iter) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ + data = self._next_data() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data + return self._process_data(data) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data + data.reraise() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise + raise exception +KeyError: Caught KeyError in DataLoader worker process 0. +Original Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc + return self._engine.get_loc(casted_key) + File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item + File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +KeyError: 'longitude' + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop + data = fetcher.fetch(index) # type: ignore[possibly-undefined] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ + longitude, latitude = row["longitude"], row["latitude"] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ + return self._get_value(key) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value + loc = self.index.get_loc(label) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc + raise KeyError(key) from err +KeyError: 'longitude' + +[rank5]: Traceback (most recent call last): +[rank5]: File "train.py", line 220, in +[rank5]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank5]: File "train.py", line 89, in train_model +[rank5]: for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ +[rank5]: for obj in iterable: +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ +[rank5]: current_batch = next(dataloader_iter) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ +[rank5]: data = self._next_data() +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data +[rank5]: return self._process_data(data) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data +[rank5]: data.reraise() +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise +[rank5]: raise exception +[rank5]: KeyError: Caught KeyError in DataLoader worker process 0. +[rank5]: Original Traceback (most recent call last): +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc +[rank5]: return self._engine.get_loc(casted_key) +[rank5]: File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc +[rank5]: File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc +[rank5]: File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank5]: File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank5]: KeyError: 'longitude' + +[rank5]: The above exception was the direct cause of the following exception: + +[rank5]: Traceback (most recent call last): +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop +[rank5]: data = fetcher.fetch(index) # type: ignore[possibly-undefined] +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch +[rank5]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in +[rank5]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank5]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ +[rank5]: longitude, latitude = row["longitude"], row["latitude"] +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ +[rank5]: return self._get_value(key) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value +[rank5]: loc = self.index.get_loc(label) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc +[rank5]: raise KeyError(key) from err +[rank5]: KeyError: 'longitude' diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-jhr5o7j4/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-jhr5o7j4/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-jhr5o7j4/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-tswlah39/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..6fb09517f4186922d052c9c976801d839b8a4fbb --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-tswlah39/files/output.log @@ -0,0 +1,92 @@ +Epoch 1 Training: 0%| | 0/11 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 89, in train_model + for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ + for obj in iterable: + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ + current_batch = next(dataloader_iter) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ + data = self._next_data() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data + return self._process_data(data) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data + data.reraise() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise + raise exception +KeyError: Caught KeyError in DataLoader worker process 0. +Original Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc + return self._engine.get_loc(casted_key) + File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item + File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +KeyError: 'longitude' + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop + data = fetcher.fetch(index) # type: ignore[possibly-undefined] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ + longitude, latitude = row["longitude"], row["latitude"] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ + return self._get_value(key) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value + loc = self.index.get_loc(label) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc + raise KeyError(key) from err +KeyError: 'longitude' + +[rank3]: Traceback (most recent call last): +[rank3]: File "train.py", line 220, in +[rank3]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank3]: File "train.py", line 89, in train_model +[rank3]: for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ +[rank3]: for obj in iterable: +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ +[rank3]: current_batch = next(dataloader_iter) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ +[rank3]: data = self._next_data() +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data +[rank3]: return self._process_data(data) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data +[rank3]: data.reraise() +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise +[rank3]: raise exception +[rank3]: KeyError: Caught KeyError in DataLoader worker process 0. +[rank3]: Original Traceback (most recent call last): +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc +[rank3]: return self._engine.get_loc(casted_key) +[rank3]: File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc +[rank3]: File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc +[rank3]: File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank3]: File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank3]: KeyError: 'longitude' + +[rank3]: The above exception was the direct cause of the following exception: + +[rank3]: Traceback (most recent call last): +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop +[rank3]: data = fetcher.fetch(index) # type: ignore[possibly-undefined] +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch +[rank3]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in +[rank3]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank3]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ +[rank3]: longitude, latitude = row["longitude"], row["latitude"] +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ +[rank3]: return self._get_value(key) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value +[rank3]: loc = self.index.get_loc(label) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc +[rank3]: raise KeyError(key) from err +[rank3]: KeyError: 'longitude' diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-tswlah39/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-tswlah39/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-tswlah39/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-w4xd9s3y/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-w4xd9s3y/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ad8ab5c83fb1c8d21799c5929524cb5f8ae0a59b --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-w4xd9s3y/files/config.yaml @@ -0,0 +1,43 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 100 +inference_time: + value: "2015" +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-w4xd9s3y/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-w4xd9s3y/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..046a04b4df668496874e608ee2d7d1f67869c69b --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-w4xd9s3y/files/output.log @@ -0,0 +1,92 @@ +Epoch 1 Training: 0%| | 0/11 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 89, in train_model + for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ + for obj in iterable: + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ + current_batch = next(dataloader_iter) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ + data = self._next_data() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data + return self._process_data(data) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data + data.reraise() + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise + raise exception +KeyError: Caught KeyError in DataLoader worker process 0. +Original Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc + return self._engine.get_loc(casted_key) + File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc + File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item + File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +KeyError: 'longitude' + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop + data = fetcher.fetch(index) # type: ignore[possibly-undefined] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in + data = [self.dataset[idx] for idx in possibly_batched_index] + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ + longitude, latitude = row["longitude"], row["latitude"] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ + return self._get_value(key) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value + loc = self.index.get_loc(label) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc + raise KeyError(key) from err +KeyError: 'longitude' + +[rank1]: Traceback (most recent call last): +[rank1]: File "train.py", line 220, in +[rank1]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank1]: File "train.py", line 89, in train_model +[rank1]: for batch_idx, (inputs, coords, targets) in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1} Training")): +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/tqdm/std.py", line 1181, in __iter__ +[rank1]: for obj in iterable: +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/accelerate/data_loader.py", line 550, in __iter__ +[rank1]: current_batch = next(dataloader_iter) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ +[rank1]: data = self._next_data() +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data +[rank1]: return self._process_data(data) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data +[rank1]: data.reraise() +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_utils.py", line 706, in reraise +[rank1]: raise exception +[rank1]: KeyError: Caught KeyError in DataLoader worker process 0. +[rank1]: Original Traceback (most recent call last): +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3653, in get_loc +[rank1]: return self._engine.get_loc(casted_key) +[rank1]: File "pandas/_libs/index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc +[rank1]: File "pandas/_libs/index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc +[rank1]: File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank1]: File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item +[rank1]: KeyError: 'longitude' + +[rank1]: The above exception was the direct cause of the following exception: + +[rank1]: Traceback (most recent call last): +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop +[rank1]: data = fetcher.fetch(index) # type: ignore[possibly-undefined] +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch +[rank1]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 52, in +[rank1]: data = [self.dataset[idx] for idx in possibly_batched_index] +[rank1]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataloaderMapping.py", line 202, in __getitem__ +[rank1]: longitude, latitude = row["longitude"], row["latitude"] +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1007, in __getitem__ +[rank1]: return self._get_value(key) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/series.py", line 1116, in _get_value +[rank1]: loc = self.index.get_loc(label) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3655, in get_loc +[rank1]: raise KeyError(key) from err +[rank1]: KeyError: 'longitude' diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-w4xd9s3y/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-w4xd9s3y/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221043-w4xd9s3y/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:07<00:00, 1.42it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.62it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.61it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.64it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.61it/s] 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--- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221202-ge8hg3eq/files/output.log @@ -0,0 +1,45 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:07<00:00, 1.42it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.62it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.61it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.64it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.61it/s] 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+100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + 10%|█████████ | 1/10 [00:01<00:11, 1.24s/it] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221202-ge8hg3eq/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221202-ge8hg3eq/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221202-ge8hg3eq/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 +eval_type_backport==0.2.2 +docker-pycreds==0.4.0 +annotated-types==0.7.0 +pydantic==2.10.5 +huggingface-hub==0.27.1 +gitdb==4.0.12 +GitPython==3.1.44 +wandb==0.19.3 +accelerate==1.0.1 +tifffile==2023.7.10 +PyWavelets==1.4.1 +lazy_loader==0.4 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"/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map", + "host": "g069", + "executable": "/home/vfourel/miniforge3/envs/socmapping/bin/python3.8", + "codePathLocal": "train.py", + "cpu_count": 36, + "cpu_count_logical": 10, + "gpu": "Quadro RTX 6000", + "gpu_count": 8, + "disk": { + "/": { + "total": "105425473536", + "used": "34225422336" + } + }, + "memory": { + "total": "505329745920" + }, + "cpu": { + "count": 36, + "countLogical": 10 + }, + "gpu_nvidia": [ + { + "name": "Quadro RTX 6000", + "memoryTotal": "25769803776", + "cudaCores": 4608, + "architecture": "Turing" + }, + { + "name": "Quadro RTX 6000", + "memoryTotal": "25769803776", + "cudaCores": 4608, + "architecture": "Turing" + }, + { + "name": "Quadro RTX 6000", + "memoryTotal": "25769803776", + "cudaCores": 4608, + "architecture": "Turing" + }, + { + "name": "Quadro RTX 6000", + "memoryTotal": "25769803776", + "cudaCores": 4608, + "architecture": "Turing" + }, + { + "name": "Quadro RTX 6000", + 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--- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221202-h6be9mmh/files/output.log @@ -0,0 +1,207 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20355 +Validation set size: 1024 +Model parameters: 40529 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:07<00:00, 1.42it/s] +Epoch 1: +Training Loss: 36243.3049 +Validation Loss: 271.7118 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.64it/s] + +Epoch 2: +Training Loss: 2142.4447 +Validation Loss: 297.0842 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.62it/s] + +Epoch 3: +Training Loss: 992.3508 +Validation Loss: 29.7119 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.62it/s] + +Epoch 4: +Training Loss: 710.4085 +Validation Loss: 38.7507 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.64it/s] + +Epoch 5: +Training Loss: 682.2555 +Validation Loss: 27.4555 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.61it/s] + +Epoch 6: +Training Loss: 616.5230 +Validation Loss: 18.6545 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 7: +Training Loss: 599.3012 +Validation Loss: 16.6607 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.62it/s] + +Epoch 8: +Training Loss: 575.8781 +Validation Loss: 13.6405 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 9: +Training Loss: 619.4537 +Validation Loss: 13.2387 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 10: +Training Loss: 576.8009 +Validation Loss: 12.0910 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 11: +Training Loss: 552.8954 +Validation Loss: 11.1056 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 12: +Training Loss: 540.6751 +Validation Loss: 10.5708 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.64it/s] + +Epoch 13: +Training Loss: 591.7637 +Validation Loss: 10.0671 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 14: +Training Loss: 570.2637 +Validation Loss: 10.0401 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 15: +Training Loss: 509.4477 +Validation Loss: 9.4105 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 16: +Training Loss: 523.2848 +Validation Loss: 8.5921 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.64it/s] + +Epoch 17: +Training Loss: 525.8915 +Validation Loss: 8.2997 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.62it/s] + +Epoch 18: +Training Loss: 487.5712 +Validation Loss: 7.8868 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 19: +Training Loss: 504.8088 +Validation Loss: 7.4638 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 20: +Training Loss: 562.0321 +Validation Loss: 7.8300 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 21: +Training Loss: 503.9750 +Validation Loss: 7.1017 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.62it/s] + +Epoch 22: +Training Loss: 494.9498 +Validation Loss: 7.4931 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 23: +Training Loss: 514.7449 +Validation Loss: 7.8080 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.64it/s] + +Epoch 24: +Training Loss: 483.2421 +Validation Loss: 7.2179 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.62it/s] + +Epoch 25: +Training Loss: 490.7699 +Validation Loss: 6.4565 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 26: +Training Loss: 451.6240 +Validation Loss: 7.9352 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 27: +Training Loss: 473.9891 +Validation Loss: 6.4218 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.62it/s] + +Epoch 28: +Training Loss: 485.1323 +Validation Loss: 6.6769 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 29: +Training Loss: 552.7412 +Validation Loss: 7.3968 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 30: +Training Loss: 450.5908 +Validation Loss: 6.9893 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.64it/s] + +Epoch 31: +Training Loss: 443.7592 +Validation Loss: 6.8380 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 32: +Training Loss: 467.2297 +Validation Loss: 6.8708 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.62it/s] + +Epoch 33: +Training Loss: 437.0836 +Validation Loss: 6.5415 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 34: +Training Loss: 431.3814 +Validation Loss: 6.7036 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 35: +Training Loss: 483.7132 +Validation Loss: 6.0114 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 36: +Training Loss: 446.4901 +Validation Loss: 6.1920 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 37: +Training Loss: 445.2684 +Validation Loss: 9.9440 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 38: +Training Loss: 451.7308 +Validation Loss: 6.0528 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] + +Epoch 39: +Training Loss: 453.8406 +Validation Loss: 5.9773 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:07<00:00, 1.42it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.62it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.61it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.64it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.61it/s] 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--- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221202-yxev21sk/files/output.log @@ -0,0 +1,45 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:07<00:00, 1.42it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.63it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.61it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.64it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.61it/s] 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+setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 +eval_type_backport==0.2.2 +docker-pycreds==0.4.0 +annotated-types==0.7.0 +pydantic==2.10.5 +huggingface-hub==0.27.1 +gitdb==4.0.12 +GitPython==3.1.44 +wandb==0.19.3 +accelerate==1.0.1 +tifffile==2023.7.10 +PyWavelets==1.4.1 +lazy_loader==0.4 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221808-2k7107mm/files/output.log @@ -0,0 +1,60 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20355 +Validation set size: 1024 +Model parameters: 100657 + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) +[rank0]: Traceback (most recent call last): +[rank0]: File "train.py", line 200, in +[rank0]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank0]: File "train.py", line 76, in train_model +[rank0]: outputs = model(features) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank0]: else self._run_ddp_forward(*inputs, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank0]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward +[rank0]: x = self.fc1(x) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank0]: return F.linear(input, self.weight, self.bias) +[rank0]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221808-2k7107mm/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221808-2k7107mm/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-6bk1ykk4/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) +[rank1]: Traceback (most recent call last): +[rank1]: File "train.py", line 200, in +[rank1]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank1]: File "train.py", line 76, in train_model +[rank1]: outputs = model(features) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank1]: else self._run_ddp_forward(*inputs, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank1]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward +[rank1]: x = self.fc1(x) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank1]: return F.linear(input, self.weight, self.bias) +[rank1]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-6bk1ykk4/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-6bk1ykk4/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-6bk1ykk4/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 +eval_type_backport==0.2.2 +docker-pycreds==0.4.0 +annotated-types==0.7.0 +pydantic==2.10.5 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-8yllj2ls/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) +[rank6]: Traceback (most recent call last): +[rank6]: File "train.py", line 200, in +[rank6]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank6]: File "train.py", line 76, in train_model +[rank6]: outputs = model(features) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank6]: return self._call_impl(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank6]: return forward_call(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank6]: else self._run_ddp_forward(*inputs, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank6]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank6]: return self._call_impl(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank6]: return forward_call(*args, **kwargs) +[rank6]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward +[rank6]: x = self.fc1(x) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank6]: return self._call_impl(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank6]: return forward_call(*args, **kwargs) +[rank6]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank6]: return F.linear(input, self.weight, self.bias) +[rank6]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-8yllj2ls/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-8yllj2ls/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-d2ed8q4p/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) +[rank3]: Traceback (most recent call last): +[rank3]: File "train.py", line 200, in +[rank3]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank3]: File "train.py", line 76, in train_model +[rank3]: outputs = model(features) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank3]: else self._run_ddp_forward(*inputs, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank3]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward +[rank3]: x = self.fc1(x) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank3]: return F.linear(input, self.weight, self.bias) +[rank3]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-d2ed8q4p/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-d2ed8q4p/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-g073m8h8/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) +[rank7]: Traceback (most recent call last): +[rank7]: File "train.py", line 200, in +[rank7]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank7]: File "train.py", line 76, in train_model +[rank7]: outputs = model(features) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank7]: return self._call_impl(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank7]: return forward_call(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank7]: else self._run_ddp_forward(*inputs, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank7]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank7]: return self._call_impl(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank7]: return forward_call(*args, **kwargs) +[rank7]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward +[rank7]: x = self.fc1(x) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank7]: return self._call_impl(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank7]: return forward_call(*args, **kwargs) +[rank7]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank7]: return F.linear(input, self.weight, self.bias) +[rank7]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-g073m8h8/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-g073m8h8/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-qmgj4o3y/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) +[rank4]: Traceback (most recent call last): +[rank4]: File "train.py", line 200, in +[rank4]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank4]: File "train.py", line 76, in train_model +[rank4]: outputs = model(features) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank4]: return self._call_impl(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank4]: return forward_call(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank4]: else self._run_ddp_forward(*inputs, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank4]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank4]: return self._call_impl(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank4]: return forward_call(*args, **kwargs) +[rank4]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward +[rank4]: x = self.fc1(x) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank4]: return self._call_impl(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank4]: return forward_call(*args, **kwargs) +[rank4]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank4]: return F.linear(input, self.weight, self.bias) +[rank4]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-qmgj4o3y/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-qmgj4o3y/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-v0usk8gm/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) +[rank2]: Traceback (most recent call last): +[rank2]: File "train.py", line 200, in +[rank2]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank2]: File "train.py", line 76, in train_model +[rank2]: outputs = model(features) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank2]: else self._run_ddp_forward(*inputs, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank2]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward +[rank2]: x = self.fc1(x) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank2]: return F.linear(input, self.weight, self.bias) +[rank2]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-v0usk8gm/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-v0usk8gm/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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0000000000000000000000000000000000000000..f0f84dd03eccda17a12e10bf5929b990ae5d7ffe --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-xcns08uh/files/config.yaml @@ -0,0 +1,41 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 100 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-xcns08uh/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-xcns08uh/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..b311921e7f593efc4cbd2fd08ef00ec88b94d476 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-xcns08uh/files/output.log @@ -0,0 +1,57 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) + 0%| | 0/10 [00:01 + model, val_outputs, val_targets = train_model(model, train_loader, val_loader, + File "train.py", line 76, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) +[rank5]: Traceback (most recent call last): +[rank5]: File "train.py", line 200, in +[rank5]: model, val_outputs, val_targets = train_model(model, train_loader, val_loader, +[rank5]: File "train.py", line 76, in train_model +[rank5]: outputs = model(features) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank5]: return self._call_impl(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank5]: return forward_call(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank5]: else self._run_ddp_forward(*inputs, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank5]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank5]: return self._call_impl(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank5]: return forward_call(*args, **kwargs) +[rank5]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/modelCNN.py", line 79, in forward +[rank5]: x = self.fc1(x) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank5]: return self._call_impl(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank5]: return forward_call(*args, **kwargs) +[rank5]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank5]: return F.linear(input, self.weight, self.bias) +[rank5]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x128 and 256x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-xcns08uh/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221809-xcns08uh/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ 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0000000000000000000000000000000000000000..980bc4c14902fa1567d087ad15bae18397a14d5b --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-24f67vcg/files/output.log @@ -0,0 +1,55 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.44it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.54it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-24f67vcg/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-24f67vcg/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 +eval_type_backport==0.2.2 +docker-pycreds==0.4.0 +annotated-types==0.7.0 +pydantic==2.10.5 +huggingface-hub==0.27.1 +gitdb==4.0.12 +GitPython==3.1.44 +wandb==0.19.3 +accelerate==1.0.1 +tifffile==2023.7.10 +PyWavelets==1.4.1 +lazy_loader==0.4 +imageio==2.35.1 +scikit-image==0.21.0 +pytorch_ssim==0.1 +fiona==1.9.5 +socmapping==0.1.0 +socmapping==0.1 +socmapping==0.1 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-24f67vcg/files/wandb-metadata.json b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-24f67vcg/files/wandb-metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9b606eddadb2af6e806b12a84e8e6cb0c9cc225e --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-24f67vcg/files/wandb-metadata.json @@ -0,0 +1,84 @@ +{ + "os": "Linux-5.15.0-89-generic-x86_64-with-glibc2.17", + "python": 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"cudaCores": 4608, + "architecture": "Turing" + }, + { + "name": "Quadro RTX 6000", + "memoryTotal": "25769803776", + "cudaCores": 4608, + "architecture": "Turing" + }, + { + "name": "Quadro RTX 6000", + "memoryTotal": "25769803776", + "cudaCores": 4608, + "architecture": "Turing" + }, + { + "name": "Quadro RTX 6000", + "memoryTotal": "25769803776", + "cudaCores": 4608, + "architecture": "Turing" + }, + { + "name": "Quadro RTX 6000", + "memoryTotal": "25769803776", + "cudaCores": 4608, + "architecture": "Turing" + }, + { + "name": "Quadro RTX 6000", + "memoryTotal": "25769803776", + "cudaCores": 4608, + "architecture": "Turing" + }, + { + "name": "Quadro RTX 6000", + "memoryTotal": "25769803776", + "cudaCores": 4608, + "architecture": "Turing" + } + ], + "cudaVersion": "12.3" +} \ No newline at end of file diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-24f67vcg/logs/debug-core.log 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Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.44it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.54it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.54it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-4judjuei/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-4judjuei/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 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Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.44it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.54it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-5ll6bdd5/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-5ll6bdd5/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 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UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20355 +Validation set size: 1024 +Model parameters: 92465 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.44it/s] +Epoch 1: +Training Loss: 1204.6274 +Validation Loss: 52.4585 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] + +Epoch 2: +Training Loss: 44.4361 +Validation Loss: 11.5753 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 3: +Training Loss: 25.5153 +Validation Loss: 8.1733 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] + +Epoch 4: +Training Loss: 20.5656 +Validation Loss: 8.0067 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 5: +Training Loss: 19.8661 +Validation Loss: 7.2001 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] + +Epoch 6: +Training Loss: 19.0024 +Validation Loss: 6.7060 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 7: +Training Loss: 18.1303 +Validation Loss: 6.0354 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 8: +Training Loss: 17.5421 +Validation Loss: 5.1824 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 9: +Training Loss: 16.4141 +Validation Loss: 3.5479 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 10: +Training Loss: 14.8463 +Validation Loss: 4.6161 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] + +Epoch 11: +Training Loss: 13.4309 +Validation Loss: 4.7527 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 12: +Training Loss: 12.9462 +Validation Loss: 3.5912 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 13: +Training Loss: 13.5118 +Validation Loss: 3.7152 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 14: +Training Loss: 12.6816 +Validation Loss: 3.7289 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 15: +Training Loss: 11.9137 +Validation Loss: 3.7893 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 16: +Training Loss: 11.7925 +Validation Loss: 3.9653 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 17: +Training Loss: 11.6234 +Validation Loss: 3.7975 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 18: +Training Loss: 11.5584 +Validation Loss: 3.9755 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 19: +Training Loss: 11.6372 +Validation Loss: 3.8491 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.59it/s] + +Epoch 20: +Training Loss: 11.8536 +Validation Loss: 3.7462 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] + +Epoch 21: +Training Loss: 11.2810 +Validation Loss: 3.7732 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 22: +Training Loss: 11.3910 +Validation Loss: 3.8646 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] + +Epoch 23: +Training Loss: 11.6418 +Validation Loss: 3.7786 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 24: +Training Loss: 11.1383 +Validation Loss: 3.7125 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] + +Epoch 25: +Training Loss: 11.0322 +Validation Loss: 3.8193 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 26: +Training Loss: 10.6222 +Validation Loss: 3.7982 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 27: +Training Loss: 10.9939 +Validation Loss: 3.7234 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 28: +Training Loss: 11.1868 +Validation Loss: 3.8606 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 29: +Training Loss: 11.5268 +Validation Loss: 3.7688 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 30: +Training Loss: 10.4067 +Validation Loss: 3.9729 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 31: +Training Loss: 10.5800 +Validation Loss: 3.6668 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 32: +Training Loss: 10.8933 +Validation Loss: 3.9252 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 33: +Training Loss: 10.5513 +Validation Loss: 3.8576 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 34: +Training Loss: 10.6270 +Validation Loss: 3.8613 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 35: +Training Loss: 10.7986 +Validation Loss: 3.8051 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 36: +Training Loss: 10.4274 +Validation Loss: 3.8734 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 37: +Training Loss: 10.3297 +Validation Loss: 3.9680 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 38: +Training Loss: 10.4009 +Validation Loss: 3.7801 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 39: +Training Loss: 10.5606 +Validation Loss: 3.9359 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 40: +Training Loss: 11.5016 +Validation Loss: 3.7595 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 41: +Training Loss: 10.6642 +Validation Loss: 4.0938 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 42: +Training Loss: 10.4939 +Validation Loss: 3.7423 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 43: +Training Loss: 10.1038 +Validation Loss: 4.1161 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 44: +Training Loss: 10.3821 +Validation Loss: 3.9336 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 45: +Training Loss: 10.8868 +Validation Loss: 3.9038 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 46: +Training Loss: 10.4056 +Validation Loss: 3.8886 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 47: +Training Loss: 10.6062 +Validation Loss: 3.8963 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 48: +Training Loss: 10.1460 +Validation Loss: 3.9059 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 49: +Training Loss: 10.7001 +Validation Loss: 4.0206 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 50: +Training Loss: 10.3897 +Validation Loss: 3.8519 + 40%|████████████████████████████████████▍ | 4/10 [00:03<00:04, 1.39it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-bu4vd694/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-bu4vd694/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-bu4vd694/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 +eval_type_backport==0.2.2 +docker-pycreds==0.4.0 +annotated-types==0.7.0 +pydantic==2.10.5 +huggingface-hub==0.27.1 +gitdb==4.0.12 +GitPython==3.1.44 +wandb==0.19.3 +accelerate==1.0.1 +tifffile==2023.7.10 +PyWavelets==1.4.1 +lazy_loader==0.4 +imageio==2.35.1 +scikit-image==0.21.0 +pytorch_ssim==0.1 +fiona==1.9.5 +socmapping==0.1.0 +socmapping==0.1 +socmapping==0.1 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-bu4vd694/files/wandb-metadata.json 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-bzwcdla2/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-bzwcdla2/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.44it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.54it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.54it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-jxpqz5en/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-jxpqz5en/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 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Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.44it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.54it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] 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Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.44it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.54it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-uk4wynt5/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_221901-uk4wynt5/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 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+ - 1 + - 49 + - 55 + - 71 + "3": + - 2 + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 100 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0g3vkyur/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0g3vkyur/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..d441eb6cce97a17865fd92412738420c23eaa521 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0g3vkyur/files/output.log @@ -0,0 +1,104 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.43it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] 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+100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.59it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0g3vkyur/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0g3vkyur/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0g3vkyur/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 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value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 2 + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 100 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0uuey5kn/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0uuey5kn/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..a2af09ae4d27d3baea9d1441e3d24109b8d77a1c --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0uuey5kn/files/output.log @@ -0,0 +1,104 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.43it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] 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+100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.59it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0uuey5kn/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0uuey5kn/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0uuey5kn/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 +eval_type_backport==0.2.2 +docker-pycreds==0.4.0 +annotated-types==0.7.0 +pydantic==2.10.5 +huggingface-hub==0.27.1 +gitdb==4.0.12 +GitPython==3.1.44 +wandb==0.19.3 +accelerate==1.0.1 +tifffile==2023.7.10 +PyWavelets==1.4.1 +lazy_loader==0.4 +imageio==2.35.1 +scikit-image==0.21.0 +pytorch_ssim==0.1 +fiona==1.9.5 +socmapping==0.1.0 +socmapping==0.1 +socmapping==0.1 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0uuey5kn/files/wandb-metadata.json b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0uuey5kn/files/wandb-metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0745d34bea977f92222f8276feb2699a75583a36 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0uuey5kn/files/wandb-metadata.json @@ -0,0 +1,84 @@ +{ + "os": "Linux-5.15.0-89-generic-x86_64-with-glibc2.17", + "python": "CPython 3.8.20", + "startedAt": "2025-03-27T21:25:38.495761Z", + "program": "train.py", + "codePath": "SimpleTimeModel/SimpleCNN_map/train.py", + "git": { + "remote": "https://github.com/ValerianFourel/SOCmapping", + "commit": "94629dd1054aa732bda11a800909b46d6faa59fc" + }, + "email": "valerian.fourel@gmail.com", + "root": 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files diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0uuey5kn/run-0uuey5kn.wandb b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0uuey5kn/run-0uuey5kn.wandb new file mode 100644 index 0000000000000000000000000000000000000000..4332c9d63bf1c46c57c4d55f6a66f20d5e597996 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-0uuey5kn/run-0uuey5kn.wandb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:888618d279f75e722ce6e4f631aaba34a40aa8ff1c12b90e327f6cc98bc00b80 +size 582590 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-falk8xkw/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-falk8xkw/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..824f43e355da72c0977fa72f6c2f583744a4055a --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-falk8xkw/files/config.yaml @@ -0,0 +1,42 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 2 + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 100 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-falk8xkw/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-falk8xkw/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..f250833b9bd0555fabc6e68aeec1b7da5fa19bae --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-falk8xkw/files/output.log @@ -0,0 +1,104 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.43it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.43it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] 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+100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.59it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.59it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-kuq3cvi7/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-kuq3cvi7/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-kuq3cvi7/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 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+100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.59it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-m3d9s14z/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-m3d9s14z/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-m3d9s14z/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 +eval_type_backport==0.2.2 +docker-pycreds==0.4.0 +annotated-types==0.7.0 +pydantic==2.10.5 +huggingface-hub==0.27.1 +gitdb==4.0.12 +GitPython==3.1.44 +wandb==0.19.3 +accelerate==1.0.1 +tifffile==2023.7.10 +PyWavelets==1.4.1 +lazy_loader==0.4 +imageio==2.35.1 +scikit-image==0.21.0 +pytorch_ssim==0.1 +fiona==1.9.5 +socmapping==0.1.0 +socmapping==0.1 +socmapping==0.1 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-m3d9s14z/files/wandb-metadata.json b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-m3d9s14z/files/wandb-metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c234dd8ac434a7ffc1b92a359f47390cb2ad1d9a --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-m3d9s14z/files/wandb-metadata.json @@ -0,0 +1,84 @@ +{ + "os": "Linux-5.15.0-89-generic-x86_64-with-glibc2.17", + "python": "CPython 3.8.20", + "startedAt": "2025-03-27T21:25:38.608407Z", + "program": "train.py", + "codePath": "SimpleTimeModel/SimpleCNN_map/train.py", + "git": { + "remote": "https://github.com/ValerianFourel/SOCmapping", + "commit": "94629dd1054aa732bda11a800909b46d6faa59fc" + }, + "email": "valerian.fourel@gmail.com", + "root": 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files diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-m3d9s14z/run-m3d9s14z.wandb b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-m3d9s14z/run-m3d9s14z.wandb new file mode 100644 index 0000000000000000000000000000000000000000..47b874c3ad978043b9a7b20d1b0e1840df1d1e66 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-m3d9s14z/run-m3d9s14z.wandb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fd27cb9e3b9bbdf973d3b7f6d4d3fe6e74342fe762546a511632214ccd6bc6c0 +size 582494 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-x28j9uww/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-x28j9uww/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..824f43e355da72c0977fa72f6c2f583744a4055a --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-x28j9uww/files/config.yaml @@ -0,0 +1,42 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 2 + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 100 +input_channels: + value: 6 +learning_rate: + value: 0.001 +max_oc: + value: 150 +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-x28j9uww/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-x28j9uww/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..6c4f643dd73afc9d3b2571efccd5007e4642e836 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222538-x28j9uww/files/output.log @@ -0,0 +1,104 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.43it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] 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+100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.59it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222542-2lzxsss9/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222542-2lzxsss9/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa579bcb27ae7cc1e902ddf4b47fe6b8990d93f3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222542-2lzxsss9/files/requirements.txt @@ -0,0 +1,157 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 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index 0000000000000000000000000000000000000000..dc7f74b6a81c04e713345f076bf05923b2323567 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250327_222547-d80nfd0k/files/output.log @@ -0,0 +1,508 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN_map/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20355 +Validation set size: 1024 +Model parameters: 92465 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.43it/s] +Epoch 1: +Training Loss: 149.9109 +Validation Loss: 20.4385 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 2: +Training Loss: 35.0207 +Validation Loss: 9.9261 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 3: +Training Loss: 20.4903 +Validation Loss: 4.7596 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] + +Epoch 4: +Training Loss: 15.4133 +Validation Loss: 3.3693 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 5: +Training Loss: 14.0289 +Validation Loss: 2.8421 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 6: +Training Loss: 12.6650 +Validation Loss: 2.4248 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] + +Epoch 7: +Training Loss: 12.4972 +Validation Loss: 2.1511 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 8: +Training Loss: 12.2068 +Validation Loss: 2.0504 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 9: +Training Loss: 12.5605 +Validation Loss: 1.9648 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 10: +Training Loss: 12.1500 +Validation Loss: 1.8742 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 11: +Training Loss: 11.8418 +Validation Loss: 1.8099 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 12: +Training Loss: 11.3541 +Validation Loss: 1.7792 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 13: +Training Loss: 12.2200 +Validation Loss: 1.7272 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 14: +Training Loss: 11.7464 +Validation Loss: 1.7124 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 15: +Training Loss: 11.1866 +Validation Loss: 1.7138 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 16: +Training Loss: 11.2098 +Validation Loss: 1.7257 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 17: +Training Loss: 11.1715 +Validation Loss: 1.7724 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 18: +Training Loss: 11.1798 +Validation Loss: 1.7092 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 19: +Training Loss: 11.3121 +Validation Loss: 1.7334 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 20: +Training Loss: 11.7609 +Validation Loss: 1.6774 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 21: +Training Loss: 11.0896 +Validation Loss: 1.7063 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 22: +Training Loss: 11.1187 +Validation Loss: 1.6826 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 23: +Training Loss: 11.4096 +Validation Loss: 1.6565 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 24: +Training Loss: 10.9707 +Validation Loss: 1.6902 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 25: +Training Loss: 10.8160 +Validation Loss: 1.7282 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 26: +Training Loss: 10.4575 +Validation Loss: 1.6999 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 27: +Training Loss: 10.5217 +Validation Loss: 1.7080 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 28: +Training Loss: 11.0958 +Validation Loss: 1.7361 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 29: +Training Loss: 11.2981 +Validation Loss: 1.7086 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 30: +Training Loss: 10.3460 +Validation Loss: 1.7307 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 31: +Training Loss: 10.5385 +Validation Loss: 1.7965 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] + +Epoch 32: +Training Loss: 10.5569 +Validation Loss: 1.7794 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 33: +Training Loss: 10.3544 +Validation Loss: 1.8203 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 34: +Training Loss: 10.5241 +Validation Loss: 1.8714 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 35: +Training Loss: 10.7073 +Validation Loss: 1.9779 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 36: +Training Loss: 10.3493 +Validation Loss: 1.9259 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 37: +Training Loss: 10.0918 +Validation Loss: 1.9240 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 38: +Training Loss: 10.2927 +Validation Loss: 1.9962 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 39: +Training Loss: 10.2246 +Validation Loss: 1.9144 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 40: +Training Loss: 10.9573 +Validation Loss: 1.9547 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 41: +Training Loss: 10.4329 +Validation Loss: 2.0325 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 42: +Training Loss: 10.0874 +Validation Loss: 2.0265 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 43: +Training Loss: 9.8127 +Validation Loss: 2.0954 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 44: +Training Loss: 10.2114 +Validation Loss: 2.1081 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 45: +Training Loss: 10.3503 +Validation Loss: 1.9865 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 46: +Training Loss: 10.0959 +Validation Loss: 2.0799 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 47: +Training Loss: 10.0429 +Validation Loss: 2.1440 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 48: +Training Loss: 9.6849 +Validation Loss: 2.0105 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 49: +Training Loss: 10.3611 +Validation Loss: 2.1558 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 50: +Training Loss: 9.7295 +Validation Loss: 2.1838 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 51: +Training Loss: 10.1310 +Validation Loss: 2.0763 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 52: +Training Loss: 9.5229 +Validation Loss: 2.2722 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 53: +Training Loss: 9.6203 +Validation Loss: 2.0747 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 54: +Training Loss: 9.6660 +Validation Loss: 2.2762 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 55: +Training Loss: 9.4121 +Validation Loss: 2.1165 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 56: +Training Loss: 9.4966 +Validation Loss: 2.3112 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 57: +Training Loss: 9.6405 +Validation Loss: 1.9835 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 58: +Training Loss: 10.1927 +Validation Loss: 2.2370 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 59: +Training Loss: 9.4770 +Validation Loss: 2.2257 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 60: +Training Loss: 9.6780 +Validation Loss: 2.0054 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 61: +Training Loss: 9.7364 +Validation Loss: 2.1250 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 62: +Training Loss: 9.3599 +Validation Loss: 2.3461 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 63: +Training Loss: 8.9999 +Validation Loss: 2.0930 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 64: +Training Loss: 9.4800 +Validation Loss: 2.1781 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.59it/s] + +Epoch 65: +Training Loss: 9.8273 +Validation Loss: 2.0731 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 66: +Training Loss: 9.4623 +Validation Loss: 2.1912 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 67: +Training Loss: 9.2452 +Validation Loss: 2.1439 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 68: +Training Loss: 9.2240 +Validation Loss: 2.1252 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 69: +Training Loss: 9.0668 +Validation Loss: 2.0301 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 70: +Training Loss: 9.0315 +Validation Loss: 2.1086 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 71: +Training Loss: 9.3370 +Validation Loss: 2.1581 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 72: +Training Loss: 9.6499 +Validation Loss: 2.4189 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 73: +Training Loss: 9.4223 +Validation Loss: 2.0197 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 74: +Training Loss: 8.8835 +Validation Loss: 2.0346 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 75: +Training Loss: 9.1068 +Validation Loss: 2.1431 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 76: +Training Loss: 8.8458 +Validation Loss: 1.9790 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 77: +Training Loss: 9.3197 +Validation Loss: 2.1379 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.59it/s] + +Epoch 78: +Training Loss: 8.9202 +Validation Loss: 2.0774 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 79: +Training Loss: 9.3993 +Validation Loss: 2.3499 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 80: +Training Loss: 8.9948 +Validation Loss: 2.1189 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 81: +Training Loss: 8.8561 +Validation Loss: 2.3628 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 82: +Training Loss: 9.0556 +Validation Loss: 2.3238 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 83: +Training Loss: 9.0342 +Validation Loss: 2.1222 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.58it/s] + +Epoch 84: +Training Loss: 9.0054 +Validation Loss: 2.3773 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 85: +Training Loss: 9.0053 +Validation Loss: 2.3113 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.59it/s] + +Epoch 86: +Training Loss: 9.5944 +Validation Loss: 2.2639 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.55it/s] + +Epoch 87: +Training Loss: 8.8680 +Validation Loss: 2.4705 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.59it/s] + +Epoch 88: +Training Loss: 8.7040 +Validation Loss: 2.3301 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.56it/s] + +Epoch 89: +Training Loss: 8.8134 +Validation Loss: 2.0912 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.59it/s] + +Epoch 90: +Training Loss: 8.3250 +Validation Loss: 2.1796 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 91: +Training Loss: 8.9468 +Validation Loss: 2.6008 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 92: +Training Loss: 8.8823 +Validation Loss: 2.1460 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 93: +Training Loss: 8.2978 +Validation Loss: 2.2340 +100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.57it/s] + +Epoch 94: +Training Loss: 8.7017 +Validation Loss: 2.4688 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falling back to `dateutil`. 