INFO:2022-10-13 03:35:55,399:[predict.py:841 - predict_main() ] Loading /project/model/ENCSR239ZLZ_split000.h5 INFO:2022-10-13 03:35:59,670:[predict.py:399 - predict() ] SEQGEN Class Name: MBPNetSequenceGenerator INFO:2022-10-13 03:35:59,691:[generators.py:508 - _pad_samples() ] mode 'test': Data size (after padding 1009 samples) - 1024 INFO:2022-10-13 03:35:59,694:[predict.py:454 - predict() ] Writing predictions to /project/predictions_and_metrics/ENCSR239ZLZ_split000_predictions.h5 DEBUG:2022-10-13 03:35:59,703:[generators.py:804 - _epoch_run() ] test spawning process 0, df size (1024, 3) sum(num_batches) 1 DEBUG:2022-10-13 03:35:59,721:[generators.py:830 - _epoch_run() ] test num_batches list [1] DEBUG:2022-10-13 03:35:59,722:[generators.py:843 - _epoch_run() ] test starting stealer thread 0 [1] DEBUG:2022-10-13 03:36:01,742:[generators.py:725 - _proc_target() ] test process 0 put 1 batches into mpq DEBUG:2022-10-13 03:36:02,226:[generators.py:749 - _stealer() ] test stealer thread 0 got 1 batches from mpq DEBUG:2022-10-13 03:36:06,260:[generators.py:887 - gen() ] test waiting to join process 0 DEBUG:2022-10-13 03:36:06,260:[generators.py:892 - gen() ] test waiting to join thread 0 DEBUG:2022-10-13 03:36:06,260:[generators.py:896 - gen() ] test join complete for process 0 DEBUG:2022-10-13 03:36:06,260:[generators.py:899 - gen() ] test Finished join for epoch DEBUG:2022-10-13 03:36:06,260:[generators.py:901 - gen() ] test Ready for next epoch INFO:2022-10-13 03:36:06,261:[predict.py:658 - predict() ] Writing to h5 file ... INFO:2022-10-13 03:36:06,286:[predict.py:674 - predict() ] Elapsed Time: 6.589142560958862 secs INFO:2022-10-13 03:36:06,286:[predict.py:678 - predict() ] Generating predicted profile bigWigs ... INFO:2022-10-13 03:36:06,286:[predict.py:680 - predict() ] predictions shape - (15, 1000, 2) INFO:2022-10-13 03:36:06,292:[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 03:36:06,370:[predict.py:728 - predict() ] min max median INFO:2022-10-13 03:36:06,370:[predict.py:733 - predict() ] mnll 1.000 1.000 1.000 INFO:2022-10-13 03:36:06,371:[predict.py:738 - predict() ] cross_entropy 1.000 1.000 1.000 INFO:2022-10-13 03:36:06,371:[predict.py:743 - predict() ] jsd 1.000 1.000 1.000 INFO:2022-10-13 03:36:06,371:[predict.py:748 - predict() ] spearman -0.004 0.279 0.067 INFO:2022-10-13 03:36:06,372:[predict.py:753 - predict() ] pearson 0.355 0.862 0.746 INFO:2022-10-13 03:36:06,372:[predict.py:758 - predict() ] mse 9038.795 388785885967.416 4411671116.495 INFO:2022-10-13 03:36:06,372:[predict.py:760 - predict() ] ============================================== INFO:2022-10-13 03:36:06,377:[predict.py:775 - predict() ] counts pearson: [0.21451825 0.17514542] INFO:2022-10-13 03:36:06,378:[predict.py:776 - predict() ] counts spearman: [0.3 0.24285714]