IOError "No such file or directory: './evaluation/temp/eval.1181043.scores "

[Daisy-123]

Hi
I tried this code " time /tools/anaconda2/bin/python train.py --train dataset/train1400.iob --dev dataset/dev300.iob --test dataset/test284.iob --tag_scheme iob > test-output0611 "
, but I got the error message " IOError: [Errno 2] No such file or directory: './evaluation/temp/eval.1181043.scores "

How can I solve this problem ?

[glample]

What does the full log file contain?
Could you check this is not a problem with permissions and that the script is allowed to create this scores file in the temp directory? (Also, does the temp directory exist?)

[Daisy-123]

• Full log :
  WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.
  Traceback (most recent call last):
  File "train.py", line 222, in
  test_data, id_to_tag, dico_tags)
  File "/mnt/Storage01/blue90211/tagger-master/utils.py", line 277, in evaluate
  eval_lines = [l.rstrip() for l in codecs.open(scores_path, 'r', 'utf8')]
  File "/tools/anaconda2/lib/python2.7/codecs.py", line 896, in open
  file = builtin.open(filename, mode, buffering)
  IOError: [Errno 2] No such file or directory: './evaluation/temp/eval.1181043.scores'
• Temp directory is generated by script (not exited before).
• I got eval.1181043.output file in temp directory ,but I can't find eval.1181043.scores.
• The process seems on going ... as below (stopped in epoch 0) :
  "
  ..............
  27900, cost average: 0.028285
  27950, cost average: 0.456446
  processed 1307517 tokens with 3937 phrases; found: 324 phrases; correct: 192.
  accuracy: 99.30%; precision: 59.26%; recall: 4.88%; FB1: 9.01
  Claim: precision: 59.26%; recall: 15.79%; FB1: 24.94 324
  PriorArt: precision: 0.00%; recall: 0.00%; FB1: 0.00 0
  ID NE Total OI-ClaimB-PriorArtI-PriorArtB-Claim Percent
  0 O12952511294615 583 0 0 53 99.951
  1I-Claim 6480 2950 3530 0 0 0 54.475
  2B-PriorArt 2721 2655 65 0 0 1 0.000
  3I-PriorArt 1849 1839 10 0 0 0 0.000
  4B-Claim 1216 922 24 0 0 270 22.204
  1298415/1307517 (99.30387%)
  processed 1133259 tokens with 4170 phrases; found: 302 phrases; correct: 171.
  accuracy: 99.20%; precision: 56.62%; recall: 4.10%; FB1: 7.65
  Claim: precision: 56.90%; recall: 14.07%; FB1: 22.56 297
  PriorArt: precision: 40.00%; recall: 0.07%; FB1: 0.13 5
  ID NE Total OI-ClaimB-PriorArtI-PriorArtB-Claim Percent
  0 O11213951120900 443 2 0 50 99.956
  1I-Claim 5686 2610 3074 0 0 2 54.063
  2B-PriorArt 2969 2906 58 2 0 3 0.067
  3I-PriorArt 2008 1981 24 1 0 2 0.000
  4B-Claim 1201 926 40 0 0 235 19.567
  1124211/1133259 (99.20159%)
  Score on dev: 9.01000
  Score on test: 7.65000
  New best score on dev.
  Saving model to disk...
  New best score on test.
  28000, cost average: 0.016292
  28050, cost average: 0.545872
  28100, cost average: 0.665584
  28150, cost average: 0.817216
  28200, cost average: 0.029983
  28250, cost average: 0.342566
  28300, cost average: 0.429246
  28350, cost average: 0.035073
  28400, cost average: 0.009134
  28450, cost average: 0.274903
  28500, cost average: 0.448717
  28550, cost average: 0.014378
  28600, cost average: 0.342188
  28650, cost average: 0.033076
  28700, cost average: 0.010674
  28750, cost average: 0.009748
  28800, cost average: 0.018098
  28850, cost average: 0.020268
  28900, cost average: 0.022872
  28950, cost average: 0.271496
  processed 1307517 tokens with 3937 phrases; found: 534 phrases; correct: 30.
  accuracy: 99.24%; precision: 5.62%; recall: 0.76%; FB1: 1.34
  Claim: precision: 5.62%; recall: 2.47%; FB1: 3.43 534
  PriorArt: precision: 0.00%; recall: 0.00%; FB1: 0.00 0
  ID NE Total OI-ClaimB-PriorArtI-PriorArtB-Claim Percent
  0 O12952511292716 2424 0 0 111 99.804
  1I-Claim 6480 1968 4509 0 0 3 69.583
  2B-PriorArt 2721 2324 370 0 0 27 0.000
  3I-PriorArt 1849 1702 137 0 0 10 0.000
  4B-Claim 1216 749 88 0 0 379 31.168
  1297604/1307517 (99.24185%)
  ....................
  "