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 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0.001, 'input_channels': 6, 'loss_type': 'L2', 'target_transform': 'normalize'} +2025-04-09 18:58:08,012 INFO MainThread:3244027 [wandb_init.py:init():745] starting backend +2025-04-09 18:58:08,249 INFO MainThread:3244027 [wandb_init.py:init():749] sending inform_init request +2025-04-09 18:58:08,289 INFO MainThread:3244027 [backend.py:_multiprocessing_setup():104] multiprocessing start_methods=fork,spawn,forkserver, using: spawn +2025-04-09 18:58:08,290 INFO MainThread:3244027 [wandb_init.py:init():764] backend started and connected +2025-04-09 18:58:08,292 INFO MainThread:3244027 [wandb_init.py:init():857] updated telemetry +2025-04-09 18:58:08,331 INFO MainThread:3244027 [wandb_init.py:init():889] communicating run to backend with 90.0 second timeout +2025-04-09 18:58:08,641 INFO MainThread:3244027 [wandb_init.py:init():941] starting run threads in backend +2025-04-09 18:58:08,951 INFO MainThread:3244027 [wandb_run.py:_console_start():2420] atexit reg +2025-04-09 18:58:08,951 INFO 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_185807-u8kd1gl2/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_185807-u8kd1gl2/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..fe0771b3a3c3cf6958196927919acf609f9c847f --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_185807-u8kd1gl2/files/output.log @@ -0,0 +1,302 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20273 +Validation set size: 1664 +Model parameters: 92465 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +Epoch 1: +Training Loss: 15346.3070 +Validation Loss: 23.2948 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 2: +Training Loss: 290.8681 +Validation Loss: 7.0379 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 3: +Training Loss: 98.8364 +Validation Loss: 3.2368 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 4: +Training Loss: 45.2510 +Validation Loss: 1.4080 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 5: +Training Loss: 33.9531 +Validation Loss: 0.7498 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 6: +Training Loss: 22.5284 +Validation Loss: 0.5149 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 7: +Training Loss: 21.2090 +Validation Loss: 0.4531 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 8: +Training Loss: 13.4129 +Validation Loss: 0.4338 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 9: +Training Loss: 13.0761 +Validation Loss: 0.3633 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 10: +Training Loss: 9.7267 +Validation Loss: 0.3763 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 11: +Training Loss: 8.8334 +Validation Loss: 0.3320 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 12: +Training Loss: 8.2171 +Validation Loss: 0.3244 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 13: +Training Loss: 6.9442 +Validation Loss: 0.3429 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 14: +Training Loss: 5.9724 +Validation Loss: 0.3447 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 15: +Training Loss: 5.0387 +Validation Loss: 0.2930 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 16: +Training Loss: 4.4447 +Validation Loss: 0.3340 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 17: +Training Loss: 3.6883 +Validation Loss: 0.2981 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 18: +Training Loss: 3.9009 +Validation Loss: 0.3047 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 19: +Training Loss: 3.8027 +Validation Loss: 0.3213 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 20: +Training Loss: 3.2290 +Validation Loss: 0.2954 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 21: +Training Loss: 3.2098 +Validation Loss: 0.2970 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 22: +Training Loss: 3.3097 +Validation Loss: 0.2941 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 23: +Training Loss: 3.1747 +Validation Loss: 0.2991 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 24: +Training Loss: 2.8253 +Validation Loss: 0.3002 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 25: +Training Loss: 2.5720 +Validation Loss: 0.2914 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 26: +Training Loss: 2.2796 +Validation Loss: 0.3135 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 27: +Training Loss: 2.4991 +Validation Loss: 0.2756 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 28: +Training Loss: 2.0375 +Validation Loss: 0.2931 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 29: +Training Loss: 2.2595 +Validation Loss: 0.2814 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 30: +Training Loss: 1.9841 +Validation Loss: 0.2923 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 31: +Training Loss: 1.8996 +Validation Loss: 0.2996 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 32: +Training Loss: 2.0111 +Validation Loss: 0.2919 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 33: +Training Loss: 1.7909 +Validation Loss: 0.2861 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 34: +Training Loss: 2.2627 +Validation Loss: 0.2859 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 35: +Training Loss: 1.8034 +Validation Loss: 0.2774 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 36: +Training Loss: 1.7649 +Validation Loss: 0.2780 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 37: +Training Loss: 1.5691 +Validation Loss: 0.2868 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 38: +Training Loss: 1.6921 +Validation Loss: 0.2901 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 39: +Training Loss: 1.8226 +Validation Loss: 0.2871 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 40: +Training Loss: 1.6003 +Validation Loss: 0.2835 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 41: +Training Loss: 1.6741 +Validation Loss: 0.2545 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 42: +Training Loss: 1.4959 +Validation Loss: 0.2777 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 43: +Training Loss: 1.4546 +Validation Loss: 0.2746 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 44: +Training Loss: 1.7336 +Validation Loss: 0.2868 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 45: +Training Loss: 1.6378 +Validation Loss: 0.2711 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 46: +Training Loss: 1.3462 +Validation Loss: 0.2844 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 47: +Training Loss: 1.5304 +Validation Loss: 0.2688 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 48: +Training Loss: 1.4151 +Validation Loss: 0.2790 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 49: +Training Loss: 1.3373 +Validation Loss: 0.3057 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 50: +Training Loss: 1.3679 +Validation Loss: 0.2758 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 51: +Training Loss: 1.3161 +Validation Loss: 0.2811 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 52: +Training Loss: 1.6960 +Validation Loss: 0.2850 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 53: +Training Loss: 1.3610 +Validation Loss: 0.2718 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 54: +Training Loss: 1.3593 +Validation Loss: 0.2829 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 55: +Training Loss: 1.3754 +Validation Loss: 0.3021 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 56: +Training Loss: 1.2956 +Validation Loss: 0.2657 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 57: +Training Loss: 1.1524 +Validation Loss: 0.2822 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 58: +Training Loss: 1.1770 +Validation Loss: 0.2882 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 59: +Training Loss: 1.5183 +Validation Loss: 0.2887 + 85%|█████████████████████████████████████████████████████████████████████████████████▌ | 17/20 [00:09<00:01, 1.87it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_185807-u8kd1gl2/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_185807-u8kd1gl2/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..993042d1e5459219a2f8e1ecb06664b577d212a8 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_185807-u8kd1gl2/files/requirements.txt @@ -0,0 +1,162 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_185809-r4bem11y/files/requirements.txt @@ -0,0 +1,162 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-5us7tt88/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..934a3b84f48be2cb53893c1a6c6017c4fa894413 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-5us7tt88/files/output.log @@ -0,0 +1,204 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-5us7tt88/files/requirements.txt @@ -0,0 +1,162 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-8lo7opb0/files/requirements.txt @@ -0,0 +1,162 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 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sha256:fe861c6a233d8edcf27602c834505f32ba3a95adb6561341716129730b2e282d +size 1856223 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-8mc45uzb/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-8mc45uzb/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e87c4b064405c33b30779ba7bf6d008904fcc634 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-8mc45uzb/files/config.yaml @@ -0,0 +1,46 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 2 + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: L2 +max_oc: + value: 150 +target_transform: + value: log +time_beginning: + value: "2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-8mc45uzb/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-8mc45uzb/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..413733a03ebf385d331692422a749166fc0e2970 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-8mc45uzb/files/output.log @@ -0,0 +1,204 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-8mc45uzb/files/requirements.txt @@ -0,0 +1,162 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 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"2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-cceoce6t/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-cceoce6t/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..962213ac07cc1b72b82182722f68f79fdc6f8720 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-cceoce6t/files/output.log @@ -0,0 +1,1008 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20273 +Validation set size: 1664 +Model parameters: 92465 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +Epoch 1: +Training Loss: 40201.7276 +Validation Loss: 435.4304 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 2: +Training Loss: 1209.7795 +Validation Loss: 71.5846 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 3: +Training Loss: 489.2463 +Validation Loss: 23.5267 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 4: +Training Loss: 286.2096 +Validation Loss: 10.5390 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 5: +Training Loss: 211.7972 +Validation Loss: 8.7565 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 6: +Training Loss: 140.0793 +Validation Loss: 7.7234 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 7: +Training Loss: 117.9194 +Validation Loss: 5.4504 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 8: +Training Loss: 84.6874 +Validation Loss: 4.2120 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 9: +Training Loss: 77.1449 +Validation Loss: 3.6340 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 10: +Training Loss: 59.0668 +Validation Loss: 3.2531 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 11: +Training Loss: 44.5979 +Validation Loss: 2.8266 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 12: +Training Loss: 42.4032 +Validation Loss: 2.6410 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 13: +Training Loss: 38.5162 +Validation Loss: 2.6192 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 14: +Training Loss: 29.4301 +Validation Loss: 2.4692 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 15: +Training Loss: 26.4987 +Validation Loss: 2.3545 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 16: +Training Loss: 24.1335 +Validation Loss: 2.2953 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 17: +Training Loss: 20.6596 +Validation Loss: 2.2051 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 18: +Training Loss: 22.8823 +Validation Loss: 2.1456 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 19: +Training Loss: 18.2842 +Validation Loss: 2.0895 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 20: +Training Loss: 16.3037 +Validation Loss: 2.1141 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 21: +Training Loss: 15.2655 +Validation Loss: 2.0334 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 22: +Training Loss: 13.4089 +Validation Loss: 1.9431 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 23: +Training Loss: 13.8198 +Validation Loss: 1.8653 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 24: +Training Loss: 10.8956 +Validation Loss: 1.9459 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 25: +Training Loss: 11.2920 +Validation Loss: 1.8126 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 26: +Training Loss: 11.9138 +Validation Loss: 1.7873 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 27: +Training Loss: 9.8738 +Validation Loss: 1.7739 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 28: +Training Loss: 9.5071 +Validation Loss: 1.6496 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 29: +Training Loss: 8.9620 +Validation Loss: 1.5385 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 30: +Training Loss: 7.5092 +Validation Loss: 1.5801 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 31: +Training Loss: 7.4127 +Validation Loss: 1.4134 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 32: +Training Loss: 7.3942 +Validation Loss: 1.4087 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 33: +Training Loss: 6.8366 +Validation Loss: 1.3717 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 34: +Training Loss: 6.1199 +Validation Loss: 1.2649 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 35: +Training Loss: 6.9342 +Validation Loss: 1.2487 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 36: +Training Loss: 6.0035 +Validation Loss: 1.2248 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 37: +Training Loss: 5.5943 +Validation Loss: 1.1367 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 38: +Training Loss: 5.2553 +Validation Loss: 1.1908 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 39: +Training Loss: 6.3232 +Validation Loss: 1.2658 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 40: +Training Loss: 4.9174 +Validation Loss: 1.0601 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 41: +Training Loss: 5.3527 +Validation Loss: 1.1491 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 42: +Training Loss: 5.1903 +Validation Loss: 1.3350 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 43: +Training Loss: 6.5865 +Validation Loss: 1.3039 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 44: +Training Loss: 4.4602 +Validation Loss: 1.3203 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 45: +Training Loss: 4.6092 +Validation Loss: 1.2950 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 46: +Training Loss: 3.8264 +Validation Loss: 1.0573 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 47: +Training Loss: 4.6398 +Validation Loss: 0.9532 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 48: +Training Loss: 5.0977 +Validation Loss: 1.0294 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 49: +Training Loss: 4.1115 +Validation Loss: 0.9656 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 50: +Training Loss: 3.7330 +Validation Loss: 1.0749 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 51: +Training Loss: 4.0190 +Validation Loss: 1.1583 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 52: +Training Loss: 4.0868 +Validation Loss: 0.9455 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 53: +Training Loss: 4.0064 +Validation Loss: 1.1203 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 54: +Training Loss: 3.7573 +Validation Loss: 0.9768 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 55: +Training Loss: 3.6184 +Validation Loss: 1.1147 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 56: +Training Loss: 3.3847 +Validation Loss: 0.9010 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 57: +Training Loss: 3.2214 +Validation Loss: 0.9674 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 58: +Training Loss: 3.6928 +Validation Loss: 1.0911 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 59: +Training Loss: 3.0069 +Validation Loss: 1.0484 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 60: +Training Loss: 3.1308 +Validation Loss: 1.0033 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 61: +Training Loss: 3.6618 +Validation Loss: 0.8091 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 62: +Training Loss: 3.0963 +Validation Loss: 1.0241 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 63: +Training Loss: 2.9643 +Validation Loss: 0.9741 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 64: +Training Loss: 3.1024 +Validation Loss: 0.7801 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 65: +Training Loss: 2.8037 +Validation Loss: 0.8515 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 66: +Training Loss: 3.1989 +Validation Loss: 1.0082 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 67: +Training Loss: 2.9015 +Validation Loss: 0.8970 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 68: +Training Loss: 2.8133 +Validation Loss: 0.9124 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 69: +Training Loss: 2.9002 +Validation Loss: 0.9459 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 70: +Training Loss: 3.1525 +Validation Loss: 0.7800 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 71: +Training Loss: 2.4986 +Validation Loss: 0.9941 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 72: +Training Loss: 3.1326 +Validation Loss: 0.9338 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 73: +Training Loss: 2.5185 +Validation Loss: 0.7754 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 74: +Training Loss: 2.5162 +Validation Loss: 0.8906 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 75: +Training Loss: 2.8438 +Validation Loss: 0.7818 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 76: +Training Loss: 2.8361 +Validation Loss: 1.0146 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 77: +Training Loss: 2.9564 +Validation Loss: 1.0120 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 78: +Training Loss: 3.0793 +Validation Loss: 1.1853 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 79: +Training Loss: 2.6329 +Validation Loss: 0.9617 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 80: +Training Loss: 2.4352 +Validation Loss: 0.8765 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 81: +Training Loss: 2.7427 +Validation Loss: 0.8618 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 82: +Training Loss: 2.5582 +Validation Loss: 0.8989 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 83: +Training Loss: 2.3543 +Validation Loss: 0.8322 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 84: +Training Loss: 2.4455 +Validation Loss: 0.9703 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 85: +Training Loss: 2.6343 +Validation Loss: 0.9383 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 86: +Training Loss: 2.2691 +Validation Loss: 0.7975 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 87: +Training Loss: 2.4370 +Validation Loss: 0.8241 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 88: +Training Loss: 2.4784 +Validation Loss: 0.8637 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 89: +Training Loss: 2.9767 +Validation Loss: 0.8478 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 90: +Training Loss: 2.3410 +Validation Loss: 0.7437 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 91: +Training Loss: 2.4729 +Validation Loss: 0.7729 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 92: +Training Loss: 2.2889 +Validation Loss: 0.8343 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 93: +Training Loss: 2.2504 +Validation Loss: 0.8224 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 94: +Training Loss: 2.2104 +Validation Loss: 0.7710 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 95: +Training Loss: 2.2645 +Validation Loss: 0.8123 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 96: +Training Loss: 2.2647 +Validation Loss: 0.8673 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 97: +Training Loss: 2.0086 +Validation Loss: 0.8869 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 98: +Training Loss: 2.0151 +Validation Loss: 0.8160 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 99: +Training Loss: 2.2164 +Validation Loss: 0.7772 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 100: +Training Loss: 2.2968 +Validation Loss: 0.8131 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 101: +Training Loss: 2.1770 +Validation Loss: 0.9969 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 102: +Training Loss: 2.0981 +Validation Loss: 0.8326 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 103: +Training Loss: 2.0409 +Validation Loss: 1.0970 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 104: +Training Loss: 2.3445 +Validation Loss: 1.0842 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 105: +Training Loss: 2.0832 +Validation Loss: 0.8305 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 106: +Training Loss: 2.0925 +Validation Loss: 0.8479 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 107: +Training Loss: 2.4650 +Validation Loss: 0.8834 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 108: +Training Loss: 2.1617 +Validation Loss: 0.9205 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 109: +Training Loss: 2.4387 +Validation Loss: 0.8632 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 110: +Training Loss: 1.9998 +Validation Loss: 0.8139 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 111: +Training Loss: 1.9029 +Validation Loss: 0.8120 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 112: +Training Loss: 1.8014 +Validation Loss: 0.8532 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 113: +Training Loss: 1.9303 +Validation Loss: 0.8520 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 114: +Training Loss: 1.9214 +Validation Loss: 0.9144 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 115: +Training Loss: 1.9186 +Validation Loss: 0.8022 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 116: +Training Loss: 1.9776 +Validation Loss: 0.9867 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 117: +Training Loss: 1.8321 +Validation Loss: 0.8762 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 118: +Training Loss: 1.8885 +Validation Loss: 0.9594 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 119: +Training Loss: 1.9384 +Validation Loss: 0.8241 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 120: +Training Loss: 1.8466 +Validation Loss: 0.9089 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 121: +Training Loss: 2.1472 +Validation Loss: 0.8066 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 122: +Training Loss: 1.9277 +Validation Loss: 0.8924 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 123: +Training Loss: 1.9638 +Validation Loss: 0.7937 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 124: +Training Loss: 1.6534 +Validation Loss: 0.8017 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 125: +Training Loss: 1.7184 +Validation Loss: 0.9801 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 126: +Training Loss: 1.7654 +Validation Loss: 0.8269 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 127: +Training Loss: 1.7393 +Validation Loss: 0.8173 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 128: +Training Loss: 2.3467 +Validation Loss: 0.7219 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 129: +Training Loss: 2.0419 +Validation Loss: 0.7692 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 130: +Training Loss: 1.9452 +Validation Loss: 0.9113 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 131: +Training Loss: 1.9020 +Validation Loss: 0.8381 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 132: +Training Loss: 1.7441 +Validation Loss: 0.8794 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 133: +Training Loss: 1.7691 +Validation Loss: 0.8668 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 134: +Training Loss: 1.7223 +Validation Loss: 0.7192 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 135: +Training Loss: 1.6842 +Validation Loss: 0.7282 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 136: +Training Loss: 1.7933 +Validation Loss: 0.7212 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 137: +Training Loss: 1.6861 +Validation Loss: 0.7229 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 138: +Training Loss: 1.7533 +Validation Loss: 0.7172 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 139: +Training Loss: 1.7160 +Validation Loss: 0.7956 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 140: +Training Loss: 2.0499 +Validation Loss: 0.9176 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 141: +Training Loss: 1.6899 +Validation Loss: 0.8337 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 142: +Training Loss: 1.6428 +Validation Loss: 0.8011 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 143: +Training Loss: 1.7168 +Validation Loss: 0.6392 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 144: +Training Loss: 1.8368 +Validation Loss: 0.7143 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 145: +Training Loss: 1.6994 +Validation Loss: 0.7244 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 146: +Training Loss: 1.7336 +Validation Loss: 0.6969 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 147: +Training Loss: 1.6190 +Validation Loss: 0.7467 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 148: +Training Loss: 1.9252 +Validation Loss: 0.7657 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 149: +Training Loss: 1.5060 +Validation Loss: 0.5934 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 150: +Training Loss: 1.5449 +Validation Loss: 0.6822 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 151: +Training Loss: 1.5920 +Validation Loss: 1.0065 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 152: +Training Loss: 1.8314 +Validation Loss: 0.8937 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 153: +Training Loss: 1.6089 +Validation Loss: 0.8167 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 154: +Training Loss: 1.7351 +Validation Loss: 0.7166 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 155: +Training Loss: 1.5645 +Validation Loss: 0.8015 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 156: +Training Loss: 1.6350 +Validation Loss: 0.7359 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 157: +Training Loss: 1.5305 +Validation Loss: 0.6377 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 158: +Training Loss: 1.7004 +Validation Loss: 0.7923 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 159: +Training Loss: 1.6363 +Validation Loss: 0.5797 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 160: +Training Loss: 1.5644 +Validation Loss: 0.8292 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 161: +Training Loss: 1.5460 +Validation Loss: 0.7915 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 162: +Training Loss: 1.7711 +Validation Loss: 0.8002 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 163: +Training Loss: 1.5655 +Validation Loss: 0.8989 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 164: +Training Loss: 1.4561 +Validation Loss: 0.8192 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 165: +Training Loss: 1.6135 +Validation Loss: 0.5848 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 166: +Training Loss: 1.4961 +Validation Loss: 0.7551 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 167: +Training Loss: 1.5913 +Validation Loss: 0.8208 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 168: +Training Loss: 1.6356 +Validation Loss: 0.7756 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 169: +Training Loss: 1.4873 +Validation Loss: 0.6470 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 170: +Training Loss: 1.4173 +Validation Loss: 0.7868 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 171: +Training Loss: 1.5235 +Validation Loss: 0.6918 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 172: +Training Loss: 1.5764 +Validation Loss: 0.8385 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 173: +Training Loss: 1.4351 +Validation Loss: 0.6935 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 174: +Training Loss: 1.4763 +Validation Loss: 0.6628 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 175: +Training Loss: 1.3978 +Validation Loss: 0.7584 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 176: +Training Loss: 1.3758 +Validation Loss: 0.7245 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 177: +Training Loss: 1.5591 +Validation Loss: 0.6726 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 178: +Training Loss: 1.3545 +Validation Loss: 0.6765 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 179: +Training Loss: 1.4167 +Validation Loss: 0.7641 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 180: +Training Loss: 1.4032 +Validation Loss: 0.6181 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 181: +Training Loss: 1.4181 +Validation Loss: 0.8635 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 182: +Training Loss: 1.3385 +Validation Loss: 0.6632 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 183: +Training Loss: 1.3068 +Validation Loss: 0.7372 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 184: +Training Loss: 1.4353 +Validation Loss: 0.6826 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[wandb_run.py:_footer_sync_info():3948] logging synced files diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-cceoce6t/run-cceoce6t.wandb b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-cceoce6t/run-cceoce6t.wandb new file mode 100644 index 0000000000000000000000000000000000000000..4e4044223c0bfd0d1c27244e6fb34da994b2def7 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_191037-cceoce6t/run-cceoce6t.wandb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eb1ec2d6e96eabacbddbd0e894e97affcf3da4742d1865433efa3b435657d1e4 +size 3549794 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214125-km47t0tp/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214125-km47t0tp/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..fd4c94c988a2733699b49932af5321519150f0dd --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214125-km47t0tp/files/output.log @@ -0,0 +1,962 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20273 +Validation set size: 1664 +Model parameters: 92465 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.81it/s] +Epoch 1: +Training Loss: 20630.5255 +Validation Loss: 139.8945 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 2: +Training Loss: 561.6409 +Validation Loss: 8.6510 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 3: +Training Loss: 174.8063 +Validation Loss: 1.0667 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 4: +Training Loss: 91.9330 +Validation Loss: 0.3335 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 5: +Training Loss: 71.8903 +Validation Loss: 0.2067 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 6: +Training Loss: 47.8752 +Validation Loss: 0.1769 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 7: +Training Loss: 40.9150 +Validation Loss: 0.1721 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 8: +Training Loss: 27.5301 +Validation Loss: 0.1731 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 9: +Training Loss: 21.6164 +Validation Loss: 0.1702 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 10: +Training Loss: 20.1464 +Validation Loss: 0.1659 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 11: +Training Loss: 15.8938 +Validation Loss: 0.1621 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 12: +Training Loss: 13.6250 +Validation Loss: 0.1609 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 13: +Training Loss: 10.5195 +Validation Loss: 0.1618 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 14: +Training Loss: 8.9502 +Validation Loss: 0.1631 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 15: +Training Loss: 8.7137 +Validation Loss: 0.1645 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 16: +Training Loss: 7.6198 +Validation Loss: 0.1650 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 17: +Training Loss: 8.0426 +Validation Loss: 0.1654 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 18: +Training Loss: 8.4045 +Validation Loss: 0.1661 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 19: +Training Loss: 6.4122 +Validation Loss: 0.1666 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 20: +Training Loss: 5.8271 +Validation Loss: 0.1669 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 21: +Training Loss: 5.2243 +Validation Loss: 0.1672 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 22: +Training Loss: 5.1325 +Validation Loss: 0.1679 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 23: +Training Loss: 4.5146 +Validation Loss: 0.1683 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 24: +Training Loss: 6.0131 +Validation Loss: 0.1685 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 25: +Training Loss: 4.0617 +Validation Loss: 0.1680 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 26: +Training Loss: 3.6794 +Validation Loss: 0.1689 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 27: +Training Loss: 3.7679 +Validation Loss: 0.1699 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 28: +Training Loss: 3.2899 +Validation Loss: 0.1707 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 29: +Training Loss: 2.7948 +Validation Loss: 0.1712 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 30: +Training Loss: 2.5444 +Validation Loss: 0.1716 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 31: +Training Loss: 2.5599 +Validation Loss: 0.1719 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 32: +Training Loss: 2.1512 +Validation Loss: 0.1722 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 33: +Training Loss: 1.9758 +Validation Loss: 0.1727 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 34: +Training Loss: 2.3548 +Validation Loss: 0.1732 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 35: +Training Loss: 3.1574 +Validation Loss: 0.1737 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 36: +Training Loss: 1.9288 +Validation Loss: 0.1740 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 37: +Training Loss: 1.8633 +Validation Loss: 0.1743 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 38: +Training Loss: 1.9127 +Validation Loss: 0.1746 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 39: +Training Loss: 2.1758 +Validation Loss: 0.1748 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+100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 55: +Training Loss: 1.3021 +Validation Loss: 0.1781 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 56: +Training Loss: 1.7540 +Validation Loss: 0.1784 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 57: +Training Loss: 1.4021 +Validation Loss: 0.1785 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 58: +Training Loss: 1.2681 +Validation Loss: 0.1787 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 59: +Training Loss: 1.9106 +Validation Loss: 0.1789 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 60: +Training Loss: 1.2088 +Validation Loss: 0.1792 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 61: +Training Loss: 1.1461 +Validation Loss: 0.1794 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 62: +Training Loss: 1.4548 +Validation Loss: 0.1796 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 63: +Training Loss: 1.2497 +Validation Loss: 0.1798 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 64: +Training Loss: 1.6136 +Validation Loss: 0.1800 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 65: +Training Loss: 1.2755 +Validation Loss: 0.1802 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 66: +Training Loss: 1.2676 +Validation Loss: 0.1805 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 67: +Training Loss: 1.9042 +Validation Loss: 0.1807 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 68: +Training Loss: 1.5007 +Validation Loss: 0.1810 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 69: +Training Loss: 1.4844 +Validation Loss: 0.1812 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 70: +Training Loss: 1.4132 +Validation Loss: 0.1815 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 71: +Training Loss: 1.4772 +Validation Loss: 0.1817 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 72: +Training Loss: 1.1924 +Validation Loss: 0.1819 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 73: +Training Loss: 1.1279 +Validation Loss: 0.1822 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 74: +Training Loss: 1.2629 +Validation Loss: 0.1824 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 75: +Training Loss: 1.5111 +Validation Loss: 0.1826 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 76: +Training Loss: 1.0503 +Validation Loss: 0.1829 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 77: +Training Loss: 1.1557 +Validation Loss: 0.1832 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 78: +Training Loss: 1.3969 +Validation Loss: 0.1834 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 79: +Training Loss: 1.2654 +Validation Loss: 0.1837 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 80: +Training Loss: 1.2814 +Validation Loss: 0.1840 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 81: +Training Loss: 1.1701 +Validation Loss: 0.1843 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 82: +Training Loss: 1.4592 +Validation Loss: 0.1846 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 83: +Training Loss: 1.4134 +Validation Loss: 0.1849 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 84: +Training Loss: 1.6007 +Validation Loss: 0.1852 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 85: +Training Loss: 1.1603 +Validation Loss: 0.1855 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 86: +Training Loss: 1.3705 +Validation Loss: 0.1857 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 87: +Training Loss: 1.0364 +Validation Loss: 0.1860 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 88: +Training Loss: 1.1861 +Validation Loss: 0.1862 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 89: +Training Loss: 1.2248 +Validation Loss: 0.1865 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 90: +Training Loss: 1.3443 +Validation Loss: 0.1868 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 91: +Training Loss: 1.4919 +Validation Loss: 0.1872 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 92: +Training Loss: 1.2383 +Validation Loss: 0.1874 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 93: +Training Loss: 1.1972 +Validation Loss: 0.1878 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 94: +Training Loss: 1.1097 +Validation Loss: 0.1881 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 95: +Training Loss: 1.0370 +Validation Loss: 0.1884 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 96: +Training Loss: 1.2118 +Validation Loss: 0.1887 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 97: +Training Loss: 1.0827 +Validation Loss: 0.1890 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 98: +Training Loss: 1.1228 +Validation Loss: 0.1893 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 99: +Training Loss: 1.1923 +Validation Loss: 0.1896 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 100: +Training Loss: 1.0426 +Validation Loss: 0.1899 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 101: +Training Loss: 1.1305 +Validation Loss: 0.1902 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 102: +Training Loss: 1.1609 +Validation Loss: 0.1906 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 103: +Training Loss: 1.0894 +Validation Loss: 0.1909 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 104: +Training Loss: 1.2425 +Validation Loss: 0.1913 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 105: +Training Loss: 1.0493 +Validation Loss: 0.1916 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 106: +Training Loss: 1.0907 +Validation Loss: 0.1920 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 107: +Training Loss: 1.2656 +Validation Loss: 0.1923 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 108: +Training Loss: 1.0021 +Validation Loss: 0.1926 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 109: +Training Loss: 1.0668 +Validation Loss: 0.1930 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 110: +Training Loss: 1.1990 +Validation Loss: 0.1933 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 111: +Training Loss: 1.1418 +Validation Loss: 0.1937 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 112: +Training Loss: 1.2242 +Validation Loss: 0.1940 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 113: +Training Loss: 1.3125 +Validation Loss: 0.1944 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 114: +Training Loss: 1.2601 +Validation Loss: 0.1948 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 115: +Training Loss: 1.1358 +Validation Loss: 0.1950 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 116: +Training Loss: 1.2150 +Validation Loss: 0.1955 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 117: +Training Loss: 1.3169 +Validation Loss: 0.1958 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 118: +Training Loss: 1.3115 +Validation Loss: 0.1960 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 119: +Training Loss: 1.0227 +Validation Loss: 0.1966 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 120: +Training Loss: 1.1608 +Validation Loss: 0.1969 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 121: +Training Loss: 1.2678 +Validation Loss: 0.1971 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 122: +Training Loss: 1.0200 +Validation Loss: 0.1978 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 123: +Training Loss: 1.1375 +Validation Loss: 0.1979 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 124: +Training Loss: 1.2166 +Validation Loss: 0.1980 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 125: +Training Loss: 1.0096 +Validation Loss: 0.1987 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 126: +Training Loss: 1.2059 +Validation Loss: 0.1991 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 127: +Training Loss: 1.1157 +Validation Loss: 0.1994 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 128: +Training Loss: 1.1343 +Validation Loss: 0.1998 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 129: +Training Loss: 1.0958 +Validation Loss: 0.2006 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 130: +Training Loss: 1.2119 +Validation Loss: 0.2006 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 131: +Training Loss: 0.9658 +Validation Loss: 0.2012 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 132: +Training Loss: 1.1561 +Validation Loss: 0.2014 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 133: +Training Loss: 1.2995 +Validation Loss: 0.2017 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 134: +Training Loss: 1.3097 +Validation Loss: 0.2024 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 135: +Training Loss: 1.2835 +Validation Loss: 0.2026 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 136: +Training Loss: 1.0442 +Validation Loss: 0.2032 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 137: +Training Loss: 1.0893 +Validation Loss: 0.2036 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 138: +Training Loss: 1.1724 +Validation Loss: 0.2037 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 139: +Training Loss: 1.0453 +Validation Loss: 0.2041 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+100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 165: +Training Loss: 1.0483 +Validation Loss: 0.2152 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 166: +Training Loss: 0.9865 +Validation Loss: 0.2141 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 167: +Training Loss: 1.3769 +Validation Loss: 0.2169 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 168: +Training Loss: 1.1047 +Validation Loss: 0.2161 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 169: +Training Loss: 1.0778 +Validation Loss: 0.2133 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+100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 175: +Training Loss: 1.0313 +Validation Loss: 0.2179 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 176: +Training Loss: 1.0553 +Validation Loss: 0.2162 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 177: +Training Loss: 1.0465 +Validation Loss: 0.2153 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 178: +Training Loss: 1.0861 +Validation Loss: 0.2206 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 179: +Training Loss: 1.1053 +Validation Loss: 0.2177 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 180: +Training Loss: 1.1677 +Validation Loss: 0.2168 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 181: +Training Loss: 1.0346 +Validation Loss: 0.2192 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 182: +Training Loss: 1.0279 +Validation Loss: 0.2178 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 183: +Training Loss: 0.9783 +Validation Loss: 0.2183 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 184: +Training Loss: 1.1218 +Validation Loss: 0.2200 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 185: +Training Loss: 1.0501 +Validation Loss: 0.2216 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 186: +Training Loss: 1.0257 +Validation Loss: 0.2206 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 187: +Training Loss: 1.0503 +Validation Loss: 0.2198 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 188: +Training Loss: 1.0807 +Validation Loss: 0.2214 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 189: +Training Loss: 1.0735 +Validation Loss: 0.2204 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 190: +Training Loss: 1.0336 +Validation Loss: 0.2232 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 191: +Training Loss: 1.1028 +Validation Loss: 0.2232 + 5%|████▊ | 1/20 [00:01<00:19, 1.03s/it] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214125-km47t0tp/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214125-km47t0tp/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..993042d1e5459219a2f8e1ecb06664b577d212a8 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214125-km47t0tp/files/requirements.txt @@ -0,0 +1,162 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 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+100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + 5%|████▊ | 1/20 [00:01<00:19, 1.02s/it] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214126-2u5i78rg/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214126-2u5i78rg/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..993042d1e5459219a2f8e1ecb06664b577d212a8 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214126-2u5i78rg/files/requirements.txt @@ -0,0 +1,162 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 +eval_type_backport==0.2.2 +docker-pycreds==0.4.0 +annotated-types==0.7.0 +pydantic==2.10.5 +huggingface-hub==0.27.1 +gitdb==4.0.12 +GitPython==3.1.44 +wandb==0.19.3 +accelerate==1.0.1 +tifffile==2023.7.10 +PyWavelets==1.4.1 +lazy_loader==0.4 +imageio==2.35.1 +scikit-image==0.21.0 +pytorch_ssim==0.1 +fiona==1.9.5 +patsy==1.0.1 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+100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + 5%|████▊ | 1/20 [00:01<00:20, 1.07s/it] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214126-dihtr2b0/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214126-dihtr2b0/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..993042d1e5459219a2f8e1ecb06664b577d212a8 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214126-dihtr2b0/files/requirements.txt @@ -0,0 +1,162 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 +eval_type_backport==0.2.2 +docker-pycreds==0.4.0 +annotated-types==0.7.0 +pydantic==2.10.5 +huggingface-hub==0.27.1 +gitdb==4.0.12 +GitPython==3.1.44 +wandb==0.19.3 +accelerate==1.0.1 +tifffile==2023.7.10 +PyWavelets==1.4.1 +lazy_loader==0.4 +imageio==2.35.1 +scikit-image==0.21.0 +pytorch_ssim==0.1 +fiona==1.9.5 +patsy==1.0.1 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+100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + 5%|████▊ | 1/20 [00:01<00:19, 1.03s/it] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214126-klufcac8/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214126-klufcac8/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..993042d1e5459219a2f8e1ecb06664b577d212a8 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_214126-klufcac8/files/requirements.txt @@ -0,0 +1,162 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_221832-abya18yj/files/requirements.txt @@ -0,0 +1,162 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_221832-i4wowo0j/files/requirements.txt @@ -0,0 +1,162 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 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"2007" +time_end: + value: "2023" diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_221832-okw7jof8/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_221832-okw7jof8/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..ec8b54288a8914dfe64287ed938533f9753ebc9b --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_221832-okw7jof8/files/output.log @@ -0,0 +1,1008 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Training set size: 20273 +Validation set size: 1664 +Model parameters: 92465 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.83it/s] +Epoch 1: +Training Loss: 30211.6894 +Validation Loss: 207.8561 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 2: +Training Loss: 