INFO:2022-10-13 03:37:03,307:[predict.py:841 - predict_main() ] Loading /project/model/ENCSR000BUB_split000.h5 INFO:2022-10-13 03:37:07,544:[predict.py:399 - predict() ] SEQGEN Class Name: MBPNetSequenceGenerator INFO:2022-10-13 03:37:07,564:[generators.py:508 - _pad_samples() ] mode 'test': Data size (after padding 1009 samples) - 1024 INFO:2022-10-13 03:37:07,567:[predict.py:454 - predict() ] Writing predictions to /project/predictions_and_metrics/ENCSR000BUB_split000_predictions.h5 DEBUG:2022-10-13 03:37:07,575:[generators.py:804 - _epoch_run() ] test spawning process 0, df size (1024, 3) sum(num_batches) 1 DEBUG:2022-10-13 03:37:07,593:[generators.py:830 - _epoch_run() ] test num_batches list [1] DEBUG:2022-10-13 03:37:07,594:[generators.py:843 - _epoch_run() ] test starting stealer thread 0 [1] DEBUG:2022-10-13 03:37:09,559:[generators.py:725 - _proc_target() ] test process 0 put 1 batches into mpq DEBUG:2022-10-13 03:37:10,058:[generators.py:749 - _stealer() ] test stealer thread 0 got 1 batches from mpq DEBUG:2022-10-13 03:37:14,085:[generators.py:887 - gen() ] test waiting to join process 0 DEBUG:2022-10-13 03:37:14,085:[generators.py:892 - gen() ] test waiting to join thread 0 DEBUG:2022-10-13 03:37:14,085:[generators.py:896 - gen() ] test join complete for process 0 DEBUG:2022-10-13 03:37:14,086:[generators.py:899 - gen() ] test Finished join for epoch DEBUG:2022-10-13 03:37:14,086:[generators.py:901 - gen() ] test Ready for next epoch INFO:2022-10-13 03:37:14,086:[predict.py:658 - predict() ] Writing to h5 file ... INFO:2022-10-13 03:37:14,106:[predict.py:674 - predict() ] Elapsed Time: 6.5362584590911865 secs INFO:2022-10-13 03:37:14,106:[predict.py:678 - predict() ] Generating predicted profile bigWigs ... INFO:2022-10-13 03:37:14,106:[predict.py:680 - predict() ] predictions shape - (15, 1000, 2) INFO:2022-10-13 03:37:14,112:[predict.py:702 - predict() ] bigWig HEADER - [('chr1', 248956422), ('chr10', 133797422), ('chr11', 135086622), ('chr12', 133275309), ('chr13', 114364328), ('chr14', 107043718), ('chr15', 101991189), ('chr16', 90338345), ('chr17', 83257441), ('chr18', 80373285), ('chr19', 58617616), ('chr2', 242193529), ('chr20', 64444167), ('chr21', 46709983), ('chr22', 50818468), ('chr3', 198295559), ('chr4', 190214555), ('chr5', 181538259), ('chr6', 170805979), ('chr7', 159345973), ('chr8', 145138636), ('chr9', 138394717), ('chrM', 16569), ('chrX', 156040895), ('chrY', 57227415)] INFO:2022-10-13 03:37:14,183:[predict.py:728 - predict() ] min max median INFO:2022-10-13 03:37:14,183:[predict.py:733 - predict() ] mnll 1.000 1.000 1.000 INFO:2022-10-13 03:37:14,184:[predict.py:738 - predict() ] cross_entropy 1.000 1.000 1.000 INFO:2022-10-13 03:37:14,184:[predict.py:743 - predict() ] jsd 1.000 1.000 1.000 INFO:2022-10-13 03:37:14,185:[predict.py:748 - predict() ] spearman 0.041 0.259 0.122 INFO:2022-10-13 03:37:14,185:[predict.py:753 - predict() ] pearson 0.252 0.767 0.593 INFO:2022-10-13 03:37:14,185:[predict.py:758 - predict() ] mse 25.197 22054353.270 57877.192 INFO:2022-10-13 03:37:14,185:[predict.py:760 - predict() ] ============================================== INFO:2022-10-13 03:37:14,190:[predict.py:775 - predict() ] counts pearson: [0.24760572 0.56081529] INFO:2022-10-13 03:37:14,191:[predict.py:776 - predict() ] counts spearman: [0.15 0.71785714]