[junchenzhi]

• Full log :
  WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.
  Traceback (most recent call last):
  File "train.py", line 222, in
  test_data, id_to_tag, dico_tags)
  File "/mnt/Storage01/blue90211/tagger-master/utils.py", line 277, in evaluate
  eval_lines = [l.rstrip() for l in codecs.open(scores_path, 'r', 'utf8')]
  File "/tools/anaconda2/lib/python2.7/codecs.py", line 896, in open
  file = builtin.open(filename, mode, buffering)
  IOError: [Errno 2] No such file or directory: './evaluation/temp/eval.1181043.scores'
• Temp directory is generated by script (not exited before).
• I got eval.1181043.output file in temp directory ,but I can't find eval.1181043.scores.
• The process seems on going ... as below (stopped in epoch 0) :
  "
  ..............
  27900, cost average: 0.028285
  27950, cost average: 0.456446
  processed 1307517 tokens with 3937 phrases; found: 324 phrases; correct: 192.
  accuracy: 99.30%; precision: 59.26%; recall: 4.88%; FB1: 9.01
  Claim: precision: 59.26%; recall: 15.79%; FB1: 24.94 324
  PriorArt: precision: 0.00%; recall: 0.00%; FB1: 0.00 0
  ID NE Total OI-ClaimB-PriorArtI-PriorArtB-Claim Percent
  0 O12952511294615 583 0 0 53 99.951
  1I-Claim 6480 2950 3530 0 0 0 54.475
  2B-PriorArt 2721 2655 65 0 0 1 0.000
  3I-PriorArt 1849 1839 10 0 0 0 0.000
  4B-Claim 1216 922 24 0 0 270 22.204
  1298415/1307517 (99.30387%)
  processed 1133259 tokens with 4170 phrases; found: 302 phrases; correct: 171.
  accuracy: 99.20%; precision: 56.62%; recall: 4.10%; FB1: 7.65
  Claim: precision: 56.90%; recall: 14.07%; FB1: 22.56 297
  PriorArt: precision: 40.00%; recall: 0.07%; FB1: 0.13 5
  ID NE Total OI-ClaimB-PriorArtI-PriorArtB-Claim Percent
  0 O11213951120900 443 2 0 50 99.956
  1I-Claim 5686 2610 3074 0 0 2 54.063
  2B-PriorArt 2969 2906 58 2 0 3 0.067
  3I-PriorArt 2008 1981 24 1 0 2 0.000
  4B-Claim 1201 926 40 0 0 235 19.567
  1124211/1133259 (99.20159%)
  Score on dev: 9.01000
  Score on test: 7.65000
  New best score on dev.
  Saving model to disk...
  New best score on test.
  28000, cost average: 0.016292
  28050, cost average: 0.545872
  28100, cost average: 0.665584
  28150, cost average: 0.817216
  28200, cost average: 0.029983
  28250, cost average: 0.342566
  28300, cost average: 0.429246
  28350, cost average: 0.035073
  28400, cost average: 0.009134
  28450, cost average: 0.274903
  28500, cost average: 0.448717
  28550, cost average: 0.014378
  28600, cost average: 0.342188
  28650, cost average: 0.033076
  28700, cost average: 0.010674
  28750, cost average: 0.009748
  28800, cost average: 0.018098
  28850, cost average: 0.020268
  28900, cost average: 0.022872
  28950, cost average: 0.271496
  processed 1307517 tokens with 3937 phrases; found: 534 phrases; correct: 30.
  accuracy: 99.24%; precision: 5.62%; recall: 0.76%; FB1: 1.34
  Claim: precision: 5.62%; recall: 2.47%; FB1: 3.43 534
  PriorArt: precision: 0.00%; recall: 0.00%; FB1: 0.00 0
  ID NE Total OI-ClaimB-PriorArtI-PriorArtB-Claim Percent
  0 O12952511292716 2424 0 0 111 99.804
  1I-Claim 6480 1968 4509 0 0 3 69.583
  2B-PriorArt 2721 2324 370 0 0 27 0.000
  3I-PriorArt 1849 1702 137 0 0 10 0.000
  4B-Claim 1216 749 88 0 0 379 31.168
  1297604/1307517 (99.24185%)
  ....................
  "

Hi, I have the same bug. Have you solved it? How did you solve it? Thanks~