1277.4290 +Validation Loss: 75.8962 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 3: +Training Loss: 775.9327 +Validation Loss: 65.1442 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 4: +Training Loss: 646.0980 +Validation Loss: 62.8611 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 5: +Training Loss: 601.0071 +Validation Loss: 55.6067 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 6: +Training Loss: 557.1270 +Validation Loss: 53.8792 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 7: +Training Loss: 522.8304 +Validation Loss: 52.2691 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 8: +Training Loss: 516.8070 +Validation Loss: 50.3100 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 9: +Training Loss: 485.8218 +Validation Loss: 49.9693 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 10: +Training Loss: 469.3049 +Validation Loss: 48.4666 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 11: +Training Loss: 477.8098 +Validation Loss: 48.4073 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 12: +Training Loss: 490.1203 +Validation Loss: 46.3968 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 13: +Training Loss: 476.9148 +Validation Loss: 46.0837 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 14: +Training Loss: 450.4372 +Validation Loss: 45.6722 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 15: +Training Loss: 474.3010 +Validation Loss: 47.8102 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 16: +Training Loss: 414.0887 +Validation Loss: 44.4694 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 17: +Training Loss: 449.6896 +Validation Loss: 44.2356 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 18: +Training Loss: 442.9535 +Validation Loss: 44.9362 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 19: +Training Loss: 433.9990 +Validation Loss: 43.5642 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 20: +Training Loss: 437.0011 +Validation Loss: 43.2742 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 21: +Training Loss: 464.7456 +Validation Loss: 43.8316 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 22: +Training Loss: 447.5724 +Validation Loss: 42.5235 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 23: +Training Loss: 435.1757 +Validation Loss: 42.2724 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 24: +Training Loss: 453.4024 +Validation Loss: 42.1280 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 25: +Training Loss: 427.6428 +Validation Loss: 42.5536 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 26: +Training Loss: 432.1302 +Validation Loss: 42.0983 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 27: +Training Loss: 480.2785 +Validation Loss: 44.5301 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 28: +Training Loss: 412.1119 +Validation Loss: 41.4286 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 29: +Training Loss: 424.3875 +Validation Loss: 41.4325 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 30: +Training Loss: 472.1849 +Validation Loss: 41.5601 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 31: +Training Loss: 435.4905 +Validation Loss: 41.3207 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 32: +Training Loss: 401.2908 +Validation Loss: 42.2697 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 33: +Training Loss: 437.7576 +Validation Loss: 41.4233 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 34: +Training Loss: 407.7494 +Validation Loss: 41.5484 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 35: +Training Loss: 413.9973 +Validation Loss: 41.4061 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 36: +Training Loss: 424.6587 +Validation Loss: 41.9808 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 37: +Training Loss: 414.6968 +Validation Loss: 41.4738 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 38: +Training Loss: 423.6708 +Validation Loss: 41.7256 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 39: +Training Loss: 398.2901 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+Validation Loss: 42.3798 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 45: +Training Loss: 408.1400 +Validation Loss: 41.2615 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 46: +Training Loss: 432.6147 +Validation Loss: 40.4372 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 47: +Training Loss: 417.3497 +Validation Loss: 40.4856 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 48: +Training Loss: 444.9492 +Validation Loss: 41.4138 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 49: +Training Loss: 409.8877 +Validation Loss: 40.2679 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 50: +Training Loss: 446.7479 +Validation Loss: 40.5887 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 51: +Training Loss: 383.7144 +Validation Loss: 39.7706 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 52: +Training Loss: 428.5482 +Validation Loss: 40.7180 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 53: +Training Loss: 406.0448 +Validation Loss: 40.6707 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 54: +Training Loss: 402.3874 +Validation Loss: 40.8555 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 55: +Training Loss: 412.2114 +Validation Loss: 40.6856 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 56: +Training Loss: 433.1100 +Validation Loss: 39.6422 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 57: +Training Loss: 405.9767 +Validation Loss: 39.9082 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 58: +Training Loss: 401.6873 +Validation Loss: 41.4500 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 59: +Training Loss: 440.4744 +Validation Loss: 39.4759 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 60: +Training Loss: 402.9340 +Validation Loss: 40.3698 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 61: +Training Loss: 394.6159 +Validation Loss: 39.9131 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 62: +Training Loss: 413.6928 +Validation Loss: 39.2001 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 63: +Training Loss: 372.6091 +Validation Loss: 39.1241 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 64: +Training Loss: 405.1531 +Validation Loss: 40.2472 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 65: +Training Loss: 433.3568 +Validation Loss: 40.0602 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 66: +Training Loss: 395.7006 +Validation Loss: 39.2638 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 67: +Training Loss: 404.3562 +Validation Loss: 39.7865 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 68: +Training Loss: 422.5988 +Validation Loss: 39.9558 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 69: +Training Loss: 423.3157 +Validation Loss: 38.4184 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 70: +Training Loss: 406.2187 +Validation Loss: 39.7745 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 71: +Training Loss: 390.3179 +Validation Loss: 39.1637 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 72: +Training Loss: 406.3099 +Validation Loss: 40.0285 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 73: +Training Loss: 378.1436 +Validation Loss: 39.2064 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 74: +Training Loss: 429.5101 +Validation Loss: 39.2701 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 75: +Training Loss: 449.1831 +Validation Loss: 38.2638 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 76: +Training Loss: 377.5275 +Validation Loss: 37.9939 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 77: +Training Loss: 379.3162 +Validation Loss: 38.4385 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 78: +Training Loss: 411.2072 +Validation Loss: 38.2817 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 79: +Training Loss: 370.2878 +Validation Loss: 38.9934 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 80: +Training Loss: 408.7027 +Validation Loss: 40.0633 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 81: +Training Loss: 380.3952 +Validation Loss: 44.7046 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 82: +Training Loss: 390.5510 +Validation Loss: 43.5543 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 83: +Training Loss: 414.7566 +Validation Loss: 39.6858 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 84: +Training Loss: 383.6034 +Validation Loss: 37.8962 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 85: +Training Loss: 410.3260 +Validation Loss: 38.8076 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 86: +Training Loss: 384.7753 +Validation Loss: 38.3196 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 87: +Training Loss: 369.7865 +Validation Loss: 38.3517 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 88: +Training Loss: 358.6980 +Validation Loss: 39.3314 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 89: +Training Loss: 388.8395 +Validation Loss: 43.0675 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 90: +Training Loss: 378.8118 +Validation Loss: 40.9924 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 91: +Training Loss: 377.6806 +Validation Loss: 37.7347 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 92: +Training Loss: 373.2643 +Validation Loss: 39.6839 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 93: +Training Loss: 399.9760 +Validation Loss: 44.7459 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 94: +Training Loss: 387.5985 +Validation Loss: 43.1377 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 95: +Training Loss: 381.6720 +Validation Loss: 43.0584 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 96: +Training Loss: 373.9655 +Validation Loss: 41.0786 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 97: +Training Loss: 365.2250 +Validation Loss: 41.6452 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 98: +Training Loss: 371.3945 +Validation Loss: 43.0737 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 99: +Training Loss: 407.2197 +Validation Loss: 41.6614 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 100: +Training Loss: 361.1463 +Validation Loss: 40.3552 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 101: +Training Loss: 362.4450 +Validation Loss: 37.8338 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 102: +Training Loss: 380.6826 +Validation Loss: 37.9108 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 103: +Training Loss: 393.5805 +Validation Loss: 38.0321 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 104: +Training Loss: 373.3692 +Validation Loss: 41.3798 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 105: +Training Loss: 370.0417 +Validation Loss: 43.1876 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 106: +Training Loss: 371.3428 +Validation Loss: 37.6458 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 107: +Training Loss: 393.4808 +Validation Loss: 37.4838 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 108: +Training Loss: 341.0202 +Validation Loss: 38.4826 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 109: +Training Loss: 340.4777 +Validation Loss: 36.6492 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 110: +Training Loss: 408.0117 +Validation Loss: 38.0041 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 111: +Training Loss: 352.2764 +Validation Loss: 39.8200 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 112: +Training Loss: 396.6479 +Validation Loss: 41.2426 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 113: +Training Loss: 364.0531 +Validation Loss: 45.7465 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 114: +Training Loss: 359.4698 +Validation Loss: 39.0819 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 115: +Training Loss: 344.4517 +Validation Loss: 38.0695 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 116: +Training Loss: 382.4421 +Validation Loss: 43.1342 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 117: +Training Loss: 348.0633 +Validation Loss: 44.4846 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 118: +Training Loss: 352.4962 +Validation Loss: 47.6455 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 119: +Training Loss: 327.3615 +Validation Loss: 48.8107 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 120: +Training Loss: 342.7624 +Validation Loss: 53.2235 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 121: +Training Loss: 330.3742 +Validation Loss: 48.6187 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 122: +Training Loss: 335.7721 +Validation Loss: 46.2577 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 123: +Training Loss: 350.4416 +Validation Loss: 44.0014 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 124: +Training Loss: 345.3304 +Validation Loss: 43.3682 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 125: +Training Loss: 320.4443 +Validation Loss: 45.2552 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 126: +Training Loss: 364.6622 +Validation Loss: 41.1728 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 127: +Training Loss: 348.3229 +Validation Loss: 42.2310 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 128: +Training Loss: 334.6038 +Validation Loss: 45.0659 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 129: +Training Loss: 344.0521 +Validation Loss: 47.6869 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 130: +Training Loss: 350.9794 +Validation Loss: 45.1502 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 131: +Training Loss: 311.2683 +Validation Loss: 48.7419 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 132: +Training Loss: 354.3963 +Validation Loss: 48.1167 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 133: +Training Loss: 325.7923 +Validation Loss: 43.2832 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 134: +Training Loss: 335.1767 +Validation Loss: 45.8797 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 135: +Training Loss: 318.5661 +Validation Loss: 41.6276 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 136: +Training Loss: 300.8984 +Validation Loss: 44.7698 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 137: +Training Loss: 341.4717 +Validation Loss: 46.1124 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 138: +Training Loss: 348.6719 +Validation Loss: 45.7112 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 139: +Training Loss: 335.2446 +Validation Loss: 43.2217 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 140: +Training Loss: 349.5773 +Validation Loss: 42.4639 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 141: +Training Loss: 329.9153 +Validation Loss: 45.1095 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 142: +Training Loss: 317.3975 +Validation Loss: 40.6804 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 143: +Training Loss: 325.5324 +Validation Loss: 43.9176 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 144: +Training Loss: 328.3457 +Validation Loss: 42.0196 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 145: +Training Loss: 341.1636 +Validation Loss: 42.5722 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 146: +Training Loss: 317.3863 +Validation Loss: 43.2947 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 147: +Training Loss: 303.3592 +Validation Loss: 42.8713 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 148: +Training Loss: 331.5470 +Validation Loss: 48.4936 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 149: +Training Loss: 321.0771 +Validation Loss: 48.4608 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 150: +Training Loss: 298.0859 +Validation Loss: 48.6276 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 151: +Training Loss: 308.9102 +Validation Loss: 48.8042 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 152: +Training Loss: 312.2853 +Validation Loss: 47.8387 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 153: +Training Loss: 306.5447 +Validation Loss: 42.2924 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 154: +Training Loss: 291.3444 +Validation Loss: 50.5676 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 155: +Training Loss: 322.1136 +Validation Loss: 54.2589 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 156: +Training Loss: 339.7329 +Validation Loss: 52.7504 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 157: +Training Loss: 328.7673 +Validation Loss: 47.5365 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 158: +Training Loss: 309.1129 +Validation Loss: 42.2746 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 159: +Training Loss: 307.6238 +Validation Loss: 44.5000 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 160: +Training Loss: 296.6082 +Validation Loss: 42.2880 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 161: +Training Loss: 329.1464 +Validation Loss: 43.8280 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 162: +Training Loss: 309.6638 +Validation Loss: 49.9770 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 163: +Training Loss: 332.6188 +Validation Loss: 43.2149 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 164: +Training Loss: 299.1682 +Validation Loss: 44.7324 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 165: +Training Loss: 291.3634 +Validation Loss: 47.6954 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 166: +Training Loss: 298.1045 +Validation Loss: 46.0703 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 167: +Training Loss: 284.3934 +Validation Loss: 48.6241 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 168: +Training Loss: 310.8657 +Validation Loss: 47.1198 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 169: +Training Loss: 306.4615 +Validation Loss: 47.0508 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 170: +Training Loss: 290.3956 +Validation Loss: 44.7296 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 171: +Training Loss: 278.7064 +Validation Loss: 45.6360 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 172: +Training Loss: 302.7458 +Validation Loss: 45.6257 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 173: +Training Loss: 309.4234 +Validation Loss: 43.8915 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 174: +Training Loss: 299.1551 +Validation Loss: 43.3367 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 175: +Training Loss: 290.4320 +Validation Loss: 47.2839 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 176: +Training Loss: 274.5516 +Validation Loss: 45.5179 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 177: +Training Loss: 303.5097 +Validation Loss: 44.7490 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 178: +Training Loss: 300.2766 +Validation Loss: 45.6268 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 179: +Training Loss: 313.8955 +Validation Loss: 45.2599 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 180: +Training Loss: 306.8194 +Validation Loss: 40.2398 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 181: +Training Loss: 293.3960 +Validation Loss: 40.7054 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 182: +Training Loss: 293.0694 +Validation Loss: 41.2617 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 183: +Training Loss: 292.1090 +Validation Loss: 42.9014 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 184: +Training Loss: 290.9123 +Validation Loss: 43.9569 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 185: +Training Loss: 305.7741 +Validation Loss: 43.5844 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 186: +Training Loss: 285.8270 +Validation Loss: 41.9449 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 187: +Training Loss: 291.5104 +Validation Loss: 41.7679 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 188: +Training Loss: 300.2455 +Validation Loss: 47.4318 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 189: +Training Loss: 294.4551 +Validation Loss: 42.0467 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 190: +Training Loss: 297.4566 +Validation Loss: 56.9218 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 2.00it/s] + +Epoch 191: +Training Loss: 301.0592 +Validation Loss: 49.4716 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 192: +Training Loss: 287.1178 +Validation Loss: 44.8864 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 193: +Training Loss: 298.8588 +Validation Loss: 41.2586 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 194: +Training Loss: 303.2215 +Validation Loss: 41.4525 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 195: +Training Loss: 295.1567 +Validation Loss: 44.6658 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 196: +Training Loss: 265.7792 +Validation Loss: 41.2527 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 197: +Training Loss: 264.6069 +Validation Loss: 41.7259 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 198: +Training Loss: 284.4422 +Validation Loss: 46.2090 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 199: +Training Loss: 289.4343 +Validation Loss: 44.8574 +100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 200: +Training Loss: 300.9736 +Validation Loss: 45.4526 + +Model trained and saved successfully! diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_221832-okw7jof8/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_221832-okw7jof8/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..993042d1e5459219a2f8e1ecb06664b577d212a8 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_221832-okw7jof8/files/requirements.txt @@ -0,0 +1,162 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250409_221832-s8edjhi0/files/requirements.txt @@ -0,0 +1,162 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 +eval_type_backport==0.2.2 +docker-pycreds==0.4.0 +annotated-types==0.7.0 +pydantic==2.10.5 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Training set size: 20273 +Validation set size: 1664 +Model parameters: 92465 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] +Epoch 1: +Training Loss: 58785.7194 +Validation Loss: 655.8146 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:09<00:00, 2.05it/s] + +Epoch 2: +Training Loss: 2307.3913 +Validation Loss: 244.9171 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:09<00:00, 2.05it/s] + +Epoch 3: +Training Loss: 1158.0137 +Validation Loss: 123.2353 + 20%|██████████████ | 4/20 [00:02<00:08, 1.83it/s] diff --git 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:09<00:00, 2.06it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:09<00:00, 2.05it/s] + 20%|██████████████ | 4/20 [00:02<00:08, 1.83it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221815-2ab4bmjw/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221815-2ab4bmjw/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:09<00:00, 2.06it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:09<00:00, 2.06it/s] + 20%|██████████████ | 4/20 [00:02<00:08, 1.83it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221815-5cqmdrvr/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221815-5cqmdrvr/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221815-aj143cxx/files/output.log @@ -0,0 +1,10 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:09<00:00, 2.06it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:09<00:00, 2.06it/s] + 20%|██████████████ | 4/20 [00:02<00:08, 1.83it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221815-aj143cxx/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221815-aj143cxx/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 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+nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221944-la18xf9v/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221944-la18xf9v/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..8811ffd4863172a658111e02100c16b4002fc274 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221944-la18xf9v/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 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+nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 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+googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 +eval_type_backport==0.2.2 +docker-pycreds==0.4.0 +annotated-types==0.7.0 +pydantic==2.10.5 +huggingface-hub==0.27.1 +gitdb==4.0.12 +GitPython==3.1.44 +wandb==0.19.3 +accelerate==1.0.1 +tifffile==2023.7.10 +PyWavelets==1.4.1 +lazy_loader==0.4 +imageio==2.35.1 +scikit-image==0.21.0 +pytorch_ssim==0.1 +fiona==1.9.5 +patsy==1.0.1 +statsmodels==0.14.1 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221944-mpzrrke7/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221944-mpzrrke7/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..2635eb96da8605832dcc115b256b62862c7a9794 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221944-mpzrrke7/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] +100%|█████████████████████████████████████████████████████████████████████| 20/20 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+nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 +eval_type_backport==0.2.2 +docker-pycreds==0.4.0 +annotated-types==0.7.0 +pydantic==2.10.5 +huggingface-hub==0.27.1 +gitdb==4.0.12 +GitPython==3.1.44 +wandb==0.19.3 +accelerate==1.0.1 +tifffile==2023.7.10 +PyWavelets==1.4.1 +lazy_loader==0.4 +imageio==2.35.1 +scikit-image==0.21.0 +pytorch_ssim==0.1 +fiona==1.9.5 +patsy==1.0.1 +statsmodels==0.14.1 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221944-owot104h/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221944-owot104h/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..8a377b8f9b6c4f185bdfe4dba7c4cceb2f14167c --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221944-owot104h/files/output.log @@ -0,0 +1,1010 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Training set size: 20273 +Validation set size: 1664 +Model parameters: 92465 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +Epoch 1: +Training Loss: 46869.9135 +Validation Loss: 226.0885 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 2: +Training Loss: 1178.4702 +Validation Loss: 88.4639 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 3: +Training Loss: 773.6104 +Validation Loss: 71.6896 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 4: +Training Loss: 642.0584 +Validation Loss: 60.4622 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 5: +Training Loss: 560.2775 +Validation Loss: 55.8328 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 6: +Training Loss: 523.6697 +Validation Loss: 53.0253 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 7: +Training Loss: 531.6694 +Validation Loss: 51.3792 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 8: +Training Loss: 515.4989 +Validation Loss: 49.8620 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 9: +Training Loss: 486.9975 +Validation Loss: 48.4857 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 10: +Training Loss: 481.9375 +Validation Loss: 47.1160 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 11: +Training Loss: 468.9247 +Validation Loss: 46.5152 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 12: +Training Loss: 483.6235 +Validation Loss: 45.8803 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 13: +Training Loss: 487.6613 +Validation Loss: 46.2409 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 14: +Training Loss: 472.8397 +Validation Loss: 45.9279 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 15: +Training Loss: 438.5983 +Validation Loss: 45.9090 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 16: +Training Loss: 465.3272 +Validation Loss: 45.8714 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 17: +Training Loss: 429.3381 +Validation Loss: 45.6636 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 18: +Training Loss: 433.6377 +Validation Loss: 44.7928 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 19: +Training Loss: 436.6299 +Validation Loss: 45.5840 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 20: +Training Loss: 438.3166 +Validation Loss: 45.3138 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 21: +Training Loss: 438.3987 +Validation Loss: 44.9987 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 22: +Training Loss: 472.5794 +Validation Loss: 44.2467 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 23: +Training Loss: 444.5253 +Validation Loss: 45.2056 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 24: +Training Loss: 439.5961 +Validation Loss: 44.3615 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 25: +Training Loss: 458.4188 +Validation Loss: 43.3402 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 26: +Training Loss: 444.2119 +Validation Loss: 45.5686 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 27: +Training Loss: 446.7647 +Validation Loss: 44.6344 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 28: +Training Loss: 501.0855 +Validation Loss: 45.7731 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 29: +Training Loss: 431.0203 +Validation Loss: 46.0893 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 30: +Training Loss: 433.3136 +Validation Loss: 43.0311 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 31: +Training Loss: 474.5550 +Validation Loss: 41.8725 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 32: +Training Loss: 444.5001 +Validation Loss: 42.1183 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 33: +Training Loss: 423.4202 +Validation Loss: 41.9497 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 34: +Training Loss: 446.4854 +Validation Loss: 41.3696 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 35: +Training Loss: 418.7996 +Validation Loss: 43.4791 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 36: +Training Loss: 420.6692 +Validation Loss: 43.1230 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 37: +Training Loss: 428.0750 +Validation Loss: 42.7181 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 38: +Training Loss: 415.5716 +Validation Loss: 44.9812 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 39: +Training Loss: 437.2808 +Validation Loss: 42.0593 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 40: +Training Loss: 410.8620 +Validation Loss: 43.3493 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 41: +Training Loss: 409.0263 +Validation Loss: 43.5543 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 42: +Training Loss: 428.5893 +Validation Loss: 41.8915 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 43: +Training Loss: 429.3985 +Validation Loss: 42.5766 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 44: +Training Loss: 464.8439 +Validation Loss: 43.8588 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 45: +Training Loss: 441.5916 +Validation Loss: 42.7127 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 46: +Training Loss: 420.2946 +Validation Loss: 41.4720 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 47: +Training Loss: 438.3635 +Validation Loss: 43.1823 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 48: +Training Loss: 434.4342 +Validation Loss: 41.8698 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 49: +Training Loss: 447.8103 +Validation Loss: 41.5096 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 50: +Training Loss: 425.1762 +Validation Loss: 41.2654 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 51: +Training Loss: 441.0154 +Validation Loss: 41.1159 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 52: +Training Loss: 394.5368 +Validation Loss: 41.3581 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 53: +Training Loss: 446.9926 +Validation Loss: 40.3808 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 54: +Training Loss: 421.2735 +Validation Loss: 40.5836 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 55: +Training Loss: 398.3863 +Validation Loss: 40.5672 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 56: +Training Loss: 423.6196 +Validation Loss: 40.8597 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 57: +Training Loss: 436.6053 +Validation Loss: 40.3708 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 58: +Training Loss: 408.8143 +Validation Loss: 39.7908 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 59: +Training Loss: 404.4864 +Validation Loss: 41.0286 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 60: +Training Loss: 455.5149 +Validation Loss: 40.8818 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 61: +Training Loss: 406.0170 +Validation Loss: 40.2930 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 62: +Training Loss: 416.0039 +Validation Loss: 39.6747 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 63: +Training Loss: 426.6832 +Validation Loss: 39.7842 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 64: +Training Loss: 387.3880 +Validation Loss: 39.6257 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 65: +Training Loss: 414.3128 +Validation Loss: 39.4050 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 66: +Training Loss: 451.7621 +Validation Loss: 40.0160 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 67: +Training Loss: 401.3750 +Validation Loss: 39.4243 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 68: +Training Loss: 420.9055 +Validation Loss: 39.4517 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 69: +Training Loss: 432.8830 +Validation Loss: 39.2355 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 70: +Training Loss: 432.7063 +Validation Loss: 38.8654 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 71: +Training Loss: 416.5584 +Validation Loss: 39.4237 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 72: +Training Loss: 403.0287 +Validation Loss: 38.5761 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 73: +Training Loss: 414.7473 +Validation Loss: 38.9275 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 74: +Training Loss: 385.5247 +Validation Loss: 38.9734 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 75: +Training Loss: 446.5270 +Validation Loss: 39.2077 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 76: +Training Loss: 461.0766 +Validation Loss: 38.7879 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 77: +Training Loss: 385.6830 +Validation Loss: 38.9781 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 78: +Training Loss: 400.2218 +Validation Loss: 37.5599 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 79: +Training Loss: 434.8260 +Validation Loss: 38.3786 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 80: +Training Loss: 383.8297 +Validation Loss: 39.4737 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 81: +Training Loss: 412.3251 +Validation Loss: 39.6117 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 82: +Training Loss: 396.0553 +Validation Loss: 41.2519 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 83: +Training Loss: 400.3054 +Validation Loss: 41.5329 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 84: +Training Loss: 430.9702 +Validation Loss: 37.8255 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 85: +Training Loss: 398.4261 +Validation Loss: 38.5922 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 86: +Training Loss: 411.4290 +Validation Loss: 41.3491 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 87: +Training Loss: 392.3670 +Validation Loss: 41.3071 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 88: +Training Loss: 381.9396 +Validation Loss: 39.6791 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 89: +Training Loss: 370.5965 +Validation Loss: 39.3239 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 90: +Training Loss: 403.2394 +Validation Loss: 40.1733 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 91: +Training Loss: 385.9417 +Validation Loss: 38.9733 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 92: +Training Loss: 380.4425 +Validation Loss: 37.2927 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 93: +Training Loss: 385.7686 +Validation Loss: 39.9347 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 94: +Training Loss: 402.6812 +Validation Loss: 40.1406 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 95: +Training Loss: 391.7234 +Validation Loss: 39.2990 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 96: +Training Loss: 385.1753 +Validation Loss: 40.9461 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 97: +Training Loss: 368.8194 +Validation Loss: 41.6622 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 98: +Training Loss: 380.3211 +Validation Loss: 40.0721 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 99: +Training Loss: 375.8670 +Validation Loss: 39.9720 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 100: +Training Loss: 400.3646 +Validation Loss: 42.5040 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 101: +Training Loss: 369.8092 +Validation Loss: 43.1868 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 102: +Training Loss: 370.8722 +Validation Loss: 39.3809 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 103: +Training Loss: 375.4742 +Validation Loss: 40.7011 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 104: +Training Loss: 386.6384 +Validation Loss: 41.0099 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 105: +Training Loss: 365.5687 +Validation Loss: 42.3734 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 106: +Training Loss: 361.0364 +Validation Loss: 44.8950 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 107: +Training Loss: 364.8025 +Validation Loss: 41.8026 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 108: +Training Loss: 386.7776 +Validation Loss: 40.4049 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 109: +Training Loss: 338.5838 +Validation Loss: 41.3779 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 110: +Training Loss: 346.5245 +Validation Loss: 41.7811 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 111: +Training Loss: 406.8473 +Validation Loss: 42.1055 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 112: +Training Loss: 339.2159 +Validation Loss: 42.8791 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 113: +Training Loss: 373.0225 +Validation Loss: 45.7936 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 114: +Training Loss: 363.1901 +Validation Loss: 49.2027 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 115: +Training Loss: 353.4546 +Validation Loss: 47.0709 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 116: +Training Loss: 324.1306 +Validation Loss: 47.7121 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 117: +Training Loss: 372.5934 +Validation Loss: 51.1184 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 118: +Training Loss: 346.5437 +Validation Loss: 47.5375 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 119: +Training Loss: 354.0857 +Validation Loss: 48.5225 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 120: +Training Loss: 322.8645 +Validation Loss: 50.3169 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 121: +Training Loss: 343.1494 +Validation Loss: 54.4861 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 122: +Training Loss: 340.7891 +Validation Loss: 47.4770 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 123: +Training Loss: 352.5436 +Validation Loss: 46.0816 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 124: +Training Loss: 357.8404 +Validation Loss: 45.0322 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 125: +Training Loss: 370.6613 +Validation Loss: 45.9997 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 126: +Training Loss: 331.4685 +Validation Loss: 45.2789 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 127: +Training Loss: 364.1758 +Validation Loss: 48.7754 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 128: +Training Loss: 351.4122 +Validation Loss: 48.0717 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 129: +Training Loss: 339.9916 +Validation Loss: 46.3991 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 130: +Training Loss: 350.9138 +Validation Loss: 47.4516 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 131: +Training Loss: 355.1289 +Validation Loss: 48.4844 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 132: +Training Loss: 328.0050 +Validation Loss: 48.4224 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 133: +Training Loss: 372.7904 +Validation Loss: 48.1736 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 134: +Training Loss: 345.7974 +Validation Loss: 44.2784 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 135: +Training Loss: 369.8072 +Validation Loss: 46.9035 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 136: +Training Loss: 345.2680 +Validation Loss: 48.4627 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 137: +Training Loss: 306.1863 +Validation Loss: 46.9448 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 138: +Training Loss: 350.6802 +Validation Loss: 46.9031 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 139: +Training Loss: 354.5749 +Validation Loss: 48.9365 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 140: +Training Loss: 349.9033 +Validation Loss: 53.9462 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 141: +Training Loss: 356.9339 +Validation Loss: 47.7763 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 142: +Training Loss: 335.0111 +Validation Loss: 51.2843 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 143: +Training Loss: 344.3599 +Validation Loss: 45.2450 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 144: +Training Loss: 334.6493 +Validation Loss: 47.1868 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 145: +Training Loss: 332.3798 +Validation Loss: 53.4584 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 146: +Training Loss: 346.7607 +Validation Loss: 51.3329 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 147: +Training Loss: 327.2786 +Validation Loss: 49.6826 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 148: +Training Loss: 313.7225 +Validation Loss: 50.1063 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 149: +Training Loss: 346.5019 +Validation Loss: 58.0549 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 150: +Training Loss: 341.2648 +Validation Loss: 50.5859 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 151: +Training Loss: 320.6766 +Validation Loss: 54.3834 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 152: +Training Loss: 321.8624 +Validation Loss: 56.1838 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 153: +Training Loss: 326.6646 +Validation Loss: 52.6955 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 154: +Training Loss: 329.4572 +Validation Loss: 51.6581 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 155: +Training Loss: 297.2274 +Validation Loss: 50.5096 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 156: +Training Loss: 340.5859 +Validation Loss: 55.5594 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 157: +Training Loss: 345.3819 +Validation Loss: 46.5702 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 158: +Training Loss: 335.5712 +Validation Loss: 47.9376 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 159: +Training Loss: 306.3297 +Validation Loss: 49.0124 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 160: +Training Loss: 316.8390 +Validation Loss: 51.3026 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 161: +Training Loss: 325.3515 +Validation Loss: 45.8514 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 162: +Training Loss: 357.5618 +Validation Loss: 48.5386 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 163: +Training Loss: 327.6543 +Validation Loss: 50.7110 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 164: +Training Loss: 334.8971 +Validation Loss: 50.6298 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 165: +Training Loss: 322.7853 +Validation Loss: 46.5522 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 166: +Training Loss: 324.8984 +Validation Loss: 50.6881 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 167: +Training Loss: 316.6188 +Validation Loss: 50.0803 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 168: +Training Loss: 297.0901 +Validation Loss: 46.9522 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 169: +Training Loss: 340.6026 +Validation Loss: 46.7724 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 170: +Training Loss: 321.4773 +Validation Loss: 49.0577 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 171: +Training Loss: 302.9223 +Validation Loss: 57.5669 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 172: +Training Loss: 302.6258 +Validation Loss: 54.0445 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 173: +Training Loss: 315.9130 +Validation Loss: 47.1944 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 174: +Training Loss: 321.1230 +Validation Loss: 50.0426 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 175: +Training Loss: 311.8109 +Validation Loss: 52.7463 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 176: +Training Loss: 308.9194 +Validation Loss: 56.7932 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 177: +Training Loss: 290.6860 +Validation Loss: 57.4485 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 178: +Training Loss: 327.6233 +Validation Loss: 49.9625 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 179: +Training Loss: 314.4842 +Validation Loss: 46.1500 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.95it/s] + +Epoch 180: +Training Loss: 340.1116 +Validation Loss: 47.8214 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.96it/s] + +Epoch 181: +Training Loss: 328.0236 +Validation Loss: 46.9886 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294.7864 +Validation Loss: 50.4679 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 188: +Training Loss: 315.2116 +Validation Loss: 48.7534 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 189: +Training Loss: 319.7235 +Validation Loss: 47.0528 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 190: +Training Loss: 304.5118 +Validation Loss: 49.2814 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 191: +Training Loss: 315.8951 +Validation Loss: 55.8702 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 192: +Training Loss: 338.0640 +Validation Loss: 52.8338 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 193: +Training Loss: 307.0729 +Validation Loss: 49.6416 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 194: +Training Loss: 324.9635 +Validation Loss: 48.2898 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 195: +Training Loss: 310.8776 +Validation Loss: 45.8666 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 196: +Training Loss: 308.4145 +Validation Loss: 47.9535 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 197: +Training Loss: 294.8562 +Validation Loss: 45.7408 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 198: +Training Loss: 307.2351 +Validation Loss: 49.7319 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 199: +Training Loss: 303.5895 +Validation Loss: 57.0424 +100%|█████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 200: +Training Loss: 298.8998 +Validation Loss: 55.4738 + +Best model saved with R²: 0.1990 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221944-owot104h/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221944-owot104h/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250412_221944-owot104h/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 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"2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_171147-6bdmxpva/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_171147-6bdmxpva/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..7b24210cfb968de1f296d1b9dcdb5ccb645dd737 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_171147-6bdmxpva/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.67it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_171147-6bdmxpva/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_171147-6bdmxpva/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 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200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: composite_l2 +max_oc: + value: 150 +target_transform: + value: log +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_171147-7rpmjcju/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_171147-7rpmjcju/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..36292d12e22217a568c684040324915e59c173ee --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_171147-7rpmjcju/files/output.log @@ -0,0 +1,1010 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.67it/s] +Epoch 1: +Training Loss: 79998.1612 +Validation Loss: 364.2924 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 2: +Training Loss: 3179.2067 +Validation Loss: 118.2743 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 3: +Training Loss: 1298.1428 +Validation Loss: 41.1160 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 4: +Training Loss: 867.8446 +Validation Loss: 22.7085 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 5: +Training Loss: 607.9270 +Validation Loss: 15.1487 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 6: +Training Loss: 447.6609 +Validation Loss: 11.0444 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 7: +Training Loss: 349.7652 +Validation Loss: 9.1354 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 8: +Training Loss: 263.0461 +Validation Loss: 6.9756 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 9: +Training Loss: 223.2303 +Validation Loss: 5.6920 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 10: +Training Loss: 179.1831 +Validation Loss: 4.9686 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 11: +Training Loss: 146.8697 +Validation Loss: 4.1753 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 12: +Training Loss: 117.0584 +Validation Loss: 3.6188 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 13: +Training Loss: 110.4277 +Validation Loss: 3.5101 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 14: +Training Loss: 87.3086 +Validation Loss: 3.4655 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 15: +Training Loss: 73.0022 +Validation Loss: 3.4874 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 16: +Training Loss: 65.3799 +Validation Loss: 3.2981 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 17: +Training Loss: 63.6256 +Validation Loss: 3.2031 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 18: +Training Loss: 54.0524 +Validation Loss: 3.1034 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 19: +Training Loss: 48.5075 +Validation Loss: 3.0417 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 20: +Training Loss: 40.9819 +Validation Loss: 2.8207 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 21: +Training Loss: 41.6482 +Validation Loss: 2.6091 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 22: +Training Loss: 34.6864 +Validation Loss: 2.3869 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 23: +Training Loss: 32.4023 +Validation Loss: 2.2697 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 24: +Training Loss: 29.2862 +Validation Loss: 2.0823 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 25: +Training Loss: 28.6937 +Validation Loss: 2.0650 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 26: +Training Loss: 26.4872 +Validation Loss: 1.9148 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 27: +Training Loss: 25.8287 +Validation Loss: 1.8881 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 28: +Training Loss: 20.5726 +Validation Loss: 1.7113 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 29: +Training Loss: 18.3977 +Validation Loss: 1.7732 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 30: +Training Loss: 17.8267 +Validation Loss: 1.6143 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 31: +Training Loss: 15.5508 +Validation Loss: 1.5584 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 32: +Training Loss: 16.9677 +Validation Loss: 1.5510 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 33: +Training Loss: 15.9039 +Validation Loss: 1.4388 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 34: +Training Loss: 13.9205 +Validation Loss: 1.6031 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 35: +Training Loss: 13.4663 +Validation Loss: 1.4208 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 36: +Training Loss: 12.7411 +Validation Loss: 1.4301 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 37: +Training Loss: 11.1191 +Validation Loss: 1.2567 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 38: +Training Loss: 12.2645 +Validation Loss: 1.4002 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 39: +Training Loss: 9.8594 +Validation Loss: 1.3668 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 40: +Training Loss: 9.9400 +Validation Loss: 1.3154 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 41: +Training Loss: 9.2727 +Validation Loss: 1.2353 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 42: +Training Loss: 9.4175 +Validation Loss: 1.3340 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 43: +Training Loss: 10.0952 +Validation Loss: 1.1466 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 44: +Training Loss: 8.7396 +Validation Loss: 1.2371 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 45: +Training Loss: 8.4373 +Validation Loss: 1.1550 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 46: +Training Loss: 7.4013 +Validation Loss: 1.1980 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 47: +Training Loss: 6.0759 +Validation Loss: 1.1142 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 48: +Training Loss: 7.1875 +Validation Loss: 1.0934 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 49: +Training Loss: 6.9739 +Validation Loss: 1.1416 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 50: +Training Loss: 7.5254 +Validation Loss: 1.1224 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 51: +Training Loss: 6.0448 +Validation Loss: 1.0604 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 52: +Training Loss: 6.4806 +Validation Loss: 1.0722 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 53: +Training Loss: 6.7739 +Validation Loss: 1.0707 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 54: +Training Loss: 6.7624 +Validation Loss: 1.0945 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 55: +Training Loss: 7.1013 +Validation Loss: 1.0368 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 56: +Training Loss: 6.4765 +Validation Loss: 1.0598 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 57: +Training Loss: 6.1440 +Validation Loss: 1.0052 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 58: +Training Loss: 4.9928 +Validation Loss: 1.0468 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 59: +Training Loss: 6.0583 +Validation Loss: 1.0056 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 60: +Training Loss: 5.2910 +Validation Loss: 0.9941 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 61: +Training Loss: 5.2506 +Validation Loss: 1.0400 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 62: +Training Loss: 5.1315 +Validation Loss: 1.0005 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 63: +Training Loss: 6.0354 +Validation Loss: 1.0149 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 64: +Training Loss: 5.3934 +Validation Loss: 1.0595 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 65: +Training Loss: 5.5677 +Validation Loss: 1.0223 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 66: +Training Loss: 4.7834 +Validation Loss: 0.9729 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 67: +Training Loss: 4.5243 +Validation Loss: 0.9614 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 68: +Training Loss: 5.0250 +Validation Loss: 0.9247 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 69: +Training Loss: 4.5116 +Validation Loss: 0.9644 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 70: +Training Loss: 4.5772 +Validation Loss: 0.8976 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 71: +Training Loss: 4.2172 +Validation Loss: 0.9725 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 72: +Training Loss: 3.9000 +Validation Loss: 0.8999 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 73: +Training Loss: 5.3613 +Validation Loss: 0.8781 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 74: +Training Loss: 3.7050 +Validation Loss: 0.9085 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 75: +Training Loss: 3.8338 +Validation Loss: 1.0005 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 76: +Training Loss: 4.1751 +Validation Loss: 0.9367 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 77: +Training Loss: 3.9934 +Validation Loss: 0.9617 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 78: +Training Loss: 3.7414 +Validation Loss: 0.9810 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 79: +Training Loss: 4.3614 +Validation Loss: 0.9095 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 80: +Training Loss: 3.8525 +Validation Loss: 0.9420 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 81: +Training Loss: 3.9176 +Validation Loss: 0.9289 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 82: +Training Loss: 3.9356 +Validation Loss: 0.8779 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 83: +Training Loss: 3.1198 +Validation Loss: 0.9264 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 84: +Training Loss: 4.0093 +Validation Loss: 0.9102 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 85: +Training Loss: 3.3361 +Validation Loss: 0.9783 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 86: +Training Loss: 3.4150 +Validation Loss: 0.8722 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 87: +Training Loss: 4.0796 +Validation Loss: 0.9109 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 88: +Training Loss: 4.5081 +Validation Loss: 0.9753 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 89: +Training Loss: 3.4520 +Validation Loss: 0.9505 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 90: +Training Loss: 3.1730 +Validation Loss: 0.8497 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 91: +Training Loss: 3.2990 +Validation Loss: 0.9565 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 92: +Training Loss: 3.3579 +Validation Loss: 0.8267 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 93: +Training Loss: 4.3075 +Validation Loss: 0.9385 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 94: +Training Loss: 4.4805 +Validation Loss: 0.9292 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 95: +Training Loss: 3.5713 +Validation Loss: 0.9351 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 96: +Training Loss: 3.1314 +Validation Loss: 0.8522 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 97: +Training Loss: 3.2784 +Validation Loss: 0.9137 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 98: +Training Loss: 3.1940 +Validation Loss: 0.9028 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 99: +Training Loss: 2.9425 +Validation Loss: 0.8337 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 100: +Training Loss: 3.3139 +Validation Loss: 1.0035 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 101: +Training Loss: 3.5844 +Validation Loss: 0.9457 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 102: +Training Loss: 3.0506 +Validation Loss: 0.9467 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 103: +Training Loss: 2.9396 +Validation Loss: 0.8376 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 104: +Training Loss: 3.2142 +Validation Loss: 0.9724 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 105: +Training Loss: 3.0039 +Validation Loss: 0.8916 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 106: +Training Loss: 2.9057 +Validation Loss: 0.8788 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 107: +Training Loss: 3.2085 +Validation Loss: 0.8720 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 108: +Training Loss: 3.2260 +Validation Loss: 0.9400 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 109: +Training Loss: 2.8493 +Validation Loss: 0.8605 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 110: +Training Loss: 2.7667 +Validation Loss: 0.8713 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 111: +Training Loss: 2.9069 +Validation Loss: 0.9107 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 112: +Training Loss: 2.7812 +Validation Loss: 0.8097 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 113: +Training Loss: 3.1488 +Validation Loss: 0.8143 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 114: +Training Loss: 3.0605 +Validation Loss: 0.8448 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 115: +Training Loss: 3.0032 +Validation Loss: 0.8861 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 116: +Training Loss: 2.8277 +Validation Loss: 0.8878 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 117: +Training Loss: 3.0146 +Validation Loss: 0.8545 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 118: +Training Loss: 2.6374 +Validation Loss: 0.9012 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 119: +Training Loss: 2.7385 +Validation Loss: 0.9258 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 120: +Training Loss: 3.1355 +Validation Loss: 0.8994 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 121: +Training Loss: 2.7766 +Validation Loss: 0.9404 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 122: +Training Loss: 3.0344 +Validation Loss: 0.8138 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 123: +Training Loss: 2.9810 +Validation Loss: 0.8412 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 124: +Training Loss: 2.8703 +Validation Loss: 0.8599 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 125: +Training Loss: 3.1029 +Validation Loss: 0.7727 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 126: +Training Loss: 2.4720 +Validation Loss: 0.9473 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 127: +Training Loss: 2.3662 +Validation Loss: 0.8910 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 128: +Training Loss: 2.8140 +Validation Loss: 0.9495 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 129: +Training Loss: 3.0709 +Validation Loss: 0.8848 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 130: +Training Loss: 2.4501 +Validation Loss: 0.8782 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 131: +Training Loss: 2.4597 +Validation Loss: 0.9174 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 132: +Training Loss: 2.7437 +Validation Loss: 0.9374 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 133: +Training Loss: 2.4615 +Validation Loss: 0.9018 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 134: +Training Loss: 2.4574 +Validation Loss: 0.8751 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 135: +Training Loss: 2.6597 +Validation Loss: 0.8589 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 136: +Training Loss: 2.8131 +Validation Loss: 1.0075 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 137: +Training Loss: 2.5677 +Validation Loss: 0.8993 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 138: +Training Loss: 2.5169 +Validation Loss: 0.8616 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 139: +Training Loss: 2.3262 +Validation Loss: 0.9946 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 140: +Training Loss: 2.4963 +Validation Loss: 0.7199 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 141: +Training Loss: 2.2984 +Validation Loss: 0.8715 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 142: +Training Loss: 2.4891 +Validation Loss: 0.8348 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 143: +Training Loss: 2.2041 +Validation Loss: 0.7917 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 144: +Training Loss: 2.3631 +Validation Loss: 0.8124 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 145: +Training Loss: 2.3250 +Validation Loss: 0.7952 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 146: +Training Loss: 2.2213 +Validation Loss: 0.7524 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 147: +Training Loss: 2.6601 +Validation Loss: 0.7915 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 148: +Training Loss: 2.2442 +Validation Loss: 0.9770 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 149: +Training Loss: 2.1724 +Validation Loss: 0.7101 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 150: +Training Loss: 2.5457 +Validation Loss: 0.7762 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 151: +Training Loss: 2.2711 +Validation Loss: 0.7859 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 152: +Training Loss: 2.4709 +Validation Loss: 0.7889 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 153: +Training Loss: 2.1886 +Validation Loss: 0.6807 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 154: +Training Loss: 2.0566 +Validation Loss: 0.6893 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 155: +Training Loss: 2.3621 +Validation Loss: 0.7069 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 156: +Training Loss: 2.0266 +Validation Loss: 0.7473 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 157: +Training Loss: 2.1539 +Validation Loss: 0.8334 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 158: +Training Loss: 2.3141 +Validation Loss: 0.6845 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 159: +Training Loss: 2.1150 +Validation Loss: 0.6405 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 160: +Training Loss: 2.2367 +Validation Loss: 0.6130 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 161: +Training Loss: 1.9250 +Validation Loss: 0.7891 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 162: +Training Loss: 2.0616 +Validation Loss: 0.6772 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 163: +Training Loss: 2.0570 +Validation Loss: 0.6353 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 164: +Training Loss: 2.1120 +Validation Loss: 0.5878 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 165: +Training Loss: 2.0156 +Validation Loss: 0.5975 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 166: +Training Loss: 2.1877 +Validation Loss: 0.6742 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 167: +Training Loss: 1.7399 +Validation Loss: 0.6684 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 168: +Training Loss: 2.1284 +Validation Loss: 0.5142 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 169: +Training Loss: 1.8937 +Validation Loss: 0.5024 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 170: +Training Loss: 2.0598 +Validation Loss: 0.5484 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 171: +Training Loss: 1.9776 +Validation Loss: 0.4874 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 172: +Training Loss: 2.4025 +Validation Loss: 0.5822 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 173: +Training Loss: 2.4970 +Validation Loss: 0.6902 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 174: +Training Loss: 1.7651 +Validation Loss: 0.4891 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 175: +Training Loss: 1.9114 +Validation Loss: 0.4557 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 176: +Training Loss: 1.8434 +Validation Loss: 0.4440 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 177: +Training Loss: 1.6894 +Validation Loss: 0.5533 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 178: +Training Loss: 1.9062 +Validation Loss: 0.4404 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 179: +Training Loss: 1.7164 +Validation Loss: 0.4374 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 180: +Training Loss: 1.6711 +Validation Loss: 0.5567 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 181: +Training Loss: 2.0572 +Validation Loss: 0.6359 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 182: +Training Loss: 1.6833 +Validation Loss: 0.5794 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 183: +Training Loss: 1.6298 +Validation Loss: 0.5890 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 184: +Training Loss: 1.6744 +Validation Loss: 0.5690 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 185: +Training Loss: 1.8244 +Validation Loss: 0.5716 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 186: +Training Loss: 1.8052 +Validation Loss: 0.5247 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 187: +Training Loss: 1.7039 +Validation Loss: 0.5036 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 188: +Training Loss: 1.7514 +Validation Loss: 0.4653 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 189: +Training Loss: 1.7116 +Validation Loss: 0.5696 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 190: +Training Loss: 1.7287 +Validation Loss: 0.5048 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 191: +Training Loss: 1.5550 +Validation Loss: 0.5009 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 192: +Training Loss: 1.8278 +Validation Loss: 0.5152 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 193: +Training Loss: 1.6415 +Validation Loss: 0.4539 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 194: +Training Loss: 1.5321 +Validation Loss: 0.5253 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 195: +Training Loss: 1.5359 +Validation Loss: 0.4889 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 196: +Training Loss: 1.6448 +Validation Loss: 0.4754 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 197: +Training Loss: 1.6472 +Validation Loss: 0.5416 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 198: +Training Loss: 1.8297 +Validation Loss: 0.5123 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 199: +Training Loss: 1.6182 +Validation Loss: 0.5010 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 200: +Training Loss: 1.7736 +Validation Loss: 0.4848 + +Best model saved with R²: 0.0630 diff --git 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_171152-ueyirhah/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..e7cb5ecf3f1be6d774b5ac42bd0b6e45b8378c61 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_171152-ueyirhah/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.67it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 +Target mean: 22.261998046875, Target std: 19.64471716765064 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.69it/s] +Epoch 1: +Training Loss: 36241.1439 +Validation Loss: 316.6411 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 2: +Training Loss: 1253.0454 +Validation Loss: 32.8366 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 3: +Training Loss: 433.3737 +Validation Loss: 13.1400 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 4: +Training Loss: 285.1484 +Validation Loss: 5.0966 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 5: +Training Loss: 184.8914 +Validation Loss: 3.2439 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 6: +Training Loss: 135.3138 +Validation Loss: 2.3211 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 7: +Training Loss: 104.2870 +Validation Loss: 1.7720 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 8: +Training Loss: 77.1323 +Validation Loss: 1.1738 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 9: +Training Loss: 65.3332 +Validation Loss: 0.7497 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 10: +Training Loss: 49.3860 +Validation Loss: 0.5076 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 11: +Training Loss: 38.3571 +Validation Loss: 0.3815 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 12: +Training Loss: 31.6033 +Validation Loss: 0.3320 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 13: +Training Loss: 28.8648 +Validation Loss: 0.3314 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 14: +Training Loss: 25.8118 +Validation Loss: 0.3437 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 15: +Training Loss: 19.3957 +Validation Loss: 0.3504 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 16: +Training Loss: 22.0784 +Validation Loss: 0.3513 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 17: +Training Loss: 15.8861 +Validation Loss: 0.3547 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 18: +Training Loss: 16.5902 +Validation Loss: 0.3545 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.74it/s] + +Epoch 19: +Training Loss: 12.9303 +Validation Loss: 0.3539 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 20: +Training Loss: 13.0529 +Validation Loss: 0.3525 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 21: +Training Loss: 13.6372 +Validation Loss: 0.3514 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 22: +Training Loss: 8.6221 +Validation Loss: 0.3500 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 23: +Training Loss: 9.5712 +Validation Loss: 0.3492 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 24: +Training Loss: 8.2145 +Validation Loss: 0.3486 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 25: +Training Loss: 7.4018 +Validation Loss: 0.3481 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 26: +Training Loss: 8.7265 +Validation Loss: 0.3477 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 27: +Training Loss: 7.6976 +Validation Loss: 0.3474 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 28: +Training Loss: 7.2131 +Validation Loss: 0.3470 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 29: +Training Loss: 6.7814 +Validation Loss: 0.3466 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 30: +Training Loss: 5.6047 +Validation Loss: 0.3462 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 31: +Training Loss: 3.9999 +Validation Loss: 0.3459 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 32: +Training Loss: 5.4982 +Validation Loss: 0.3455 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 33: +Training Loss: 6.4200 +Validation Loss: 0.3452 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 34: +Training Loss: 5.4244 +Validation Loss: 0.3448 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 35: +Training Loss: 4.6334 +Validation Loss: 0.3446 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide + c /= stddev[:, None] +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/numpy/lib/function_base.py:2855: RuntimeWarning: invalid value encountered in divide + c /= stddev[None, :] + +Epoch 36: +Training Loss: 4.7439 +Validation Loss: 0.3444 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 37: +Training Loss: 4.3819 +Validation Loss: 0.3443 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 38: +Training Loss: 3.9031 +Validation Loss: 0.3442 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 39: +Training Loss: 3.5114 +Validation Loss: 0.3440 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 40: +Training Loss: 3.5179 +Validation Loss: 0.3439 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 41: +Training Loss: 3.1295 +Validation Loss: 0.3437 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 42: +Training Loss: 2.6800 +Validation Loss: 0.3436 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 43: +Training Loss: 2.8364 +Validation Loss: 0.3434 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 44: +Training Loss: 3.8584 +Validation Loss: 0.3433 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 45: +Training Loss: 2.6990 +Validation Loss: 0.3431 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 46: +Training Loss: 3.1165 +Validation Loss: 0.3430 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 47: +Training Loss: 2.4590 +Validation Loss: 0.3428 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 48: +Training Loss: 2.6620 +Validation Loss: 0.3427 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 49: +Training Loss: 2.7474 +Validation Loss: 0.3425 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 50: +Training Loss: 3.3671 +Validation Loss: 0.3423 + 10%|████▍ | 2/20 [00:01<00:14, 1.25it/s] diff --git 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+/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:12<00:00, 1.65it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] 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100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_175450-8d5pouxi/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.69it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] 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:] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + 10%|████▍ | 2/20 [00:01<00:14, 1.26it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_175450-kqn128dl/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_175450-kqn128dl/files/requirements.txt new file mode 100644 index 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.67it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] 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infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.67it/s] +Epoch 1: +Training Loss: 122759.8227 +Validation Loss: 551.8312 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 2: +Training Loss: 4002.5114 +Validation Loss: 282.1192 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 3: +Training Loss: 1788.3309 +Validation Loss: 208.2409 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 4: +Training Loss: 1261.6034 +Validation Loss: 179.9966 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 5: +Training Loss: 987.1453 +Validation Loss: 134.5996 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 6: +Training Loss: 904.4836 +Validation Loss: 103.8749 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 7: +Training Loss: 771.0203 +Validation Loss: 87.2264 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 8: +Training Loss: 706.7924 +Validation Loss: 78.4195 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 9: +Training Loss: 679.4089 +Validation Loss: 71.7362 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 10: +Training Loss: 615.7330 +Validation Loss: 69.4730 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 11: +Training Loss: 585.6842 +Validation Loss: 68.0436 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 12: +Training Loss: 591.6265 +Validation Loss: 66.2224 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 13: +Training Loss: 586.8219 +Validation Loss: 66.8748 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 14: +Training Loss: 536.0062 +Validation Loss: 63.8756 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 15: +Training Loss: 535.1741 +Validation Loss: 61.6161 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 16: +Training Loss: 546.1463 +Validation Loss: 59.9578 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 17: +Training Loss: 497.8165 +Validation Loss: 59.7659 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 18: +Training Loss: 518.6086 +Validation Loss: 56.6349 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 19: +Training Loss: 495.1478 +Validation Loss: 55.0472 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 20: +Training Loss: 487.4831 +Validation Loss: 54.4410 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 21: +Training Loss: 493.0768 +Validation Loss: 52.2373 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 22: +Training Loss: 525.2703 +Validation Loss: 51.5318 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 23: +Training Loss: 498.1439 +Validation Loss: 51.2326 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 24: +Training Loss: 473.7925 +Validation Loss: 51.3087 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 25: +Training Loss: 497.4362 +Validation Loss: 50.8374 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 26: +Training Loss: 465.6519 +Validation Loss: 51.4979 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 27: +Training Loss: 479.5868 +Validation Loss: 51.5639 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 28: +Training Loss: 528.1320 +Validation Loss: 52.1622 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 29: +Training Loss: 450.3891 +Validation Loss: 52.8497 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 30: +Training Loss: 456.0607 +Validation Loss: 51.7778 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 31: +Training Loss: 505.6629 +Validation Loss: 51.8108 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 32: +Training Loss: 491.5638 +Validation Loss: 53.3281 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 33: +Training Loss: 440.3005 +Validation Loss: 51.8764 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 34: +Training Loss: 474.0065 +Validation Loss: 51.7916 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 35: +Training Loss: 453.7177 +Validation Loss: 51.6417 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 36: +Training Loss: 454.5677 +Validation Loss: 51.6306 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 37: +Training Loss: 454.7123 +Validation Loss: 50.2451 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 38: +Training Loss: 438.0218 +Validation Loss: 51.2255 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 39: +Training Loss: 459.1354 +Validation Loss: 50.6644 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 40: +Training Loss: 434.2723 +Validation Loss: 50.2778 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 41: +Training Loss: 427.2262 +Validation Loss: 50.7995 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 42: +Training Loss: 461.8302 +Validation Loss: 49.2713 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 43: +Training Loss: 442.6156 +Validation Loss: 49.3593 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 44: +Training Loss: 490.5829 +Validation Loss: 48.3418 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 45: +Training Loss: 445.0257 +Validation Loss: 49.7427 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 46: +Training Loss: 438.3184 +Validation Loss: 48.0191 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 47: +Training Loss: 454.4023 +Validation Loss: 49.5618 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 48: +Training Loss: 439.1319 +Validation Loss: 49.2764 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 49: +Training Loss: 473.3982 +Validation Loss: 48.3499 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 50: +Training Loss: 446.2909 +Validation Loss: 49.1831 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 51: +Training Loss: 460.3613 +Validation Loss: 48.7855 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 52: +Training Loss: 410.9151 +Validation Loss: 46.8492 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 53: +Training Loss: 462.6896 +Validation Loss: 48.2914 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 54: +Training Loss: 437.3685 +Validation Loss: 47.1301 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 55: +Training Loss: 422.6924 +Validation Loss: 48.3122 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 56: +Training Loss: 431.8128 +Validation Loss: 47.1706 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 57: +Training Loss: 453.7282 +Validation Loss: 47.0133 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 58: +Training Loss: 421.8272 +Validation Loss: 46.4592 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 59: +Training Loss: 426.4534 +Validation Loss: 46.4125 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 60: +Training Loss: 466.4033 +Validation Loss: 46.1006 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 61: +Training Loss: 431.4438 +Validation Loss: 46.2832 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 62: +Training Loss: 425.3701 +Validation Loss: 45.7003 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 63: +Training Loss: 437.3545 +Validation Loss: 46.9145 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 64: +Training Loss: 404.8329 +Validation Loss: 45.5762 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 65: +Training Loss: 440.5254 +Validation Loss: 46.8511 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 66: +Training Loss: 456.9837 +Validation Loss: 45.7423 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 67: +Training Loss: 424.8099 +Validation Loss: 46.0859 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 68: +Training Loss: 424.4812 +Validation Loss: 45.1720 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 69: +Training Loss: 445.5628 +Validation Loss: 46.9833 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 70: +Training Loss: 455.9631 +Validation Loss: 45.4199 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 71: +Training Loss: 426.9863 +Validation Loss: 45.8725 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 72: +Training Loss: 411.8335 +Validation Loss: 44.3511 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 73: +Training Loss: 441.8900 +Validation Loss: 44.6242 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 74: +Training Loss: 402.5460 +Validation Loss: 45.2374 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 75: +Training Loss: 464.5474 +Validation Loss: 44.4573 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 76: +Training Loss: 460.2390 +Validation Loss: 44.6780 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 77: +Training Loss: 401.8837 +Validation Loss: 46.6641 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 78: +Training Loss: 406.9837 +Validation Loss: 44.8370 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 79: +Training Loss: 444.6349 +Validation Loss: 45.2297 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 80: +Training Loss: 396.5495 +Validation Loss: 44.5931 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 81: +Training Loss: 435.2264 +Validation Loss: 44.2398 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 82: +Training Loss: 408.8494 +Validation Loss: 44.7747 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 83: +Training Loss: 416.0830 +Validation Loss: 43.5111 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 84: +Training Loss: 437.9570 +Validation Loss: 45.0459 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 85: +Training Loss: 414.5285 +Validation Loss: 43.4201 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 86: +Training Loss: 433.1586 +Validation Loss: 43.9509 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 87: +Training Loss: 408.4658 +Validation Loss: 43.5428 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 88: +Training Loss: 393.7231 +Validation Loss: 44.0859 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 89: +Training Loss: 391.8888 +Validation Loss: 43.4652 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 90: +Training Loss: 409.4807 +Validation Loss: 43.3072 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 91: +Training Loss: 413.0413 +Validation Loss: 44.1858 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 92: +Training Loss: 407.5214 +Validation Loss: 44.2853 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 93: +Training Loss: 405.6313 +Validation Loss: 43.4633 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 94: +Training Loss: 434.6116 +Validation Loss: 43.0288 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 95: +Training Loss: 421.2164 +Validation Loss: 43.2064 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 96: +Training Loss: 410.0473 +Validation Loss: 43.7538 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 97: +Training Loss: 406.2635 +Validation Loss: 42.5294 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 98: +Training Loss: 409.7801 +Validation Loss: 43.3068 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 99: +Training Loss: 413.5391 +Validation Loss: 42.3288 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 100: +Training Loss: 439.9739 +Validation Loss: 42.6670 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 101: +Training Loss: 388.0968 +Validation Loss: 43.6037 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 102: +Training Loss: 404.4340 +Validation Loss: 42.0731 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 103: +Training Loss: 418.8938 +Validation Loss: 42.8729 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 104: +Training Loss: 422.1758 +Validation Loss: 42.1097 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 105: +Training Loss: 415.0069 +Validation Loss: 41.8749 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 106: +Training Loss: 406.7194 +Validation Loss: 42.2436 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 107: +Training Loss: 401.2421 +Validation Loss: 42.1443 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 108: +Training Loss: 437.3947 +Validation Loss: 42.2827 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 109: +Training Loss: 381.0847 +Validation Loss: 42.6443 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 110: +Training Loss: 383.1036 +Validation Loss: 42.2966 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 111: +Training Loss: 464.1353 +Validation Loss: 42.0196 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 112: +Training Loss: 390.5474 +Validation Loss: 42.2230 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 113: +Training Loss: 430.9902 +Validation Loss: 42.7499 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 114: +Training Loss: 421.0577 +Validation Loss: 41.7265 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 115: +Training Loss: 409.5497 +Validation Loss: 41.4226 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 116: +Training Loss: 390.3571 +Validation Loss: 41.6656 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 117: +Training Loss: 448.8874 +Validation Loss: 42.5607 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 118: +Training Loss: 406.0766 +Validation Loss: 40.7104 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 119: +Training Loss: 411.3797 +Validation Loss: 41.9291 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 120: +Training Loss: 382.2399 +Validation Loss: 41.0665 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 121: +Training Loss: 407.5899 +Validation Loss: 42.9363 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 122: +Training Loss: 404.6395 +Validation Loss: 41.2239 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 123: +Training Loss: 411.0294 +Validation Loss: 42.7181 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 124: +Training Loss: 413.3473 +Validation Loss: 40.0918 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 125: +Training Loss: 416.6962 +Validation Loss: 40.9720 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 126: +Training Loss: 389.8040 +Validation Loss: 40.8517 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 127: +Training Loss: 430.5175 +Validation Loss: 40.4603 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 128: +Training Loss: 404.3229 +Validation Loss: 39.6940 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 129: +Training Loss: 395.5366 +Validation Loss: 40.5133 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 130: +Training Loss: 414.7466 +Validation Loss: 40.1701 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 131: +Training Loss: 416.7155 +Validation Loss: 40.7789 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 132: +Training Loss: 377.8326 +Validation Loss: 39.9997 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 133: +Training Loss: 438.9495 +Validation Loss: 40.3937 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 134: 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1.76it/s] + +Epoch 141: +Training Loss: 410.5800 +Validation Loss: 40.7908 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 142: +Training Loss: 417.0356 +Validation Loss: 40.3641 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 143: +Training Loss: 404.1747 +Validation Loss: 38.9004 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 144: +Training Loss: 391.3197 +Validation Loss: 39.0768 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 145: +Training Loss: 393.9666 +Validation Loss: 38.9893 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 146: +Training Loss: 420.8792 +Validation Loss: 39.7655 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 147: +Training Loss: 387.8346 +Validation Loss: 39.2333 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1.76it/s] + +Epoch 168: +Training Loss: 360.0798 +Validation Loss: 38.4433 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 169: +Training Loss: 390.9132 +Validation Loss: 36.2091 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 170: +Training Loss: 383.7057 +Validation Loss: 35.6415 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 171: +Training Loss: 353.6129 +Validation Loss: 38.5809 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 172: +Training Loss: 344.0728 +Validation Loss: 40.2128 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 173: +Training Loss: 359.5686 +Validation Loss: 39.4555 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 174: +Training Loss: 374.6690 +Validation Loss: 36.6408 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 175: +Training Loss: 372.3627 +Validation Loss: 39.0295 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 176: +Training Loss: 359.1992 +Validation Loss: 43.6402 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 177: +Training Loss: 330.9439 +Validation Loss: 38.4079 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 178: +Training Loss: 377.2271 +Validation Loss: 37.3922 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 179: +Training Loss: 359.7268 +Validation Loss: 37.3633 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 180: +Training Loss: 370.6963 +Validation Loss: 36.7184 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 181: +Training Loss: 377.7471 +Validation Loss: 35.8462 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 182: +Training Loss: 364.2189 +Validation Loss: 38.0413 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 183: +Training Loss: 343.2559 +Validation Loss: 36.7390 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 184: +Training Loss: 355.8235 +Validation Loss: 47.6170 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 185: +Training Loss: 351.9348 +Validation Loss: 40.6077 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 186: +Training Loss: 377.0578 +Validation Loss: 36.0512 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 187: +Training Loss: 345.4705 +Validation Loss: 36.7295 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 188: +Training Loss: 355.4031 +Validation Loss: 40.4930 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 189: +Training Loss: 382.9763 +Validation Loss: 40.1104 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 190: +Training Loss: 359.8709 +Validation Loss: 38.5679 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 191: +Training Loss: 352.9821 +Validation Loss: 40.0467 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 192: +Training Loss: 383.6323 +Validation Loss: 41.0958 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 193: +Training Loss: 365.6138 +Validation Loss: 37.0702 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 194: +Training Loss: 357.9394 +Validation Loss: 39.7657 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 195: +Training Loss: 356.4019 +Validation Loss: 35.8273 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 196: +Training Loss: 353.9924 +Validation Loss: 36.8652 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 197: +Training Loss: 323.6502 +Validation Loss: 37.1095 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 198: +Training Loss: 343.8700 +Validation Loss: 38.8266 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 199: +Training Loss: 351.2679 +Validation Loss: 39.6188 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 200: +Training Loss: 338.7258 +Validation Loss: 39.7251 + +Best model saved with R²: 0.1951 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_180606-bbt4tgtt/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_180606-bbt4tgtt/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_180606-bbt4tgtt/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_180606-k4svlpo7/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..8b05d406cbfbbe6b49607e33b1ce7580b95c269c --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_180606-k4svlpo7/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.67it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] 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each element will be parsed individually, falling back to `dateutil`. 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file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-e4bjzury/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 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value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-elv1u9wv/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-elv1u9wv/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..848c5f89f8f4d9b8a37e10627fd1e544211b21e4 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-elv1u9wv/files/output.log @@ -0,0 +1,1210 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.68it/s] +Epoch 1: +Training Loss: 67633.6306 +Validation Loss: 83.1008 +RPIQ: 0.5183 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 2: +Training Loss: 1969.6405 +Validation Loss: 133.9715 +RPIQ: 0.5401 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 3: +Training Loss: 1088.5258 +Validation Loss: 75.5571 +RPIQ: 0.5490 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 4: +Training Loss: 829.5408 +Validation Loss: 62.4974 +RPIQ: 0.5364 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 5: +Training Loss: 664.1966 +Validation Loss: 61.7892 +RPIQ: 0.5270 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 6: +Training Loss: 607.0955 +Validation Loss: 61.6172 +RPIQ: 0.5251 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 7: +Training Loss: 585.1861 +Validation Loss: 61.3237 +RPIQ: 0.5231 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 8: +Training Loss: 557.4785 +Validation Loss: 58.9964 +RPIQ: 0.5262 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 9: +Training Loss: 548.2082 +Validation Loss: 57.5616 +RPIQ: 0.5272 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 10: +Training Loss: 481.8337 +Validation Loss: 56.2549 +RPIQ: 0.5274 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 11: +Training Loss: 480.7145 +Validation Loss: 55.3115 +RPIQ: 0.5268 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 12: +Training Loss: 477.0902 +Validation Loss: 54.3351 +RPIQ: 0.5313 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 13: +Training Loss: 493.5817 +Validation Loss: 52.9205 +RPIQ: 0.5322 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 14: +Training Loss: 468.2947 +Validation Loss: 51.7584 +RPIQ: 0.5342 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 15: +Training Loss: 441.3899 +Validation Loss: 50.9630 +RPIQ: 0.5360 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 16: +Training Loss: 466.4573 +Validation Loss: 50.5613 +RPIQ: 0.5383 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 17: +Training Loss: 432.2925 +Validation Loss: 50.4229 +RPIQ: 0.5396 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 18: +Training Loss: 441.4535 +Validation Loss: 49.9693 +RPIQ: 0.5378 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 19: +Training Loss: 448.3010 +Validation Loss: 49.5256 +RPIQ: 0.5384 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 20: +Training Loss: 440.0492 +Validation Loss: 49.7656 +RPIQ: 0.5413 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 21: +Training Loss: 453.2427 +Validation Loss: 49.3051 +RPIQ: 0.5420 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 22: +Training Loss: 494.5553 +Validation Loss: 48.3359 +RPIQ: 0.5396 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 23: +Training Loss: 444.5571 +Validation Loss: 48.3403 +RPIQ: 0.5427 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 24: +Training Loss: 440.4235 +Validation Loss: 48.4839 +RPIQ: 0.5442 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 25: +Training Loss: 463.7144 +Validation Loss: 48.1409 +RPIQ: 0.5447 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 26: +Training Loss: 443.9044 +Validation Loss: 48.9276 +RPIQ: 0.5478 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 27: +Training Loss: 442.8566 +Validation Loss: 47.7579 +RPIQ: 0.5458 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 28: +Training Loss: 483.7983 +Validation Loss: 48.6840 +RPIQ: 0.5487 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 29: +Training Loss: 421.1237 +Validation Loss: 48.4738 +RPIQ: 0.5486 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 30: +Training Loss: 426.3396 +Validation Loss: 47.9545 +RPIQ: 0.5477 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 31: +Training Loss: 479.7740 +Validation Loss: 47.7365 +RPIQ: 0.5481 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 32: +Training Loss: 443.2853 +Validation Loss: 48.8195 +RPIQ: 0.5504 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 33: +Training Loss: 415.8674 +Validation Loss: 47.2993 +RPIQ: 0.5485 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 34: +Training Loss: 448.8163 +Validation Loss: 47.6066 +RPIQ: 0.5495 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 35: +Training Loss: 418.9055 +Validation Loss: 47.4172 +RPIQ: 0.5496 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 36: +Training Loss: 413.5606 +Validation Loss: 47.8453 +RPIQ: 0.5508 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 37: +Training Loss: 429.2789 +Validation Loss: 46.0518 +RPIQ: 0.5477 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 38: +Training Loss: 403.5953 +Validation Loss: 47.9405 +RPIQ: 0.5519 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 39: +Training Loss: 424.3756 +Validation Loss: 47.6427 +RPIQ: 0.5515 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.82it/s] + +Epoch 40: +Training Loss: 405.6880 +Validation Loss: 48.6841 +RPIQ: 0.5535 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 41: +Training Loss: 400.8735 +Validation Loss: 49.4540 +RPIQ: 0.5547 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 42: +Training Loss: 428.9896 +Validation Loss: 45.5022 +RPIQ: 0.5494 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 43: +Training Loss: 430.7472 +Validation Loss: 47.1366 +RPIQ: 0.5528 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 44: +Training Loss: 453.5883 +Validation Loss: 45.3129 +RPIQ: 0.5512 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 45: +Training Loss: 432.5877 +Validation Loss: 46.7591 +RPIQ: 0.5535 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 46: +Training Loss: 424.9671 +Validation Loss: 44.3974 +RPIQ: 0.5477 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 47: +Training Loss: 426.0148 +Validation Loss: 47.3120 +RPIQ: 0.5547 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 48: +Training Loss: 422.3739 +Validation Loss: 47.3151 +RPIQ: 0.5552 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 49: +Training Loss: 444.7640 +Validation Loss: 45.8863 +RPIQ: 0.5534 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 50: +Training Loss: 437.1517 +Validation Loss: 46.9558 +RPIQ: 0.5545 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 51: +Training Loss: 445.7757 +Validation Loss: 45.0674 +RPIQ: 0.5522 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 52: +Training Loss: 385.9022 +Validation Loss: 45.4166 +RPIQ: 0.5537 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 53: +Training Loss: 433.6329 +Validation Loss: 44.4682 +RPIQ: 0.5528 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 54: +Training Loss: 411.8977 +Validation Loss: 45.2993 +RPIQ: 0.5542 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 55: +Training Loss: 407.9524 +Validation Loss: 45.8314 +RPIQ: 0.5550 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 56: +Training Loss: 411.6666 +Validation Loss: 46.0656 +RPIQ: 0.5558 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 57: +Training Loss: 441.0518 +Validation Loss: 46.6964 +RPIQ: 0.5569 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 58: +Training Loss: 406.3832 +Validation Loss: 45.6246 +RPIQ: 0.5553 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 59: +Training Loss: 415.8471 +Validation Loss: 43.9117 +RPIQ: 0.5526 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 60: +Training Loss: 453.3268 +Validation Loss: 45.0551 +RPIQ: 0.5557 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 61: +Training Loss: 422.2052 +Validation Loss: 44.6424 +RPIQ: 0.5561 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 62: +Training Loss: 410.8341 +Validation Loss: 44.5099 +RPIQ: 0.5554 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 63: +Training Loss: 425.4375 +Validation Loss: 44.5242 +RPIQ: 0.5554 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 64: +Training Loss: 386.4295 +Validation Loss: 43.5160 +RPIQ: 0.5527 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 65: +Training Loss: 422.1747 +Validation Loss: 43.8916 +RPIQ: 0.5549 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 66: +Training Loss: 444.5620 +Validation Loss: 42.7375 +RPIQ: 0.5527 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 67: +Training Loss: 409.1306 +Validation Loss: 46.1197 +RPIQ: 0.5593 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 68: +Training Loss: 418.7197 +Validation Loss: 43.6943 +RPIQ: 0.5550 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 69: +Training Loss: 434.7197 +Validation Loss: 44.5923 +RPIQ: 0.5585 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 70: +Training Loss: 443.9743 +Validation Loss: 43.1330 +RPIQ: 0.5566 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 71: +Training Loss: 412.9744 +Validation Loss: 43.2755 +RPIQ: 0.5569 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 72: +Training Loss: 405.3903 +Validation Loss: 42.8253 +RPIQ: 0.5566 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 73: +Training Loss: 425.9271 +Validation Loss: 41.1362 +RPIQ: 0.5519 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 74: +Training Loss: 401.5000 +Validation Loss: 43.7836 +RPIQ: 0.5589 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 75: +Training Loss: 448.1160 +Validation Loss: 41.3379 +RPIQ: 0.5540 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 76: +Training Loss: 456.5245 +Validation Loss: 43.8815 +RPIQ: 0.5600 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 77: +Training Loss: 394.5108 +Validation Loss: 44.1917 +RPIQ: 0.5601 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 78: +Training Loss: 403.3099 +Validation Loss: 42.4125 +RPIQ: 0.5568 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 79: +Training Loss: 440.3044 +Validation Loss: 42.7554 +RPIQ: 0.5584 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 80: +Training Loss: 392.5634 +Validation Loss: 43.1310 +RPIQ: 0.5596 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 81: +Training Loss: 420.3787 +Validation Loss: 40.7642 +RPIQ: 0.5535 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 82: +Training Loss: 391.6705 +Validation Loss: 40.6663 +RPIQ: 0.5518 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 83: +Training Loss: 410.4444 +Validation Loss: 41.0413 +RPIQ: 0.5570 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 84: +Training Loss: 428.1643 +Validation Loss: 45.4713 +RPIQ: 0.5634 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 85: +Training Loss: 410.9450 +Validation Loss: 40.5695 +RPIQ: 0.5550 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 86: +Training Loss: 428.7525 +Validation Loss: 41.4091 +RPIQ: 0.5583 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 87: +Training Loss: 403.6948 +Validation Loss: 40.6939 +RPIQ: 0.5554 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 88: +Training Loss: 388.6349 +Validation Loss: 40.0608 +RPIQ: 0.5531 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 89: +Training Loss: 373.4267 +Validation Loss: 40.1368 +RPIQ: 0.5553 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 90: +Training Loss: 407.9485 +Validation Loss: 39.6123 +RPIQ: 0.5537 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 91: +Training Loss: 407.5772 +Validation Loss: 41.2428 +RPIQ: 0.5609 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 92: +Training Loss: 400.6044 +Validation Loss: 41.7574 +RPIQ: 0.5621 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 93: +Training Loss: 403.5633 +Validation Loss: 40.8405 +RPIQ: 0.5587 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 94: +Training Loss: 425.4254 +Validation Loss: 40.2504 +RPIQ: 0.5576 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 95: +Training Loss: 411.2578 +Validation Loss: 40.5279 +RPIQ: 0.5552 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 96: +Training Loss: 400.9648 +Validation Loss: 40.0739 +RPIQ: 0.5583 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 97: +Training Loss: 396.4956 +Validation Loss: 40.6472 +RPIQ: 0.5569 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 98: +Training Loss: 398.5754 +Validation Loss: 39.9398 +RPIQ: 0.5588 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+100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 104: +Training Loss: 424.3398 +Validation Loss: 40.2074 +RPIQ: 0.5623 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 105: +Training Loss: 402.4409 +Validation Loss: 39.1132 +RPIQ: 0.5559 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 106: +Training Loss: 397.8138 +Validation Loss: 39.2222 +RPIQ: 0.5572 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 107: +Training Loss: 403.2477 +Validation Loss: 40.9726 +RPIQ: 0.5660 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 108: +Training Loss: 422.4399 +Validation Loss: 41.9241 +RPIQ: 0.5673 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 109: +Training Loss: 386.5376 +Validation Loss: 41.1508 +RPIQ: 0.5633 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 110: +Training Loss: 387.3367 +Validation Loss: 39.8140 +RPIQ: 0.5632 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 111: +Training Loss: 448.7097 +Validation Loss: 40.0182 +RPIQ: 0.5610 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 112: +Training Loss: 390.6557 +Validation Loss: 39.9886 +RPIQ: 0.5645 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 113: +Training Loss: 434.8210 +Validation Loss: 39.4255 +RPIQ: 0.5648 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 114: +Training Loss: 413.6199 +Validation Loss: 37.8538 +RPIQ: 0.5551 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 115: +Training Loss: 415.7648 +Validation Loss: 38.5247 +RPIQ: 0.5575 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 116: +Training Loss: 381.1382 +Validation Loss: 38.2346 +RPIQ: 0.5550 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 117: +Training Loss: 426.1488 +Validation Loss: 37.8338 +RPIQ: 0.5544 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 118: +Training Loss: 403.5989 +Validation Loss: 38.8212 +RPIQ: 0.5516 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 119: +Training Loss: 414.9579 +Validation Loss: 37.9684 +RPIQ: 0.5557 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 120: +Training Loss: 386.2690 +Validation Loss: 37.6823 +RPIQ: 0.5568 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 121: +Training Loss: 403.2100 +Validation Loss: 38.7791 +RPIQ: 0.5478 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 122: +Training Loss: 397.1722 +Validation Loss: 38.2272 +RPIQ: 0.5499 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 123: +Training Loss: 404.9808 +Validation Loss: 37.6261 +RPIQ: 0.5567 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 124: +Training Loss: 418.8438 +Validation Loss: 38.3302 +RPIQ: 0.5637 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 125: +Training Loss: 422.3928 +Validation Loss: 37.3294 +RPIQ: 0.5586 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 126: +Training Loss: 385.2442 +Validation Loss: 37.2790 +RPIQ: 0.5582 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 127: +Training Loss: 423.7232 +Validation Loss: 36.7961 +RPIQ: 0.5604 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 128: +Training Loss: 413.9531 +Validation Loss: 37.4832 +RPIQ: 0.5559 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 129: +Training Loss: 388.4897 +Validation Loss: 36.6115 +RPIQ: 0.5573 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 130: +Training Loss: 410.2000 +Validation Loss: 37.2212 +RPIQ: 0.5550 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 131: +Training Loss: 425.2388 +Validation Loss: 37.3430 +RPIQ: 0.5602 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 132: +Training Loss: 368.7941 +Validation Loss: 36.8458 +RPIQ: 0.5586 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 133: +Training Loss: 434.6989 +Validation Loss: 37.3258 +RPIQ: 0.5587 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 134: +Training Loss: 392.4532 +Validation Loss: 36.3422 +RPIQ: 0.5655 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 135: +Training Loss: 415.0868 +Validation Loss: 36.9824 +RPIQ: 0.5578 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 136: +Training Loss: 395.8871 +Validation Loss: 36.4239 +RPIQ: 0.5595 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 137: +Training Loss: 365.5272 +Validation Loss: 37.0710 +RPIQ: 0.5668 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 138: +Training Loss: 420.3598 +Validation Loss: 36.9325 +RPIQ: 0.5605 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 139: +Training Loss: 411.0426 +Validation Loss: 36.2870 +RPIQ: 0.5616 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 140: +Training Loss: 401.4847 +Validation Loss: 36.6363 +RPIQ: 0.5587 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 141: +Training Loss: 408.7271 +Validation Loss: 36.8963 +RPIQ: 0.5570 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 142: +Training Loss: 408.5203 +Validation Loss: 38.8045 +RPIQ: 0.5490 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 143: +Training Loss: 383.1739 +Validation Loss: 35.5718 +RPIQ: 0.5650 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 144: +Training Loss: 396.8371 +Validation Loss: 35.8146 +RPIQ: 0.5656 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 145: +Training Loss: 399.0640 +Validation Loss: 35.6019 +RPIQ: 0.5667 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 146: +Training Loss: 422.4746 +Validation Loss: 35.6226 +RPIQ: 0.5734 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 147: +Training Loss: 385.8141 +Validation Loss: 35.2001 +RPIQ: 0.5649 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 148: +Training Loss: 366.2092 +Validation Loss: 34.8373 +RPIQ: 0.5713 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 149: +Training Loss: 402.1051 +Validation Loss: 36.7221 +RPIQ: 0.5549 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 150: +Training Loss: 401.7995 +Validation Loss: 35.0747 +RPIQ: 0.5675 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 151: +Training Loss: 371.0697 +Validation Loss: 35.5552 +RPIQ: 0.5693 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 152: +Training Loss: 381.4518 +Validation Loss: 36.7393 +RPIQ: 0.5597 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 153: +Training Loss: 383.4856 +Validation Loss: 38.1589 +RPIQ: 0.5524 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 154: +Training Loss: 381.5057 +Validation Loss: 36.6865 +RPIQ: 0.5589 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 155: +Training Loss: 352.5840 +Validation Loss: 35.9133 +RPIQ: 0.5651 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 156: +Training Loss: 404.4202 +Validation Loss: 35.0029 +RPIQ: 0.5743 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 157: +Training Loss: 415.2218 +Validation Loss: 34.9652 +RPIQ: 0.5757 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 158: +Training Loss: 410.6991 +Validation Loss: 34.3906 +RPIQ: 0.5733 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 159: +Training Loss: 378.7489 +Validation Loss: 34.4757 +RPIQ: 0.5728 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 160: +Training Loss: 383.1189 +Validation Loss: 36.2778 +RPIQ: 0.5638 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 161: +Training Loss: 364.9546 +Validation Loss: 35.0623 +RPIQ: 0.5697 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 162: +Training Loss: 420.5597 +Validation Loss: 34.8111 +RPIQ: 0.5723 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 163: +Training Loss: 380.2283 +Validation Loss: 34.4044 +RPIQ: 0.5785 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 164: +Training Loss: 400.3661 +Validation Loss: 34.9977 +RPIQ: 0.5822 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.81it/s] + +Epoch 165: +Training Loss: 377.6152 +Validation Loss: 34.5312 +RPIQ: 0.5881 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 166: +Training Loss: 376.4350 +Validation Loss: 34.5673 +RPIQ: 0.5860 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 167: +Training Loss: 367.9520 +Validation Loss: 35.0103 +RPIQ: 0.5757 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 168: +Training Loss: 348.2001 +Validation Loss: 34.8718 +RPIQ: 0.5816 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+100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 199: +Training Loss: 339.4298 +Validation Loss: 38.5849 +RPIQ: 0.6209 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.80it/s] + +Epoch 200: +Training Loss: 325.3371 +Validation Loss: 43.8060 +RPIQ: 0.6004 + +Best model saved with R²: 0.3106 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-elv1u9wv/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-elv1u9wv/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-elv1u9wv/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.68it/s] +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] 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file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-ktvvdg1p/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 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sha256:a28a50a3d80bec2c1a9023bbecbd24de5f6fd5bcedf0089a29b528ceee5ddbf6 +size 1659427 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-r56lduxc/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-r56lduxc/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..41f051cbab30bb915a5f406cd842daec6f7406b9 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-r56lduxc/files/config.yaml @@ -0,0 +1,48 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 2 + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: composite_l2 +max_oc: + value: 150 +target_transform: + value: none +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-r56lduxc/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-r56lduxc/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..f5349e9bcb0a9d2c425a0ea69260ba2a2d275a36 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-r56lduxc/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.68it/s] +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] +100%|████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] 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file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211726-r56lduxc/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 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+time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211908-ah2ed6ba/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211908-ah2ed6ba/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..c12a30845e9f27a20cbba17dbc68bacbb5c126ab --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211908-ah2ed6ba/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00, 1.66it/s] +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] 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mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211908-ah2ed6ba/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 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so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00, 1.66it/s] +Epoch 1: +Training Loss: 78509.4440 +Validation Loss: 751.7707 +RPIQ: 0.3384 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 2: +Training Loss: 2606.3689 +Validation Loss: 372.7746 +RPIQ: 0.3598 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 3: +Training Loss: 1665.6926 +Validation Loss: 218.6797 +RPIQ: 0.4284 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 4: +Training Loss: 1208.0345 +Validation Loss: 168.3582 +RPIQ: 0.4429 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 5: +Training Loss: 1015.0034 +Validation Loss: 133.6579 +RPIQ: 0.4590 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 6: +Training Loss: 872.0258 +Validation Loss: 113.6359 +RPIQ: 0.4683 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 7: +Training Loss: 763.0389 +Validation Loss: 99.6010 +RPIQ: 0.4766 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 8: +Training Loss: 708.5955 +Validation Loss: 82.0116 +RPIQ: 0.4890 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 9: +Training Loss: 667.2071 +Validation Loss: 63.5857 +RPIQ: 0.5105 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 10: +Training Loss: 587.8529 +Validation Loss: 56.5750 +RPIQ: 0.5197 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 11: +Training Loss: 552.4827 +Validation Loss: 59.1002 +RPIQ: 0.5172 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 12: +Training Loss: 532.4012 +Validation Loss: 62.7173 +RPIQ: 0.5172 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 13: +Training Loss: 554.0569 +Validation Loss: 59.8437 +RPIQ: 0.5273 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 14: +Training Loss: 511.4231 +Validation Loss: 59.3017 +RPIQ: 0.5325 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 15: +Training Loss: 475.7706 +Validation Loss: 57.0651 +RPIQ: 0.5426 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 16: +Training Loss: 489.1539 +Validation Loss: 60.2275 +RPIQ: 0.5388 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 17: +Training Loss: 439.1010 +Validation Loss: 57.4014 +RPIQ: 0.5462 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 18: +Training Loss: 456.3989 +Validation Loss: 51.1623 +RPIQ: 0.5666 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 19: +Training Loss: 422.4429 +Validation Loss: 52.1222 +RPIQ: 0.5650 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 20: +Training Loss: 426.4543 +Validation Loss: 50.0876 +RPIQ: 0.5720 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 21: +Training Loss: 411.5492 +Validation Loss: 51.0135 +RPIQ: 0.5701 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 22: +Training Loss: 451.5246 +Validation Loss: 46.5493 +RPIQ: 0.5843 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 23: +Training Loss: 429.9418 +Validation Loss: 45.4890 +RPIQ: 0.5896 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 24: +Training Loss: 427.7161 +Validation Loss: 46.8263 +RPIQ: 0.5846 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 25: +Training Loss: 419.0081 +Validation Loss: 51.2876 +RPIQ: 0.5665 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 26: +Training Loss: 411.3617 +Validation Loss: 46.5527 +RPIQ: 0.5803 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 27: +Training Loss: 394.8047 +Validation Loss: 46.8094 +RPIQ: 0.5782 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 28: +Training Loss: 453.3234 +Validation Loss: 42.8631 +RPIQ: 0.5944 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 29: +Training Loss: 392.3167 +Validation Loss: 44.7538 +RPIQ: 0.5894 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 30: +Training Loss: 386.4247 +Validation Loss: 44.5202 +RPIQ: 0.5906 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 31: +Training Loss: 438.1946 +Validation Loss: 44.0445 +RPIQ: 0.5878 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 32: +Training Loss: 421.5489 +Validation Loss: 43.2282 +RPIQ: 0.5905 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 33: +Training Loss: 369.2750 +Validation Loss: 42.7906 +RPIQ: 0.5956 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 34: +Training Loss: 398.4264 +Validation Loss: 44.2543 +RPIQ: 0.5916 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 35: +Training Loss: 372.5524 +Validation Loss: 43.1584 +RPIQ: 0.5929 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 36: +Training Loss: 375.5637 +Validation Loss: 41.4252 +RPIQ: 0.6073 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 37: +Training Loss: 388.9373 +Validation Loss: 41.5058 +RPIQ: 0.6028 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 38: +Training Loss: 365.0967 +Validation Loss: 41.5307 +RPIQ: 0.6069 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 39: +Training Loss: 395.8187 +Validation Loss: 43.5930 +RPIQ: 0.5931 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 40: +Training Loss: 375.6940 +Validation Loss: 41.2334 +RPIQ: 0.6079 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 41: +Training Loss: 361.7918 +Validation Loss: 41.5209 +RPIQ: 0.6042 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 42: +Training Loss: 374.6882 +Validation Loss: 46.0257 +RPIQ: 0.5828 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 43: +Training Loss: 377.9486 +Validation Loss: 42.9305 +RPIQ: 0.5969 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 44: +Training Loss: 420.4144 +Validation Loss: 40.7721 +RPIQ: 0.6123 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 45: +Training Loss: 373.1412 +Validation Loss: 45.1279 +RPIQ: 0.5883 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 46: +Training Loss: 393.4887 +Validation Loss: 44.9929 +RPIQ: 0.5848 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 47: +Training Loss: 386.3369 +Validation Loss: 43.6046 +RPIQ: 0.5924 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 48: +Training Loss: 382.4992 +Validation Loss: 41.4244 +RPIQ: 0.6056 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 49: +Training Loss: 423.7794 +Validation Loss: 45.2239 +RPIQ: 0.5875 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 50: +Training Loss: 391.9687 +Validation Loss: 46.0904 +RPIQ: 0.5826 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 51: +Training Loss: 396.3961 +Validation Loss: 42.4762 +RPIQ: 0.5945 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 52: +Training Loss: 341.8308 +Validation Loss: 40.8980 +RPIQ: 0.6039 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 53: +Training Loss: 394.5497 +Validation Loss: 40.6415 +RPIQ: 0.6034 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 54: +Training Loss: 360.9080 +Validation Loss: 42.5380 +RPIQ: 0.5961 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 55: +Training Loss: 357.6643 +Validation Loss: 41.3511 +RPIQ: 0.5988 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 56: +Training Loss: 371.3133 +Validation Loss: 40.2093 +RPIQ: 0.6043 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 57: +Training Loss: 383.4473 +Validation Loss: 39.1692 +RPIQ: 0.6076 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 58: +Training Loss: 356.3867 +Validation Loss: 41.3313 +RPIQ: 0.5962 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 59: +Training Loss: 353.9510 +Validation Loss: 41.3151 +RPIQ: 0.5985 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 60: +Training Loss: 396.9243 +Validation Loss: 40.3450 +RPIQ: 0.6040 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 61: +Training Loss: 367.9288 +Validation Loss: 42.5006 +RPIQ: 0.5936 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 62: +Training Loss: 369.8367 +Validation Loss: 39.7910 +RPIQ: 0.6020 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 63: +Training Loss: 385.6916 +Validation Loss: 38.1073 +RPIQ: 0.6111 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 64: +Training Loss: 351.3241 +Validation Loss: 37.6690 +RPIQ: 0.6164 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 65: +Training Loss: 373.3083 +Validation Loss: 39.6396 +RPIQ: 0.6032 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 66: +Training Loss: 401.9618 +Validation Loss: 42.1404 +RPIQ: 0.5909 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 67: +Training Loss: 371.4175 +Validation Loss: 39.2342 +RPIQ: 0.6036 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 68: +Training Loss: 368.5469 +Validation Loss: 43.1659 +RPIQ: 0.5914 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 69: +Training Loss: 386.4622 +Validation Loss: 44.8423 +RPIQ: 0.5864 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 70: +Training Loss: 383.3946 +Validation Loss: 38.0700 +RPIQ: 0.6139 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 71: +Training Loss: 369.5550 +Validation Loss: 40.5657 +RPIQ: 0.6003 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 72: +Training Loss: 363.7245 +Validation Loss: 40.9092 +RPIQ: 0.5995 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 73: +Training Loss: 374.4486 +Validation Loss: 43.9150 +RPIQ: 0.5893 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 74: +Training Loss: 348.2626 +Validation Loss: 40.3562 +RPIQ: 0.6004 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 75: +Training Loss: 405.3164 +Validation Loss: 40.1222 +RPIQ: 0.6028 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 76: +Training Loss: 401.4517 +Validation Loss: 41.7393 +RPIQ: 0.5977 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 77: +Training Loss: 344.7389 +Validation Loss: 37.3085 +RPIQ: 0.6266 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 78: +Training Loss: 342.6932 +Validation Loss: 40.0038 +RPIQ: 0.6066 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 79: +Training Loss: 380.3050 +Validation Loss: 44.2637 +RPIQ: 0.5873 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 80: +Training Loss: 357.2121 +Validation Loss: 43.0735 +RPIQ: 0.5912 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 81: +Training Loss: 377.9343 +Validation Loss: 42.1612 +RPIQ: 0.5945 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 82: +Training Loss: 342.1488 +Validation Loss: 41.7545 +RPIQ: 0.5955 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 83: +Training Loss: 358.2102 +Validation Loss: 39.9247 +RPIQ: 0.6039 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 84: +Training Loss: 373.9427 +Validation Loss: 38.3271 +RPIQ: 0.6105 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 85: +Training Loss: 368.9560 +Validation Loss: 39.5739 +RPIQ: 0.6047 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 86: +Training Loss: 381.9875 +Validation Loss: 41.8094 +RPIQ: 0.5954 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 87: +Training Loss: 340.9149 +Validation Loss: 40.7343 +RPIQ: 0.6003 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 88: +Training Loss: 336.9633 +Validation Loss: 46.6624 +RPIQ: 0.5839 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 89: +Training Loss: 333.2603 +Validation Loss: 50.2404 +RPIQ: 0.5713 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 90: +Training Loss: 367.0414 +Validation Loss: 40.0371 +RPIQ: 0.6000 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 91: +Training Loss: 367.8142 +Validation Loss: 38.3802 +RPIQ: 0.6112 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 92: +Training Loss: 358.2195 +Validation Loss: 38.2123 +RPIQ: 0.6094 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 93: +Training Loss: 353.0515 +Validation Loss: 38.0162 +RPIQ: 0.6105 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 94: +Training Loss: 372.8371 +Validation Loss: 42.7826 +RPIQ: 0.5952 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 95: +Training Loss: 358.2847 +Validation Loss: 44.9705 +RPIQ: 0.5864 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 96: +Training Loss: 352.3530 +Validation Loss: 43.5341 +RPIQ: 0.5903 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 97: +Training Loss: 341.4241 +Validation Loss: 48.4094 +RPIQ: 0.5832 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 98: +Training Loss: 347.7199 +Validation Loss: 38.5308 +RPIQ: 0.6103 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 99: +Training Loss: 336.3005 +Validation Loss: 40.9052 +RPIQ: 0.6055 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 100: +Training Loss: 362.4822 +Validation Loss: 45.2953 +RPIQ: 0.5884 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 101: +Training Loss: 348.9783 +Validation Loss: 44.3998 +RPIQ: 0.5890 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 102: +Training Loss: 340.9522 +Validation Loss: 40.7021 +RPIQ: 0.6037 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 103: +Training Loss: 366.7376 +Validation Loss: 40.0166 +RPIQ: 0.6072 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 104: +Training Loss: 365.7521 +Validation Loss: 41.2143 +RPIQ: 0.6067 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 105: +Training Loss: 357.6220 +Validation Loss: 53.4616 +RPIQ: 0.5759 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 106: +Training Loss: 337.7519 +Validation Loss: 46.7504 +RPIQ: 0.5881 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 107: +Training Loss: 359.3546 +Validation Loss: 36.8968 +RPIQ: 0.6199 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 108: +Training Loss: 374.6671 +Validation Loss: 36.5048 +RPIQ: 0.6253 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+100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 129: +Training Loss: 336.5028 +Validation Loss: 43.2162 +RPIQ: 0.6005 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 130: +Training Loss: 356.4563 +Validation Loss: 39.9808 +RPIQ: 0.6054 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 131: +Training Loss: 356.3698 +Validation Loss: 38.1237 +RPIQ: 0.6154 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 132: +Training Loss: 332.5389 +Validation Loss: 41.6067 +RPIQ: 0.6067 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 133: +Training Loss: 363.7725 +Validation Loss: 44.9660 +RPIQ: 0.5967 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 134: +Training Loss: 343.3670 +Validation Loss: 36.8514 +RPIQ: 0.6193 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 135: +Training Loss: 356.8249 +Validation Loss: 38.9035 +RPIQ: 0.6094 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 136: +Training Loss: 335.7062 +Validation Loss: 49.0199 +RPIQ: 0.5880 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 137: +Training Loss: 316.5204 +Validation Loss: 42.6661 +RPIQ: 0.6005 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 138: +Training Loss: 345.4365 +Validation Loss: 38.1247 +RPIQ: 0.6162 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 139: +Training Loss: 347.0754 +Validation Loss: 36.7342 +RPIQ: 0.6242 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 140: +Training Loss: 352.0356 +Validation Loss: 40.8680 +RPIQ: 0.6020 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 141: +Training Loss: 358.0738 +Validation Loss: 37.4564 +RPIQ: 0.6214 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 142: +Training Loss: 344.8305 +Validation Loss: 41.1658 +RPIQ: 0.6100 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 143: +Training Loss: 345.3955 +Validation Loss: 37.3301 +RPIQ: 0.6281 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 144: +Training Loss: 345.9122 +Validation Loss: 35.7787 +RPIQ: 0.6242 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 145: +Training Loss: 346.2656 +Validation Loss: 37.0307 +RPIQ: 0.6236 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 146: +Training Loss: 359.3081 +Validation Loss: 35.8108 +RPIQ: 0.6287 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 147: +Training Loss: 337.6538 +Validation Loss: 36.0708 +RPIQ: 0.6279 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 148: +Training Loss: 334.2815 +Validation Loss: 34.8974 +RPIQ: 0.6381 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 149: +Training Loss: 348.9708 +Validation Loss: 39.5243 +RPIQ: 0.6161 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 150: +Training Loss: 356.9901 +Validation Loss: 42.0506 +RPIQ: 0.6088 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 151: +Training Loss: 320.0203 +Validation Loss: 42.3668 +RPIQ: 0.6079 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 152: +Training Loss: 330.5814 +Validation Loss: 47.1911 +RPIQ: 0.5919 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 153: +Training Loss: 332.0526 +Validation Loss: 43.6611 +RPIQ: 0.6038 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 154: +Training Loss: 347.6326 +Validation Loss: 38.8219 +RPIQ: 0.6112 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 155: +Training Loss: 314.2280 +Validation Loss: 45.5150 +RPIQ: 0.5946 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 156: +Training Loss: 348.6066 +Validation Loss: 44.5520 +RPIQ: 0.6058 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 157: +Training Loss: 367.2296 +Validation Loss: 41.8698 +RPIQ: 0.6062 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 158: +Training Loss: 353.8799 +Validation Loss: 38.6384 +RPIQ: 0.6207 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 159: +Training Loss: 317.0057 +Validation Loss: 38.7497 +RPIQ: 0.6144 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 160: +Training Loss: 341.4123 +Validation Loss: 45.3072 +RPIQ: 0.6110 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 161: +Training Loss: 321.1328 +Validation Loss: 36.2927 +RPIQ: 0.6245 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 162: +Training Loss: 370.3949 +Validation Loss: 37.0596 +RPIQ: 0.6286 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 163: +Training Loss: 334.5265 +Validation Loss: 42.7650 +RPIQ: 0.6090 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 164: +Training Loss: 361.8775 +Validation Loss: 39.2242 +RPIQ: 0.6200 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 165: +Training Loss: 329.8784 +Validation Loss: 38.5706 +RPIQ: 0.6238 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 166: +Training Loss: 346.7035 +Validation Loss: 38.1807 +RPIQ: 0.6263 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 167: +Training Loss: 334.1222 +Validation Loss: 46.5719 +RPIQ: 0.6024 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 168: +Training Loss: 327.3168 +Validation Loss: 40.5896 +RPIQ: 0.6136 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 169: +Training Loss: 342.7731 +Validation Loss: 41.3765 +RPIQ: 0.6156 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 170: +Training Loss: 355.8186 +Validation Loss: 40.2549 +RPIQ: 0.6156 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 171: +Training Loss: 324.2760 +Validation Loss: 44.1964 +RPIQ: 0.6032 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 172: +Training Loss: 304.0425 +Validation Loss: 48.9571 +RPIQ: 0.5982 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 173: +Training Loss: 322.2577 +Validation Loss: 41.2434 +RPIQ: 0.6169 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 174: +Training Loss: 321.3394 +Validation Loss: 45.7239 +RPIQ: 0.6061 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 175: +Training Loss: 344.2302 +Validation Loss: 40.2087 +RPIQ: 0.6173 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 176: +Training Loss: 319.9808 +Validation Loss: 46.6497 +RPIQ: 0.6003 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 177: +Training Loss: 296.1331 +Validation Loss: 37.2529 +RPIQ: 0.6280 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 178: +Training Loss: 348.4295 +Validation Loss: 41.1267 +RPIQ: 0.6146 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 179: +Training Loss: 314.1590 +Validation Loss: 41.1287 +RPIQ: 0.6242 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 180: +Training Loss: 344.2222 +Validation Loss: 36.6840 +RPIQ: 0.6335 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 181: +Training Loss: 337.5692 +Validation Loss: 38.7471 +RPIQ: 0.6295 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 182: +Training Loss: 322.2840 +Validation Loss: 37.5213 +RPIQ: 0.6291 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 183: +Training Loss: 319.8057 +Validation Loss: 42.2968 +RPIQ: 0.6167 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mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211908-w4xv1y42/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 +sentry-sdk==2.20.0 +safetensors==0.5.2 +PyYAML==6.0.2 +pydantic_core==2.27.2 +psutil==6.1.1 +platformdirs==4.3.6 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00, 1.66it/s] +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|███████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] 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mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211908-x3liy91h/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 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0000000000000000000000000000000000000000..98390357c5f722060e76b81ba8d1f8c68b542fbe --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_211908-x3liy91h/run-x3liy91h.wandb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b68647c3d0c0fee62ca0b9f12d8d96bab09db952735b3620917eff935dcdbbfa +size 1656223 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-3njuhsyn/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-3njuhsyn/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4fffb7f74f2b2a8cc5e2aa9ce9fb055714a36c63 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-3njuhsyn/files/config.yaml @@ -0,0 +1,48 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 2 + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: l1 +max_oc: + value: 150 +target_transform: + value: none +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-3njuhsyn/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-3njuhsyn/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..6ed20f5dce090e790b74bc2294ce1b723d9b7842 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-3njuhsyn/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.69it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-5zfpqm8j/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-5zfpqm8j/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..4ef77f7d651aaa19cac8fe61c054ca9b80650b39 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-5zfpqm8j/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.69it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] 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+100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-5zfpqm8j/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-5zfpqm8j/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-5zfpqm8j/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-fwohu6ft/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-fwohu6ft/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..b95e0b317eba002fb500a6807972b62e29e5a8b0 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-fwohu6ft/files/output.log @@ -0,0 +1,1210 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.69it/s] +Epoch 1: +Training Loss: 68.5800 +Validation Loss: 8.7777 +RPIQ: 0.4566 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 2: +Training Loss: 14.3721 +Validation Loss: 6.9549 +RPIQ: 0.4978 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 3: +Training Loss: 12.7258 +Validation Loss: 6.5460 +RPIQ: 0.5086 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 4: +Training Loss: 12.1132 +Validation Loss: 6.3766 +RPIQ: 0.5101 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 5: +Training Loss: 11.8972 +Validation Loss: 6.3434 +RPIQ: 0.5088 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 6: +Training Loss: 11.8800 +Validation Loss: 6.3005 +RPIQ: 0.5085 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 7: +Training Loss: 11.4889 +Validation Loss: 6.2742 +RPIQ: 0.5072 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 8: +Training Loss: 11.4726 +Validation Loss: 6.1703 +RPIQ: 0.5139 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 9: +Training Loss: 11.2974 +Validation Loss: 6.1237 +RPIQ: 0.5147 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 10: +Training Loss: 11.0538 +Validation Loss: 6.0564 +RPIQ: 0.5171 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 11: +Training Loss: 10.9112 +Validation Loss: 6.0616 +RPIQ: 0.5116 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 12: +Training Loss: 11.1666 +Validation Loss: 6.0980 +RPIQ: 0.5098 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 13: +Training Loss: 11.3455 +Validation Loss: 6.1377 +RPIQ: 0.5062 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 14: +Training Loss: 11.0838 +Validation Loss: 5.8485 +RPIQ: 0.5195 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 15: +Training Loss: 10.8177 +Validation Loss: 5.9691 +RPIQ: 0.5129 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 16: +Training Loss: 10.9519 +Validation Loss: 6.1131 +RPIQ: 0.5074 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 17: +Training Loss: 10.5579 +Validation Loss: 5.7897 +RPIQ: 0.5200 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 18: +Training Loss: 10.5074 +Validation Loss: 5.7822 +RPIQ: 0.5214 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 19: +Training Loss: 10.4338 +Validation Loss: 5.7130 +RPIQ: 0.5240 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 20: +Training Loss: 10.2242 +Validation Loss: 5.8839 +RPIQ: 0.5207 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 21: +Training Loss: 10.3673 +Validation Loss: 5.7314 +RPIQ: 0.5322 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 22: +Training Loss: 10.6103 +Validation Loss: 5.7403 +RPIQ: 0.5360 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 23: +Training Loss: 10.2361 +Validation Loss: 5.7209 +RPIQ: 0.5382 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 24: +Training Loss: 10.1139 +Validation Loss: 6.0841 +RPIQ: 0.5317 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 25: +Training Loss: 10.2014 +Validation Loss: 5.7460 +RPIQ: 0.5476 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 26: +Training Loss: 10.0366 +Validation Loss: 5.7503 +RPIQ: 0.5505 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 27: +Training Loss: 9.9668 +Validation Loss: 5.5853 +RPIQ: 0.5570 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 28: +Training Loss: 10.4381 +Validation Loss: 5.7470 +RPIQ: 0.5549 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 29: +Training Loss: 9.7527 +Validation Loss: 5.6061 +RPIQ: 0.5589 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 30: +Training Loss: 9.7403 +Validation Loss: 6.0466 +RPIQ: 0.5482 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 31: +Training Loss: 10.1685 +Validation Loss: 5.8203 +RPIQ: 0.5539 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 32: +Training Loss: 9.9973 +Validation Loss: 5.8177 +RPIQ: 0.5512 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 33: +Training Loss: 9.3989 +Validation Loss: 5.6319 +RPIQ: 0.5608 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 34: +Training Loss: 9.5356 +Validation Loss: 5.2905 +RPIQ: 0.5731 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 35: +Training Loss: 9.2612 +Validation Loss: 5.0792 +RPIQ: 0.5769 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 36: +Training Loss: 9.3977 +Validation Loss: 5.5075 +RPIQ: 0.5609 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + 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+100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 43: +Training Loss: 9.3323 +Validation Loss: 5.9214 +RPIQ: 0.5585 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 44: +Training Loss: 9.5875 +Validation Loss: 5.5642 +RPIQ: 0.5721 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 45: +Training Loss: 9.2182 +Validation Loss: 5.8113 +RPIQ: 0.5687 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 46: +Training Loss: 9.1116 +Validation Loss: 5.0197 +RPIQ: 0.5890 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 47: +Training Loss: 9.1522 +Validation Loss: 5.6609 +RPIQ: 0.5705 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 48: +Training Loss: 8.8797 +Validation Loss: 4.9037 +RPIQ: 0.5912 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 49: +Training Loss: 9.1433 +Validation Loss: 5.5708 +RPIQ: 0.5769 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 50: +Training Loss: 9.0449 +Validation Loss: 5.8802 +RPIQ: 0.5612 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 51: +Training Loss: 9.2377 +Validation Loss: 5.9011 +RPIQ: 0.5629 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 52: +Training Loss: 8.7219 +Validation Loss: 5.6323 +RPIQ: 0.5688 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 53: +Training Loss: 9.1144 +Validation Loss: 5.8382 +RPIQ: 0.5629 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 54: +Training Loss: 8.9264 +Validation Loss: 5.4137 +RPIQ: 0.5799 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 55: +Training Loss: 8.8586 +Validation Loss: 5.9905 +RPIQ: 0.5666 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 56: +Training Loss: 8.7539 +Validation Loss: 5.4620 +RPIQ: 0.5810 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 57: +Training Loss: 9.0500 +Validation Loss: 4.9509 +RPIQ: 0.5935 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 58: +Training Loss: 8.5679 +Validation Loss: 5.0965 +RPIQ: 0.5904 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 59: +Training Loss: 8.7286 +Validation Loss: 5.6483 +RPIQ: 0.5776 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 60: +Training Loss: 8.9657 +Validation Loss: 5.5975 +RPIQ: 0.5867 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 61: +Training Loss: 8.7362 +Validation Loss: 5.2542 +RPIQ: 0.5874 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 62: +Training Loss: 8.4614 +Validation Loss: 5.4491 +RPIQ: 0.5813 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 63: +Training Loss: 8.9659 +Validation Loss: 6.0041 +RPIQ: 0.5660 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 64: +Training Loss: 8.3974 +Validation Loss: 5.5416 +RPIQ: 0.5823 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 65: +Training Loss: 8.7602 +Validation Loss: 5.9874 +RPIQ: 0.5693 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 66: +Training Loss: 9.0192 +Validation Loss: 5.5483 +RPIQ: 0.5809 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 67: +Training Loss: 8.4368 +Validation Loss: 5.0251 +RPIQ: 0.5986 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 68: +Training Loss: 8.4284 +Validation Loss: 5.2515 +RPIQ: 0.5879 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 69: +Training Loss: 8.7565 +Validation Loss: 5.7587 +RPIQ: 0.5806 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 70: +Training Loss: 8.6782 +Validation Loss: 5.1272 +RPIQ: 0.5943 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 71: +Training Loss: 8.4351 +Validation Loss: 5.3461 +RPIQ: 0.5959 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 72: +Training Loss: 8.4014 +Validation Loss: 5.5386 +RPIQ: 0.5855 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 73: +Training Loss: 8.5953 +Validation Loss: 5.9976 +RPIQ: 0.5750 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 74: +Training Loss: 8.3834 +Validation Loss: 5.7271 +RPIQ: 0.5877 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 75: +Training Loss: 8.8557 +Validation Loss: 5.4233 +RPIQ: 0.5818 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 76: +Training Loss: 8.6178 +Validation Loss: 5.6476 +RPIQ: 0.5882 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 77: +Training Loss: 8.1797 +Validation Loss: 5.5714 +RPIQ: 0.5911 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 78: +Training Loss: 8.1850 +Validation Loss: 5.2565 +RPIQ: 0.5954 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 79: +Training Loss: 8.5644 +Validation Loss: 5.8573 +RPIQ: 0.5851 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 80: +Training Loss: 8.1126 +Validation Loss: 4.8721 +RPIQ: 0.6079 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 81: +Training Loss: 8.4018 +Validation Loss: 5.1270 +RPIQ: 0.6015 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 82: +Training Loss: 7.9833 +Validation Loss: 5.5885 +RPIQ: 0.5928 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 83: +Training Loss: 8.1243 +Validation Loss: 5.0341 +RPIQ: 0.5975 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 84: +Training Loss: 8.5710 +Validation Loss: 5.2326 +RPIQ: 0.6098 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 85: +Training Loss: 8.3089 +Validation Loss: 5.5914 +RPIQ: 0.5848 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 86: +Training Loss: 8.3133 +Validation Loss: 5.4961 +RPIQ: 0.5958 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 87: +Training Loss: 8.3325 +Validation Loss: 5.1158 +RPIQ: 0.5960 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 88: +Training Loss: 8.0530 +Validation Loss: 5.9445 +RPIQ: 0.5840 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 89: +Training Loss: 7.7986 +Validation Loss: 4.9492 +RPIQ: 0.6052 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 90: +Training Loss: 8.1730 +Validation Loss: 5.0458 +RPIQ: 0.6053 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 91: +Training Loss: 8.0954 +Validation Loss: 5.5119 +RPIQ: 0.5944 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 92: +Training Loss: 8.0034 +Validation Loss: 5.2121 +RPIQ: 0.6048 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 93: +Training Loss: 8.0229 +Validation Loss: 5.4094 +RPIQ: 0.5914 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 94: +Training Loss: 8.2673 +Validation Loss: 4.9038 +RPIQ: 0.6123 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 95: +Training Loss: 8.1605 +Validation Loss: 5.1803 +RPIQ: 0.6024 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 96: +Training Loss: 8.0214 +Validation Loss: 5.1377 +RPIQ: 0.6038 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 97: +Training Loss: 7.8500 +Validation Loss: 4.8850 +RPIQ: 0.6144 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 98: +Training Loss: 7.9362 +Validation Loss: 5.0725 +RPIQ: 0.6078 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 99: +Training Loss: 7.9459 +Validation Loss: 4.4041 +RPIQ: 0.6273 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 100: +Training Loss: 8.2294 +Validation Loss: 4.7726 +RPIQ: 0.6167 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 101: +Training Loss: 7.7313 +Validation Loss: 5.0054 +RPIQ: 0.6094 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 102: +Training Loss: 7.8284 +Validation Loss: 5.4539 +RPIQ: 0.5966 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 103: +Training Loss: 8.0663 +Validation Loss: 5.5108 +RPIQ: 0.6037 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 104: +Training Loss: 8.0171 +Validation Loss: 5.0134 +RPIQ: 0.6098 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 105: +Training Loss: 8.0283 +Validation Loss: 5.4316 +RPIQ: 0.6000 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 106: +Training Loss: 7.8102 +Validation Loss: 5.2000 +RPIQ: 0.6190 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 107: +Training Loss: 7.8058 +Validation Loss: 5.1296 +RPIQ: 0.6159 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 108: +Training Loss: 8.0602 +Validation Loss: 5.0179 +RPIQ: 0.6142 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 109: +Training Loss: 7.5932 +Validation Loss: 5.0530 +RPIQ: 0.6083 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 110: +Training Loss: 7.6123 +Validation Loss: 4.7203 +RPIQ: 0.6187 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 111: +Training Loss: 8.3302 +Validation Loss: 5.3226 +RPIQ: 0.6014 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 112: +Training Loss: 7.7616 +Validation Loss: 4.2226 +RPIQ: 0.6431 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 113: +Training Loss: 8.0752 +Validation Loss: 5.5299 +RPIQ: 0.5974 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 114: +Training Loss: 7.9320 +Validation Loss: 5.3746 +RPIQ: 0.6035 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 115: +Training Loss: 7.9950 +Validation Loss: 5.1675 +RPIQ: 0.6090 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 116: +Training Loss: 7.5667 +Validation Loss: 4.8003 +RPIQ: 0.6221 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 117: +Training Loss: 8.0061 +Validation Loss: 4.7600 +RPIQ: 0.6193 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 118: +Training Loss: 7.7635 +Validation Loss: 4.6865 +RPIQ: 0.6282 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 119: +Training Loss: 7.7613 +Validation Loss: 5.0510 +RPIQ: 0.6183 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 120: +Training Loss: 7.6165 +Validation Loss: 5.0778 +RPIQ: 0.6085 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 121: +Training Loss: 7.7803 +Validation Loss: 4.8411 +RPIQ: 0.6251 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 122: +Training Loss: 7.6016 +Validation Loss: 5.0149 +RPIQ: 0.6007 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 123: +Training Loss: 7.9558 +Validation Loss: 5.1623 +RPIQ: 0.6153 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 124: +Training Loss: 7.8800 +Validation Loss: 4.7743 +RPIQ: 0.6309 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 125: +Training Loss: 7.8280 +Validation Loss: 5.0649 +RPIQ: 0.6158 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 126: +Training Loss: 7.6657 +Validation Loss: 5.4671 +RPIQ: 0.6128 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 127: +Training Loss: 7.8841 +Validation Loss: 4.7834 +RPIQ: 0.6271 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 128: +Training Loss: 7.6260 +Validation Loss: 5.0427 +RPIQ: 0.6142 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 129: +Training Loss: 7.6918 +Validation Loss: 4.2752 +RPIQ: 0.6375 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 130: +Training Loss: 7.8235 +Validation Loss: 5.1582 +RPIQ: 0.6183 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1.78it/s] + +Epoch 136: +Training Loss: 7.4980 +Validation Loss: 4.8405 +RPIQ: 0.6224 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 137: +Training Loss: 7.4382 +Validation Loss: 5.6612 +RPIQ: 0.5958 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 138: +Training Loss: 7.8499 +Validation Loss: 4.6812 +RPIQ: 0.6377 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 139: +Training Loss: 7.6426 +Validation Loss: 4.7137 +RPIQ: 0.6228 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 140: +Training Loss: 7.6191 +Validation Loss: 4.6894 +RPIQ: 0.6266 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 141: +Training Loss: 7.8090 +Validation Loss: 4.9250 +RPIQ: 0.6208 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1.78it/s] + +Epoch 147: +Training Loss: 7.6572 +Validation Loss: 5.0504 +RPIQ: 0.6250 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 148: +Training Loss: 7.2583 +Validation Loss: 5.0989 +RPIQ: 0.6289 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 149: +Training Loss: 7.5671 +Validation Loss: 4.3204 +RPIQ: 0.6385 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 150: +Training Loss: 7.7350 +Validation Loss: 4.8127 +RPIQ: 0.6351 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 151: +Training Loss: 7.1037 +Validation Loss: 5.3326 +RPIQ: 0.6186 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 152: +Training Loss: 7.5066 +Validation Loss: 4.7929 +RPIQ: 0.6292 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 153: +Training Loss: 7.3451 +Validation Loss: 5.1209 +RPIQ: 0.6168 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 154: +Training Loss: 7.6680 +Validation Loss: 4.8053 +RPIQ: 0.6302 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 155: +Training Loss: 7.2752 +Validation Loss: 4.6745 +RPIQ: 0.6364 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 156: +Training Loss: 7.5312 +Validation Loss: 4.3792 +RPIQ: 0.6431 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 157: +Training Loss: 7.6744 +Validation Loss: 4.9191 +RPIQ: 0.6252 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 158: +Training Loss: 7.5694 +Validation Loss: 4.8039 +RPIQ: 0.6320 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 159: +Training Loss: 7.5282 +Validation Loss: 4.7390 +RPIQ: 0.6285 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 160: +Training Loss: 7.6282 +Validation Loss: 4.6973 +RPIQ: 0.6271 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 161: +Training Loss: 7.4006 +Validation Loss: 4.9969 +RPIQ: 0.6292 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 162: +Training Loss: 7.8255 +Validation Loss: 4.9450 +RPIQ: 0.6331 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 163: +Training Loss: 7.3577 +Validation Loss: 4.7387 +RPIQ: 0.6231 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 164: +Training Loss: 7.5947 +Validation Loss: 4.9471 +RPIQ: 0.6300 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 165: +Training Loss: 7.5147 +Validation Loss: 4.5083 +RPIQ: 0.6484 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 166: +Training Loss: 7.5253 +Validation Loss: 4.4648 +RPIQ: 0.6496 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 167: +Training Loss: 7.3029 +Validation Loss: 5.4231 +RPIQ: 0.6129 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 168: +Training Loss: 7.4079 +Validation Loss: 4.2869 +RPIQ: 0.6580 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 169: +Training Loss: 7.4378 +Validation Loss: 5.0792 +RPIQ: 0.6288 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 170: +Training Loss: 7.4075 +Validation Loss: 5.0132 +RPIQ: 0.6315 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 171: +Training Loss: 7.3625 +Validation Loss: 4.8609 +RPIQ: 0.6320 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 172: +Training Loss: 7.1536 +Validation Loss: 4.7063 +RPIQ: 0.6311 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 173: +Training Loss: 7.3054 +Validation Loss: 5.0615 +RPIQ: 0.6264 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 174: +Training Loss: 7.5087 +Validation Loss: 4.5943 +RPIQ: 0.6391 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 175: +Training Loss: 7.4477 +Validation Loss: 4.3870 +RPIQ: 0.6475 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 176: +Training Loss: 7.2574 +Validation Loss: 4.7061 +RPIQ: 0.6334 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 177: +Training Loss: 7.0210 +Validation Loss: 4.1525 +RPIQ: 0.6599 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 178: +Training Loss: 7.5935 +Validation Loss: 4.8787 +RPIQ: 0.6354 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 179: +Training Loss: 7.3243 +Validation Loss: 4.9533 +RPIQ: 0.6361 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 180: +Training Loss: 7.4513 +Validation Loss: 4.7657 +RPIQ: 0.6343 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 181: +Training Loss: 7.4816 +Validation Loss: 4.5424 +RPIQ: 0.6458 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 182: +Training Loss: 7.2479 +Validation Loss: 4.9914 +RPIQ: 0.6293 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 183: +Training Loss: 7.1447 +Validation Loss: 4.7732 +RPIQ: 0.6355 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 184: +Training Loss: 6.9736 +Validation Loss: 5.0702 +RPIQ: 0.6238 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 185: +Training Loss: 7.3146 +Validation Loss: 4.1987 +RPIQ: 0.6482 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 186: +Training Loss: 7.3701 +Validation Loss: 5.3000 +RPIQ: 0.6239 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 187: +Training Loss: 7.0560 +Validation Loss: 4.6713 +RPIQ: 0.6418 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 188: +Training Loss: 7.1064 +Validation Loss: 4.0250 +RPIQ: 0.6540 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 189: +Training Loss: 7.5062 +Validation Loss: 4.2260 +RPIQ: 0.6378 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 190: +Training Loss: 7.4040 +Validation Loss: 4.8220 +RPIQ: 0.6276 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 191: +Training Loss: 7.4121 +Validation Loss: 4.6116 +RPIQ: 0.6467 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 192: +Training Loss: 7.5815 +Validation Loss: 4.7174 +RPIQ: 0.6395 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 193: +Training Loss: 7.2480 +Validation Loss: 4.5016 +RPIQ: 0.6394 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 194: +Training Loss: 7.3475 +Validation Loss: 3.8666 +RPIQ: 0.6601 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 195: +Training Loss: 7.1295 +Validation Loss: 4.7584 +RPIQ: 0.6413 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 196: +Training Loss: 7.4428 +Validation Loss: 4.8976 +RPIQ: 0.6381 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 197: +Training Loss: 6.9757 +Validation Loss: 4.9280 +RPIQ: 0.6380 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 198: +Training Loss: 7.3306 +Validation Loss: 4.2049 +RPIQ: 0.6575 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 199: +Training Loss: 7.1898 +Validation Loss: 4.9315 +RPIQ: 0.6402 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 200: +Training Loss: 7.1450 +Validation Loss: 4.3104 +RPIQ: 0.6519 + +Best model saved with R²: 0.4472 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-fwohu6ft/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-fwohu6ft/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-fwohu6ft/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 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value: 6 +learning_rate: + value: 0.001 +loss_type: + value: l1 +max_oc: + value: 150 +target_transform: + value: none +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-g1xjfq98/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-g1xjfq98/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..a8528ea5f9a65fa90bf85b2488933e1cb27d6d37 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212000-g1xjfq98/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.69it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|██████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.68it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212116-f498fob8/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212116-f498fob8/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 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index 0000000000000000000000000000000000000000..49872986f1fa8e0fa61efebcf4884c331517af0a --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212116-f498fob8/run-f498fob8.wandb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:93a6b708db7611dca63d34c54017c06f200c3e037f523846ba630099f7ea2299 +size 1385622 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212116-ivzci0v8/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212116-ivzci0v8/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..37b9065066a6b533a0d7f989b886b7d165d08ba2 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212116-ivzci0v8/files/config.yaml @@ -0,0 +1,48 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 2 + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: composite_l1 +max_oc: + value: 150 +target_transform: + value: none +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212116-ivzci0v8/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212116-ivzci0v8/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..81e5ed5b25682c7ba3468e77b59481d174abdeb6 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212116-ivzci0v8/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.68it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212116-ivzci0v8/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212116-ivzci0v8/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.68it/s] +Epoch 1: +Training Loss: 130.3774 +Validation Loss: 14.6598 +RPIQ: 0.3849 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 2: +Training Loss: 18.1762 +Validation Loss: 6.7349 +RPIQ: 0.5077 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 3: +Training Loss: 12.9656 +Validation Loss: 6.7299 +RPIQ: 0.5092 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 4: +Training Loss: 12.0736 +Validation Loss: 6.5308 +RPIQ: 0.5099 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 5: +Training Loss: 11.9224 +Validation Loss: 6.3453 +RPIQ: 0.5113 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 6: +Training Loss: 11.7549 +Validation Loss: 6.3001 +RPIQ: 0.5135 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 7: +Training Loss: 11.2749 +Validation Loss: 6.0985 +RPIQ: 0.5226 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 8: +Training Loss: 11.2969 +Validation Loss: 6.1624 +RPIQ: 0.5158 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 9: +Training Loss: 11.2121 +Validation Loss: 6.1061 +RPIQ: 0.5187 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 10: +Training Loss: 10.8738 +Validation Loss: 6.0448 +RPIQ: 0.5236 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 11: +Training Loss: 10.8390 +Validation Loss: 6.1206 +RPIQ: 0.5063 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 12: +Training Loss: 10.9307 +Validation Loss: 5.9719 +RPIQ: 0.5200 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 13: +Training Loss: 11.3091 +Validation Loss: 5.9535 +RPIQ: 0.5231 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 14: +Training Loss: 10.8964 +Validation Loss: 5.9253 +RPIQ: 0.5194 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 15: +Training Loss: 10.7310 +Validation Loss: 6.1508 +RPIQ: 0.5022 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 16: +Training Loss: 10.7681 +Validation Loss: 5.9806 +RPIQ: 0.5126 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 17: +Training Loss: 10.3308 +Validation Loss: 5.8786 +RPIQ: 0.5190 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 18: +Training Loss: 10.6267 +Validation Loss: 5.8652 +RPIQ: 0.5195 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 19: +Training Loss: 10.7114 +Validation Loss: 5.8554 +RPIQ: 0.5197 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 20: +Training Loss: 10.5339 +Validation Loss: 5.8198 +RPIQ: 0.5186 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 21: +Training Loss: 10.6002 +Validation Loss: 5.8608 +RPIQ: 0.5153 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 22: +Training Loss: 10.9871 +Validation Loss: 5.7263 +RPIQ: 0.5238 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 23: +Training Loss: 10.6454 +Validation Loss: 5.7482 +RPIQ: 0.5212 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 24: +Training Loss: 10.6832 +Validation Loss: 5.7184 +RPIQ: 0.5210 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 25: +Training Loss: 10.7040 +Validation Loss: 5.5969 +RPIQ: 0.5288 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 26: +Training Loss: 10.4136 +Validation Loss: 5.6356 +RPIQ: 0.5241 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 27: +Training Loss: 10.2644 +Validation Loss: 6.0602 +RPIQ: 0.5067 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 28: +Training Loss: 10.9837 +Validation Loss: 5.7743 +RPIQ: 0.5160 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 29: +Training Loss: 10.0713 +Validation Loss: 5.7704 +RPIQ: 0.5174 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 30: +Training Loss: 10.0492 +Validation Loss: 5.6928 +RPIQ: 0.5216 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 31: +Training Loss: 10.6676 +Validation Loss: 5.7423 +RPIQ: 0.5218 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 32: +Training Loss: 10.3089 +Validation Loss: 5.8512 +RPIQ: 0.5201 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 33: +Training Loss: 9.8793 +Validation Loss: 5.6655 +RPIQ: 0.5272 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 34: +Training Loss: 9.9672 +Validation Loss: 5.7207 +RPIQ: 0.5291 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 35: +Training Loss: 9.9122 +Validation Loss: 5.4774 +RPIQ: 0.5374 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 36: +Training Loss: 9.8960 +Validation Loss: 5.0964 +RPIQ: 0.5516 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 37: +Training Loss: 9.7986 +Validation Loss: 5.7743 +RPIQ: 0.5337 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 38: +Training Loss: 9.5600 +Validation Loss: 5.7659 +RPIQ: 0.5366 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 39: +Training Loss: 9.7513 +Validation Loss: 5.2795 +RPIQ: 0.5524 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 40: +Training Loss: 9.6126 +Validation Loss: 5.2546 +RPIQ: 0.5582 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 41: +Training Loss: 9.4385 +Validation Loss: 5.2109 +RPIQ: 0.5605 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 42: +Training Loss: 9.6651 +Validation Loss: 4.8291 +RPIQ: 0.5810 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 43: +Training Loss: 9.6571 +Validation Loss: 5.7734 +RPIQ: 0.5540 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 44: +Training Loss: 9.8708 +Validation Loss: 5.1717 +RPIQ: 0.5710 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 45: +Training Loss: 9.5261 +Validation Loss: 5.5055 +RPIQ: 0.5650 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 46: +Training Loss: 9.5563 +Validation Loss: 5.6057 +RPIQ: 0.5734 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 47: +Training Loss: 9.5216 +Validation Loss: 5.6155 +RPIQ: 0.5718 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 48: +Training Loss: 9.2755 +Validation Loss: 5.2535 +RPIQ: 0.5835 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 49: +Training Loss: 9.6871 +Validation Loss: 5.7103 +RPIQ: 0.5780 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 50: +Training Loss: 9.4071 +Validation Loss: 6.0776 +RPIQ: 0.5694 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 51: +Training Loss: 9.5345 +Validation Loss: 5.5304 +RPIQ: 0.5815 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 52: +Training Loss: 8.9383 +Validation Loss: 5.5053 +RPIQ: 0.5844 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 53: +Training Loss: 9.4351 +Validation Loss: 5.5556 +RPIQ: 0.5790 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 54: +Training Loss: 9.1499 +Validation Loss: 5.0547 +RPIQ: 0.6004 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 55: +Training Loss: 9.3318 +Validation Loss: 5.1217 +RPIQ: 0.5917 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 56: +Training Loss: 9.0863 +Validation Loss: 5.4836 +RPIQ: 0.5876 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 57: +Training Loss: 9.4375 +Validation Loss: 5.4564 +RPIQ: 0.5891 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 58: +Training Loss: 8.9992 +Validation Loss: 5.3390 +RPIQ: 0.5891 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 59: +Training Loss: 9.0410 +Validation Loss: 5.2962 +RPIQ: 0.5947 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 60: +Training Loss: 9.3638 +Validation Loss: 5.7476 +RPIQ: 0.5830 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 61: +Training Loss: 9.1038 +Validation Loss: 4.5309 +RPIQ: 0.6206 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 62: +Training Loss: 8.9966 +Validation Loss: 6.1666 +RPIQ: 0.5755 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 63: +Training Loss: 9.0546 +Validation Loss: 5.2114 +RPIQ: 0.5985 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 64: +Training Loss: 8.7583 +Validation Loss: 5.5066 +RPIQ: 0.5997 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 65: +Training Loss: 9.2536 +Validation Loss: 5.6736 +RPIQ: 0.5862 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 66: +Training Loss: 9.2651 +Validation Loss: 4.9993 +RPIQ: 0.6085 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 67: +Training Loss: 9.0677 +Validation Loss: 5.5494 +RPIQ: 0.5947 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 68: +Training Loss: 8.7251 +Validation Loss: 5.3339 +RPIQ: 0.5961 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 69: +Training Loss: 9.2430 +Validation Loss: 5.6396 +RPIQ: 0.5882 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 70: +Training Loss: 9.1084 +Validation Loss: 4.9803 +RPIQ: 0.6102 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 71: +Training Loss: 9.0724 +Validation Loss: 4.7624 +RPIQ: 0.6203 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 72: +Training Loss: 8.9006 +Validation Loss: 4.9495 +RPIQ: 0.6176 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 73: +Training Loss: 9.0152 +Validation Loss: 5.1242 +RPIQ: 0.6063 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 74: +Training Loss: 8.6721 +Validation Loss: 4.5315 +RPIQ: 0.6223 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 75: +Training Loss: 9.2307 +Validation Loss: 5.0045 +RPIQ: 0.6185 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 76: +Training Loss: 9.1881 +Validation Loss: 4.7034 +RPIQ: 0.6178 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 77: +Training Loss: 8.6229 +Validation Loss: 4.9486 +RPIQ: 0.6081 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 78: +Training Loss: 8.7795 +Validation Loss: 5.7918 +RPIQ: 0.5830 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 79: +Training Loss: 9.1198 +Validation Loss: 4.8882 +RPIQ: 0.6197 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 80: +Training Loss: 8.6013 +Validation Loss: 4.9110 +RPIQ: 0.6147 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 81: +Training Loss: 8.8384 +Validation Loss: 5.9848 +RPIQ: 0.5796 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 82: +Training Loss: 8.6953 +Validation Loss: 4.1382 +RPIQ: 0.6451 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 83: +Training Loss: 8.8575 +Validation Loss: 5.8520 +RPIQ: 0.5919 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 84: +Training Loss: 9.0674 +Validation Loss: 4.4128 +RPIQ: 0.6443 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 85: +Training Loss: 8.8303 +Validation Loss: 5.8656 +RPIQ: 0.5937 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 86: +Training Loss: 8.8760 +Validation Loss: 5.2041 +RPIQ: 0.6071 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 87: +Training Loss: 8.6840 +Validation Loss: 4.5457 +RPIQ: 0.6323 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 88: +Training Loss: 8.6745 +Validation Loss: 5.5359 +RPIQ: 0.6005 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 89: +Training Loss: 8.4140 +Validation Loss: 5.0123 +RPIQ: 0.6096 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 90: +Training Loss: 8.6027 +Validation Loss: 5.0279 +RPIQ: 0.6095 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 91: +Training Loss: 8.6555 +Validation Loss: 5.0426 +RPIQ: 0.6107 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 92: +Training Loss: 8.6028 +Validation Loss: 5.3495 +RPIQ: 0.5964 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 93: +Training Loss: 8.5466 +Validation Loss: 4.9544 +RPIQ: 0.6113 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 94: +Training Loss: 8.7067 +Validation Loss: 4.9627 +RPIQ: 0.6166 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 95: +Training Loss: 8.4160 +Validation Loss: 4.7837 +RPIQ: 0.6153 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 96: +Training Loss: 8.4019 +Validation Loss: 5.1667 +RPIQ: 0.6007 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 97: +Training Loss: 8.5455 +Validation Loss: 4.6190 +RPIQ: 0.6326 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 98: +Training Loss: 8.4259 +Validation Loss: 4.4307 +RPIQ: 0.6344 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 99: +Training Loss: 8.3296 +Validation Loss: 5.0538 +RPIQ: 0.6195 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 100: +Training Loss: 8.8004 +Validation Loss: 4.4824 +RPIQ: 0.6408 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 101: +Training Loss: 8.3869 +Validation Loss: 5.2990 +RPIQ: 0.6106 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 102: +Training Loss: 8.4965 +Validation Loss: 4.9285 +RPIQ: 0.6118 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 103: +Training Loss: 8.6217 +Validation Loss: 4.6668 +RPIQ: 0.6407 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 104: +Training Loss: 8.6595 +Validation Loss: 5.2488 +RPIQ: 0.6068 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 105: +Training Loss: 8.5975 +Validation Loss: 4.8921 +RPIQ: 0.6228 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 106: +Training Loss: 8.4067 +Validation Loss: 5.0928 +RPIQ: 0.6205 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 107: +Training Loss: 8.2367 +Validation Loss: 5.3427 +RPIQ: 0.6061 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 108: +Training Loss: 8.6202 +Validation Loss: 4.3511 +RPIQ: 0.6401 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 109: +Training Loss: 8.1843 +Validation Loss: 4.8652 +RPIQ: 0.6347 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 110: +Training Loss: 8.3521 +Validation Loss: 5.1071 +RPIQ: 0.6232 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 111: +Training Loss: 8.9706 +Validation Loss: 5.3219 +RPIQ: 0.6217 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 112: +Training Loss: 8.3213 +Validation Loss: 4.8474 +RPIQ: 0.6309 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 113: +Training Loss: 8.5975 +Validation Loss: 5.2385 +RPIQ: 0.6168 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 114: +Training Loss: 8.5789 +Validation Loss: 4.7196 +RPIQ: 0.6381 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 115: +Training Loss: 8.4976 +Validation Loss: 4.5329 +RPIQ: 0.6417 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 116: +Training Loss: 8.2434 +Validation Loss: 5.4917 +RPIQ: 0.6051 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 117: +Training Loss: 8.7773 +Validation Loss: 4.8769 +RPIQ: 0.6296 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 118: +Training Loss: 8.4872 +Validation Loss: 4.7985 +RPIQ: 0.6350 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 119: +Training Loss: 8.5598 +Validation Loss: 4.7164 +RPIQ: 0.6403 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 120: +Training Loss: 8.1880 +Validation Loss: 5.1695 +RPIQ: 0.6167 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 121: +Training Loss: 8.3701 +Validation Loss: 4.4246 +RPIQ: 0.6476 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 122: +Training Loss: 8.3580 +Validation Loss: 4.9662 +RPIQ: 0.6239 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 123: +Training Loss: 8.3809 +Validation Loss: 4.7582 +RPIQ: 0.6391 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 124: +Training Loss: 8.4705 +Validation Loss: 4.5920 +RPIQ: 0.6439 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 125: +Training Loss: 8.4769 +Validation Loss: 4.6698 +RPIQ: 0.6380 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 126: +Training Loss: 8.2002 +Validation Loss: 5.4431 +RPIQ: 0.6224 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 127: +Training Loss: 8.3780 +Validation Loss: 5.1666 +RPIQ: 0.6263 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 128: +Training Loss: 8.3517 +Validation Loss: 5.4563 +RPIQ: 0.6119 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] + +Epoch 129: +Training Loss: 8.3451 +Validation Loss: 5.5495 +RPIQ: 0.6128 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 130: +Training Loss: 8.5014 +Validation Loss: 5.2423 +RPIQ: 0.6204 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 131: +Training Loss: 8.3722 +Validation Loss: 5.1831 +RPIQ: 0.6320 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 132: +Training Loss: 8.2614 +Validation Loss: 4.5114 +RPIQ: 0.6594 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 133: +Training Loss: 8.2492 +Validation Loss: 4.5963 +RPIQ: 0.6464 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 134: +Training Loss: 8.1873 +Validation Loss: 4.9466 +RPIQ: 0.6352 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 135: +Training Loss: 8.2820 +Validation Loss: 5.3001 +RPIQ: 0.6109 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 136: +Training Loss: 8.3391 +Validation Loss: 4.8754 +RPIQ: 0.6390 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 137: +Training Loss: 8.1180 +Validation Loss: 4.0130 +RPIQ: 0.6624 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 138: +Training Loss: 8.3735 +Validation Loss: 4.5959 +RPIQ: 0.6436 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 139: +Training Loss: 8.4195 +Validation Loss: 5.0696 +RPIQ: 0.6289 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 140: +Training Loss: 8.1754 +Validation Loss: 4.9557 +RPIQ: 0.6335 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 141: +Training Loss: 8.1051 +Validation Loss: 5.2002 +RPIQ: 0.6358 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 142: +Training Loss: 8.2075 +Validation Loss: 4.6279 +RPIQ: 0.6635 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 143: +Training Loss: 8.2524 +Validation Loss: 4.1390 +RPIQ: 0.6681 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 144: +Training Loss: 8.3546 +Validation Loss: 4.6962 +RPIQ: 0.6424 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 145: +Training Loss: 8.1070 +Validation Loss: 5.0384 +RPIQ: 0.6402 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 146: +Training Loss: 8.2191 +Validation Loss: 4.3350 +RPIQ: 0.6641 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 147: +Training Loss: 8.2172 +Validation Loss: 4.7638 +RPIQ: 0.6491 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 148: +Training Loss: 7.8726 +Validation Loss: 5.1688 +RPIQ: 0.6349 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 149: +Training Loss: 8.2099 +Validation Loss: 4.6173 +RPIQ: 0.6537 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 150: +Training Loss: 8.3207 +Validation Loss: 4.3044 +RPIQ: 0.6587 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 151: +Training Loss: 7.7298 +Validation Loss: 5.2199 +RPIQ: 0.6335 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 152: +Training Loss: 8.0345 +Validation Loss: 5.1395 +RPIQ: 0.6259 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 153: +Training Loss: 7.9339 +Validation Loss: 4.8570 +RPIQ: 0.6352 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 154: +Training Loss: 7.9633 +Validation Loss: 4.5598 +RPIQ: 0.6539 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] + +Epoch 155: +Training Loss: 7.6128 +Validation Loss: 4.9669 +RPIQ: 0.6396 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 156: +Training Loss: 8.2937 +Validation Loss: 4.4968 +RPIQ: 0.6482 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 157: +Training Loss: 8.2250 +Validation Loss: 4.3456 +RPIQ: 0.6588 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 158: +Training Loss: 8.2086 +Validation Loss: 4.4993 +RPIQ: 0.6620 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 159: +Training Loss: 7.9104 +Validation Loss: 4.5494 +RPIQ: 0.6450 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 160: +Training Loss: 8.1119 +Validation Loss: 5.2225 +RPIQ: 0.6291 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 161: +Training Loss: 7.8788 +Validation Loss: 4.2307 +RPIQ: 0.6632 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 162: +Training Loss: 8.2838 +Validation Loss: 4.2069 +RPIQ: 0.6716 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 163: +Training Loss: 7.8676 +Validation Loss: 4.5455 +RPIQ: 0.6541 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 164: +Training Loss: 7.9603 +Validation Loss: 4.5899 +RPIQ: 0.6476 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 165: +Training Loss: 8.0693 +Validation Loss: 4.3691 +RPIQ: 0.6588 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 166: +Training Loss: 8.0026 +Validation Loss: 4.7631 +RPIQ: 0.6574 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 167: +Training Loss: 7.9374 +Validation Loss: 5.0252 +RPIQ: 0.6371 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 168: +Training Loss: 7.7660 +Validation Loss: 4.9988 +RPIQ: 0.6539 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 169: +Training Loss: 8.1572 +Validation Loss: 5.0607 +RPIQ: 0.6407 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 170: +Training Loss: 8.0480 +Validation Loss: 5.2918 +RPIQ: 0.6358 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 171: +Training Loss: 7.8647 +Validation Loss: 4.9915 +RPIQ: 0.6385 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 172: +Training Loss: 7.7038 +Validation Loss: 4.6071 +RPIQ: 0.6587 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 173: +Training Loss: 7.8190 +Validation Loss: 4.0024 +RPIQ: 0.6795 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 174: +Training Loss: 7.9269 +Validation Loss: 4.1791 +RPIQ: 0.6813 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 175: +Training Loss: 7.9740 +Validation Loss: 4.7728 +RPIQ: 0.6530 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 176: +Training Loss: 7.6193 +Validation Loss: 4.6421 +RPIQ: 0.6651 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 177: +Training Loss: 7.5413 +Validation Loss: 4.9361 +RPIQ: 0.6409 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 178: +Training Loss: 8.1250 +Validation Loss: 5.1945 +RPIQ: 0.6377 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 179: +Training Loss: 7.6517 +Validation Loss: 4.9572 +RPIQ: 0.6533 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 180: +Training Loss: 8.0222 +Validation Loss: 4.5021 +RPIQ: 0.6537 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 181: +Training Loss: 7.8641 +Validation Loss: 4.5745 +RPIQ: 0.6470 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 182: +Training Loss: 7.7550 +Validation Loss: 4.8591 +RPIQ: 0.6498 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 183: +Training Loss: 7.8789 +Validation Loss: 4.7954 +RPIQ: 0.6514 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 184: +Training Loss: 7.7822 +Validation Loss: 4.6006 +RPIQ: 0.6631 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 185: +Training Loss: 7.7681 +Validation Loss: 4.6927 +RPIQ: 0.6571 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 186: +Training Loss: 7.9012 +Validation Loss: 4.6886 +RPIQ: 0.6576 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 187: +Training Loss: 7.8657 +Validation Loss: 4.7046 +RPIQ: 0.6497 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 188: +Training Loss: 7.7907 +Validation Loss: 5.1293 +RPIQ: 0.6408 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 189: +Training Loss: 7.9310 +Validation Loss: 4.5813 +RPIQ: 0.6567 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 190: +Training Loss: 7.9351 +Validation Loss: 4.4650 +RPIQ: 0.6761 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 191: +Training Loss: 7.7848 +Validation Loss: 4.8768 +RPIQ: 0.6550 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 192: +Training Loss: 7.9289 +Validation Loss: 5.2311 +RPIQ: 0.6347 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 193: +Training Loss: 7.8345 +Validation Loss: 4.3667 +RPIQ: 0.6550 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 194: +Training Loss: 8.1131 +Validation Loss: 4.6872 +RPIQ: 0.6594 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 195: +Training Loss: 7.7815 +Validation Loss: 4.8768 +RPIQ: 0.6570 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] + +Epoch 196: +Training Loss: 7.8962 +Validation Loss: 4.2811 +RPIQ: 0.6711 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 197: +Training Loss: 7.3046 +Validation Loss: 4.8117 +RPIQ: 0.6683 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 198: +Training Loss: 7.5231 +Validation Loss: 4.6516 +RPIQ: 0.6699 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] + +Epoch 199: +Training Loss: 7.6169 +Validation Loss: 4.6442 +RPIQ: 0.6572 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.76it/s] + +Epoch 200: +Training Loss: 7.5898 +Validation Loss: 4.9521 +RPIQ: 0.6529 + +Best model saved with R²: 0.5107 diff --git 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212116-zfmm6n7w/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212116-zfmm6n7w/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 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"12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: mse +max_oc: + value: 150 +target_transform: + value: normalize +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212222-8wcfo5oa/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212222-8wcfo5oa/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..08316464ab3206f078e8c09a8a5ae946c022c28e --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212222-8wcfo5oa/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.82it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212222-8wcfo5oa/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212222-8wcfo5oa/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 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+ - 1 + - 49 + - 55 + - 71 + "3": + - 2 + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: mse +max_oc: + value: 150 +target_transform: + value: normalize +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212222-9thi1s6p/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212222-9thi1s6p/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..54d377d1d1562ef75358988725cbd91122a52505 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212222-9thi1s6p/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.78it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 +Target mean: 22.236005794270838, Target std: 19.5023913440399 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.79it/s] +Epoch 1: +Training Loss: 107794.5532 +Validation Loss: 1045.3342 +RPIQ: 0.0167 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 2: +Training Loss: 3952.5439 +Validation Loss: 161.9570 +RPIQ: 0.0401 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 3: +Training Loss: 1551.4733 +Validation Loss: 102.5322 +RPIQ: 0.0500 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 4: +Training Loss: 970.5708 +Validation Loss: 60.3168 +RPIQ: 0.0663 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 5: +Training Loss: 691.4512 +Validation Loss: 41.0105 +RPIQ: 0.0820 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 6: +Training Loss: 484.5722 +Validation Loss: 27.8856 +RPIQ: 0.0991 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 7: +Training Loss: 427.7418 +Validation Loss: 17.5861 +RPIQ: 0.1234 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 8: +Training Loss: 323.8828 +Validation Loss: 11.5579 +RPIQ: 0.1515 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 9: +Training Loss: 254.7261 +Validation Loss: 7.8094 +RPIQ: 0.1823 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 10: +Training Loss: 211.2102 +Validation Loss: 5.4053 +RPIQ: 0.2126 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 11: +Training Loss: 166.4547 +Validation Loss: 3.5952 +RPIQ: 0.2495 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 12: +Training Loss: 146.2505 +Validation Loss: 2.5805 +RPIQ: 0.2806 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 13: +Training Loss: 127.6262 +Validation Loss: 1.9623 +RPIQ: 0.3087 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 14: +Training Loss: 95.4168 +Validation Loss: 1.5575 +RPIQ: 0.3393 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 15: +Training Loss: 78.1257 +Validation Loss: 1.2925 +RPIQ: 0.3704 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 16: +Training Loss: 75.0336 +Validation Loss: 1.1067 +RPIQ: 0.3976 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 17: +Training Loss: 61.4103 +Validation Loss: 0.9268 +RPIQ: 0.4212 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 18: +Training Loss: 61.6677 +Validation Loss: 0.8271 +RPIQ: 0.4409 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 19: +Training Loss: 47.7124 +Validation Loss: 0.7215 +RPIQ: 0.4597 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 20: +Training Loss: 42.5040 +Validation Loss: 0.6202 +RPIQ: 0.4790 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 21: +Training Loss: 45.9989 +Validation Loss: 0.5550 +RPIQ: 0.4950 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 22: +Training Loss: 35.4594 +Validation Loss: 0.5141 +RPIQ: 0.5070 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 23: +Training Loss: 31.7209 +Validation Loss: 0.4783 +RPIQ: 0.5176 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 24: +Training Loss: 29.7389 +Validation Loss: 0.4573 +RPIQ: 0.5253 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 25: +Training Loss: 25.6042 +Validation Loss: 0.4135 +RPIQ: 0.5351 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 26: +Training Loss: 25.8959 +Validation Loss: 0.3738 +RPIQ: 0.5441 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 27: +Training Loss: 20.0107 +Validation Loss: 0.3825 +RPIQ: 0.5477 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 28: +Training Loss: 19.7048 +Validation Loss: 0.3580 +RPIQ: 0.5539 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 29: +Training Loss: 20.4643 +Validation Loss: 0.3585 +RPIQ: 0.5565 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 30: +Training Loss: 19.7246 +Validation Loss: 0.3417 +RPIQ: 0.5609 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 31: +Training Loss: 15.5149 +Validation Loss: 0.3484 +RPIQ: 0.5619 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 32: +Training Loss: 16.6044 +Validation Loss: 0.3180 +RPIQ: 0.5672 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 33: +Training Loss: 14.6234 +Validation Loss: 0.3180 +RPIQ: 0.5684 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 34: +Training Loss: 15.6180 +Validation Loss: 0.3098 +RPIQ: 0.5702 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 35: +Training Loss: 14.0889 +Validation Loss: 0.3229 +RPIQ: 0.5686 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 36: +Training Loss: 12.6867 +Validation Loss: 0.2905 +RPIQ: 0.5735 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 37: +Training Loss: 11.5297 +Validation Loss: 0.2834 +RPIQ: 0.5746 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 38: +Training Loss: 11.2532 +Validation Loss: 0.3071 +RPIQ: 0.5715 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 39: +Training Loss: 12.0332 +Validation Loss: 0.2947 +RPIQ: 0.5733 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 40: +Training Loss: 10.7361 +Validation Loss: 0.3093 +RPIQ: 0.5714 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 41: +Training Loss: 9.4682 +Validation Loss: 0.2919 +RPIQ: 0.5739 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 42: +Training Loss: 10.5350 +Validation Loss: 0.2749 +RPIQ: 0.5762 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 43: +Training Loss: 7.3517 +Validation Loss: 0.2782 +RPIQ: 0.5757 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 44: +Training Loss: 7.2207 +Validation Loss: 0.2835 +RPIQ: 0.5750 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 45: +Training Loss: 5.8037 +Validation Loss: 0.2790 +RPIQ: 0.5756 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 46: +Training Loss: 7.6784 +Validation Loss: 0.2787 +RPIQ: 0.5757 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 47: +Training Loss: 5.9989 +Validation Loss: 0.2871 +RPIQ: 0.5745 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 48: +Training Loss: 7.0701 +Validation Loss: 0.2934 +RPIQ: 0.5737 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 49: +Training Loss: 6.5351 +Validation Loss: 0.2754 +RPIQ: 0.5761 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 50: +Training Loss: 5.9468 +Validation Loss: 0.2816 +RPIQ: 0.5753 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 51: +Training Loss: 7.1066 +Validation Loss: 0.2825 +RPIQ: 0.5752 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 52: +Training Loss: 6.4202 +Validation Loss: 0.2686 +RPIQ: 0.5770 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 53: +Training Loss: 5.7304 +Validation Loss: 0.2668 +RPIQ: 0.5772 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 54: +Training Loss: 5.8379 +Validation Loss: 0.2778 +RPIQ: 0.5758 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 55: +Training Loss: 4.9328 +Validation Loss: 0.2782 +RPIQ: 0.5758 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 56: +Training Loss: 4.6366 +Validation Loss: 0.2770 +RPIQ: 0.5759 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 57: +Training Loss: 5.8399 +Validation Loss: 0.2959 +RPIQ: 0.5734 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 58: +Training Loss: 3.7390 +Validation Loss: 0.2713 +RPIQ: 0.5767 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 59: +Training Loss: 5.0198 +Validation Loss: 0.2593 +RPIQ: 0.5782 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 60: +Training Loss: 5.2456 +Validation Loss: 0.2758 +RPIQ: 0.5761 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 61: +Training Loss: 3.9742 +Validation Loss: 0.2716 +RPIQ: 0.5766 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 62: +Training Loss: 5.1094 +Validation Loss: 0.2800 +RPIQ: 0.5755 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 63: +Training Loss: 5.0272 +Validation Loss: 0.2690 +RPIQ: 0.5769 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 64: +Training Loss: 3.5797 +Validation Loss: 0.2781 +RPIQ: 0.5758 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 65: +Training Loss: 3.6316 +Validation Loss: 0.2799 +RPIQ: 0.5755 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 66: +Training Loss: 4.3968 +Validation Loss: 0.2930 +RPIQ: 0.5738 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 67: +Training Loss: 3.6893 +Validation Loss: 0.2697 +RPIQ: 0.5768 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 68: +Training Loss: 3.1053 +Validation Loss: 0.2788 +RPIQ: 0.5756 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 69: +Training Loss: 2.3902 +Validation Loss: 0.2770 +RPIQ: 0.5759 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 70: +Training Loss: 3.7731 +Validation Loss: 0.2815 +RPIQ: 0.5753 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 71: +Training Loss: 2.5008 +Validation Loss: 0.2804 +RPIQ: 0.5754 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 72: +Training Loss: 3.7829 +Validation Loss: 0.2707 +RPIQ: 0.5767 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 73: +Training Loss: 2.6118 +Validation Loss: 0.2857 +RPIQ: 0.5747 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 74: +Training Loss: 3.1790 +Validation Loss: 0.2781 +RPIQ: 0.5757 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 75: +Training Loss: 3.9808 +Validation Loss: 0.2673 +RPIQ: 0.5771 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 76: +Training Loss: 2.8364 +Validation Loss: 0.2692 +RPIQ: 0.5769 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 77: +Training Loss: 3.5103 +Validation Loss: 0.2516 +RPIQ: 0.5790 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 78: +Training Loss: 2.5672 +Validation Loss: 0.2865 +RPIQ: 0.5746 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 79: +Training Loss: 1.9060 +Validation Loss: 0.2756 +RPIQ: 0.5760 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 80: +Training Loss: 2.1788 +Validation Loss: 0.2787 +RPIQ: 0.5756 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 81: +Training Loss: 2.7937 +Validation Loss: 0.2772 +RPIQ: 0.5758 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 82: +Training Loss: 2.5952 +Validation Loss: 0.2875 +RPIQ: 0.5745 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 83: +Training Loss: 1.9239 +Validation Loss: 0.2754 +RPIQ: 0.5761 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 84: +Training Loss: 3.5321 +Validation Loss: 0.2641 +RPIQ: 0.5775 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 85: +Training Loss: 1.8394 +Validation Loss: 0.2766 +RPIQ: 0.5759 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 86: +Training Loss: 2.4104 +Validation Loss: 0.2723 +RPIQ: 0.5765 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 87: +Training Loss: 2.2871 +Validation Loss: 0.2789 +RPIQ: 0.5756 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 88: +Training Loss: 1.5275 +Validation Loss: 0.2924 +RPIQ: 0.5738 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 89: +Training Loss: 1.9555 +Validation Loss: 0.2697 +RPIQ: 0.5768 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 90: +Training Loss: 2.3721 +Validation Loss: 0.2784 +RPIQ: 0.5757 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 91: +Training Loss: 1.5028 +Validation Loss: 0.2805 +RPIQ: 0.5754 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 92: +Training Loss: 2.3491 +Validation Loss: 0.2718 +RPIQ: 0.5765 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 93: +Training Loss: 2.7926 +Validation Loss: 0.2511 +RPIQ: 0.5790 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 94: +Training Loss: 2.1772 +Validation Loss: 0.2796 +RPIQ: 0.5755 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 95: +Training Loss: 2.5333 +Validation Loss: 0.2750 +RPIQ: 0.5761 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 96: +Training Loss: 1.8437 +Validation Loss: 0.2830 +RPIQ: 0.5751 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 97: +Training Loss: 2.6100 +Validation Loss: 0.2649 +RPIQ: 0.5774 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 98: +Training Loss: 2.5518 +Validation Loss: 0.2728 +RPIQ: 0.5764 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 99: +Training Loss: 3.4470 +Validation Loss: 0.2642 +RPIQ: 0.5775 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 100: +Training Loss: 1.3644 +Validation Loss: 0.2646 +RPIQ: 0.5774 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 101: +Training Loss: 2.4517 +Validation Loss: 0.2608 +RPIQ: 0.5779 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 102: +Training Loss: 1.6434 +Validation Loss: 0.2740 +RPIQ: 0.5762 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 103: +Training Loss: 1.7211 +Validation Loss: 0.2526 +RPIQ: 0.5788 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 104: +Training Loss: 1.8619 +Validation Loss: 0.2600 +RPIQ: 0.5780 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 105: +Training Loss: 1.7031 +Validation Loss: 0.2865 +RPIQ: 0.5746 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 106: +Training Loss: 1.7278 +Validation Loss: 0.2669 +RPIQ: 0.5771 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 107: +Training Loss: 1.8827 +Validation Loss: 0.2689 +RPIQ: 0.5769 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 108: +Training Loss: 2.0221 +Validation Loss: 0.2637 +RPIQ: 0.5775 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 109: +Training Loss: 1.5453 +Validation Loss: 0.2641 +RPIQ: 0.5775 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 110: +Training Loss: 2.1218 +Validation Loss: 0.2664 +RPIQ: 0.5772 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 111: +Training Loss: 1.3830 +Validation Loss: 0.2718 +RPIQ: 0.5765 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 112: +Training Loss: 1.7814 +Validation Loss: 0.2569 +RPIQ: 0.5783 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 113: +Training Loss: 2.1548 +Validation Loss: 0.2432 +RPIQ: 0.5799 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 114: +Training Loss: 1.5138 +Validation Loss: 0.2894 +RPIQ: 0.5742 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 115: +Training Loss: 1.3944 +Validation Loss: 0.2779 +RPIQ: 0.5757 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 116: +Training Loss: 1.8822 +Validation Loss: 0.2636 +RPIQ: 0.5775 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 117: +Training Loss: 1.4414 +Validation Loss: 0.2764 +RPIQ: 0.5759 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 118: +Training Loss: 2.4341 +Validation Loss: 0.2654 +RPIQ: 0.5773 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 119: +Training Loss: 1.2304 +Validation Loss: 0.2640 +RPIQ: 0.5775 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 120: +Training Loss: 1.4452 +Validation Loss: 0.2653 +RPIQ: 0.5773 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 121: +Training Loss: 1.2162 +Validation Loss: 0.2574 +RPIQ: 0.5782 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 122: +Training Loss: 1.2178 +Validation Loss: 0.2799 +RPIQ: 0.5755 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 123: +Training Loss: 1.5494 +Validation Loss: 0.2863 +RPIQ: 0.5746 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 124: +Training Loss: 1.7610 +Validation Loss: 0.2711 +RPIQ: 0.5766 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 125: +Training Loss: 1.2225 +Validation Loss: 0.2762 +RPIQ: 0.5759 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 126: +Training Loss: 1.8478 +Validation Loss: 0.2779 +RPIQ: 0.5757 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 127: +Training Loss: 1.2771 +Validation Loss: 0.2759 +RPIQ: 0.5760 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 128: +Training Loss: 1.5677 +Validation Loss: 0.2772 +RPIQ: 0.5758 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 129: +Training Loss: 1.5569 +Validation Loss: 0.2619 +RPIQ: 0.5777 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 130: +Training Loss: 1.4426 +Validation Loss: 0.2603 +RPIQ: 0.5779 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 131: +Training Loss: 1.1328 +Validation Loss: 0.2682 +RPIQ: 0.5769 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 132: +Training Loss: 1.4963 +Validation Loss: 0.2682 +RPIQ: 0.5769 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 133: +Training Loss: 1.2331 +Validation Loss: 0.2831 +RPIQ: 0.5750 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 134: +Training Loss: 1.3983 +Validation Loss: 0.2450 +RPIQ: 0.5796 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 135: +Training Loss: 1.4094 +Validation Loss: 0.2792 +RPIQ: 0.5755 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 136: +Training Loss: 1.7059 +Validation Loss: 0.2639 +RPIQ: 0.5775 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 137: +Training Loss: 1.2854 +Validation Loss: 0.2694 +RPIQ: 0.5768 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 138: +Training Loss: 1.4755 +Validation Loss: 0.2577 +RPIQ: 0.5782 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 139: +Training Loss: 1.1424 +Validation Loss: 0.2659 +RPIQ: 0.5772 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 140: +Training Loss: 1.3422 +Validation Loss: 0.2616 +RPIQ: 0.5777 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 141: +Training Loss: 1.2906 +Validation Loss: 0.2902 +RPIQ: 0.5741 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 142: +Training Loss: 1.3921 +Validation Loss: 0.3035 +RPIQ: 0.5723 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 143: +Training Loss: 1.7496 +Validation Loss: 0.2702 +RPIQ: 0.5767 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 144: +Training Loss: 1.2142 +Validation Loss: 0.2833 +RPIQ: 0.5750 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 145: +Training Loss: 1.3444 +Validation Loss: 0.2915 +RPIQ: 0.5739 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 146: +Training Loss: 1.6739 +Validation Loss: 0.2541 +RPIQ: 0.5786 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 147: +Training Loss: 1.2448 +Validation Loss: 0.2678 +RPIQ: 0.5770 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 148: +Training Loss: 1.1999 +Validation Loss: 0.2708 +RPIQ: 0.5766 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 149: +Training Loss: 1.1749 +Validation Loss: 0.2620 +RPIQ: 0.5777 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 150: +Training Loss: 1.3049 +Validation Loss: 0.2563 +RPIQ: 0.5784 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 151: +Training Loss: 1.1552 +Validation Loss: 0.2566 +RPIQ: 0.5783 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 152: +Training Loss: 1.4217 +Validation Loss: 0.2725 +RPIQ: 0.5764 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 153: +Training Loss: 1.3604 +Validation Loss: 0.2604 +RPIQ: 0.5779 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 154: +Training Loss: 1.2949 +Validation Loss: 0.2821 +RPIQ: 0.5752 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 155: +Training Loss: 1.2589 +Validation Loss: 0.2512 +RPIQ: 0.5789 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 156: +Training Loss: 1.2229 +Validation Loss: 0.2720 +RPIQ: 0.5765 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 157: +Training Loss: 1.3810 +Validation Loss: 0.2753 +RPIQ: 0.5760 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 158: +Training Loss: 1.1506 +Validation Loss: 0.2787 +RPIQ: 0.5756 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 159: +Training Loss: 1.1887 +Validation Loss: 0.2641 +RPIQ: 0.5774 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 160: +Training Loss: 1.1394 +Validation Loss: 0.2826 +RPIQ: 0.5751 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 161: +Training Loss: 1.7056 +Validation Loss: 0.2870 +RPIQ: 0.5745 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 162: +Training Loss: 1.1290 +Validation Loss: 0.2427 +RPIQ: 0.5798 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 163: +Training Loss: 1.5976 +Validation Loss: 0.2660 +RPIQ: 0.5772 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 164: +Training Loss: 1.0985 +Validation Loss: 0.2854 +RPIQ: 0.5747 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 165: +Training Loss: 1.2387 +Validation Loss: 0.2975 +RPIQ: 0.5731 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 166: +Training Loss: 1.0474 +Validation Loss: 0.2649 +RPIQ: 0.5773 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 167: +Training Loss: 1.1415 +Validation Loss: 0.2714 +RPIQ: 0.5765 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 168: +Training Loss: 1.1504 +Validation Loss: 0.2879 +RPIQ: 0.5744 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 169: +Training Loss: 1.3914 +Validation Loss: 0.3074 +RPIQ: 0.5717 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 170: +Training Loss: 1.1225 +Validation Loss: 0.2748 +RPIQ: 0.5761 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 171: +Training Loss: 1.0296 +Validation Loss: 0.2707 +RPIQ: 0.5766 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 172: +Training Loss: 1.0291 +Validation Loss: 0.2501 +RPIQ: 0.5790 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 173: +Training Loss: 1.1635 +Validation Loss: 0.2496 +RPIQ: 0.5791 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 174: +Training Loss: 1.2398 +Validation Loss: 0.2706 +RPIQ: 0.5766 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 175: +Training Loss: 1.2201 +Validation Loss: 0.2519 +RPIQ: 0.5788 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 176: +Training Loss: 1.1136 +Validation Loss: 0.2624 +RPIQ: 0.5776 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 177: +Training Loss: 1.7789 +Validation Loss: 0.2835 +RPIQ: 0.5750 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 178: +Training Loss: 1.0743 +Validation Loss: 0.2873 +RPIQ: 0.5745 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 179: +Training Loss: 1.1144 +Validation Loss: 0.2601 +RPIQ: 0.5779 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 180: +Training Loss: 1.3718 +Validation Loss: 0.2824 +RPIQ: 0.5751 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 181: +Training Loss: 1.1204 +Validation Loss: 0.2590 +RPIQ: 0.5780 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 182: +Training Loss: 1.1536 +Validation Loss: 0.2948 +RPIQ: 0.5735 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 183: +Training Loss: 1.1722 +Validation Loss: 0.2534 +RPIQ: 0.5787 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 184: +Training Loss: 1.0585 +Validation Loss: 0.2462 +RPIQ: 0.5795 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 185: +Training Loss: 1.1763 +Validation Loss: 0.2868 +RPIQ: 0.5746 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 186: +Training Loss: 1.1797 +Validation Loss: 0.2796 +RPIQ: 0.5755 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 187: +Training Loss: 1.0804 +Validation Loss: 0.2846 +RPIQ: 0.5748 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.94it/s] + +Epoch 188: +Training Loss: 1.3284 +Validation Loss: 0.2747 +RPIQ: 0.5761 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 189: +Training Loss: 1.2076 +Validation Loss: 0.2332 +RPIQ: 0.5808 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 190: +Training Loss: 1.0706 +Validation Loss: 0.2823 +RPIQ: 0.5751 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 191: +Training Loss: 1.2725 +Validation Loss: 0.2855 +RPIQ: 0.5747 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 192: +Training Loss: 1.1099 +Validation Loss: 0.2267 +RPIQ: 0.5814 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 193: +Training Loss: 1.0826 +Validation Loss: 0.2760 +RPIQ: 0.5760 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 194: +Training Loss: 1.1191 +Validation Loss: 0.2675 +RPIQ: 0.5770 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.93it/s] + +Epoch 195: +Training Loss: 1.1117 +Validation Loss: 0.2770 +RPIQ: 0.5758 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 196: +Training Loss: 1.0342 +Validation Loss: 0.2771 +RPIQ: 0.5758 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 197: +Training Loss: 1.0386 +Validation Loss: 0.2724 +RPIQ: 0.5764 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 198: +Training Loss: 1.0858 +Validation Loss: 0.2397 +RPIQ: 0.5801 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 199: +Training Loss: 1.0281 +Validation Loss: 0.2880 +RPIQ: 0.5744 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 200: +Training Loss: 1.3537 +Validation Loss: 0.2850 +RPIQ: 0.5748 + +Best model saved with R²: 0.0024 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212222-aomawblj/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212222-aomawblj/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212222-aomawblj/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212222-cx8kvf7l/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212222-cx8kvf7l/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 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+/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.81it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212324-6m3yxetq/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212324-6m3yxetq/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 +prompt_toolkit==3.0.48 +pexpect==4.9.0 +parso==0.8.4 +jupyterlab_widgets==3.0.13 +jupyter-leaflet==0.19.2 +idna==3.10 +httplib2==0.22.0 +google-crc32c==1.5.0 +future==1.0.0 +executing==2.1.0 +decorator==5.1.1 +charset-normalizer==3.4.0 +cachetools==5.5.0 +asttokens==3.0.0 +traittypes==0.2.1 +stack-data==0.6.3 +rsa==4.9 +requests==2.32.3 +ratelim==0.1.6 +pyasn1_modules==0.4.1 +proto-plus==1.25.0 +plotly==5.24.1 +matplotlib-inline==0.1.7 +jedi==0.19.2 +googleapis-common-protos==1.66.0 +google-resumable-media==2.7.2 +comm==0.2.2 +branca==0.8.0 +ipython==8.12.3 +google-auth==2.37.0 +geocoder==1.38.1 +folium==0.18.0 +ipywidgets==8.1.5 +google-auth-httplib2==0.2.0 +google-api-core==2.24.0 +ipytree==0.2.2 +ipyleaflet==0.19.2 +ipyfilechooser==0.6.0 +ipyevents==2.0.2 +google-cloud-core==2.4.1 +google-api-python-client==2.155.0 +bqplot==0.12.43 +google-cloud-storage==2.19.0 +earthengine-api==1.1.5 +eerepr==0.0.4 +geemap==0.34.3 +tqdm==4.67.1 +smmap==5.0.2 +setproctitle==1.3.4 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"8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: composite_l2 +max_oc: + value: 150 +target_transform: + value: normalize +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212324-97a7kz72/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212324-97a7kz72/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..39f37c4bcd815170a19e243c4a6c74a1bd59a4be --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212324-97a7kz72/files/output.log @@ -0,0 +1,1211 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 +Target mean: 22.248163671875, Target std: 19.61666863529859 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.77it/s] +Epoch 1: +Training Loss: 89218.3344 +Validation Loss: 187.6492 +RPIQ: 0.0348 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 2: +Training Loss: 3351.1230 +Validation Loss: 118.1384 +RPIQ: 0.0500 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 3: +Training Loss: 1307.9308 +Validation Loss: 33.3067 +RPIQ: 0.0883 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 4: +Training Loss: 740.4781 +Validation Loss: 14.4359 +RPIQ: 0.1318 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 5: +Training Loss: 514.0577 +Validation Loss: 6.5712 +RPIQ: 0.1924 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 6: +Training Loss: 377.0575 +Validation Loss: 2.9453 +RPIQ: 0.2695 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 7: +Training Loss: 262.1354 +Validation Loss: 1.2877 +RPIQ: 0.3733 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 8: +Training Loss: 195.0013 +Validation Loss: 0.6484 +RPIQ: 0.4513 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 9: +Training Loss: 148.3585 +Validation Loss: 0.4104 +RPIQ: 0.5099 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 10: +Training Loss: 120.1030 +Validation Loss: 0.3206 +RPIQ: 0.5441 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 11: +Training Loss: 97.7696 +Validation Loss: 0.2970 +RPIQ: 0.5672 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 12: +Training Loss: 66.8589 +Validation Loss: 0.2901 +RPIQ: 0.5735 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 13: +Training Loss: 71.5444 +Validation Loss: 0.2581 +RPIQ: 0.5792 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 14: +Training Loss: 68.0667 +Validation Loss: 0.2465 +RPIQ: 0.5816 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 15: +Training Loss: 51.3065 +Validation Loss: 0.2327 +RPIQ: 0.5839 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 16: +Training Loss: 42.9049 +Validation Loss: 0.2229 +RPIQ: 0.5859 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 17: +Training Loss: 35.1163 +Validation Loss: 0.2215 +RPIQ: 0.5870 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 18: +Training Loss: 28.9437 +Validation Loss: 0.2078 +RPIQ: 0.5882 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 19: +Training Loss: 31.0465 +Validation Loss: 0.2144 +RPIQ: 0.5882 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 20: +Training Loss: 25.5002 +Validation Loss: 0.2124 +RPIQ: 0.5886 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 21: +Training Loss: 23.5201 +Validation Loss: 0.2089 +RPIQ: 0.5891 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 22: +Training Loss: 19.6862 +Validation Loss: 0.2033 +RPIQ: 0.5896 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 23: +Training Loss: 20.6505 +Validation Loss: 0.2038 +RPIQ: 0.5897 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 24: +Training Loss: 16.9833 +Validation Loss: 0.1954 +RPIQ: 0.5902 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 25: +Training Loss: 16.4628 +Validation Loss: 0.2020 +RPIQ: 0.5900 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 26: +Training Loss: 13.0364 +Validation Loss: 0.1989 +RPIQ: 0.5901 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 27: +Training Loss: 15.0043 +Validation Loss: 0.1995 +RPIQ: 0.5901 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 28: +Training Loss: 12.5364 +Validation Loss: 0.2012 +RPIQ: 0.5900 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 29: +Training Loss: 14.0720 +Validation Loss: 0.1879 +RPIQ: 0.5907 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 30: +Training Loss: 11.4852 +Validation Loss: 0.2149 +RPIQ: 0.5891 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 31: +Training Loss: 8.4095 +Validation Loss: 0.2094 +RPIQ: 0.5895 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 32: +Training Loss: 11.4006 +Validation Loss: 0.1998 +RPIQ: 0.5901 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 33: +Training Loss: 7.7889 +Validation Loss: 0.1982 +RPIQ: 0.5902 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 34: +Training Loss: 10.9332 +Validation Loss: 0.1910 +RPIQ: 0.5906 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 35: +Training Loss: 6.7384 +Validation Loss: 0.1935 +RPIQ: 0.5905 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 36: +Training Loss: 11.1213 +Validation Loss: 0.2130 +RPIQ: 0.5892 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 37: +Training Loss: 6.4265 +Validation Loss: 0.1907 +RPIQ: 0.5906 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 38: +Training Loss: 7.0508 +Validation Loss: 0.1967 +RPIQ: 0.5903 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 39: +Training Loss: 8.1679 +Validation Loss: 0.2046 +RPIQ: 0.5898 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 40: +Training Loss: 7.0516 +Validation Loss: 0.1971 +RPIQ: 0.5903 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 41: +Training Loss: 7.2165 +Validation Loss: 0.1971 +RPIQ: 0.5903 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 42: +Training Loss: 8.1905 +Validation Loss: 0.1940 +RPIQ: 0.5904 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 43: +Training Loss: 4.6651 +Validation Loss: 0.2043 +RPIQ: 0.5899 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 44: +Training Loss: 5.0761 +Validation Loss: 0.2063 +RPIQ: 0.5897 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 45: +Training Loss: 5.0975 +Validation Loss: 0.2034 +RPIQ: 0.5899 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 46: +Training Loss: 4.5079 +Validation Loss: 0.2052 +RPIQ: 0.5898 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 47: +Training Loss: 3.7345 +Validation Loss: 0.2040 +RPIQ: 0.5899 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 48: +Training Loss: 4.2119 +Validation Loss: 0.2124 +RPIQ: 0.5893 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 49: +Training Loss: 6.3971 +Validation Loss: 0.2034 +RPIQ: 0.5899 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 50: +Training Loss: 5.6813 +Validation Loss: 0.2005 +RPIQ: 0.5901 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 51: +Training Loss: 5.2400 +Validation Loss: 0.2051 +RPIQ: 0.5898 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 52: +Training Loss: 3.8012 +Validation Loss: 0.2052 +RPIQ: 0.5898 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 53: +Training Loss: 4.0870 +Validation Loss: 0.2014 +RPIQ: 0.5900 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 54: +Training Loss: 3.0998 +Validation Loss: 0.2108 +RPIQ: 0.5894 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 55: +Training Loss: 5.4365 +Validation Loss: 0.2032 +RPIQ: 0.5899 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 56: +Training Loss: 2.5230 +Validation Loss: 0.2188 +RPIQ: 0.5888 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 57: +Training Loss: 3.8913 +Validation Loss: 0.2018 +RPIQ: 0.5900 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 58: +Training Loss: 3.7064 +Validation Loss: 0.2010 +RPIQ: 0.5901 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 59: +Training Loss: 3.2915 +Validation Loss: 0.2068 +RPIQ: 0.5897 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 60: +Training Loss: 3.1292 +Validation Loss: 0.2047 +RPIQ: 0.5898 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 61: +Training Loss: 3.3843 +Validation Loss: 0.2149 +RPIQ: 0.5891 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 62: +Training Loss: 2.9510 +Validation Loss: 0.2128 +RPIQ: 0.5892 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 63: +Training Loss: 2.8916 +Validation Loss: 0.2026 +RPIQ: 0.5900 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 64: +Training Loss: 2.4189 +Validation Loss: 0.2076 +RPIQ: 0.5896 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 65: +Training Loss: 2.2126 +Validation Loss: 0.2086 +RPIQ: 0.5895 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 66: +Training Loss: 4.2797 +Validation Loss: 0.2062 +RPIQ: 0.5897 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 67: +Training Loss: 3.3079 +Validation Loss: 0.2117 +RPIQ: 0.5893 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 68: +Training Loss: 2.6781 +Validation Loss: 0.2114 +RPIQ: 0.5893 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 69: +Training Loss: 2.6563 +Validation Loss: 0.1999 +RPIQ: 0.5901 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 70: +Training Loss: 2.5447 +Validation Loss: 0.2144 +RPIQ: 0.5891 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 71: +Training Loss: 2.7867 +Validation Loss: 0.2124 +RPIQ: 0.5892 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 72: +Training Loss: 2.7438 +Validation Loss: 0.2078 +RPIQ: 0.5896 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 73: +Training Loss: 2.7158 +Validation Loss: 0.2056 +RPIQ: 0.5897 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 74: +Training Loss: 2.4144 +Validation Loss: 0.2116 +RPIQ: 0.5893 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 75: +Training Loss: 2.8377 +Validation Loss: 0.2181 +RPIQ: 0.5888 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 76: +Training Loss: 2.0926 +Validation Loss: 0.2125 +RPIQ: 0.5892 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 77: +Training Loss: 1.7193 +Validation Loss: 0.2046 +RPIQ: 0.5898 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 78: +Training Loss: 2.1736 +Validation Loss: 0.2057 +RPIQ: 0.5897 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 79: +Training Loss: 2.9486 +Validation Loss: 0.2129 +RPIQ: 0.5892 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 80: +Training Loss: 2.0853 +Validation Loss: 0.2134 +RPIQ: 0.5892 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 81: +Training Loss: 2.9441 +Validation Loss: 0.2118 +RPIQ: 0.5893 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 82: +Training Loss: 1.7850 +Validation Loss: 0.2066 +RPIQ: 0.5897 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 83: +Training Loss: 2.2931 +Validation Loss: 0.2109 +RPIQ: 0.5894 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 84: +Training Loss: 3.0236 +Validation Loss: 0.2150 +RPIQ: 0.5890 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 85: +Training Loss: 2.0945 +Validation Loss: 0.2055 +RPIQ: 0.5897 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 86: +Training Loss: 1.9640 +Validation Loss: 0.2065 +RPIQ: 0.5897 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 87: +Training Loss: 2.2562 +Validation Loss: 0.2104 +RPIQ: 0.5894 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 88: +Training Loss: 2.0260 +Validation Loss: 0.2076 +RPIQ: 0.5896 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 89: +Training Loss: 1.7079 +Validation Loss: 0.2095 +RPIQ: 0.5895 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 90: +Training Loss: 1.8620 +Validation Loss: 0.2160 +RPIQ: 0.5889 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 91: +Training Loss: 1.4316 +Validation Loss: 0.2117 +RPIQ: 0.5893 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 92: +Training Loss: 1.7942 +Validation Loss: 0.2111 +RPIQ: 0.5893 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 93: +Training Loss: 1.7106 +Validation Loss: 0.2157 +RPIQ: 0.5890 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 94: +Training Loss: 1.5616 +Validation Loss: 0.2075 +RPIQ: 0.5896 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 95: +Training Loss: 1.7724 +Validation Loss: 0.2103 +RPIQ: 0.5894 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 96: +Training Loss: 1.6187 +Validation Loss: 0.2094 +RPIQ: 0.5895 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 97: +Training Loss: 2.0553 +Validation Loss: 0.2112 +RPIQ: 0.5893 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 98: +Training Loss: 2.0470 +Validation Loss: 0.2173 +RPIQ: 0.5888 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 99: +Training Loss: 1.8555 +Validation Loss: 0.2102 +RPIQ: 0.5894 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 100: +Training Loss: 1.4020 +Validation Loss: 0.2115 +RPIQ: 0.5893 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 101: +Training Loss: 1.4127 +Validation Loss: 0.1997 +RPIQ: 0.5901 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 102: +Training Loss: 1.9623 +Validation Loss: 0.2179 +RPIQ: 0.5888 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 103: +Training Loss: 1.9993 +Validation Loss: 0.2181 +RPIQ: 0.5888 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 104: +Training Loss: 1.4281 +Validation Loss: 0.2057 +RPIQ: 0.5897 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 105: +Training Loss: 1.3642 +Validation Loss: 0.2196 +RPIQ: 0.5886 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 106: +Training Loss: 1.6333 +Validation Loss: 0.2052 +RPIQ: 0.5898 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 107: +Training Loss: 2.1532 +Validation Loss: 0.2146 +RPIQ: 0.5891 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 108: +Training Loss: 1.3155 +Validation Loss: 0.2100 +RPIQ: 0.5894 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 109: +Training Loss: 1.5169 +Validation Loss: 0.2072 +RPIQ: 0.5896 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 110: +Training Loss: 1.4591 +Validation Loss: 0.2112 +RPIQ: 0.5893 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 111: +Training Loss: 1.3794 +Validation Loss: 0.2149 +RPIQ: 0.5890 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 112: +Training Loss: 2.2079 +Validation Loss: 0.2119 +RPIQ: 0.5893 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 113: +Training Loss: 1.7273 +Validation Loss: 0.2179 +RPIQ: 0.5888 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 114: +Training Loss: 1.8845 +Validation Loss: 0.2146 +RPIQ: 0.5891 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 115: +Training Loss: 1.2401 +Validation Loss: 0.2131 +RPIQ: 0.5892 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 116: +Training Loss: 1.4200 +Validation Loss: 0.2139 +RPIQ: 0.5891 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 117: +Training Loss: 1.7270 +Validation Loss: 0.2170 +RPIQ: 0.5889 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 118: +Training Loss: 1.4584 +Validation Loss: 0.2144 +RPIQ: 0.5891 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 119: +Training Loss: 1.1442 +Validation Loss: 0.2082 +RPIQ: 0.5896 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 120: +Training Loss: 1.4824 +Validation Loss: 0.2159 +RPIQ: 0.5890 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 121: +Training Loss: 1.4529 +Validation Loss: 0.2137 +RPIQ: 0.5892 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 122: +Training Loss: 1.3115 +Validation Loss: 0.2206 +RPIQ: 0.5886 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 123: +Training Loss: 1.7368 +Validation Loss: 0.2136 +RPIQ: 0.5892 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 124: +Training Loss: 1.4367 +Validation Loss: 0.2125 +RPIQ: 0.5892 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 125: +Training Loss: 1.2749 +Validation Loss: 0.2273 +RPIQ: 0.5880 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 126: +Training Loss: 1.4107 +Validation Loss: 0.2054 +RPIQ: 0.5898 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 127: +Training Loss: 1.5072 +Validation Loss: 0.2214 +RPIQ: 0.5885 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 128: +Training Loss: 1.4857 +Validation Loss: 0.2098 +RPIQ: 0.5894 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 129: +Training Loss: 1.3856 +Validation Loss: 0.2088 +RPIQ: 0.5895 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 130: +Training Loss: 1.3404 +Validation Loss: 0.2228 +RPIQ: 0.5884 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 131: +Training Loss: 1.1913 +Validation Loss: 0.2215 +RPIQ: 0.5885 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 132: +Training Loss: 1.7384 +Validation Loss: 0.2222 +RPIQ: 0.5884 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 133: +Training Loss: 1.1301 +Validation Loss: 0.2108 +RPIQ: 0.5894 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 134: +Training Loss: 1.4692 +Validation Loss: 0.1987 +RPIQ: 0.5902 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 135: +Training Loss: 1.1539 +Validation Loss: 0.2310 +RPIQ: 0.5876 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 136: +Training Loss: 1.4466 +Validation Loss: 0.2131 +RPIQ: 0.5892 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 137: +Training Loss: 1.3394 +Validation Loss: 0.2153 +RPIQ: 0.5890 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 138: +Training Loss: 1.5566 +Validation Loss: 0.2223 +RPIQ: 0.5884 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 139: +Training Loss: 1.3111 +Validation Loss: 0.2109 +RPIQ: 0.5894 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 140: +Training Loss: 1.2517 +Validation Loss: 0.1999 +RPIQ: 0.5901 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 141: +Training Loss: 1.3271 +Validation Loss: 0.2329 +RPIQ: 0.5874 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 142: +Training Loss: 1.1634 +Validation Loss: 0.2141 +RPIQ: 0.5891 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 143: +Training Loss: 1.5521 +Validation Loss: 0.2068 +RPIQ: 0.5897 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 144: +Training Loss: 1.4946 +Validation Loss: 0.2161 +RPIQ: 0.5890 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 145: +Training Loss: 1.4718 +Validation Loss: 0.2095 +RPIQ: 0.5895 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 146: +Training Loss: 1.1561 +Validation Loss: 0.2137 +RPIQ: 0.5892 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.92it/s] + +Epoch 147: +Training Loss: 1.0961 +Validation Loss: 0.2091 +RPIQ: 0.5895 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 148: +Training Loss: 1.3261 +Validation Loss: 0.2194 +RPIQ: 0.5887 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 149: +Training Loss: 1.4808 +Validation Loss: 0.2146 +RPIQ: 0.5891 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 150: +Training Loss: 1.2072 +Validation Loss: 0.2082 +RPIQ: 0.5896 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 151: +Training Loss: 1.3514 +Validation Loss: 0.2067 +RPIQ: 0.5897 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 152: +Training Loss: 1.1472 +Validation Loss: 0.2155 +RPIQ: 0.5890 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 153: +Training Loss: 1.6355 +Validation Loss: 0.2230 +RPIQ: 0.5883 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 154: +Training Loss: 1.2775 +Validation Loss: 0.2230 +RPIQ: 0.5884 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 155: +Training Loss: 1.2541 +Validation Loss: 0.2182 +RPIQ: 0.5888 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 156: +Training Loss: 1.1731 +Validation Loss: 0.2096 +RPIQ: 0.5895 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 157: +Training Loss: 1.1955 +Validation Loss: 0.2195 +RPIQ: 0.5887 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.91it/s] + +Epoch 158: +Training Loss: 1.0680 +Validation Loss: 0.2265 +RPIQ: 0.5880 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 159: +Training Loss: 1.2047 +Validation Loss: 0.2177 +RPIQ: 0.5889 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 160: +Training Loss: 1.2566 +Validation Loss: 0.2311 +RPIQ: 0.5876 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 161: +Training Loss: 1.4219 +Validation Loss: 0.2163 +RPIQ: 0.5890 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 162: +Training Loss: 1.4747 +Validation Loss: 0.2045 +RPIQ: 0.5898 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 163: +Training Loss: 1.0847 +Validation Loss: 0.2154 +RPIQ: 0.5890 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 164: +Training Loss: 1.1427 +Validation Loss: 0.2261 +RPIQ: 0.5881 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 165: +Training Loss: 1.3130 +Validation Loss: 0.2221 +RPIQ: 0.5885 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 166: +Training Loss: 1.5183 +Validation Loss: 0.2304 +RPIQ: 0.5877 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] + +Epoch 167: +Training Loss: 1.2025 +Validation Loss: 0.2111 +RPIQ: 0.5894 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 168: +Training Loss: 1.1773 +Validation Loss: 0.2096 +RPIQ: 0.5895 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 169: +Training Loss: 1.0785 +Validation Loss: 0.2084 +RPIQ: 0.5896 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 170: +Training Loss: 1.1200 +Validation Loss: 0.2029 +RPIQ: 0.5900 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 171: +Training Loss: 1.0254 +Validation Loss: 0.2122 +RPIQ: 0.5893 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 172: +Training Loss: 1.0212 +Validation Loss: 0.2247 +RPIQ: 0.5882 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 173: +Training Loss: 1.0310 +Validation Loss: 0.2177 +RPIQ: 0.5888 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.90it/s] + +Epoch 174: +Training Loss: 1.2360 +Validation Loss: 0.2307 +RPIQ: 0.5876 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] + +Epoch 175: +Training Loss: 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model saved with R²: 0.0026 diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212324-97a7kz72/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212324-97a7kz72/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212324-97a7kz72/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 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"12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: composite_l2 +max_oc: + value: 150 +target_transform: + value: normalize +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212324-zvd10x1m/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212324-zvd10x1m/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..2120a99d3cdcf78e72e14b281c7a180d3393931c --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212324-zvd10x1m/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212324-zvd10x1m/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212324-zvd10x1m/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 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"12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: mse +max_oc: + value: 150 +target_transform: + value: log +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212431-0konuzzu/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212431-0konuzzu/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..696ca9ee02f1fba7d1b627ddf2791d8933e93138 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212431-0konuzzu/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212431-0konuzzu/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212431-0konuzzu/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 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+time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212431-5tgcfp1z/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212431-5tgcfp1z/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..57418d36b3dbfea30a7ab59db6846e1fa59f1674 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212431-5tgcfp1z/files/output.log @@ -0,0 +1,206 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] 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b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212431-eqtevkeh/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212431-eqtevkeh/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 +kiwisolver==1.4.7 +fonttools==4.55.3 +cycler==0.12.1 +contourpy==1.1.1 +importlib_resources==6.4.5 +matplotlib==3.7.5 +seaborn==0.13.2 +scipy==1.10.1 +xgboost==2.1.3 +shapely==2.0.6 +importlib_metadata==8.5.0 +click==8.1.7 +certifi==2024.8.30 +attrs==24.2.0 +pyproj==3.5.0 +cligj==0.7.2 +click-plugins==1.1.1 +geopandas==0.13.2 +et_xmlfile==2.0.0 +openpyxl==3.1.5 +threadpoolctl==3.5.0 +joblib==1.4.2 +scikit-learn==1.3.2 +munch==4.0.0 +torchvision==0.19.1+cu118 +torchaudio==2.4.1+cu118 +snuggs==1.4.7 +affine==2.4.0 +rasterio==1.3.11 +wcwidth==0.2.13 +pyperclip==1.9.0 +pure_eval==0.2.3 +ptyprocess==0.7.0 +pickleshare==0.7.5 +colour==0.1.5 +backcall==0.2.0 +xyzservices==2024.9.0 +widgetsnbextension==4.0.13 +urllib3==2.2.3 +uritemplate==4.1.1 +traitlets==5.14.3 +tenacity==9.0.0 +scooby==0.10.0 +python-box==7.2.0 +pyshp==2.3.1 +Pygments==2.18.0 +pyasn1==0.6.1 +protobuf==5.29.1 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"2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212431-u0b3ok15/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212431-u0b3ok15/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..5c6b2a88e1ef971c2d987d28cf752191e098faf3 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250413_212431-u0b3ok15/files/output.log @@ -0,0 +1,1210 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 +100%|███████████████████████████████████████████| 20/20 [00:11<00:00, 1.75it/s] +Epoch 1: +Training Loss: 19461.6523 +Validation Loss: 136.5185 +RPIQ: 0.0000 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 2: +Training Loss: 784.5709 +Validation Loss: 24.4409 +RPIQ: 0.0000 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 3: +Training Loss: 313.7197 +Validation Loss: 10.2964 +RPIQ: 0.0000 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 4: +Training Loss: 181.0690 +Validation Loss: 6.7326 +RPIQ: 0.0001 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 5: +Training Loss: 127.8528 +Validation Loss: 4.4458 +RPIQ: 0.0010 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 6: +Training Loss: 91.5810 +Validation Loss: 3.5093 +RPIQ: 0.0052 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 7: +Training Loss: 71.4470 +Validation Loss: 3.2500 +RPIQ: 0.0160 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 8: +Training Loss: 53.3813 +Validation Loss: 2.9875 +RPIQ: 0.0380 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 9: +Training Loss: 43.6654 +Validation Loss: 2.9583 +RPIQ: 0.0843 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 10: +Training Loss: 34.0470 +Validation Loss: 3.0172 +RPIQ: 0.1791 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 11: +Training Loss: 27.5198 +Validation Loss: 3.1379 +RPIQ: 0.2718 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 12: +Training Loss: 23.6725 +Validation Loss: 3.1705 +RPIQ: 0.3542 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 13: +Training Loss: 21.6306 +Validation Loss: 3.1386 +RPIQ: 0.3902 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 14: +Training Loss: 17.5108 +Validation Loss: 3.1777 +RPIQ: 0.4084 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 15: +Training Loss: 14.6621 +Validation Loss: 3.1766 +RPIQ: 0.4108 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 16: +Training Loss: 13.0140 +Validation Loss: 3.0518 +RPIQ: 0.4123 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 17: +Training Loss: 12.2899 +Validation Loss: 2.8629 +RPIQ: 0.4142 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 18: +Training Loss: 10.6232 +Validation Loss: 2.8041 +RPIQ: 0.4148 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 19: +Training Loss: 10.3784 +Validation Loss: 2.5433 +RPIQ: 0.4175 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 20: +Training Loss: 10.6529 +Validation Loss: 2.4378 +RPIQ: 0.4187 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 21: +Training Loss: 8.1285 +Validation Loss: 2.2618 +RPIQ: 0.4211 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 22: +Training Loss: 8.1088 +Validation Loss: 2.1572 +RPIQ: 0.4226 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 23: +Training Loss: 6.6081 +Validation Loss: 2.0291 +RPIQ: 0.4246 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 24: +Training Loss: 6.7157 +Validation Loss: 1.9461 +RPIQ: 0.4259 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 25: +Training Loss: 5.6913 +Validation Loss: 1.8920 +RPIQ: 0.4267 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 26: +Training Loss: 5.5544 +Validation Loss: 1.9073 +RPIQ: 0.4264 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 27: +Training Loss: 5.4259 +Validation Loss: 1.7137 +RPIQ: 0.4302 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 28: +Training Loss: 4.9072 +Validation Loss: 1.7502 +RPIQ: 0.4293 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 29: +Training Loss: 5.4332 +Validation Loss: 1.6553 +RPIQ: 0.4313 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 30: +Training Loss: 4.7654 +Validation Loss: 1.6225 +RPIQ: 0.4320 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 31: +Training Loss: 4.2515 +Validation Loss: 1.6407 +RPIQ: 0.4315 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 32: +Training Loss: 4.8573 +Validation Loss: 1.5578 +RPIQ: 0.4334 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 33: +Training Loss: 3.9788 +Validation Loss: 1.5256 +RPIQ: 0.4342 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 34: +Training Loss: 3.4436 +Validation Loss: 1.4782 +RPIQ: 0.4354 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 35: +Training Loss: 3.5884 +Validation Loss: 1.4168 +RPIQ: 0.4371 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 36: +Training Loss: 3.8547 +Validation Loss: 1.5061 +RPIQ: 0.4346 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 37: +Training Loss: 3.0702 +Validation Loss: 1.4724 +RPIQ: 0.4354 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 38: +Training Loss: 3.2160 +Validation Loss: 1.3931 +RPIQ: 0.4376 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 39: +Training Loss: 3.4074 +Validation Loss: 1.2832 +RPIQ: 0.4409 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 40: +Training Loss: 2.9137 +Validation Loss: 1.3015 +RPIQ: 0.4403 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 41: +Training Loss: 3.0812 +Validation Loss: 1.2963 +RPIQ: 0.4404 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 42: +Training Loss: 2.9367 +Validation Loss: 1.2810 +RPIQ: 0.4409 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 43: +Training Loss: 3.0879 +Validation Loss: 1.2522 +RPIQ: 0.4418 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 44: +Training Loss: 2.7423 +Validation Loss: 1.2218 +RPIQ: 0.4428 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 45: +Training Loss: 2.8995 +Validation Loss: 1.1742 +RPIQ: 0.4445 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 46: +Training Loss: 2.6801 +Validation Loss: 1.1184 +RPIQ: 0.4465 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 47: +Training Loss: 2.6158 +Validation Loss: 1.0590 +RPIQ: 0.4487 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 48: +Training Loss: 2.5637 +Validation Loss: 1.0511 +RPIQ: 0.4489 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 49: +Training Loss: 2.8383 +Validation Loss: 1.0377 +RPIQ: 0.4494 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 50: +Training Loss: 2.8914 +Validation Loss: 1.0251 +RPIQ: 0.4497 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 51: +Training Loss: 2.4677 +Validation Loss: 1.0083 +RPIQ: 0.4504 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 52: +Training Loss: 2.3347 +Validation Loss: 1.0168 +RPIQ: 0.4500 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 53: +Training Loss: 2.2690 +Validation Loss: 0.9943 +RPIQ: 0.4510 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 54: +Training Loss: 2.7823 +Validation Loss: 0.9437 +RPIQ: 0.4530 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 55: +Training Loss: 2.0356 +Validation Loss: 0.9309 +RPIQ: 0.4536 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 56: +Training Loss: 2.7231 +Validation Loss: 0.8290 +RPIQ: 0.4588 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 57: +Training Loss: 2.1596 +Validation Loss: 0.9437 +RPIQ: 0.4527 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 58: +Training Loss: 2.0211 +Validation Loss: 0.9293 +RPIQ: 0.4533 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 59: +Training Loss: 2.1253 +Validation Loss: 0.8472 +RPIQ: 0.4574 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 60: +Training Loss: 1.9992 +Validation Loss: 0.7934 +RPIQ: 0.4607 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 61: +Training Loss: 2.0423 +Validation Loss: 0.8752 +RPIQ: 0.4557 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 62: +Training Loss: 2.0215 +Validation Loss: 0.8485 +RPIQ: 0.4571 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 63: +Training Loss: 1.9819 +Validation Loss: 0.8236 +RPIQ: 0.4586 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 64: +Training Loss: 2.0610 +Validation Loss: 0.7909 +RPIQ: 0.4603 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 65: +Training Loss: 2.0694 +Validation Loss: 0.7986 +RPIQ: 0.4599 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 66: +Training Loss: 2.0943 +Validation Loss: 0.7814 +RPIQ: 0.4609 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 67: +Training Loss: 1.8475 +Validation Loss: 0.7809 +RPIQ: 0.4608 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 68: +Training Loss: 1.8953 +Validation Loss: 0.7980 +RPIQ: 0.4597 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 69: +Training Loss: 1.6450 +Validation Loss: 0.6828 +RPIQ: 0.4672 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 70: +Training Loss: 1.9302 +Validation Loss: 0.7571 +RPIQ: 0.4620 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 71: +Training Loss: 1.8426 +Validation Loss: 0.6879 +RPIQ: 0.4667 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.83it/s] + +Epoch 72: +Training Loss: 1.7165 +Validation Loss: 0.7743 +RPIQ: 0.4609 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 73: +Training Loss: 1.6747 +Validation Loss: 0.7072 +RPIQ: 0.4652 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 74: +Training Loss: 1.8661 +Validation Loss: 0.7114 +RPIQ: 0.4648 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 75: +Training Loss: 1.7171 +Validation Loss: 0.7704 +RPIQ: 0.4609 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 76: +Training Loss: 1.8332 +Validation Loss: 0.7124 +RPIQ: 0.4646 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 77: +Training Loss: 1.7158 +Validation Loss: 0.6818 +RPIQ: 0.4667 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 78: +Training Loss: 1.8624 +Validation Loss: 0.6734 +RPIQ: 0.4673 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 79: +Training Loss: 1.5651 +Validation Loss: 0.6984 +RPIQ: 0.4653 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 80: +Training Loss: 1.6116 +Validation Loss: 0.7047 +RPIQ: 0.4649 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 81: +Training Loss: 1.5233 +Validation Loss: 0.6212 +RPIQ: 0.4713 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 82: +Training Loss: 1.4847 +Validation Loss: 0.7262 +RPIQ: 0.4633 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 83: +Training Loss: 1.7639 +Validation Loss: 0.6632 +RPIQ: 0.4678 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 84: +Training Loss: 1.7042 +Validation Loss: 0.6657 +RPIQ: 0.4675 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 85: +Training Loss: 1.6248 +Validation Loss: 0.6326 +RPIQ: 0.4703 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 86: +Training Loss: 1.5829 +Validation Loss: 0.7270 +RPIQ: 0.4630 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 87: +Training Loss: 1.6468 +Validation Loss: 0.6144 +RPIQ: 0.4717 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 88: +Training Loss: 1.4831 +Validation Loss: 0.6231 +RPIQ: 0.4708 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 89: +Training Loss: 1.5153 +Validation Loss: 0.6286 +RPIQ: 0.4703 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 90: +Training Loss: 1.5519 +Validation Loss: 0.6390 +RPIQ: 0.4695 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 91: +Training Loss: 1.6483 +Validation Loss: 0.6186 +RPIQ: 0.4711 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 92: +Training Loss: 1.8289 +Validation Loss: 0.6121 +RPIQ: 0.4716 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 93: +Training Loss: 1.4633 +Validation Loss: 0.6455 +RPIQ: 0.4688 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 94: +Training Loss: 1.5161 +Validation Loss: 0.6338 +RPIQ: 0.4698 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 95: +Training Loss: 1.4578 +Validation Loss: 0.6664 +RPIQ: 0.4672 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 96: +Training Loss: 1.5717 +Validation Loss: 0.6143 +RPIQ: 0.4713 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 97: +Training Loss: 1.3455 +Validation Loss: 0.6322 +RPIQ: 0.4697 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 98: +Training Loss: 1.4019 +Validation Loss: 0.6581 +RPIQ: 0.4677 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 99: +Training Loss: 1.4275 +Validation Loss: 0.5934 +RPIQ: 0.4731 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 100: +Training Loss: 1.3776 +Validation Loss: 0.7319 +RPIQ: 0.4624 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 101: +Training Loss: 1.4200 +Validation Loss: 0.5707 +RPIQ: 0.4752 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 102: +Training Loss: 1.3180 +Validation Loss: 0.5331 +RPIQ: 0.4791 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 103: +Training Loss: 1.4154 +Validation Loss: 0.6652 +RPIQ: 0.4670 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 104: +Training Loss: 1.4110 +Validation Loss: 0.5589 +RPIQ: 0.4762 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 105: +Training Loss: 1.4881 +Validation Loss: 0.6138 +RPIQ: 0.4713 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 106: +Training Loss: 1.3349 +Validation Loss: 0.6015 +RPIQ: 0.4724 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 107: +Training Loss: 1.3409 +Validation Loss: 0.5644 +RPIQ: 0.4758 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 108: +Training Loss: 1.3808 +Validation Loss: 0.6011 +RPIQ: 0.4723 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 109: +Training Loss: 1.2829 +Validation Loss: 0.5612 +RPIQ: 0.4760 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 110: +Training Loss: 1.3517 +Validation Loss: 0.5912 +RPIQ: 0.4730 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 111: +Training Loss: 1.4689 +Validation Loss: 0.5936 +RPIQ: 0.4729 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 112: +Training Loss: 1.3281 +Validation Loss: 0.5666 +RPIQ: 0.4755 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 113: +Training Loss: 1.2900 +Validation Loss: 0.5233 +RPIQ: 0.4798 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 114: +Training Loss: 1.4361 +Validation Loss: 0.5993 +RPIQ: 0.4723 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 115: +Training Loss: 1.2607 +Validation Loss: 0.5763 +RPIQ: 0.4744 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 116: +Training Loss: 1.2638 +Validation Loss: 0.5987 +RPIQ: 0.4723 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 117: +Training Loss: 1.2213 +Validation Loss: 0.5341 +RPIQ: 0.4785 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 118: +Training Loss: 1.2884 +Validation Loss: 0.5629 +RPIQ: 0.4756 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 119: +Training Loss: 1.2591 +Validation Loss: 0.6011 +RPIQ: 0.4721 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 120: +Training Loss: 1.3509 +Validation Loss: 0.5359 +RPIQ: 0.4784 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 121: +Training Loss: 1.3293 +Validation Loss: 0.5585 +RPIQ: 0.4760 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 122: +Training Loss: 1.4361 +Validation Loss: 0.6325 +RPIQ: 0.4694 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 123: +Training Loss: 1.2107 +Validation Loss: 0.5616 +RPIQ: 0.4757 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 124: +Training Loss: 1.4392 +Validation Loss: 0.5235 +RPIQ: 0.4796 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 125: +Training Loss: 1.2515 +Validation Loss: 0.5747 +RPIQ: 0.4745 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 126: +Training Loss: 1.2484 +Validation Loss: 0.6019 +RPIQ: 0.4720 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 127: +Training Loss: 1.3180 +Validation Loss: 0.5751 +RPIQ: 0.4745 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 128: +Training Loss: 1.2652 +Validation Loss: 0.5213 +RPIQ: 0.4798 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 129: +Training Loss: 1.1797 +Validation Loss: 0.5807 +RPIQ: 0.4737 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 130: +Training Loss: 1.2903 +Validation Loss: 0.5812 +RPIQ: 0.4739 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 131: +Training Loss: 1.3097 +Validation Loss: 0.5234 +RPIQ: 0.4795 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 132: +Training Loss: 1.2082 +Validation Loss: 0.5686 +RPIQ: 0.4748 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 133: +Training Loss: 1.2661 +Validation Loss: 0.5622 +RPIQ: 0.4756 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 134: +Training Loss: 1.2989 +Validation Loss: 0.5041 +RPIQ: 0.4815 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 135: +Training Loss: 1.2096 +Validation Loss: 0.5174 +RPIQ: 0.4801 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 136: +Training Loss: 1.2176 +Validation Loss: 0.5392 +RPIQ: 0.4778 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 137: +Training Loss: 1.2050 +Validation Loss: 0.5076 +RPIQ: 0.4813 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 138: +Training Loss: 1.1550 +Validation Loss: 0.5108 +RPIQ: 0.4808 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 139: +Training Loss: 1.1943 +Validation Loss: 0.5372 +RPIQ: 0.4780 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 140: +Training Loss: 1.1660 +Validation Loss: 0.4802 +RPIQ: 0.4840 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 141: +Training Loss: 1.1806 +Validation Loss: 0.5353 +RPIQ: 0.4784 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 142: +Training Loss: 1.1482 +Validation Loss: 0.5148 +RPIQ: 0.4801 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 143: +Training Loss: 1.2418 +Validation Loss: 0.4683 +RPIQ: 0.4857 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 144: +Training Loss: 1.1608 +Validation Loss: 0.5806 +RPIQ: 0.4734 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 145: +Training Loss: 1.1819 +Validation Loss: 0.4577 +RPIQ: 0.4869 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 146: +Training Loss: 1.1180 +Validation Loss: 0.5273 +RPIQ: 0.4786 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 147: +Training Loss: 1.1374 +Validation Loss: 0.5329 +RPIQ: 0.4780 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 148: +Training Loss: 1.0852 +Validation Loss: 0.5719 +RPIQ: 0.4744 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 149: +Training Loss: 1.1404 +Validation Loss: 0.4374 +RPIQ: 0.4899 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 150: +Training Loss: 1.1520 +Validation Loss: 0.5497 +RPIQ: 0.4763 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 151: +Training Loss: 1.1046 +Validation Loss: 0.4770 +RPIQ: 0.4843 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 152: +Training Loss: 1.1713 +Validation Loss: 0.5676 +RPIQ: 0.4745 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 153: +Training Loss: 1.1940 +Validation Loss: 0.4284 +RPIQ: 0.4910 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 154: +Training Loss: 1.0966 +Validation Loss: 0.5155 +RPIQ: 0.4798 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 155: +Training Loss: 1.0985 +Validation Loss: 0.4955 +RPIQ: 0.4817 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 156: +Training Loss: 1.2166 +Validation Loss: 0.4797 +RPIQ: 0.4837 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 157: +Training Loss: 1.0993 +Validation Loss: 0.5408 +RPIQ: 0.4768 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 158: +Training Loss: 1.1502 +Validation Loss: 0.4878 +RPIQ: 0.4827 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 159: +Training Loss: 1.0709 +Validation Loss: 0.4876 +RPIQ: 0.4827 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 160: +Training Loss: 1.3264 +Validation Loss: 0.4449 +RPIQ: 0.4878 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 161: +Training Loss: 1.0407 +Validation Loss: 0.5055 +RPIQ: 0.4807 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 162: +Training Loss: 1.1753 +Validation Loss: 0.4591 +RPIQ: 0.4861 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 163: +Training Loss: 1.1235 +Validation Loss: 0.5366 +RPIQ: 0.4774 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 164: +Training Loss: 1.0294 +Validation Loss: 0.4250 +RPIQ: 0.4909 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 165: +Training Loss: 1.1479 +Validation Loss: 0.4602 +RPIQ: 0.4858 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 166: +Training Loss: 1.0874 +Validation Loss: 0.4785 +RPIQ: 0.4838 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 167: +Training Loss: 1.1067 +Validation Loss: 0.5119 +RPIQ: 0.4800 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 168: +Training Loss: 1.0807 +Validation Loss: 0.5455 +RPIQ: 0.4764 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 169: +Training Loss: 1.1559 +Validation Loss: 0.4677 +RPIQ: 0.4849 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 170: +Training Loss: 1.0163 +Validation Loss: 0.4664 +RPIQ: 0.4854 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 171: +Training Loss: 1.1346 +Validation Loss: 0.4379 +RPIQ: 0.4888 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 172: +Training Loss: 1.0556 +Validation Loss: 0.4715 +RPIQ: 0.4845 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 173: +Training Loss: 1.0279 +Validation Loss: 0.4674 +RPIQ: 0.4848 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 174: +Training Loss: 1.0146 +Validation Loss: 0.3825 +RPIQ: 0.4974 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 175: +Training Loss: 1.0680 +Validation Loss: 0.4651 +RPIQ: 0.4849 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 176: +Training Loss: 1.0790 +Validation Loss: 0.4148 +RPIQ: 0.4917 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 177: +Training Loss: 0.9716 +Validation Loss: 0.5212 +RPIQ: 0.4785 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 178: +Training Loss: 1.0658 +Validation Loss: 0.5333 +RPIQ: 0.4771 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 179: +Training Loss: 1.0188 +Validation Loss: 0.4389 +RPIQ: 0.4883 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 180: +Training Loss: 1.0016 +Validation Loss: 0.4068 +RPIQ: 0.4927 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 181: +Training Loss: 1.0198 +Validation Loss: 0.4409 +RPIQ: 0.4878 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 182: +Training Loss: 1.0323 +Validation Loss: 0.4483 +RPIQ: 0.4867 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 183: +Training Loss: 1.0141 +Validation Loss: 0.4822 +RPIQ: 0.4826 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 184: +Training Loss: 0.9946 +Validation Loss: 0.4961 +RPIQ: 0.4810 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 185: +Training Loss: 1.0069 +Validation Loss: 0.5195 +RPIQ: 0.4785 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 186: +Training Loss: 0.9828 +Validation Loss: 0.5424 +RPIQ: 0.4763 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 187: +Training Loss: 1.0813 +Validation Loss: 0.5073 +RPIQ: 0.4797 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.86it/s] + +Epoch 188: +Training Loss: 1.0647 +Validation Loss: 0.4151 +RPIQ: 0.4913 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 189: +Training Loss: 1.1222 +Validation Loss: 0.4066 +RPIQ: 0.4928 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 190: +Training Loss: 0.9664 +Validation Loss: 0.4604 +RPIQ: 0.4848 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 191: +Training Loss: 0.9953 +Validation Loss: 0.4338 +RPIQ: 0.4883 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.84it/s] + +Epoch 192: +Training Loss: 0.9819 +Validation Loss: 0.4831 +RPIQ: 0.4823 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.85it/s] + +Epoch 193: +Training Loss: 0.9845 +Validation Loss: 0.3808 +RPIQ: 0.4967 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.87it/s] + +Epoch 194: +Training Loss: 1.0052 +Validation Loss: 0.4406 +RPIQ: 0.4873 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] +100%|███████████████████████████████████████████| 20/20 [00:09<00:00, 2.01it/s] 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] +100%|███████████████████████████████████████████| 20/20 [00:09<00:00, 2.01it/s] 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index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_000657-ryx67wnx/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 +nvidia-cusolver-cu12==11.4.5.107 +torch==2.4.1 +pytz==2024.2 +tzdata==2024.2 +six==1.17.0 +numpy==1.24.4 +python-dateutil==2.9.0.post0 +pandas==2.0.3 +zipp==3.20.2 +pyparsing==3.1.4 +pillow==10.4.0 +packaging==24.2 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.89it/s] +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/numpy/core/_methods.py:239: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) +train.py:203: RuntimeWarning: overflow encountered in square + mse = np.mean((original_val_outputs - original_val_targets) ** 2) +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/numpy/core/_methods.py:181: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(arr, axis, dtype, out, keepdims, where=where) +Epoch 1: +Training Loss: 165832.2709 +Validation Loss: 858.0169 +RPIQ: 0.0000 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/numpy/core/_methods.py:250: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) + +Epoch 2: +Training Loss: 6102.8245 +Validation Loss: 247.2239 +RPIQ: 0.0000 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 3: +Training Loss: 2084.1149 +Validation Loss: 75.6728 +RPIQ: 0.0000 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 4: +Training Loss: 1117.4275 +Validation Loss: 28.2337 +RPIQ: 0.0002 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 5: +Training Loss: 728.5841 +Validation Loss: 18.9816 +RPIQ: 0.0079 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 6: +Training Loss: 507.0556 +Validation Loss: 12.3021 +RPIQ: 0.0356 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 7: +Training Loss: 402.0923 +Validation Loss: 9.9641 +RPIQ: 0.1372 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 8: +Training Loss: 325.6294 +Validation Loss: 8.9327 +RPIQ: 0.2749 +100%|███████████████████████████████████████████| 20/20 [00:09<00:00, 2.01it/s] + +Epoch 9: +Training Loss: 248.9647 +Validation Loss: 8.7552 +RPIQ: 0.3793 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 10: +Training Loss: 190.8343 +Validation Loss: 8.2233 +RPIQ: 0.3866 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 11: +Training Loss: 177.8710 +Validation Loss: 7.9567 +RPIQ: 0.3874 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 12: +Training Loss: 138.1365 +Validation Loss: 7.8050 +RPIQ: 0.3870 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 13: +Training Loss: 114.2048 +Validation Loss: 7.6653 +RPIQ: 0.3867 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 14: +Training Loss: 107.5218 +Validation Loss: 7.5963 +RPIQ: 0.3866 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 15: +Training Loss: 87.1691 +Validation Loss: 7.4916 +RPIQ: 0.3867 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 16: +Training Loss: 75.7898 +Validation Loss: 7.4337 +RPIQ: 0.3867 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 17: +Training Loss: 70.7243 +Validation Loss: 7.3690 +RPIQ: 0.3868 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 18: +Training Loss: 60.1663 +Validation Loss: 7.3001 +RPIQ: 0.3869 + 25%|███████████ | 5/20 [00:03<00:08, 1.81it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_000657-x01bwl7v/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_000657-x01bwl7v/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_000657-x01bwl7v/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 +Jinja2==3.1.4 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+learning_rate: + value: 0.001 +loss_type: + value: mse +max_oc: + value: 150 +target_transform: + value: log +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-5fsl3b0r/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-5fsl3b0r/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..f5a038cdaf722cdb1954d50454f46591ba6e5fa5 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-5fsl3b0r/files/output.log @@ -0,0 +1,67 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 + 0%| | 0/20 [00:01 + model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( + File "train.py", line 140, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 61, in forward + x3_pooled = self.pool(x3) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/pooling.py", line 164, in forward + return F.max_pool2d(input, self.kernel_size, self.stride, + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_jit_internal.py", line 503, in fn + return if_false(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/functional.py", line 796, in _max_pool2d + return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) +RuntimeError: Given input size: (64x1x1). Calculated output size: (64x0x0). Output size is too small +[rank1]: Traceback (most recent call last): +[rank1]: File "train.py", line 312, in +[rank1]: model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( +[rank1]: File "train.py", line 140, in train_model +[rank1]: outputs = model(features) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank1]: else self._run_ddp_forward(*inputs, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank1]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 61, in forward +[rank1]: x3_pooled = self.pool(x3) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/pooling.py", line 164, in forward +[rank1]: return F.max_pool2d(input, self.kernel_size, self.stride, +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_jit_internal.py", line 503, in fn +[rank1]: return if_false(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/functional.py", line 796, in _max_pool2d +[rank1]: return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) +[rank1]: RuntimeError: Given input size: (64x1x1). Calculated output size: (64x0x0). Output size is too small diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-5fsl3b0r/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-5fsl3b0r/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-5fsl3b0r/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 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16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: mse +max_oc: + value: 150 +target_transform: + value: log +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-n4kr6lns/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-n4kr6lns/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..26ebf82085ca805e2681d03cd7bd49443120b605 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-n4kr6lns/files/output.log @@ -0,0 +1,67 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 + 0%| | 0/20 [00:01 + model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( + File "train.py", line 140, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 61, in forward + x3_pooled = self.pool(x3) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/pooling.py", line 164, in forward + return F.max_pool2d(input, self.kernel_size, self.stride, + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_jit_internal.py", line 503, in fn + return if_false(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/functional.py", line 796, in _max_pool2d + return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) +RuntimeError: Given input size: (64x1x1). Calculated output size: (64x0x0). Output size is too small +[rank3]: Traceback (most recent call last): +[rank3]: File "train.py", line 312, in +[rank3]: model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( +[rank3]: File "train.py", line 140, in train_model +[rank3]: outputs = model(features) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank3]: else self._run_ddp_forward(*inputs, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank3]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 61, in forward +[rank3]: x3_pooled = self.pool(x3) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/pooling.py", line 164, in forward +[rank3]: return F.max_pool2d(input, self.kernel_size, self.stride, +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_jit_internal.py", line 503, in fn +[rank3]: return if_false(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/functional.py", line 796, in _max_pool2d +[rank3]: return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) +[rank3]: RuntimeError: Given input size: (64x1x1). Calculated output size: (64x0x0). Output size is too small diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-n4kr6lns/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-n4kr6lns/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-n4kr6lns/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 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`dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 + 0%| | 0/20 [00:01 + model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( + File "train.py", line 140, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 61, in forward + x3_pooled = self.pool(x3) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/pooling.py", line 164, in forward + return F.max_pool2d(input, self.kernel_size, self.stride, + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_jit_internal.py", line 503, in fn + return if_false(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/functional.py", line 796, in _max_pool2d + return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) +RuntimeError: Given input size: (64x1x1). Calculated output size: (64x0x0). Output size is too small +[rank2]: Traceback (most recent call last): +[rank2]: File "train.py", line 312, in +[rank2]: model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( +[rank2]: File "train.py", line 140, in train_model +[rank2]: outputs = model(features) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank2]: else self._run_ddp_forward(*inputs, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank2]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 61, in forward +[rank2]: x3_pooled = self.pool(x3) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/pooling.py", line 164, in forward +[rank2]: return F.max_pool2d(input, self.kernel_size, self.stride, +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_jit_internal.py", line 503, in fn +[rank2]: return if_false(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/functional.py", line 796, in _max_pool2d +[rank2]: return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) +[rank2]: RuntimeError: Given input size: (64x1x1). Calculated output size: (64x0x0). Output size is too small diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-nxgh5yjl/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-nxgh5yjl/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-nxgh5yjl/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 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16 + - 23 + - 55 + - 62 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: mse +max_oc: + value: 150 +target_transform: + value: log +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-yl190gul/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-yl190gul/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..404599d9f90780e982bc93e4a32aaeded76c7c2b --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-yl190gul/files/output.log @@ -0,0 +1,70 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 + 0%| | 0/20 [00:01 + model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( + File "train.py", line 140, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 61, in forward + x3_pooled = self.pool(x3) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/pooling.py", line 164, in forward + return F.max_pool2d(input, self.kernel_size, self.stride, + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_jit_internal.py", line 503, in fn + return if_false(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/functional.py", line 796, in _max_pool2d + return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) +RuntimeError: Given input size: (64x1x1). Calculated output size: (64x0x0). Output size is too small +[rank0]: Traceback (most recent call last): +[rank0]: File "train.py", line 312, in +[rank0]: model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( +[rank0]: File "train.py", line 140, in train_model +[rank0]: outputs = model(features) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank0]: else self._run_ddp_forward(*inputs, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank0]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 61, in forward +[rank0]: x3_pooled = self.pool(x3) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/pooling.py", line 164, in forward +[rank0]: return F.max_pool2d(input, self.kernel_size, self.stride, +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/_jit_internal.py", line 503, in fn +[rank0]: return if_false(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/functional.py", line 796, in _max_pool2d +[rank0]: return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) +[rank0]: RuntimeError: Given input size: (64x1x1). Calculated output size: (64x0x0). Output size is too small diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-yl190gul/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-yl190gul/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001100-yl190gul/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 +nvidia-curand-cu12==10.3.2.106 +nvidia-cufft-cu12==11.0.2.54 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cublas-cu12==12.1.3.1 +networkx==3.1 +MarkupSafe==2.1.5 +fsspec==2024.10.0 +filelock==3.16.1 +triton==3.0.0 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cudnn-cu12==9.1.0.70 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16 + - 23 + - 55 + - 62 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: mse +max_oc: + value: 150 +target_transform: + value: log +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-6mx2zwe5/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-6mx2zwe5/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..64bb7a0ad65a1ca4ed7000c7df196bdb338ca182 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-6mx2zwe5/files/output.log @@ -0,0 +1,62 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 + 0%| | 0/20 [00:01 + model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( + File "train.py", line 140, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 79, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x800 and 128x64) +[rank0]: Traceback (most recent call last): +[rank0]: File "train.py", line 312, in +[rank0]: model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( +[rank0]: File "train.py", line 140, in train_model +[rank0]: outputs = model(features) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank0]: else self._run_ddp_forward(*inputs, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank0]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 79, in forward +[rank0]: x = self.fc1(x) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank0]: return self._call_impl(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank0]: return forward_call(*args, **kwargs) +[rank0]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank0]: return F.linear(input, self.weight, self.bias) +[rank0]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x800 and 128x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-6mx2zwe5/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-6mx2zwe5/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-g4c03jgm/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-g4c03jgm/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4e222afb3c6904ed7d25911aefcd70d1adc1e135 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-g4c03jgm/files/config.yaml @@ -0,0 +1,47 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: mse +max_oc: + value: 150 +target_transform: + value: log +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-g4c03jgm/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-g4c03jgm/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..72aaf9315eb0640b998f25a7320f894ee7fc880f --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-g4c03jgm/files/output.log @@ -0,0 +1,59 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 + 0%| | 0/20 [00:01 + model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( + File "train.py", line 140, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 79, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x800 and 128x64) +[rank3]: Traceback (most recent call last): +[rank3]: File "train.py", line 312, in +[rank3]: model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( +[rank3]: File "train.py", line 140, in train_model +[rank3]: outputs = model(features) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank3]: else self._run_ddp_forward(*inputs, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank3]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 79, in forward +[rank3]: x = self.fc1(x) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank3]: return self._call_impl(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank3]: return forward_call(*args, **kwargs) +[rank3]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank3]: return F.linear(input, self.weight, self.bias) +[rank3]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x800 and 128x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-g4c03jgm/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-g4c03jgm/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-pc0inzgo/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-pc0inzgo/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..6024774dad497bcea063b4a1e724243a63ba31b2 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-pc0inzgo/files/output.log @@ -0,0 +1,59 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 + 0%| | 0/20 [00:01 + model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( + File "train.py", line 140, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 79, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x800 and 128x64) +[rank1]: Traceback (most recent call last): +[rank1]: File "train.py", line 312, in +[rank1]: model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( +[rank1]: File "train.py", line 140, in train_model +[rank1]: outputs = model(features) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank1]: else self._run_ddp_forward(*inputs, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank1]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 79, in forward +[rank1]: x = self.fc1(x) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank1]: return self._call_impl(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank1]: return forward_call(*args, **kwargs) +[rank1]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank1]: return F.linear(input, self.weight, self.bias) +[rank1]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x800 and 128x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-pc0inzgo/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-pc0inzgo/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ 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a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-vplbtbbj/files/config.yaml b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-vplbtbbj/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4e222afb3c6904ed7d25911aefcd70d1adc1e135 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-vplbtbbj/files/config.yaml @@ -0,0 +1,47 @@ +_wandb: + value: + cli_version: 0.19.3 + m: [] + python_version: 3.8.20 + t: + "1": + - 1 + - 49 + - 55 + - 71 + "2": + - 1 + - 49 + - 55 + - 71 + "3": + - 14 + - 16 + - 23 + - 55 + "4": 3.8.20 + "5": 0.19.3 + "8": + - 5 + "12": 0.19.3 + "13": linux-x86_64 +batch_size: + value: 256 +epochs: + value: 200 +input_channels: + value: 6 +learning_rate: + value: 0.001 +loss_type: + value: mse +max_oc: + value: 150 +target_transform: + value: log +time_beginning: + value: "2007" +time_end: + value: "2023" +use_validation: + value: true diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-vplbtbbj/files/output.log b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-vplbtbbj/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..e3ffc9994f3977e87110f340ad3953d0564d5113 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-vplbtbbj/files/output.log @@ -0,0 +1,59 @@ +/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/dataloader/dataframe_loader.py:158: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 + 0%| | 0/20 [00:01 + model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( + File "train.py", line 140, in train_model + outputs = model(features) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward + else self._run_ddp_forward(*inputs, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward + return self.module(*inputs, **kwargs) # type: ignore[index] + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 79, in forward + x = self.fc1(x) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl + return forward_call(*args, **kwargs) + File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward + return F.linear(input, self.weight, self.bias) +RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x800 and 128x64) +[rank2]: Traceback (most recent call last): +[rank2]: File "train.py", line 312, in +[rank2]: model, val_outputs, val_targets, best_model_state, best_r_squared = train_model( +[rank2]: File "train.py", line 140, in train_model +[rank2]: outputs = model(features) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward +[rank2]: else self._run_ddp_forward(*inputs, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward +[rank2]: return self.module(*inputs, **kwargs) # type: ignore[index] +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/lustre/home/vfourel/SOCProject/SOCmapping/SimpleTimeModel/SimpleCNN/modelCNN.py", line 79, in forward +[rank2]: x = self.fc1(x) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl +[rank2]: return self._call_impl(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl +[rank2]: return forward_call(*args, **kwargs) +[rank2]: File "/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 117, in forward +[rank2]: return F.linear(input, self.weight, self.bias) +[rank2]: RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x800 and 128x64) diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-vplbtbbj/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001140-vplbtbbj/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ 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To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Size of the training set: 20273 +Size of the validation set: 1664 +Model parameters: 92465 +Training set size: 20273 +Validation set size: 1664 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.88it/s] +Epoch 1: +Training Loss: 39197.9809 +Validation Loss: 627.9223 +RPIQ: 0.0000 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/numpy/core/_methods.py:239: RuntimeWarning: overflow encountered in multiply + x = um.multiply(x, x, out=x) +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/numpy/core/_methods.py:250: RuntimeWarning: overflow encountered in reduce + ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where) +train.py:203: RuntimeWarning: overflow encountered in square + mse = np.mean((original_val_outputs - original_val_targets) ** 2) + +Epoch 2: +Training Loss: 1794.2090 +Validation Loss: 58.4131 +RPIQ: 0.0000 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 2.00it/s] + +Epoch 3: +Training Loss: 734.4253 +Validation Loss: 24.1357 +RPIQ: 0.0000 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 4: +Training Loss: 447.5931 +Validation Loss: 17.5684 +RPIQ: 0.0000 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 5: +Training Loss: 319.8674 +Validation Loss: 15.0343 +RPIQ: 0.0000 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.99it/s] + +Epoch 6: +Training Loss: 237.6801 +Validation Loss: 11.2647 +RPIQ: 0.0000 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.97it/s] + +Epoch 7: +Training Loss: 183.2783 +Validation Loss: 8.3543 +RPIQ: 0.0002 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 8: +Training Loss: 136.0936 +Validation Loss: 6.8100 +RPIQ: 0.0028 +100%|███████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s] + +Epoch 9: +Training Loss: 101.4203 +Validation Loss: 6.0932 +RPIQ: 0.0075 + 15%|██████▌ | 3/20 [00:02<00:10, 1.62it/s] diff --git a/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001208-9jh4iwl3/files/requirements.txt b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001208-9jh4iwl3/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa8ff88f5f6d561382c8b5b1b21ce933db538e72 --- /dev/null +++ b/Archive/SimpleTimeModel/SimpleCNN/wandb/run-20250416_001208-9jh4iwl3/files/requirements.txt @@ -0,0 +1,167 @@ +setuptools==75.3.0 +wheel==0.45.1 +pip==24.3.1 +mpmath==1.3.0 +typing_extensions==4.12.2 +sympy==1.13.3 +nvidia-nvtx-cu12==12.1.105 +nvidia-nvjitlink-cu12==12.6.85 +nvidia-nccl-cu12==2.20.5 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batch 271: Too many concurrent aggregations. diff --git a/Archive/landcover_bavaria_Visual_results_npy_s/errors_200000_400000.txt b/Archive/landcover_bavaria_Visual_results_npy_s/errors_200000_400000.txt new file mode 100644 index 0000000000000000000000000000000000000000..018adf8acb9dc19a9a7df1e38409858f79c291f8 --- /dev/null +++ b/Archive/landcover_bavaria_Visual_results_npy_s/errors_200000_400000.txt @@ -0,0 +1 @@ +Error in batch 379: Too many concurrent aggregations. diff --git a/Archive/landcover_bavaria_Visual_results_npy_s/errors_400000_600000.txt b/Archive/landcover_bavaria_Visual_results_npy_s/errors_400000_600000.txt new file mode 100644 index 0000000000000000000000000000000000000000..cc4f87ed62df17e9b3dcfdeca25987015c018802 --- /dev/null +++ b/Archive/landcover_bavaria_Visual_results_npy_s/errors_400000_600000.txt @@ -0,0 +1 @@ +Error in batch 289: Too many concurrent aggregations. diff --git a/Archive/landcover_bavaria_Visual_results_npy_s/errors_600000_800000.txt 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b/Archive/landcover_bavaria_Visual_results_npy_s/summary_0_200000.txt new file mode 100644 index 0000000000000000000000000000000000000000..aa5af5421489008237fa9114cbbc2f578923cbee --- /dev/null +++ b/Archive/landcover_bavaria_Visual_results_npy_s/summary_0_200000.txt @@ -0,0 +1,15 @@ +Land Cover Class Distribution (indices 0-200000): +-------------------------------------------------- +Total points: 200000 +Points with data: 199500 +Points without data: 500 +-------------------------------------------------- +Tree cover: 93,512 points (46.76%) +Grassland: 47,629 points (23.81%) +Cropland: 44,858 points (22.43%) +Built-up: 9,098 points (4.55%) +Permanent water bodies: 2,500 points (1.25%) +Bare / sparse vegetation: 1,594 points (0.80%) +Moss and lichen: 186 points (0.09%) +Herbaceous wetland: 74 points (0.04%) +Snow and ice: 49 points (0.02%) diff --git a/Archive/landcover_bavaria_Visual_results_npy_s/summary_1000000_1200000.txt b/Archive/landcover_bavaria_Visual_results_npy_s/summary_1000000_1200000.txt new file mode 100644 index 0000000000000000000000000000000000000000..a7f7bd10c391a4330989e52c11a043d5173ba4bd --- /dev/null +++ b/Archive/landcover_bavaria_Visual_results_npy_s/summary_1000000_1200000.txt @@ -0,0 +1,16 @@ +Land Cover Class Distribution (indices 1000000-1200000): +-------------------------------------------------- +Total points: 200000 +Points with data: 198500 +Points without data: 1500 +-------------------------------------------------- +Tree cover: 93,573 points (46.79%) +Grassland: 46,656 points (23.33%) +Cropland: 44,697 points (22.35%) +Built-up: 9,083 points (4.54%) +Permanent water bodies: 2,527 points (1.26%) +Bare / sparse vegetation: 1,657 points (0.83%) +Moss and lichen: 203 points (0.10%) +Herbaceous wetland: 56 points (0.03%) +Snow and ice: 47 points (0.02%) +Shrubland: 1 points (0.00%) diff --git a/Archive/landcover_bavaria_Visual_results_npy_s/summary_1200000_1250000.txt 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b/Archive/landcover_bavaria_Visual_results_npy_s/summary_400000_600000.txt new file mode 100644 index 0000000000000000000000000000000000000000..d977f33858cae2f482847e20d004cb8af503949c --- /dev/null +++ b/Archive/landcover_bavaria_Visual_results_npy_s/summary_400000_600000.txt @@ -0,0 +1,16 @@ +Land Cover Class Distribution (indices 400000-600000): +-------------------------------------------------- +Total points: 200000 +Points with data: 199500 +Points without data: 500 +-------------------------------------------------- +Tree cover: 94,132 points (47.07%) +Grassland: 47,065 points (23.53%) +Cropland: 44,829 points (22.41%) +Built-up: 9,072 points (4.54%) +Permanent water bodies: 2,497 points (1.25%) +Bare / sparse vegetation: 1,616 points (0.81%) +Moss and lichen: 185 points (0.09%) +Herbaceous wetland: 62 points (0.03%) +Snow and ice: 41 points (0.02%) +Shrubland: 1 points (0.00%) diff --git a/Archive/landcover_bavaria_Visual_results_npy_s/summary_600000_800000.txt 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b/Archive/landcover_bavaria_Visual_results_npy_s/summary_800000_1000000.txt new file mode 100644 index 0000000000000000000000000000000000000000..57e1052d5bc63628f959d78d59d57398486fcc2a --- /dev/null +++ b/Archive/landcover_bavaria_Visual_results_npy_s/summary_800000_1000000.txt @@ -0,0 +1,16 @@ +Land Cover Class Distribution (indices 800000-1000000): +-------------------------------------------------- +Total points: 200000 +Points with data: 199500 +Points without data: 500 +-------------------------------------------------- +Tree cover: 94,173 points (47.09%) +Grassland: 47,256 points (23.63%) +Cropland: 44,714 points (22.36%) +Built-up: 8,972 points (4.49%) +Permanent water bodies: 2,485 points (1.24%) +Bare / sparse vegetation: 1,628 points (0.81%) +Moss and lichen: 152 points (0.08%) +Herbaceous wetland: 70 points (0.03%) +Snow and ice: 47 points (0.02%) +Shrubland: 3 points (0.00%) diff --git a/Archive/landcover_bavaria_Visual_results_npy_s/summary_merged.txt b/Archive/landcover_bavaria_Visual_results_npy_s/summary_merged.txt new file mode 100644 index 0000000000000000000000000000000000000000..34177e38b9fc8b12cefcb6868db7340512308972 --- /dev/null +++ b/Archive/landcover_bavaria_Visual_results_npy_s/summary_merged.txt @@ -0,0 +1,16 @@ +Merged Land Cover Class Distribution: +-------------------------------------------------- +Total points: 1,300,000 +Points with data: 1,295,500 +Points without data: 4,500 +-------------------------------------------------- +Tree cover: 609,990 points (46.92%) +Shrubland: 8 points (0.00%) +Grassland: 307,257 points (23.64%) +Cropland: 290,643 points (22.36%) +Built-up: 58,876 points (4.53%) +Bare / sparse vegetation: 10,577 points (0.81%) +Snow and ice: 299 points (0.02%) +Permanent water bodies: 16,227 points (1.25%) +Herbaceous wetland: 443 points (0.03%) +Moss and lichen: 1,180 points (0.09%) diff --git a/Archive/parquetTrainingValidationSet/final_training_df.parquet 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b/Archive/parquetTrainingValidationSet/original_launch.txt new file mode 100644 index 0000000000000000000000000000000000000000..0c8b55748992f357e9b4040bb0eee161edb2366e --- /dev/null +++ b/Archive/parquetTrainingValidationSet/original_launch.txt @@ -0,0 +1,71 @@ +(socmapping) vfourel@g058:/lustre/home/vfourel/SOCProject/SOCmapping/balancedDataset$ python nonRandomVal_v2.py --use-gpu +Using device: cuda +/lustre/home/vfourel/SOCProject/SOCmapping/balancedDataset/dataframe_loader.py:301: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. + dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date']) +Initial shape: (30451, 7) +Final filtered shape: (16514, 7) +Loaded 16514 rows +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/scipy/stats/_continuous_distns.py:709: RuntimeWarning: invalid value encountered in sqrt + sk = 2*(b-a)*np.sqrt(a + b + 1) / (a + b + 2) / np.sqrt(a*b) +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/scipy/optimize/_minpack_py.py:178: RuntimeWarning: The iteration is not making good progress, as measured by the + improvement from the last ten iterations. + warnings.warn(msg, RuntimeWarning) +/home/vfourel/miniforge3/envs/socmapping/lib/python3.8/site-packages/scipy/stats/_distn_infrastructure.py:2789: RuntimeWarning: invalid value encountered in scalar multiply + Lhat = muhat - Shat*mu +Best fitting distribution: Inverse Gamma +Parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} +KS statistic: 0.0566 +Flipping 1917 points (distance < 1.7 km) +Validation set size 0.39% < 10.0%. Increasing ratio. +Flipping 2253 points (distance < 1.7 km) +Validation set size 0.35% < 10.0%. Increasing ratio. +Flipping 2550 points (distance < 1.7 km) +Validation set size 0.56% < 10.0%. Increasing ratio. +Flipping 2885 points (distance < 1.7 km) +Validation set size 0.53% < 10.0%. Increasing ratio. +Flipping 3191 points (distance < 1.7 km) +Validation set size 0.67% < 10.0%. Increasing ratio. +Flipping 3486 points (distance < 1.7 km) +Validation set size 0.89% < 10.0%. Increasing ratio. +Flipping 3813 points (distance < 1.7 km) +Validation set size 0.91% < 10.0%. Increasing ratio. +Flipping 4131 points (distance < 1.7 km) +Validation set size 0.98% < 10.0%. Increasing ratio. +Flipping 4444 points (distance < 1.7 km) +Validation set size 1.08% < 10.0%. Increasing ratio. +Flipping 4763 points (distance < 1.7 km) +Validation set size 1.16% < 10.0%. Increasing ratio. +Flipping 5058 points (distance < 1.7 km) +Validation set size 1.37% < 10.0%. Increasing ratio. +Flipping 5328 points (distance < 1.7 km) +Validation set size 1.73% < 10.0%. Increasing ratio. +Flipping 5636 points (distance < 1.7 km) +Validation set size 1.87% < 10.0%. Increasing ratio. +Flipping 5922 points (distance < 1.7 km) +Validation set size 2.14% < 10.0%. Increasing ratio. +Flipping 6218 points (distance < 1.7 km) +Validation set size 2.34% < 10.0%. Increasing ratio. +Flipping 6472 points (distance < 1.7 km) +Validation set size 2.80% < 10.0%. Increasing ratio. +Flipping 6703 points (distance < 1.7 km) +Validation set size 3.41% < 10.0%. Increasing ratio. +Flipping 7013 points (distance < 1.7 km) +Validation set size 3.53% < 10.0%. Increasing ratio. +Flipping 7196 points (distance < 1.7 km) +Validation set size 4.42% < 10.0%. Increasing ratio. +Flipping 7486 points (distance < 1.7 km) +Validation set size 4.67% < 10.0%. Increasing ratio. +Flipping 7599 points (distance < 1.7 km) +Validation set size 5.98% < 10.0%. Increasing ratio. +Flipping 7820 points (distance < 1.7 km) +Validation set size 6.64% < 10.0%. Increasing ratio. +Flipping 7937 points (distance < 1.7 km) +Validation set size 7.93% < 10.0%. Increasing ratio. +Flipping 8111 points (distance < 1.7 km) +Validation set size 8.88% < 10.0%. Increasing ratio. +Flipping 8210 points (distance < 1.7 km) +Full dataset size: 16514 +Final validation set: 1698 (10.28%) +Final training set: 14816 +Minimum distance (km): 1.43 +Validation OC distribution matched to Inverse Gamma with parameters: {'a': 3.5093212085018015, 'loc': -0.2207140712018134, 'scale': 55.73445932737795} diff --git a/Archive/predictions_plots_33_by_33_plots_2015/3dcnn_plots/100_150_2015_version_2015_bavaria_triptych_MAX_OC_150_Beginning_2007_End_2023__InferenceTime2015_20250325_152416.png b/Archive/predictions_plots_33_by_33_plots_2015/3dcnn_plots/100_150_2015_version_2015_bavaria_triptych_MAX_OC_150_Beginning_2007_End_2023__InferenceTime2015_20250325_152416.png new file mode 100644 index 0000000000000000000000000000000000000000..7465421e1b780928cfbc769b9a9e8eb8159a2174 --- /dev/null +++ b/Archive/predictions_plots_33_by_33_plots_2015/3dcnn_plots/100_150_2015_version_2015_bavaria_triptych_MAX_OC_150_Beginning_2007_End_2023__InferenceTime2015_20250325_152416.png 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run_4 at: https://wandb.ai/valerian-fourel/socmapping-SimpleTFT/runs/fy5a359t +wandb: ⭐️ View project at: https://wandb.ai/valerian-fourel/socmapping-SimpleTFT +wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s) +wandb: Find logs at: ./wandb/run-20250526_042857-fy5a359t/logs + +Average Metrics Across Runs: +train_loss_avg: 0.4248 +val_loss: 0.2090 +correlation: 0.5900 +r_squared: 0.3806 +mse: 52.5077 +rmse: 7.1665 +mae: 3.8612 +rpiq: 0.7369 + +Average Min Distance Statistics: +avg_mean: 64.0200 +std_mean: 3.5669 +avg_median: 4.5607 +std_median: 0.2014 +avg_min: 1.0640 +std_min: 0.2351 +avg_max: 726.2149 +std_max: 123.6940 +avg_std: 107.7294 +std_std: 5.7107 + +Best Metrics Across Runs: +r_squared - Mean: 0.6034, Std: 0.0271 +rmse - Mean: 5.7222, Std: 0.8980 +mae - Mean: 2.9016, Std: 0.3522 +rpiq - Mean: 0.9258, Std: 0.1325 + +Metrics saved to: output/training_metrics_tft_11f1e6eb.txt +wandb: Tracking run with wandb version 0.19.3 +wandb: Run data is saved locally in /lustre/home/vfourel/SOCProject/SOCmapping/TemporalFusionTransformer/wandb/run-20250526_055614-ay745p74 +wandb: Run `wandb offline` to turn off syncing. +wandb: Syncing run average_metrics +wandb: ⭐️ View project at https://wandb.ai/valerian-fourel/socmapping-SimpleTFT +wandb: 🚀 View run at https://wandb.ai/valerian-fourel/socmapping-SimpleTFT/runs/ay745p74 + +**Best overall model from run 3 saved** +Best R²: 0.6287 +Best MAE: 2.6474 +Best RMSE: 5.0902 +Best RPIQ: 1.0412 +Average R² across all runs: 0.6034 +wandb: +wandb: +wandb: Run summary: +wandb: avg_mae 2.90162 +wandb: avg_r_squared 0.60339 +wandb: avg_rmse 5.72224 +wandb: avg_rpiq 0.92575 +wandb: std_mae 0.35216 +wandb: std_r_squared 0.02712 +wandb: std_rmse 0.89799 +wandb: std_rpiq 0.13254 +wandb: +wandb: 🚀 View run average_metrics at: https://wandb.ai/valerian-fourel/socmapping-SimpleTFT/runs/ay745p74 +wandb: ⭐️ View project at: https://wandb.ai/valerian-fourel/socmapping-SimpleTFT +wandb: Synced 5 W&B file(s), 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b/Archive/results_prediction_41by41WindowSize/FoundationalModels/L1Loss_ZeroCenteredTarget/run2015_MAXOC150_v1/tmp.py @@ -0,0 +1,36 @@ +import numpy as np +import os + +# Set base and output directory +base_dir = "/lustre/home/vfourel/SOCProject/SOCmapping/results_prediction_41by41WindowSize/FoundationalModels/L1Loss_ZeroCenteredTarget/run2015_MAXOC150_v1" +output_dir = os.path.join(base_dir, "concatenatedResults") +os.makedirs(output_dir, exist_ok=True) + +# Ordered file names +coord_files = [ + "coordinates_1mil_1stQuarter.npy", + "coordinates_1mil_2ndQuarter.npy", + "coordinates_1mil_3rdQuarter.npy", + "coordinates_1mil_4thQuarter.npy" +] + +pred_files = [ + "predictions_1mil_1stQuarter.npy", + "predictions_1mil_2ndQuarter.npy", + "predictions_1mil_3rdQuarter.npy", + "predictions_1mil_4thQuarter.npy" +] + +# Load and stack coordinates +coordinates = np.vstack([np.load(os.path.join(base_dir, f)) for f in coord_files]) + +# Load, reshape and stack predictions +predictions = np.vstack([np.load(os.path.join(base_dir, f)).reshape(-1, 1) for f in pred_files]) + +# Save outputs +np.save(os.path.join(output_dir, "coordinates.npy"), coordinates) +np.save(os.path.join(output_dir, "predictions.npy"), predictions) + +print("✅ Saved to 'concatenatedResults/'") +print(f"coordinates shape: {coordinates.shape}") +print(f"predictions shape: {predictions.shape}") diff --git a/Archive/results_prediction_41by41WindowSize/Maps/mappingAllPredictions.py b/Archive/results_prediction_41by41WindowSize/Maps/mappingAllPredictions.py new file mode 100644 index 0000000000000000000000000000000000000000..0f8fe48999384dfe497d25efaa61a68a6d6eabae --- /dev/null +++ b/Archive/results_prediction_41by41WindowSize/Maps/mappingAllPredictions.py @@ -0,0 +1,194 @@ +import os +import numpy as np +import matplotlib.pyplot as plt +from scipy.interpolate import griddata +import geopandas as gpd +from pathlib import Path +import scipy # Added for QhullError + +def load_inference_data(): + """ + Load .npy files with explicit paths, print shapes, and ensure correct dimensions. + Trims arrays, aligns coordinates and predictions, and picks max prediction for duplicate coords. + """ + root_dir = "/home/vfourel/SOCProject/SOCmapping/results_predictions" + method_data = {} + + method_configs = { + "3DCNN": { + "subfolder": "2015InferenceRun_v1", + "files": ["coordinates_1mil.npy", "predictions_1mil.npy"] + }, + "CNNLSTM": { + "subfolder": "2015Inference", + "files": ["coordinates_1mil.npy", "predictions_1mil.npy"] + }, + "FoundationalModels": { + "subfolder": "run2015_MAXOC150_v1", + "files": [ + "coordinates_1mil_1stQuarter.npy", # "coordinates_1mil_2ndQuarter.npy" + # "coordinates_1mil_3rdQuarter.npy", "coordinates_1mil_4thQuarter.npy", + "predictions_1mil_1stQuarter.npy"# "predictions_1mil_2ndQuarter.npy", + # "predictions_1mil_3rdQuarter.npy", "predictions_1mil_4thQuarter.npy" + ] + }, + "RandomForest": { + "subfolder": "2015Inference", + "files": ["coordinates_1mil.npy", "predictions_1mil.npy"] + }, + "SimpleCNN": { + "subfolder": "2015Inference", + "files": ["coordinates_1mil.npy", "predictions_1mil.npy"] + }, + "SimpleTransformer": { + "subfolder": "2015Inference", + "files": [ + "coordinates_1mil_1stThird.npy", "coordinates_1mil_2ndThird.npy", + "coordinates_1mil_3rdThird.npy", "predictions_1mil_1stThird.npy", + "predictions_1mil_2ndThird.npy", "predictions_1mil_3rdThird.npy" + ] + }, + "XGBoost": { + "subfolder": "2015Inference", + "files": ["coordinates_1mil.npy", "predictions_1mil.npy"] + } + } + + for method, config in method_configs.items(): + subfolder_path = f"{root_dir}/{method}/{config['subfolder']}" + coords_list = [] + preds_list = [] + + print(f"\nLoading files for {method}:") + for file_name in config["files"]: + file_path = f"{subfolder_path}/{file_name}" + data = np.load(file_path) + print(f" {file_name}: shape={data.shape}") + + if "coordinates" in file_name: + # Ensure coordinates are (N, 2) + if data.shape[1] != 2: + data = data.T # Transpose if loaded as (2, N) + coords_list.append(data) + elif "predictions" in file_name: + # Ensure predictions are 1D + if data.ndim > 1: + data = data.flatten() + preds_list.append(data) + + if len(coords_list) > 1: + # Trim coordinates to smallest N + min_coords_n = min(arr.shape[0] for arr in coords_list) + coords_list = [arr[:min_coords_n, :] for arr in coords_list] + coordinates = np.concatenate(coords_list, axis=0) + + # Trim predictions to smallest N + min_preds_n = min(arr.shape[0] for arr in preds_list) + preds_list = [arr[:min_preds_n] for arr in preds_list] + predictions = np.concatenate(preds_list, axis=0) + else: + coordinates = coords_list[0] + predictions = preds_list[0] + + # Ensure coordinates and predictions have the same number of points + min_points = min(coordinates.shape[0], predictions.shape[0]) + coordinates = coordinates[:min_points, :] + predictions = predictions[:min_points] + + # Handle duplicate coordinates by picking the max prediction + unique_coords, indices, counts = np.unique(coordinates, axis=0, return_inverse=True, return_counts=1) + if np.any(counts > 1): + print(f"{method}: Found {np.sum(counts > 1)} duplicate coordinate pairs, aggregating by max prediction") + unique_predictions = np.zeros(unique_coords.shape[0]) + for i in range(unique_coords.shape[0]): + # Find all predictions for this unique coordinate + pred_indices = np.where(indices == i)[0] + unique_predictions[i] = np.max(predictions[pred_indices]) + coordinates = unique_coords + predictions = unique_predictions + else: + print(f"{method}: No duplicate coordinates found") + + method_data[method] = { + 'coordinates': coordinates, # (N, 2) + 'predictions': predictions # (N,) + } + print(f"{method} final: coords shape={coordinates.shape}, preds shape={predictions.shape}") + + return method_data + +def plot_interpolated_maps(method_data, + save_path="/home/vfourel/SOCProject/SOCmapping/results_predictions/output/maps", + year='2015'): + """ + Create and save interpolated maps with Bavaria contours. + Falls back to 'nearest' if 'linear' fails. Maxxed out contours with rainbow vibes. + """ + bavaria = gpd.read_file('https://raw.githubusercontent.com/isellsoap/deutschlandGeoJSON/main/2_bundeslaender/4_niedrig.geo.json') + bavaria = bavaria[bavaria['name'] == 'Bayern'] + + n_methods = len(method_data) + fig, axes = plt.subplots(nrows=(n_methods + 1) // 2, ncols=2, + figsize=(12, 6 * ((n_methods + 1) // 2))) + axes = axes.flatten() if n_methods > 1 else [axes] + + all_predictions = [data['predictions'] for data in method_data.values()] + vmin = np.min(np.concatenate(all_predictions)) + vmax = np.max(np.concatenate(all_predictions)) + + for idx, (method, data) in enumerate(method_data.items()): + coords = data['coordinates'] + preds = data['predictions'] + + unique_coords = np.unique(coords, axis=0) + print(f"{method}: unique coords={len(unique_coords)}, preds={len(preds)}") + + if len(unique_coords) < 3: + print(f"{method}: Too few unique points, skipping plot") + axes[idx].text(0.5, 0.5, f"{method}: Insufficient unique points ({len(unique_coords)})", + ha='center', va='center', transform=axes[idx].transAxes) + continue + + grid_x = np.linspace(coords[:, 0].min(), coords[:, 0].max(), 300) + grid_y = np.linspace(coords[:, 1].min(), coords[:, 1].max(), 300) + grid_x, grid_y = np.meshgrid(grid_x, grid_y) + + try: + grid_z = griddata(coords, preds, (grid_x, grid_y), method='linear') + if np.all(np.isnan(grid_z)): + raise ValueError("Linear interpolation produced all NaNs") + except (ValueError, scipy.spatial.qhull.QhullError) as e: + print(f"{method}: Linear interpolation failed ({str(e)}), using nearest") + grid_z = griddata(coords, preds, (grid_x, grid_y), method='nearest') + + ax = axes[idx] + contour = ax.contourf(grid_x, grid_y, grid_z, + levels=100, # Maxxing contours with finer transitions + cmap='hsv', # Rainbowed max contrast + vmin=vmin, vmax=vmax, + alpha=0.9) # Upped alpha for that GG glow + + bavaria.boundary.plot(ax=ax, color='black', linewidth=1.5) # Thicker boundary for contrast + ax.set_title(f'{method} - Interpolated Predictions', fontsize=12, fontweight='bold') + ax.set_xlabel('Longitude') + ax.set_ylabel('Latitude') + ax.grid(True, linestyle='--', alpha=0.5) # Subtle grid to keep focus on colors + + fig.subplots_adjust(right=0.8) + cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) + cbar = fig.colorbar(contour, cax=cbar_ax, label='Predicted Values') + cbar.ax.tick_params(labelsize=10) # Readable colorbar ticks + + for idx in range(len(method_data), len(axes)): + fig.delaxes(axes[idx]) + + os.makedirs(save_path, exist_ok=True) + output_file = os.path.join(save_path, f'{year}_bavaria_interpolated_comparison.png') + plt.savefig(output_file, dpi=300, bbox_inches='tight') + plt.close() + + print(f"Saved interpolated maps to: {output_file}") + +if __name__ == "__main__": + method_data = load_inference_data() + plot_interpolated_maps(method_data) \ No newline at end of file diff --git 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/dev/null +++ b/Archive/unzipped.py @@ -0,0 +1,60 @@ +import os +import shutil +from pathlib import Path + +def reconstruct_split_folders(*part_paths, output_dir="reconstructed"): + """ + Reconstruct split folders from multiple parts. + + Args: + *part_paths: Variable number of paths to the unzipped part folders + output_dir: Directory where the reconstructed structure will be created + """ + # Create output directory if it doesn't exist + os.makedirs(output_dir, exist_ok=True) + + for part_path in part_paths: + # Convert to Path object + part_dir = Path(part_path) + + # Get the first directory inside the unzipped folder + try: + first_subdir = next(p for p in part_dir.iterdir() if p.is_dir()) + except StopIteration: + print(f"No subdirectories found in {part_path}") + continue + + # Walk through all files in the first subdirectory + for root, _, files in os.walk(first_subdir): + for file in files: + # Get the full source path + src_path = os.path.join(root, file) + + # Calculate relative path from the first subdirectory + rel_path = os.path.relpath(src_path, first_subdir) + + # Create destination path + dst_path = os.path.join(output_dir, rel_path) + + # Create destination directory if it doesn't exist + os.makedirs(os.path.dirname(dst_path), exist_ok=True) + + # Copy the file to its new location + shutil.copy2(src_path, dst_path) + print(f"Copied: {rel_path}") + +# Example usage: +# reconstruct_split_folders( +# "/path/to/part1", +# "/path/to/part2", +# "/path/to/part3", +# output_dir="reconstructed_folder" +# ) + +# Example with three parts +reconstruct_split_folders( + "/home/vfourel/SOCmapping/Data/Coordinates-20241211T155342Z-001", + "/home/vfourel/SOCmapping/Data/Coordinates-20241211T155342Z-002", + output_dir="/home/vfourel/SOCmapping/Data/Coordinates" +) + diff --git a/BaselinesXGBoostAndRF/RandomForestFinalResults2023/coordinates_1mil_rf.npy b/BaselinesXGBoostAndRF/RandomForestFinalResults2023/coordinates_1mil_rf.npy new file mode 100644 index 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a/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/compareNumberOfParametersBaselines.txt b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/compareNumberOfParametersBaselines.txt new file mode 100644 index 0000000000000000000000000000000000000000..74491aa5bea0888cebab6fa594a754cf26115457 --- /dev/null +++ b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/compareNumberOfParametersBaselines.txt @@ -0,0 +1,220 @@ + +Resampled training size: 19715 +Processing training data... +Processing validation data... +Training features shape: (19715, 28830) +Validation features shape: (1539, 28830) + +Training models... +Training XGBoost with n_estimators=5... +Training Random Forest with n_estimators=5... + +Training Metrics (XGBoost_5): +R² (correlation squared): 0.8351 +MAE: 8.7026 +RMSE: 14.2975 +RPIQ: 0.8113 + +Validation Metrics (XGBoost_5): +R² (correlation squared): 0.1600 +MAE: 7.9862 +RMSE: 10.1359 +RPIQ: 0.4736 + +Training Metrics (RandomForest_5): +R² (correlation squared): 0.7724 +MAE: 5.4969 +RMSE: 9.7671 +RPIQ: 1.1877 + +Validation Metrics (RandomForest_5): +R² (correlation squared): 0.1537 +MAE: 8.1563 +RMSE: 13.4566 +RPIQ: 0.3567 +Training XGBoost with n_estimators=10... +Training Random Forest with n_estimators=10... + +Training Metrics (XGBoost_10): +R² (correlation squared): 0.8574 +MAE: 6.3575 +RMSE: 10.8093 +RPIQ: 1.0732 + +Validation Metrics (XGBoost_10): +R² (correlation squared): 0.1955 +MAE: 7.5836 +RMSE: 10.6899 +RPIQ: 0.4490 + +Training Metrics (RandomForest_10): +R² (correlation squared): 0.7935 +MAE: 5.3497 +RMSE: 9.3822 +RPIQ: 1.2364 + +Validation Metrics (RandomForest_10): +R² (correlation squared): 0.1895 +MAE: 7.9308 +RMSE: 12.7443 +RPIQ: 0.3766 +Training XGBoost with n_estimators=25... +Training Random Forest with n_estimators=25... + +Training Metrics (XGBoost_25): +R² (correlation squared): 0.8797 +MAE: 3.4068 +RMSE: 7.3850 +RPIQ: 1.5707 + +Validation Metrics (XGBoost_25): +R² (correlation squared): 0.2192 +MAE: 7.6038 +RMSE: 12.4826 +RPIQ: 0.3845 + +Training Metrics (RandomForest_25): +R² (correlation squared): 0.8027 +MAE: 5.2472 +RMSE: 9.2326 +RPIQ: 1.2564 + +Validation Metrics (RandomForest_25): +R² (correlation squared): 0.2001 +MAE: 7.5560 +RMSE: 12.1447 +RPIQ: 0.3952 +Training XGBoost with n_estimators=50... +Training Random Forest with n_estimators=50... + +Training Metrics (XGBoost_50): +R² (correlation squared): 0.8865 +MAE: 2.4768 +RMSE: 6.8527 +RPIQ: 1.6928 + +Validation Metrics (XGBoost_50): +R² (correlation squared): 0.2285 +MAE: 7.8809 +RMSE: 13.2981 +RPIQ: 0.3610 + +Training Metrics (RandomForest_50): +R² (correlation squared): 0.8048 +MAE: 5.2530 +RMSE: 9.1994 +RPIQ: 1.2610 + +Validation Metrics (RandomForest_50): +R² (correlation squared): 0.1999 +MAE: 7.5012 +RMSE: 12.0967 +RPIQ: 0.3968 + +Training XGBoost with n_estimators=100... +Training Random Forest with n_estimators=100... + + +Training Metrics (XGBoost_100): +R² (correlation squared): 0.8865 +MAE: 2.2710 +RMSE: 6.8134 +RPIQ: 1.6879 + +Validation Metrics (XGBoost_100): +R² (correlation squared): 0.2591 +MAE: 8.3674 +RMSE: 13.5819 +RPIQ: 0.3829 + +Training Metrics (RandomForest_100): +R² (correlation squared): 0.7971 +MAE: 5.3179 +RMSE: 9.3606 +RPIQ: 1.2286 + +Validation Metrics (RandomForest_100): +R² (correlation squared): 0.2627 +MAE: 7.2462 +RMSE: 11.0320 +RPIQ: 0.4714 +Training XGBoost with n_estimators=500... +Training Random Forest with n_estimators=500... + +Training Metrics (XGBoost_500): +R² (correlation squared): 0.8869 +MAE: 2.1388 +RMSE: 6.7995 +RPIQ: 1.6913 + +Validation Metrics (XGBoost_500): +R² (correlation squared): 0.2589 +MAE: 8.4390 +RMSE: 13.6490 +RPIQ: 0.3810 + +Training Metrics (RandomForest_500): +R² (correlation squared): 0.8013 +MAE: 5.2696 +RMSE: 9.2782 +RPIQ: 1.2395 + +Validation Metrics (RandomForest_500): +R² (correlation squared): 0.2787 +MAE: 7.1976 +RMSE: 10.8957 +RPIQ: 0.4773 +Training XGBoost with n_estimators=1000... +Training Random Forest with n_estimators=1000... + +Training Metrics (XGBoost_1000): +R² (correlation squared): 0.8869 +MAE: 2.1371 +RMSE: 6.7995 +RPIQ: 1.6913 + +Validation Metrics (XGBoost_1000): +R² (correlation squared): 0.2589 +MAE: 8.4404 +RMSE: 13.6498 +RPIQ: 0.3810 + +Training Metrics (RandomForest_1000): +R² (correlation squared): 0.8011 +MAE: 5.2778 +RMSE: 9.2850 +RPIQ: 1.2386 + +Validation Metrics (RandomForest_1000): +R² (correlation squared): 0.2805 +MAE: 7.1726 +RMSE: 10.8482 +RPIQ: 0.4793 +Training XGBoost with n_estimators=2000... +Training Random Forest with n_estimators=2000... + + + +Training Metrics (XGBoost_2000): +R² (correlation squared): 0.8869 +MAE: 2.1371 +RMSE: 6.7995 +RPIQ: 1.6913 + +Validation Metrics (XGBoost_2000): +R² (correlation squared): 0.2589 +MAE: 8.4404 +RMSE: 13.6498 +RPIQ: 0.3810 + +Training Metrics (RandomForest_2000): +R² (correlation squared): 0.8011 +MAE: 5.2779 +RMSE: 9.2848 +RPIQ: 1.2386 + +Validation Metrics (RandomForest_2000): +R² (correlation squared): 0.2804 +MAE: 7.1685 +RMSE: 10.8321 +RPIQ: 0.4801 diff --git a/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/model_statistics.txt b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/model_statistics.txt new file mode 100644 index 0000000000000000000000000000000000000000..15b18aed4cda59446e514507569ce2ad0c581a0a --- /dev/null +++ b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/model_statistics.txt @@ -0,0 +1,6 @@ +Final Statistics: +Correlation: 0.8153 +R²: 0.6647 +MSE: 115.4239 +RMSE: 10.7436 +MAE: 5.5319 diff --git a/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultMultiplerunsOn5x5x5x6x5_Dataset_classic_1.25kmby1.25km.txt b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultMultiplerunsOn5x5x5x6x5_Dataset_classic_1.25kmby1.25km.txt new file mode 100644 index 0000000000000000000000000000000000000000..f89cb38f6227063bb1771d79ee64955245dd94ce --- /dev/null +++ b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultMultiplerunsOn5x5x5x6x5_Dataset_classic_1.25kmby1.25km.txt @@ -0,0 +1,17 @@ +Summary of all runs: +XGBoost_Train R²: 0.8922 ± 0.0012 +XGBoost_Train MAE: 2.0835 ± 0.0112 +XGBoost_Train RMSE: 6.6537 ± 0.0300 +XGBoost_Train RPIQ: 1.7280 ± 0.0147 +XGBoost_Val R²: 0.2774 ± 0.0412 +XGBoost_Val MAE: 7.9392 ± 0.2897 +XGBoost_Val RMSE: 13.5768 ± 0.4270 +XGBoost_Val RPIQ: 0.4057 ± 0.0213 +RandomForest_Train R²: 0.7878 ± 0.0021 +RandomForest_Train MAE: 5.4989 ± 0.0240 +RandomForest_Train RMSE: 9.6019 ± 0.0401 +RandomForest_Train RPIQ: 1.1975 ± 0.0096 +RandomForest_Val R²: 0.2715 ± 0.0514 +RandomForest_Val MAE: 7.1997 ± 0.2135 +RandomForest_Val RMSE: 11.4534 ± 0.3556 +RandomForest_Val RPIQ: 0.4810 ± 0.0277 diff --git a/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultsMultiRunsIncreasingSizeofRaster.txt b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultsMultiRunsIncreasingSizeofRaster.txt new file mode 100644 index 0000000000000000000000000000000000000000..c52d17b0ed1004f85aa88cb1f6ed720243643033 --- /dev/null +++ b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultsMultiRunsIncreasingSizeofRaster.txt @@ -0,0 +1,82 @@ + +resultsMultipleRunsOn5x5Dataset_classic_1.25kmx1.25km + +Summary of all runs: +XGBoost_Train R²: 0.8922 ± 0.0012 +XGBoost_Train MAE: 2.0835 ± 0.0112 +XGBoost_Train RMSE: 6.6537 ± 0.0300 +XGBoost_Train RPIQ: 1.7280 ± 0.0147 +XGBoost_Val R²: 0.2774 ± 0.0412 +XGBoost_Val MAE: 7.9392 ± 0.2897 +XGBoost_Val RMSE: 13.5768 ± 0.4270 +XGBoost_Val RPIQ: 0.4057 ± 0.0213 +RandomForest_Train R²: 0.7878 ± 0.0021 +RandomForest_Train MAE: 5.4989 ± 0.0240 +RandomForest_Train RMSE: 9.6019 ± 0.0401 +RandomForest_Train RPIQ: 1.1975 ± 0.0096 +RandomForest_Val R²: 0.2715 ± 0.0514 +RandomForest_Val MAE: 7.1997 ± 0.2135 +RandomForest_Val RMSE: 11.4534 ± 0.3556 +RandomForest_Val RPIQ: 0.4810 ± 0.0277 + + +resultsMultipleRunsOn11x11Dataset_classic_2.75kmx2.75km + +Summary of all runs: +XGBoost_Train R²: 0.8914 ± 0.0011 +XGBoost_Train MAE: 2.0858 ± 0.0183 +XGBoost_Train RMSE: 6.6643 ± 0.0488 +XGBoost_Train RPIQ: 1.7196 ± 0.0095 +XGBoost_Val R²: 0.2584 ± 0.0267 +XGBoost_Val MAE: 8.0976 ± 0.5327 +XGBoost_Val RMSE: 13.6813 ± 0.6788 +XGBoost_Val RPIQ: 0.4085 ± 0.0181 +RandomForest_Train R²: 0.7948 ± 0.0033 +RandomForest_Train MAE: 5.4005 ± 0.0493 +RandomForest_Train RMSE: 9.4471 ± 0.0850 +RandomForest_Train RPIQ: 1.2131 ± 0.0095 +RandomForest_Val R²: 0.2579 ± 0.0297 +RandomForest_Val MAE: 7.2134 ± 0.2395 +RandomForest_Val RMSE: 11.5239 ± 0.3234 +RandomForest_Val RPIQ: 0.4844 ± 0.0174 + + +resultsMultipleRunsOn21x21Dataset_classic_5.25kmx5.25km + +Summary of all runs: +XGBoost_Train R²: 0.8910 ± 0.0014 +XGBoost_Train MAE: 2.0928 ± 0.0159 +XGBoost_Train RMSE: 6.6935 ± 0.0402 +XGBoost_Train RPIQ: 1.7329 ± 0.0166 +XGBoost_Val R²: 0.2811 ± 0.0251 +XGBoost_Val MAE: 7.5397 ± 0.3092 +XGBoost_Val RMSE: 12.8340 ± 0.4135 +XGBoost_Val RPIQ: 0.3976 ± 0.0263 +RandomForest_Train R²: 0.8086 ± 0.0027 +RandomForest_Train MAE: 5.2006 ± 0.0473 +RandomForest_Train RMSE: 9.1547 ± 0.0616 +RandomForest_Train RPIQ: 1.2670 ± 0.0132 +RandomForest_Val R²: 0.3015 ± 0.0272 +RandomForest_Val MAE: 6.6739 ± 0.1845 +RandomForest_Val RMSE: 10.6355 ± 0.4390 +RandomForest_Val RPIQ: 0.4800 ± 0.0334 + +resultsMultipleRunsOn31x31Dataset_Classic_7.75kmx7.75km + +Summary of all runs: +XGBoost_Train R²: 0.8914 ± 0.0013 +XGBoost_Train MAE: 2.0849 ± 0.0128 +XGBoost_Train RMSE: 6.6744 ± 0.0323 +XGBoost_Train RPIQ: 1.7239 ± 0.0184 +XGBoost_Val R²: 0.2640 ± 0.0481 +XGBoost_Val MAE: 7.8728 ± 0.3600 +XGBoost_Val RMSE: 13.2403 ± 0.3841 +XGBoost_Val RPIQ: 0.4135 ± 0.0308 +RandomForest_Train R²: 0.8083 ± 0.0022 +RandomForest_Train MAE: 5.2054 ± 0.0249 +RandomForest_Train RMSE: 9.1140 ± 0.0293 +RandomForest_Train RPIQ: 1.2625 ± 0.0160 +RandomForest_Val R²: 0.2647 ± 0.0453 +RandomForest_Val MAE: 7.0780 ± 0.2922 +RandomForest_Val RMSE: 11.2258 ± 0.4014 +RandomForest_Val RPIQ: 0.4876 ± 0.0328 diff --git a/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultsMultipleRunsOn11x11Dataset_classic_2.75kmx2.75km.txt b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultsMultipleRunsOn11x11Dataset_classic_2.75kmx2.75km.txt new file mode 100644 index 0000000000000000000000000000000000000000..57b0dfde11b3b84ebb5682ff1adee7011f252acf --- /dev/null +++ b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultsMultipleRunsOn11x11Dataset_classic_2.75kmx2.75km.txt @@ -0,0 +1,17 @@ +Summary of all runs: +XGBoost_Train R²: 0.8914 ± 0.0011 +XGBoost_Train MAE: 2.0858 ± 0.0183 +XGBoost_Train RMSE: 6.6643 ± 0.0488 +XGBoost_Train RPIQ: 1.7196 ± 0.0095 +XGBoost_Val R²: 0.2584 ± 0.0267 +XGBoost_Val MAE: 8.0976 ± 0.5327 +XGBoost_Val RMSE: 13.6813 ± 0.6788 +XGBoost_Val RPIQ: 0.4085 ± 0.0181 +RandomForest_Train R²: 0.7948 ± 0.0033 +RandomForest_Train MAE: 5.4005 ± 0.0493 +RandomForest_Train RMSE: 9.4471 ± 0.0850 +RandomForest_Train RPIQ: 1.2131 ± 0.0095 +RandomForest_Val R²: 0.2579 ± 0.0297 +RandomForest_Val MAE: 7.2134 ± 0.2395 +RandomForest_Val RMSE: 11.5239 ± 0.3234 +RandomForest_Val RPIQ: 0.4844 ± 0.0174 diff --git a/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultsMultipleRunsOn21x21Dataset_classic_5.25kmx5.25km.txt b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultsMultipleRunsOn21x21Dataset_classic_5.25kmx5.25km.txt new file mode 100644 index 0000000000000000000000000000000000000000..e3c924ab46f76767bfdbe87ab67b2131e55025e3 --- /dev/null +++ b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultsMultipleRunsOn21x21Dataset_classic_5.25kmx5.25km.txt @@ -0,0 +1,17 @@ +Summary of all runs: +XGBoost_Train R²: 0.8910 ± 0.0014 +XGBoost_Train MAE: 2.0928 ± 0.0159 +XGBoost_Train RMSE: 6.6935 ± 0.0402 +XGBoost_Train RPIQ: 1.7329 ± 0.0166 +XGBoost_Val R²: 0.2811 ± 0.0251 +XGBoost_Val MAE: 7.5397 ± 0.3092 +XGBoost_Val RMSE: 12.8340 ± 0.4135 +XGBoost_Val RPIQ: 0.3976 ± 0.0263 +RandomForest_Train R²: 0.8086 ± 0.0027 +RandomForest_Train MAE: 5.2006 ± 0.0473 +RandomForest_Train RMSE: 9.1547 ± 0.0616 +RandomForest_Train RPIQ: 1.2670 ± 0.0132 +RandomForest_Val R²: 0.3015 ± 0.0272 +RandomForest_Val MAE: 6.6739 ± 0.1845 +RandomForest_Val RMSE: 10.6355 ± 0.4390 +RandomForest_Val RPIQ: 0.4800 ± 0.0334 diff --git a/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultsMultipleRunsOn31x31Dataset_Classic_7.75kmx7.75km.txt b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultsMultipleRunsOn31x31Dataset_Classic_7.75kmx7.75km.txt new file mode 100644 index 0000000000000000000000000000000000000000..e88b39ec0924c5314f12b35570d438b810909276 --- /dev/null +++ b/BaselinesXGBoostAndRF/resultsMultipleRunsOn11x11Dataset_classic/resultsMultipleRunsOn31x31Dataset_Classic_7.75kmx7.75km.txt @@ -0,0 +1,17 @@ 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▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇▇▇▇█ +wandb: mae ▆▆▆▆██▅▆▅▅▅▄▂▂▃▃▂▄▂▂▂▂▁▂▂▂▁▂▁▂▁▁▁▂▁▁▁▁▁▁ +wandb: mse ▆▅▅▆██▆▅▄▄▄▃▄▄▃▂▂▃▃▃▂▂▁▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁ +wandb: r_squared ▁▁▁▂▂▃▃▃▃▃▄▄▄▆▅▆▅▆▅▆▇▇▇▇▇▇▇▇▇▇▇▇▇███████ +wandb: rmse ▇▆▇█▆▆▅▃▅▄▆▅▃▄▄▄▃▃▃▃▂▃▂▃▂▂▂▂▂▂▁▂▂▁▁▂▂▂▁▁ +wandb: rpiq ▂▂▂▂▁▃▃▃▄▅▃▄▄▆▆▆▆▆▆▆▆▆▇▇▇▆▇▇▆▇▆▇▇██▇██▇█ +wandb: train_loss ▆█▃▅▄▅▃▄▂▂▂▂▂▂▃▂▂▂▃▂▃▂▂▂▃▃▃▁▂▂▂▁▃▂▂▂▂▂▂▁ +wandb: train_loss_avg ██▇▆▆▅▆▅▅▅▄▄▃▃▃▂▃▂▂▂▂▂▂▂▁▁▂▂▂▂▁▁▁▁▁▁▁▁▂▁ +wandb: val_loss ▅▅▆▆██▅▅▄▃▆▃▃▄▃▃▃▃▂▂▂▂▂▂▂▂▂▁▂▂▂▁▁▂▁▁▁▁▁▁ +wandb: +wandb: Run summary: +wandb: batch 2160 +wandb: best_r2 0.65156 +wandb: correlation 0.79959 +wandb: epoch 270 +wandb: mae 2.98709 +wandb: model_parameters 1120546 +wandb: mse 26.47703 +wandb: r_squared 0.63934 +wandb: rmse 5.14558 +wandb: rpiq 1.04944 +wandb: train_loss 0.18655 +wandb: train_loss_avg 0.18984 +wandb: train_size 15069 +wandb: val_loss 0.06844 +wandb: val_size 1445 +wandb: +wandb: 🚀 View run run_4 at: https://wandb.ai/valerian-fourel/socmapping-SimpleTFT/runs/btemlpe5 +wandb: ⭐️ View project at: https://wandb.ai/valerian-fourel/socmapping-SimpleTFT +wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s) +wandb: Find logs at: ./wandb/run-20250526_043327-btemlpe5/logs + +Average Metrics Across Runs: +train_loss_avg: 0.2940 +val_loss: 0.1271 +correlation: 0.7162 +r_squared: 0.5230 +mse: 49.1742 +rmse: 6.7284 +mae: 3.8210 +rpiq: 0.8425 + +Average Min Distance Statistics: +avg_mean: 66.2693 +std_mean: 7.3105 +avg_median: 4.3304 +std_median: 0.1153 +avg_min: 0.9949 +std_min: 0.3089 +avg_max: 845.0818 +std_max: 156.5115 +avg_std: 115.8760 +std_std: 15.2691 + +Best Metrics Across Runs: +r_squared - Mean: 0.7018, Std: 0.0368 +rmse - Mean: 4.4491, Std: 0.4608 +mae - Mean: 2.5305, Std: 0.1659 +rpiq - Mean: 1.1907, Std: 0.1091 + +Metrics saved to: output/training_metrics_tft_b19a8d69.txt +wandb: Tracking run with wandb version 0.19.3 +wandb: Run data is saved locally in /lustre/home/vfourel/SOCProject/SOCmapping/TemporalFusionTransformer/wandb/run-20250526_055623-ihxnapb2 +wandb: Run `wandb offline` to turn off syncing. +wandb: Syncing run average_metrics +wandb: ⭐️ View project at https://wandb.ai/valerian-fourel/socmapping-SimpleTFT +wandb: 🚀 View run at https://wandb.ai/valerian-fourel/socmapping-SimpleTFT/runs/ihxnapb2 + +**Best overall model from run 1 saved** +Best R²: 0.7553 +Best MAE: 2.4169 +Best RMSE: 3.9094 +Best RPIQ: 1.3557 +Average R² across all runs: 0.7018 +wandb: +wandb: +wandb: Run summary: +wandb: avg_mae 2.53046 +wandb: avg_r_squared 0.70178 +wandb: avg_rmse 4.44914 +wandb: avg_rpiq 1.19067 +wandb: std_mae 0.16593 +wandb: std_r_squared 0.03677 +wandb: std_rmse 0.46078 +wandb: std_rpiq 0.10906 +wandb: +wandb: 🚀 View run average_metrics at: https://wandb.ai/valerian-fourel/socmapping-SimpleTFT/runs/ihxnapb2 +wandb: ⭐️ View project at: https://wandb.ai/valerian-fourel/socmapping-SimpleTFT +wandb: Synced 5 W&B file(s), 0 media file(s), 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